diff --git a/.github/workflows/sync-to-space.yml b/.github/workflows/sync-to-space.yml new file mode 100644 index 0000000000000000000000000000000000000000..39454decf67067adb1fa45648db0a66eeab39885 --- /dev/null +++ b/.github/workflows/sync-to-space.yml @@ -0,0 +1,17 @@ +name: Sync to Hugging Face Space +on: + push: + branches: [main] + +jobs: + sync-to-space: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + - name: Push to Space + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: | + git push https://USER:$HF_TOKEN@huggingface.co/spaces/USER/SPACE_NAME main diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..696519b6c7588db9d7fe31c53fa4e0e996bcf501 --- /dev/null +++ b/.gitignore @@ -0,0 +1,91 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual Environment +venv/ +env/ +ENV/ + +# IDE +.idea/ +.vscode/ +*.swp +*.swo + +# Logs +*.log +logs/ +log/ + +# Local development +.env +.env.local +.env.*.local + +# Data +data/ +*.db +*.sqlite3 + +# Model files +*.pt +*.pth +*.ckpt +*.bin +*.onnx + +# Temporary files +.DS_Store +Thumbs.db +*.tmp +*.bak +*.swp +*~ + +# Distribution +dist/ +build/ +*.egg-info/ + +# Documentation +docs/_build/ +site/ + +# Testing +.coverage +htmlcov/ +.pytest_cache/ +.tox/ +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Jupyter Notebook +.ipynb_checkpoints +*.ipynb + +# Project specific +outputs/ +results/ +experiments/ +checkpoints/ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0e51dea88808c1bbc5735d210d0546f20c57024c --- /dev/null +++ b/Dockerfile @@ -0,0 +1,52 @@ +FROM python:3.10-slim + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + DEBIAN_FRONTEND=noninteractive \ + REQUESTS_TIMEOUT=30 \ + PYTHONPATH=/app + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + build-essential \ + curl \ + git \ + dnsutils \ + iputils-ping \ + && rm -rf /var/lib/apt/lists/* + +# Set working directory +WORKDIR /app + +# Set up DNS configuration with multiple DNS servers for redundancy +RUN echo "nameserver 8.8.8.8" > /etc/resolv.conf && \ + echo "nameserver 8.8.4.4" >> /etc/resolv.conf && \ + echo "nameserver 1.1.1.1" >> /etc/resolv.conf && \ + echo "options timeout:1 attempts:5" >> /etc/resolv.conf + +# Copy requirements first to leverage Docker cache +COPY requirements.txt . + +# Install Python dependencies with retry mechanism and explicit Gradio upgrade +RUN pip install --no-cache-dir -r requirements.txt || \ + (sleep 5 && pip install --no-cache-dir -r requirements.txt) || \ + (sleep 10 && pip install --no-cache-dir -r requirements.txt) && \ + pip install --no-cache-dir gradio==4.44.1 + +# Copy application code +COPY . . + +# Expose port +EXPOSE 7860 + +# Add network verification script +RUN echo '#!/bin/sh\n\ +ping -c 1 huggingface.co || ping -c 1 8.8.8.8\n\ +' > /healthcheck.sh && chmod +x /healthcheck.sh + +# Healthcheck with more lenient settings +HEALTHCHECK --interval=30s --timeout=10s --start-period=30s --retries=5 \ + CMD /healthcheck.sh || exit 1 + +# Command to run the application +CMD ["python", "app.py"] diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a1fbf458227289f0ddcc20ddbc9975811ac71ffd --- /dev/null +++ b/README.md @@ -0,0 +1,113 @@ +--- +title: Advanced Reasoning System 🤖 +emoji: 🤖 +colorFrom: blue +colorTo: purple +sdk: gradio +sdk_version: 4.16.0 +app_file: app.py +pinned: false +license: mit +--- + +# Advanced Reasoning System 🤖 + +A sophisticated multi-agent reasoning system that combines multiple strategies for advanced problem-solving. + +## Features + +- 🧠 Multiple Reasoning Strategies: + - Chain of Thought + - Tree of Thoughts + - Meta Learning + - Local LLM Integration + - Recursive Reasoning + - Analogical Reasoning + +- 🤝 Multi-Agent System: + - Dynamic Team Formation + - Cross-Team Collaboration + - Resource Management + - Task Orchestration + +- 🔄 Adaptive Learning: + - Performance Tracking + - Strategy Weight Adjustment + - Pattern Recognition + - Meta-Learning Integration + +## Quick Start + +1. **Environment Setup**: + ```bash + python -m venv venv + source venv/bin/activate # On Windows: .\venv\Scripts\activate + pip install -r requirements.txt + ``` + +2. **Configuration**: + ```bash + cp .env.example .env + # Edit .env with your settings + ``` + +3. **Run the Application**: + ```bash + python app.py + ``` + +## Docker Support + +Build and run with Docker: + +```bash +docker build -t advanced-reasoning . +docker run -p 7860:7860 advanced-reasoning +``` + +## API Endpoints + +- `/`: Main interface +- `/health`: Health check endpoint +- `/api/process_query`: Process queries via API + +## Components + +1. **Reasoning Engine**: + - Unified reasoning combining multiple strategies + - Dynamic strategy selection + - Result synthesis + +2. **Team Management**: + - Specialized teams (Coders, Business, Research, Trading) + - Cross-team collaboration + - Resource sharing + +3. **Orchestration**: + - Task planning and decomposition + - Resource allocation + - Performance monitoring + +## Contributing + +1. Fork the repository +2. Create your feature branch +3. Commit your changes +4. Push to the branch +5. Create a Pull Request + +## License + +MIT License - see LICENSE file for details + +## Files +- `app.py`: Main application with Gradio interface and API integration +- `requirements.txt`: Project dependencies +- `.env.example`: Example environment variables (for reference) + +## Dependencies +- gradio==4.16.0 +- requests==2.31.0 + +--- +Created with ❤️ using Gradio and Hugging Face diff --git a/agentic_system.py b/agentic_system.py new file mode 100644 index 0000000000000000000000000000000000000000..f53cb01064680bc9e7c65432eb064efaa9b2c6a3 --- /dev/null +++ b/agentic_system.py @@ -0,0 +1,558 @@ +""" +Advanced Agentic System +---------------------- +A sophisticated multi-agent system with: + +Core Components: +1. Agent Management +2. Task Execution +3. Learning & Adaptation +4. Communication +5. Resource Management + +Advanced Features: +1. Self-Improvement +2. Multi-Agent Coordination +3. Dynamic Role Assignment +4. Emergent Behavior +""" + +import logging +from typing import Dict, Any, List, Optional, Union, TypeVar +from dataclasses import dataclass, field +from enum import Enum +import json +import asyncio +from datetime import datetime +import uuid +from concurrent.futures import ThreadPoolExecutor +import numpy as np +from collections import defaultdict + +from orchestrator import ( + AgentOrchestrator, + AgentRole, + AgentState, + TaskPriority, + Task +) +from reasoning import UnifiedReasoningEngine as ReasoningEngine, StrategyType as ReasoningMode +from reasoning.meta_learning import MetaLearningStrategy + +class AgentCapability(Enum): + """Core capabilities of agents.""" + REASONING = "reasoning" + LEARNING = "learning" + EXECUTION = "execution" + COORDINATION = "coordination" + MONITORING = "monitoring" + +class AgentPersonality(Enum): + """Different personality types for agents.""" + ANALYTICAL = "analytical" + CREATIVE = "creative" + CAUTIOUS = "cautious" + PROACTIVE = "proactive" + ADAPTIVE = "adaptive" + +@dataclass +class AgentProfile: + """Profile defining an agent's characteristics.""" + id: str + name: str + role: AgentRole + capabilities: List[AgentCapability] + personality: AgentPersonality + expertise_areas: List[str] + learning_rate: float + risk_tolerance: float + created_at: datetime + metadata: Dict[str, Any] + +class Agent: + """Advanced autonomous agent with learning capabilities.""" + + def __init__( + self, + profile: AgentProfile, + reasoning_engine: ReasoningEngine, + meta_learning: MetaLearningStrategy, + config: Dict[str, Any] = None + ): + self.profile = profile + self.reasoning_engine = reasoning_engine + self.meta_learning = meta_learning + self.config = config or {} + + # State management + self.state = AgentState.IDLE + self.current_task: Optional[Task] = None + self.task_history: List[Task] = [] + + # Learning and adaptation + self.knowledge_base: Dict[str, Any] = {} + self.learned_patterns: List[Dict[str, Any]] = [] + self.adaptation_history: List[Dict[str, Any]] = [] + + # Performance metrics + self.metrics: Dict[str, List[float]] = defaultdict(list) + self.performance_history: List[Dict[str, float]] = [] + + # Communication + self.message_queue = asyncio.Queue() + self.response_queue = asyncio.Queue() + + # Resource management + self.resource_usage: Dict[str, float] = {} + self.resource_limits: Dict[str, float] = {} + + # Async support + self.executor = ThreadPoolExecutor(max_workers=2) + self.lock = asyncio.Lock() + + # Logging + self.logger = logging.getLogger(f"Agent-{profile.id}") + + # Initialize components + self._init_components() + + def _init_components(self): + """Initialize agent components.""" + # Set up knowledge base + self.knowledge_base = { + "expertise": {area: 0.5 for area in self.profile.expertise_areas}, + "learned_skills": set(), + "interaction_patterns": defaultdict(int), + "success_patterns": defaultdict(float) + } + + # Set up resource limits + self.resource_limits = { + "cpu": 1.0, + "memory": 1000, + "api_calls": 100, + "learning_capacity": 0.8 + } + + async def process_task(self, task: Task) -> Dict[str, Any]: + """Process an assigned task.""" + try: + self.current_task = task + self.state = AgentState.BUSY + + # Analyze task + analysis = await self._analyze_task(task) + + # Plan execution + plan = await self._plan_execution(analysis) + + # Execute plan + result = await self._execute_plan(plan) + + # Learn from execution + await self._learn_from_execution(task, result) + + # Update metrics + self._update_metrics(task, result) + + return { + "success": True, + "task_id": task.id, + "result": result, + "metrics": self._get_execution_metrics() + } + + except Exception as e: + self.logger.error(f"Error processing task: {e}") + self.state = AgentState.ERROR + return { + "success": False, + "task_id": task.id, + "error": str(e) + } + finally: + self.state = AgentState.IDLE + self.current_task = None + + async def _analyze_task(self, task: Task) -> Dict[str, Any]: + """Analyze task requirements and constraints.""" + # Use reasoning engine for analysis + analysis = await self.reasoning_engine.reason( + query=task.description, + context={ + "agent_profile": self.profile.__dict__, + "task_history": self.task_history, + "knowledge_base": self.knowledge_base + }, + mode=ReasoningMode.ANALYTICAL + ) + + return { + "requirements": analysis.get("requirements", []), + "constraints": analysis.get("constraints", []), + "complexity": analysis.get("complexity", 0.5), + "estimated_duration": analysis.get("estimated_duration", 3600), + "required_capabilities": analysis.get("required_capabilities", []) + } + + async def _plan_execution(self, analysis: Dict[str, Any]) -> List[Dict[str, Any]]: + """Plan task execution based on analysis.""" + # Use reasoning engine for planning + plan = await self.reasoning_engine.reason( + query="Plan execution steps", + context={ + "analysis": analysis, + "agent_capabilities": self.profile.capabilities, + "resource_limits": self.resource_limits + }, + mode=ReasoningMode.FOCUSED + ) + + return plan.get("steps", []) + + async def _execute_plan(self, plan: List[Dict[str, Any]]) -> Dict[str, Any]: + """Execute the planned steps.""" + results = [] + + for step in plan: + try: + # Check resources + if not self._check_resources(step): + raise RuntimeError("Insufficient resources for step execution") + + # Execute step + step_result = await self._execute_step(step) + results.append(step_result) + + # Update resource usage + self._update_resource_usage(step) + + # Learn from step execution + await self._learn_from_step(step, step_result) + + except Exception as e: + self.logger.error(f"Error executing step: {e}") + results.append({"error": str(e)}) + + return { + "success": all(r.get("success", False) for r in results), + "results": results + } + + async def _execute_step(self, step: Dict[str, Any]) -> Dict[str, Any]: + """Execute a single step of the plan.""" + step_type = step.get("type", "unknown") + + if step_type == "reasoning": + return await self._execute_reasoning_step(step) + elif step_type == "learning": + return await self._execute_learning_step(step) + elif step_type == "action": + return await self._execute_action_step(step) + else: + raise ValueError(f"Unknown step type: {step_type}") + + async def _execute_reasoning_step(self, step: Dict[str, Any]) -> Dict[str, Any]: + """Execute a reasoning step.""" + result = await self.reasoning_engine.reason( + query=step["query"], + context=step.get("context", {}), + mode=ReasoningMode.ANALYTICAL + ) + + return { + "success": result.get("success", False), + "reasoning_result": result + } + + async def _execute_learning_step(self, step: Dict[str, Any]) -> Dict[str, Any]: + """Execute a learning step.""" + result = await self.meta_learning.learn( + data=step["data"], + context=step.get("context", {}) + ) + + return { + "success": result.get("success", False), + "learning_result": result + } + + async def _execute_action_step(self, step: Dict[str, Any]) -> Dict[str, Any]: + """Execute an action step.""" + action_type = step.get("action_type") + + if action_type == "api_call": + return await self._make_api_call(step) + elif action_type == "data_processing": + return await self._process_data(step) + elif action_type == "coordination": + return await self._coordinate_action(step) + else: + raise ValueError(f"Unknown action type: {action_type}") + + def _check_resources(self, step: Dict[str, Any]) -> bool: + """Check if sufficient resources are available.""" + required_resources = step.get("required_resources", {}) + + for resource, amount in required_resources.items(): + if self.resource_usage.get(resource, 0) + amount > self.resource_limits.get(resource, float('inf')): + return False + + return True + + def _update_resource_usage(self, step: Dict[str, Any]): + """Update resource usage after step execution.""" + used_resources = step.get("used_resources", {}) + + for resource, amount in used_resources.items(): + self.resource_usage[resource] = self.resource_usage.get(resource, 0) + amount + + async def _learn_from_execution(self, task: Task, result: Dict[str, Any]): + """Learn from task execution experience.""" + # Prepare learning data + learning_data = { + "task": task.__dict__, + "result": result, + "context": { + "agent_state": self.state, + "resource_usage": self.resource_usage, + "performance_metrics": self._get_execution_metrics() + } + } + + # Learn patterns + patterns = await self.meta_learning.learn( + data=learning_data, + context=self.knowledge_base + ) + + # Update knowledge base + self._update_knowledge_base(patterns) + + # Record adaptation + self.adaptation_history.append({ + "timestamp": datetime.now(), + "patterns": patterns, + "metrics": self._get_execution_metrics() + }) + + async def _learn_from_step(self, step: Dict[str, Any], result: Dict[str, Any]): + """Learn from individual step execution.""" + if result.get("success", False): + # Update success patterns + pattern_key = f"{step['type']}:{step.get('action_type', 'none')}" + self.knowledge_base["success_patterns"][pattern_key] += 1 + + # Learn from successful execution + await self.meta_learning.learn( + data={ + "step": step, + "result": result + }, + context={"pattern_key": pattern_key} + ) + + def _update_knowledge_base(self, patterns: Dict[str, Any]): + """Update knowledge base with new patterns.""" + # Update expertise levels + for area, pattern in patterns.get("expertise_patterns", {}).items(): + if area in self.knowledge_base["expertise"]: + current = self.knowledge_base["expertise"][area] + self.knowledge_base["expertise"][area] = current * 0.9 + pattern * 0.1 + + # Add new learned skills + new_skills = patterns.get("learned_skills", set()) + self.knowledge_base["learned_skills"].update(new_skills) + + # Update interaction patterns + for pattern, count in patterns.get("interaction_patterns", {}).items(): + self.knowledge_base["interaction_patterns"][pattern] += count + + def _update_metrics(self, task: Task, result: Dict[str, Any]): + """Update performance metrics.""" + metrics = { + "success": float(result.get("success", False)), + "duration": (datetime.now() - task.created_at).total_seconds(), + "resource_efficiency": self._calculate_resource_efficiency(), + "learning_progress": self._calculate_learning_progress() + } + + for key, value in metrics.items(): + self.metrics[key].append(value) + + self.performance_history.append({ + "timestamp": datetime.now(), + "metrics": metrics + }) + + def _calculate_resource_efficiency(self) -> float: + """Calculate resource usage efficiency.""" + if not self.resource_limits: + return 1.0 + + efficiencies = [] + for resource, usage in self.resource_usage.items(): + limit = self.resource_limits.get(resource, float('inf')) + if limit > 0: + efficiencies.append(1 - (usage / limit)) + + return sum(efficiencies) / len(efficiencies) if efficiencies else 1.0 + + def _calculate_learning_progress(self) -> float: + """Calculate learning progress.""" + if not self.knowledge_base["expertise"]: + return 0.0 + + return sum(self.knowledge_base["expertise"].values()) / len(self.knowledge_base["expertise"]) + + def _get_execution_metrics(self) -> Dict[str, float]: + """Get current execution metrics.""" + return { + key: sum(values[-10:]) / len(values[-10:]) + for key, values in self.metrics.items() + if values + } + +class AgenticSystem: + """Advanced multi-agent system with orchestration.""" + + def __init__(self, config: Dict[str, Any] = None): + self.config = config or {} + + # Initialize orchestrator + self.orchestrator = AgentOrchestrator(config) + + # Initialize components + self.agents: Dict[str, Agent] = {} + self.reasoning_engine = ReasoningEngine( + min_confidence=self.config.get('min_confidence', 0.7), + parallel_threshold=self.config.get('parallel_threshold', 3), + learning_rate=self.config.get('learning_rate', 0.1), + strategy_weights=self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + ) + self.meta_learning = MetaLearningStrategy(config) + + # System state + self.state = "initialized" + self.metrics: Dict[str, List[float]] = defaultdict(list) + + # Async support + self.executor = ThreadPoolExecutor(max_workers=4) + self.lock = asyncio.Lock() + + # Logging + self.logger = logging.getLogger("AgenticSystem") + + async def create_agent( + self, + name: str, + role: AgentRole, + capabilities: List[AgentCapability], + personality: AgentPersonality, + expertise_areas: List[str] + ) -> str: + """Create a new agent.""" + # Create agent profile + profile = AgentProfile( + id=str(uuid.uuid4()), + name=name, + role=role, + capabilities=capabilities, + personality=personality, + expertise_areas=expertise_areas, + learning_rate=0.1, + risk_tolerance=0.5, + created_at=datetime.now(), + metadata={} + ) + + # Create agent instance + agent = Agent( + profile=profile, + reasoning_engine=self.reasoning_engine, + meta_learning=self.meta_learning, + config=self.config.get("agent_config", {}) + ) + + # Register with orchestrator + agent_id = await self.orchestrator.register_agent( + role=role, + capabilities=[c.value for c in capabilities] + ) + + # Store agent + async with self.lock: + self.agents[agent_id] = agent + + return agent_id + + async def submit_task( + self, + description: str, + priority: TaskPriority = TaskPriority.MEDIUM, + deadline: Optional[datetime] = None + ) -> str: + """Submit a task to the system.""" + return await self.orchestrator.submit_task( + description=description, + priority=priority, + deadline=deadline + ) + + async def get_task_status(self, task_id: str) -> Dict[str, Any]: + """Get status of a task.""" + return await self.orchestrator.get_task_status(task_id) + + async def get_agent_status(self, agent_id: str) -> Dict[str, Any]: + """Get status of an agent.""" + agent = self.agents.get(agent_id) + if not agent: + raise ValueError(f"Unknown agent: {agent_id}") + + return { + "profile": agent.profile.__dict__, + "state": agent.state, + "current_task": agent.current_task.__dict__ if agent.current_task else None, + "metrics": agent._get_execution_metrics(), + "resource_usage": agent.resource_usage + } + + async def get_system_status(self) -> Dict[str, Any]: + """Get overall system status.""" + return { + "state": self.state, + "agent_count": len(self.agents), + "active_tasks": len([a for a in self.agents.values() if a.state == AgentState.BUSY]), + "performance_metrics": self._calculate_system_metrics(), + "resource_usage": self._calculate_resource_usage() + } + + def _calculate_system_metrics(self) -> Dict[str, float]: + """Calculate overall system metrics.""" + metrics = defaultdict(list) + + for agent in self.agents.values(): + agent_metrics = agent._get_execution_metrics() + for key, value in agent_metrics.items(): + metrics[key].append(value) + + return { + key: sum(values) / len(values) + for key, values in metrics.items() + if values + } + + def _calculate_resource_usage(self) -> Dict[str, float]: + """Calculate overall resource usage.""" + usage = defaultdict(float) + + for agent in self.agents.values(): + for resource, amount in agent.resource_usage.items(): + usage[resource] += amount + + return dict(usage) diff --git a/api/__init__.py b/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cbc456e3702f3a717ea2c4d09f0d7371c94ea7de --- /dev/null +++ b/api/__init__.py @@ -0,0 +1,6 @@ +"""API package for the agentic system.""" + +from .openai_compatible import OpenAICompatibleAPI +from .venture_api import VentureAPI + +__all__ = ['OpenAICompatibleAPI', 'VentureAPI'] diff --git a/api/openai_compatible.py b/api/openai_compatible.py new file mode 100644 index 0000000000000000000000000000000000000000..a01830cb3a9e3a22393f823e9cda44dbd95a2003 --- /dev/null +++ b/api/openai_compatible.py @@ -0,0 +1,139 @@ +"""OpenAI-compatible API endpoints.""" + +from typing import Dict, List, Optional, Union +from pydantic import BaseModel, Field +from fastapi import APIRouter, HTTPException, Depends +import time +import json +import asyncio +from datetime import datetime + +class ChatMessage(BaseModel): + """OpenAI-compatible chat message.""" + role: str = Field(..., description="The role of the message author (system/user/assistant)") + content: str = Field(..., description="The content of the message") + name: Optional[str] = Field(None, description="The name of the author") + +class ChatCompletionRequest(BaseModel): + """OpenAI-compatible chat completion request.""" + model: str = Field(..., description="Model to use") + messages: List[ChatMessage] + temperature: Optional[float] = Field(0.7, description="Sampling temperature") + top_p: Optional[float] = Field(1.0, description="Nucleus sampling parameter") + n: Optional[int] = Field(1, description="Number of completions") + stream: Optional[bool] = Field(False, description="Whether to stream responses") + stop: Optional[Union[str, List[str]]] = Field(None, description="Stop sequences") + max_tokens: Optional[int] = Field(None, description="Maximum tokens to generate") + presence_penalty: Optional[float] = Field(0.0, description="Presence penalty") + frequency_penalty: Optional[float] = Field(0.0, description="Frequency penalty") + user: Optional[str] = Field(None, description="User identifier") + +class ChatCompletionResponse(BaseModel): + """OpenAI-compatible chat completion response.""" + id: str = Field(..., description="Unique identifier for the completion") + object: str = Field("chat.completion", description="Object type") + created: int = Field(..., description="Unix timestamp of creation") + model: str = Field(..., description="Model used") + choices: List[Dict] = Field(..., description="Completion choices") + usage: Dict[str, int] = Field(..., description="Token usage statistics") + +class OpenAICompatibleAPI: + """OpenAI-compatible API implementation.""" + + def __init__(self, reasoning_engine): + self.reasoning_engine = reasoning_engine + self.router = APIRouter() + self.setup_routes() + + def setup_routes(self): + """Setup API routes.""" + + @self.router.post("/v1/chat/completions") + async def create_chat_completion(request: ChatCompletionRequest) -> ChatCompletionResponse: + try: + # Convert chat history to context + context = self._prepare_context(request.messages) + + # Get the last user message + user_message = next( + (msg.content for msg in reversed(request.messages) + if msg.role == "user"), + None + ) + + if not user_message: + raise HTTPException(status_code=400, detail="No user message found") + + # Process with reasoning engine + result = await self.reasoning_engine.reason( + query=user_message, + context={ + "chat_history": context, + "temperature": request.temperature, + "top_p": request.top_p, + "max_tokens": request.max_tokens, + "stream": request.stream + } + ) + + # Format response + response = { + "id": f"chatcmpl-{int(time.time()*1000)}", + "object": "chat.completion", + "created": int(time.time()), + "model": request.model, + "choices": [{ + "index": 0, + "message": { + "role": "assistant", + "content": result.answer + }, + "finish_reason": "stop" + }], + "usage": { + "prompt_tokens": self._estimate_tokens(user_message), + "completion_tokens": self._estimate_tokens(result.answer), + "total_tokens": self._estimate_tokens(user_message) + + self._estimate_tokens(result.answer) + } + } + + return ChatCompletionResponse(**response) + + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + @self.router.get("/v1/models") + async def list_models(): + """List available models.""" + return { + "object": "list", + "data": [ + { + "id": "venture-gpt-1", + "object": "model", + "created": int(time.time()), + "owned_by": "venture-ai", + "permission": [], + "root": "venture-gpt-1", + "parent": None + } + ] + } + + def _prepare_context(self, messages: List[ChatMessage]) -> List[Dict]: + """Convert messages to context format.""" + return [ + { + "role": msg.role, + "content": msg.content, + "name": msg.name, + "timestamp": datetime.now().isoformat() + } + for msg in messages + ] + + def _estimate_tokens(self, text: str) -> int: + """Estimate token count for a text.""" + # Simple estimation: ~4 characters per token + return len(text) // 4 diff --git a/api/venture_api.py b/api/venture_api.py new file mode 100644 index 0000000000000000000000000000000000000000..356d6aca14d108b6a91bcc29f493ffe97414c4c6 --- /dev/null +++ b/api/venture_api.py @@ -0,0 +1,194 @@ +"""API endpoints for venture strategies and analysis.""" + +from fastapi import APIRouter, HTTPException, Depends +from typing import List, Dict, Any, Optional +from pydantic import BaseModel, Field +from datetime import datetime + +from reasoning.venture_strategies import ( + AIStartupStrategy, SaaSVentureStrategy, AutomationVentureStrategy, + DataVentureStrategy, APIVentureStrategy, MarketplaceVentureStrategy, + AIInfrastructureStrategy, AIConsultingStrategy, AIProductStrategy, + FinTechStrategy, HealthTechStrategy, EdTechStrategy, + BlockchainStrategy, AIMarketplaceStrategy +) +from reasoning.market_analysis import MarketAnalyzer +from reasoning.portfolio_optimization import PortfolioOptimizer +from reasoning.monetization import MonetizationOptimizer + +router = APIRouter(prefix="/api/ventures", tags=["ventures"]) + +# Models +class VentureRequest(BaseModel): + """Venture analysis request.""" + venture_type: str + query: str + context: Dict[str, Any] = Field(default_factory=dict) + +class MarketRequest(BaseModel): + """Market analysis request.""" + segment: str + context: Dict[str, Any] = Field(default_factory=dict) + +class PortfolioRequest(BaseModel): + """Portfolio optimization request.""" + ventures: List[str] + context: Dict[str, Any] = Field(default_factory=dict) + +class MonetizationRequest(BaseModel): + """Monetization optimization request.""" + venture_type: str + context: Dict[str, Any] = Field(default_factory=dict) + +# Strategy mapping +VENTURE_STRATEGIES = { + "ai_startup": AIStartupStrategy(), + "saas": SaaSVentureStrategy(), + "automation": AutomationVentureStrategy(), + "data": DataVentureStrategy(), + "api": APIVentureStrategy(), + "marketplace": MarketplaceVentureStrategy(), + "ai_infrastructure": AIInfrastructureStrategy(), + "ai_consulting": AIConsultingStrategy(), + "ai_product": AIProductStrategy(), + "fintech": FinTechStrategy(), + "healthtech": HealthTechStrategy(), + "edtech": EdTechStrategy(), + "blockchain": BlockchainStrategy(), + "ai_marketplace": AIMarketplaceStrategy() +} + +# Endpoints +@router.post("/analyze") +async def analyze_venture(request: VentureRequest): + """Analyze venture opportunity.""" + try: + strategy = VENTURE_STRATEGIES.get(request.venture_type) + if not strategy: + raise HTTPException( + status_code=400, + detail=f"Invalid venture type: {request.venture_type}" + ) + + result = await strategy.reason(request.query, request.context) + return { + "success": True, + "result": result, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.post("/market") +async def analyze_market(request: MarketRequest): + """Analyze market opportunity.""" + try: + analyzer = MarketAnalyzer() + result = await analyzer.analyze_market(request.segment, request.context) + return { + "success": True, + "result": result, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.post("/portfolio") +async def optimize_portfolio(request: PortfolioRequest): + """Optimize venture portfolio.""" + try: + optimizer = PortfolioOptimizer() + result = await optimizer.optimize_portfolio(request.ventures, request.context) + return { + "success": True, + "result": result, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.post("/monetization") +async def optimize_monetization(request: MonetizationRequest): + """Optimize venture monetization.""" + try: + optimizer = MonetizationOptimizer() + result = await optimizer.optimize_monetization( + request.venture_type, request.context) + return { + "success": True, + "result": result, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.get("/strategies") +async def list_strategies(): + """List available venture strategies.""" + return { + "success": True, + "strategies": list(VENTURE_STRATEGIES.keys()), + "timestamp": datetime.now().isoformat() + } + +@router.get("/metrics/{venture_type}") +async def get_venture_metrics(venture_type: str): + """Get venture performance metrics.""" + try: + strategy = VENTURE_STRATEGIES.get(venture_type) + if not strategy: + raise HTTPException( + status_code=400, + detail=f"Invalid venture type: {venture_type}" + ) + + metrics = strategy.get_venture_metrics() + return { + "success": True, + "metrics": metrics, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.get("/insights") +async def get_market_insights(): + """Get comprehensive market insights.""" + try: + analyzer = MarketAnalyzer() + insights = analyzer.get_market_insights() + return { + "success": True, + "insights": insights, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.get("/portfolio/insights") +async def get_portfolio_insights(): + """Get comprehensive portfolio insights.""" + try: + optimizer = PortfolioOptimizer() + insights = optimizer.get_portfolio_insights() + return { + "success": True, + "insights": insights, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.get("/monetization/metrics") +async def get_monetization_metrics(): + """Get comprehensive monetization metrics.""" + try: + optimizer = MonetizationOptimizer() + metrics = optimizer.get_monetization_metrics() + return { + "success": True, + "metrics": metrics, + "timestamp": datetime.now().isoformat() + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..d76be2859e9eb75e1ea7197b098b7511168ea94e --- /dev/null +++ b/app.py @@ -0,0 +1,633 @@ +""" +Advanced Agentic System Interface +------------------------------- +Provides a chat interface to interact with the autonomous agent teams: +- Team A: Coders (App/Software Developers) +- Team B: Business (Entrepreneurs) +- Team C: Research (Deep Online Research) +- Team D: Crypto & Sports Trading +""" + +import gradio as gr +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +import uvicorn +from typing import Dict, Any, List, Tuple, Optional +import logging +from pathlib import Path +import asyncio +from datetime import datetime +import json +from requests.adapters import HTTPAdapter, Retry +from urllib3.util.retry import Retry +import time + +from agentic_system import AgenticSystem +from team_management import TeamManager, TeamType, TeamObjective +from orchestrator import AgentOrchestrator +from reasoning import UnifiedReasoningEngine as ReasoningEngine + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Configure network settings +TIMEOUT = int(os.getenv('REQUESTS_TIMEOUT', '30')) +MAX_RETRIES = 5 +RETRY_BACKOFF = 1 + +def setup_requests_session(): + """Configure requests session with retries.""" + session = requests.Session() + retry_strategy = Retry( + total=MAX_RETRIES, + backoff_factor=RETRY_BACKOFF, + status_forcelist=[408, 429, 500, 502, 503, 504], + allowed_methods=["HEAD", "GET", "PUT", "DELETE", "OPTIONS", "TRACE"] + ) + adapter = HTTPAdapter(max_retries=retry_strategy) + session.mount("https://", adapter) + session.mount("http://", adapter) + return session + +def check_network(max_attempts=3): + """Check network connectivity with retries.""" + session = setup_requests_session() + + for attempt in range(max_attempts): + try: + # Try multiple DNS servers + for dns in ['8.8.8.8', '8.8.4.4', '1.1.1.1']: + try: + socket.gethostbyname('huggingface.co') + break + except socket.gaierror: + continue + + # Test connection to Hugging Face + response = session.get('https://huggingface.co/api/health', + timeout=TIMEOUT) + if response.status_code == 200: + return True + + except (requests.RequestException, socket.gaierror) as e: + logger.warning(f"Network check attempt {attempt + 1} failed: {e}") + if attempt < max_attempts - 1: + time.sleep(RETRY_BACKOFF * (attempt + 1)) + continue + + logger.error("Network connectivity check failed after all attempts") + return False + +class ChatInterface: + def __init__(self): + # Check network connectivity + if not check_network(): + logger.warning("Network connectivity issues detected - continuing with degraded functionality") + + # Initialize core components with consistent configuration + config = { + "min_confidence": 0.7, + "parallel_threshold": 3, + "learning_rate": 0.1, + "strategy_weights": { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + } + } + + self.orchestrator = AgentOrchestrator(config) + self.agentic_system = AgenticSystem(config) + self.team_manager = TeamManager(self.orchestrator) + self.chat_history = [] + self.active_objectives = {} + + # Set up network session + self.session = setup_requests_session() + + # Initialize teams + asyncio.run(self.team_manager.initialize_team_agents()) + + async def process_message( + self, + message: str, + history: List[List[str]] + ) -> Tuple[str, List[List[str]]]: + """Process incoming chat message.""" + try: + # Update chat history + self.chat_history = history + + # Process message + response = await self._handle_message(message) + + # Update history + if response: + history.append([message, response]) + + return response, history + + except Exception as e: + logger.error(f"Error processing message: {str(e)}") + error_msg = "I apologize, but I encountered an error. Please try again." + history.append([message, error_msg]) + return error_msg, history + + async def _handle_message(self, message: str) -> str: + """Handle message processing with error recovery.""" + try: + # Analyze intent + intent = await self._analyze_intent(message) + intent_type = self._get_intent_type(intent) + + # Route to appropriate handler + if intent_type == "query": + return await self._handle_query(message) + elif intent_type == "objective": + return await self._handle_objective(message) + elif intent_type == "status": + return await self._handle_status_request(message) + else: + return await self._handle_general_chat(message) + + except Exception as e: + logger.error(f"Error in message handling: {str(e)}") + return "I apologize, but I encountered an error processing your message. Please try again." + + def _get_intent_type(self, intent) -> str: + """Safely extract intent type from various result formats.""" + if isinstance(intent, dict): + return intent.get("type", "general") + return "general" + + async def _analyze_intent(self, message: str) -> Dict[str, Any]: + """Analyze user message intent with error handling.""" + try: + # Use reasoning engine to analyze intent + analysis = await self.orchestrator.reasoning_engine.reason( + query=message, + context={ + "chat_history": self.chat_history, + "active_objectives": self.active_objectives + } + ) + + return { + "type": analysis.get("intent_type", "general"), + "confidence": analysis.get("confidence", 0.5), + "entities": analysis.get("entities", []), + "action_required": analysis.get("action_required", False) + } + except Exception as e: + logger.error(f"Error analyzing intent: {str(e)}") + return {"type": "general", "confidence": 0.5} + + async def _handle_query(self, message: str) -> str: + """Handle information queries.""" + try: + # Get relevant teams for the query + recommended_teams = await self.team_manager.get_team_recommendations(message) + + # Get responses from relevant teams + responses = [] + for team_type in recommended_teams: + response = await self._get_team_response(team_type, message) + if response: + responses.append(response) + + if not responses: + return "I apologize, but I couldn't find a relevant answer to your query." + + # Combine and format responses + return self._format_team_responses(responses) + + except Exception as e: + logger.error(f"Error handling query: {str(e)}") + return "I apologize, but I encountered an error processing your query. Please try again." + + async def _handle_objective(self, message: str) -> str: + """Handle new objective creation.""" + try: + # Create new objective + objective_id = await self.team_manager.create_objective(message) + if not objective_id: + return "I apologize, but I couldn't create the objective. Please try again." + + # Format and return response + return self._format_objective_creation(objective_id) + + except Exception as e: + logger.error(f"Error creating objective: {str(e)}") + return "I apologize, but I encountered an error creating the objective. Please try again." + + async def _handle_status_request(self, message: str) -> str: + """Handle status check requests.""" + try: + # Get system status + system_status = await self.agentic_system.get_system_status() + + # Get team status + team_status = {} + for team_id, team in self.team_manager.teams.items(): + team_status[team.name] = await self.team_manager.monitor_objective_progress(team_id) + + # Get objective status + objective_status = {} + for obj_id, obj in self.active_objectives.items(): + objective_status[obj_id] = await self.team_manager.monitor_objective_progress(obj_id) + + return self._format_status_response(system_status, team_status, objective_status) + + except Exception as e: + logger.error(f"Error getting status: {str(e)}") + return "I apologize, but I encountered an error getting the status. Please try again." + + async def _handle_general_chat(self, message: str) -> str: + """Handle general chat interactions with error recovery.""" + try: + # Use reasoning engine for response generation + response = await self.orchestrator.reasoning_engine.reason( + query=message, + context={ + "chat_history": self.chat_history, + "system_state": await self.agentic_system.get_system_status() + } + ) + + if not response or not response.get("response"): + return "I apologize, but I couldn't generate a meaningful response. Please try again." + + return response["response"] + + except Exception as e: + logger.error(f"Error in general chat: {str(e)}") + return "I apologize, but I encountered an error processing your message. Please try again." + + async def _get_team_response(self, team_type: TeamType, query: str) -> Dict[str, Any]: + """Get response from a specific team.""" + try: + team = self.team_manager.teams.get(team_type.value) + if not team: + return None + + # Get response from team's agents + responses = [] + for agent in team.agents: + response = await agent.process_query(query) + if response: + responses.append(response) + + if not responses: + return None + + # Return best response + return self._combine_agent_responses(responses) + + except Exception as e: + logger.error(f"Error getting team response: {str(e)}") + return None + + def _combine_agent_responses(self, responses: List[Dict[str, Any]]) -> Dict[str, Any]: + """Combine multiple agent responses into a coherent response.""" + try: + # Sort by confidence + valid_responses = [ + r for r in responses + if r.get("success", False) and r.get("response") + ] + + if not valid_responses: + return None + + sorted_responses = sorted( + valid_responses, + key=lambda x: x.get("confidence", 0), + reverse=True + ) + + # Take the highest confidence response + return sorted_responses[0] + + except Exception as e: + logger.error(f"Error combining responses: {str(e)}") + return None + + def _format_team_responses(self, responses: List[Dict[str, Any]]) -> str: + """Format team responses into a readable message.""" + try: + if not responses: + return "No team responses available." + + formatted = [] + for resp in responses: + if resp and resp.get("response"): + team_name = resp.get("team_name", "Unknown Team") + confidence = resp.get("confidence", 0) + formatted.append( + f"\n{team_name} (Confidence: {confidence:.2%}):\n{resp['response']}" + ) + + if not formatted: + return "No valid team responses available." + + return "\n".join(formatted) + + except Exception as e: + logger.error(f"Error formatting responses: {str(e)}") + return "Error formatting team responses." + + def _format_objective_creation(self, objective_id: str) -> str: + """Format objective creation response.""" + try: + obj = self.active_objectives.get(objective_id) + if not obj: + return "Objective created but details not available." + + return "\n".join([ + "New Objective Created:", + f"Description: {obj['description']}", + f"Status: {obj['status']}", + f"Assigned Teams: {', '.join(t.value for t in obj['teams'])}" + ]) + + except Exception as e: + logger.error(f"Error formatting objective: {str(e)}") + return "Error formatting objective details." + + def _format_status_response( + self, + system_status: Dict[str, Any], + team_status: Dict[str, Any], + objective_status: Dict[str, Any] + ) -> str: + """Format status response.""" + try: + # Format system status + status = [ + "System Status:", + f"- State: {system_status['state']}", + f"- Active Agents: {system_status['agent_count']}", + f"- Active Tasks: {system_status['active_tasks']}", + "\nTeam Status:" + ] + + # Add team status + for team_name, team_info in team_status.items(): + status.extend([ + f"\n{team_name}:", + f"- Active Agents: {team_info['active_agents']}", + f"- Completion Rate: {team_info['completion_rate']:.2%}", + f"- Collaboration Score: {team_info['collaboration_score']:.2f}" + ]) + + # Add objective status + if objective_status: + status.append("\nActive Objectives:") + for obj_id, obj_info in objective_status.items(): + obj = self.active_objectives[obj_id] + status.extend([ + f"\n{obj['description']}:", + f"- Status: {obj['status']}", + f"- Teams: {', '.join(t.value for t in obj['teams'])}", + f"- Progress: {sum(t['completion_rate'] for t in obj_info.values())/len(obj_info):.2%}" + ]) + + return "\n".join(status) + + except Exception as e: + logger.error(f"Error formatting status: {str(e)}") + return "Error formatting status information." + +class VentureUI: + def __init__(self, app): + self.app = app + + def create_interface(self): + """Create the Gradio interface.""" + with gr.Blocks( + theme=gr.themes.Soft(), + analytics_enabled=False, + title="Advanced Agentic System" + ) as interface: + # Verify Gradio version + gr.Markdown(f""" + # Advanced Agentic System Chat Interface v{gr.__version__} + + Chat with our autonomous agent teams: + - Team A: Coders (App/Software Developers) + - Team B: Business (Entrepreneurs) + - Team C: Research (Deep Online Research) + - Team D: Crypto & Sports Trading + + You can: + 1. Ask questions + 2. Create new objectives + 3. Check status of teams and objectives + 4. Get insights and recommendations + """) + + chatbot = gr.Chatbot( + label="Chat History", + height=400, + bubble_full_width=False, + show_copy_button=True, + render_markdown=True + ) + + with gr.Row(): + msg = gr.Textbox( + label="Message", + placeholder="Chat with the Agentic System...", + lines=2, + scale=9, + autofocus=True, + container=True + ) + submit = gr.Button( + "Send", + scale=1, + variant="primary" + ) + + with gr.Row(): + clear = gr.ClearButton( + [msg, chatbot], + value="Clear Chat", + variant="secondary", + scale=1 + ) + retry = gr.Button( + "Retry Last", + variant="secondary", + scale=1 + ) + + async def respond(message, history): + try: + # Convert history to the format expected by process_message + history_list = [[x, y] for x, y in history] if history else [] + response, history_list = await self.app(message, history_list) + + # Update history + if history is None: + history = [] + history.append((message, response)) + + return "", history + except Exception as e: + logger.error(f"Error in chat response: {str(e)}") + error_msg = "I apologize, but I encountered an error. Please try again." + + if history is None: + history = [] + history.append((message, error_msg)) + + return "", history + + async def retry_last(history): + if not history: + return history + last_user_msg = history[-1][0] + history = history[:-1] # Remove last exchange + return await respond(last_user_msg, history) + + msg.submit( + respond, + [msg, chatbot], + [msg, chatbot], + api_name="chat" + ).then( + lambda: gr.update(interactive=True), + None, + [msg, submit], + queue=False + ) + + submit.click( + respond, + [msg, chatbot], + [msg, chatbot], + api_name="submit" + ).then( + lambda: gr.update(interactive=True), + None, + [msg, submit], + queue=False + ) + + retry.click( + retry_last, + [chatbot], + [chatbot], + api_name="retry" + ) + + # Event handlers for better UX + msg.change(lambda x: gr.update(interactive=bool(x.strip())), [msg], [submit]) + + # Add example inputs + gr.Examples( + examples=[ + "What can Team A (Coders) help me with?", + "Create a new objective: Analyze market trends", + "What's the status of all teams?", + "Give me insights about recent developments" + ], + inputs=msg, + label="Example Queries" + ) + + return interface + +def create_chat_interface() -> gr.Blocks: + """Create Gradio chat interface.""" + chat = ChatInterface() + ui = VentureUI(chat.process_message) + return ui.create_interface() + +# Initialize FastAPI +app = FastAPI( + title="Advanced Agentic System", + description="Venture Strategy Optimizer with OpenAI-compatible API", + version="1.0.0" +) + +# Add CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Include OpenAI-compatible routes +from api.openai_compatible import OpenAICompatibleAPI +reasoning_engine = UnifiedReasoningEngine() +openai_api = OpenAICompatibleAPI(reasoning_engine) +app.include_router(openai_api.router, tags=["OpenAI Compatible"]) + +# Original API routes +@app.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return { + "status": "healthy", + "version": "1.0.0", + "endpoints": { + "openai_compatible": "/v1/chat/completions", + "venture": "/api/venture", + "ui": "/" + } + } + +@app.post("/api/reason") +async def reason(query: str, context: Optional[Dict[str, Any]] = None): + """Reasoning endpoint.""" + try: + result = await reasoning_engine.reason(query, context or {}) + return result + except Exception as e: + logger.error(f"Reasoning error: {e}") + raise HTTPException(status_code=500, detail=str(e)) + +@app.post("/api/venture/analyze") +async def analyze_venture( + venture_type: str, + description: str, + metrics: Optional[Dict[str, Any]] = None +): + """Venture analysis endpoint.""" + try: + result = await VentureAPI(reasoning_engine).analyze_venture( + venture_type=venture_type, + description=description, + metrics=metrics or {} + ) + return result + except Exception as e: + logger.error(f"Analysis error: {e}") + raise HTTPException(status_code=500, detail=str(e)) + +@app.get("/api/venture/types") +async def get_venture_types(): + """Get available venture types.""" + return VentureAPI(reasoning_engine).get_venture_types() + +# Create Gradio interface +interface = create_chat_interface() + +# Mount Gradio app to FastAPI +app = gr.mount_gradio_app(app, interface, path="/") + +if __name__ == "__main__": + # Run with uvicorn when called directly + uvicorn.run( + "app:app", + host="0.0.0.0", + port=7860, + reload=True, + workers=4 + ) diff --git a/app.yaml b/app.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bfe7ed4dfc1c8a5c17b1b6ea267433fe83a7eef3 --- /dev/null +++ b/app.yaml @@ -0,0 +1,9 @@ +title: Advanced Reasoning System +emoji: 🧠 +colorFrom: indigo +colorTo: purple +sdk: gradio +sdk_version: 4.16.0 +app_file: app.py +pinned: false +license: mit diff --git a/app_space.sh b/app_space.sh new file mode 100644 index 0000000000000000000000000000000000000000..0298fce586bb0f03b4d057cb43fba246cc776756 --- /dev/null +++ b/app_space.sh @@ -0,0 +1,34 @@ +#!/bin/bash + +# Exit on error +set -e + +echo "Starting Advanced Agentic System initialization..." + +# Create necessary directories +mkdir -p /data/models +mkdir -p logs + +# Install system dependencies +apt-get update && apt-get install -y \ + git \ + git-lfs \ + cmake \ + build-essential \ + pkg-config \ + libcurl4-openssl-dev + +# Initialize git-lfs +git lfs install + +# Upgrade pip and install requirements +python -m pip install --upgrade pip +pip install -r requirements.txt + +# Download and initialize models +echo "Initializing models..." +python download_models_space.py + +# Start the application +echo "Starting Gradio interface..." +python app.py diff --git a/check_space_status.py b/check_space_status.py new file mode 100644 index 0000000000000000000000000000000000000000..cc6d3ad5c3e46f3d3c225815899671c1b648e51a --- /dev/null +++ b/check_space_status.py @@ -0,0 +1,55 @@ +from huggingface_hub import HfApi +import time +import os +import requests + +def check_space_status(): + api = HfApi() + space_name = "nananie143/Agentic_llm" + + try: + # First try direct API request + response = requests.get( + f"https://huggingface.co/api/spaces/{space_name}/runtime", + headers={"Authorization": f"Bearer {os.environ['HUGGINGFACE_TOKEN']}"} + ) + print(f"\nAPI Response Status: {response.status_code}") + if response.ok: + data = response.json() + print(f"Space Info: {data}") + return data.get("stage") + + # Fallback to HF API + space_info = api.space_info(space_name) + print(f"\nSpace Info via HF API: {space_info}") + + if hasattr(space_info, 'runtime'): + status = space_info.runtime.stage + print(f"Status: {status}") + return status + + print("No status information available") + return None + + except Exception as e: + print(f"Error checking status: {e}") + return None + +print("Starting Space status check...") +print("Will check every 30 seconds until the Space is running...") + +while True: + status = check_space_status() + print(f"Current status: {status}") + + if status == "RUNNING": + print("\nSpace is now running! ") + print(f"Access your Space at: https://huggingface.co/spaces/nananie143/Agentic_llm") + break + elif status == "FAILED": + print("\nSpace build failed! Please check the logs for details.") + break + elif status is None: + print("\nCouldn't determine status. Will try again...") + + time.sleep(30) diff --git a/check_versions.py b/check_versions.py new file mode 100755 index 0000000000000000000000000000000000000000..aee938539456441154b528981d01ea83845bf0a6 --- /dev/null +++ b/check_versions.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 + +import sys +import pkg_resources +import logging + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +def check_package_version(package_name): + """Check installed version of a package.""" + try: + version = pkg_resources.get_distribution(package_name).version + logger.info(f"{package_name} version: {version}") + return version + except pkg_resources.DistributionNotFound: + logger.error(f"{package_name} is not installed") + return None + +def main(): + """Check versions of key packages.""" + packages = [ + "gradio", + "torch", + "transformers", + "huggingface-hub", + "pydantic", + "fastapi", + "uvicorn" + ] + + logger.info("Checking package versions...") + for package in packages: + version = check_package_version(package) + if version is None: + sys.exit(1) + + # Verify Gradio version specifically + gradio_version = check_package_version("gradio") + if gradio_version: + major, minor, patch = map(int, gradio_version.split(".")) + if major < 4 or (major == 4 and minor < 44): + logger.error(f"Gradio version {gradio_version} is too old. Please upgrade to 4.44.1 or later") + sys.exit(1) + + logger.info("All package versions verified successfully") + return 0 + +if __name__ == "__main__": + sys.exit(main()) diff --git a/cleanup.sh b/cleanup.sh new file mode 100755 index 0000000000000000000000000000000000000000..a7bde9e44ef75908a9990536904c98f7f701b181 --- /dev/null +++ b/cleanup.sh @@ -0,0 +1,45 @@ +#!/bin/bash + +# Exit on error +set -e + +echo "Starting cleanup..." + +# Define core files to keep +CORE_FILES=( + "agentic_system.py" + "orchestrator.py" + "team_management.py" + "meta_learning.py" + "config.py" + "space.yml" + "app.py" + "startup.sh" + "check_versions.py" + "requirements.txt" + "upload_to_hub.py" + "app_space.sh" + ".gitattributes" +) + +# Remove backup and temporary files (excluding reasoning directory) +find . -type f ! -path "./reasoning/*" \( -name "*.bak*" -o -name "*.backup" -o -name "*.temp" -o -name "*.log" \) -delete + +# Remove cache files (excluding reasoning directory) +find . -type d ! -path "./reasoning/*" -name "__pycache__" -exec rm -rf {} + +find . -type f ! -path "./reasoning/*" -name "*.pyc" -delete + +# Remove sample and simplified files +rm -f simple_reasoning.py quick_check.py +rm -rf simple_reasoning/ + +# Remove environment files (after backing up if needed) +if [ -f .env ]; then + mv .env .env.backup +fi +rm -f .env.example + +# Remove quantum_learning.py since its functionality exists in reasoning/quantum.py +rm -f quantum_learning.py + +echo "Cleanup complete! All files in the reasoning directory have been preserved." diff --git a/config.py b/config.py new file mode 100644 index 0000000000000000000000000000000000000000..66d0e917aefcec7ed49106596f6a4c218acf268a --- /dev/null +++ b/config.py @@ -0,0 +1,452 @@ +""" +System Configuration +------------------ +Central configuration for the Agentic System including: +1. Local Model Settings +2. Team Settings +3. System Parameters +4. Resource Limits +5. Free API Configurations +""" + +import os +from typing import Dict, Any, Optional +from pathlib import Path +import json +import logging +from dataclasses import dataclass, field + +logger = logging.getLogger(__name__) + +@dataclass +class Config: + """Configuration for the Advanced Agentic System.""" + + # Core settings + min_confidence: float = 0.7 + parallel_threshold: int = 3 + learning_rate: float = 0.1 + + # Model settings + model_backend: str = field(default_factory=lambda: os.getenv('MODEL_BACKEND', 'huggingface')) + groq_api_key: Optional[str] = field(default_factory=lambda: os.getenv('GROQ_API_KEY')) + huggingface_token: Optional[str] = field(default_factory=lambda: os.getenv('HUGGINGFACE_TOKEN')) + + # API settings + enable_openai_compatibility: bool = True + api_rate_limit: int = 100 + api_timeout: int = 30 + + # Resource limits + max_parallel_requests: int = field( + default_factory=lambda: int(os.getenv('MAX_PARALLEL_REQUESTS', '10')) + ) + request_timeout: int = field( + default_factory=lambda: int(os.getenv('REQUEST_TIMEOUT', '30')) + ) + batch_size: int = field( + default_factory=lambda: int(os.getenv('BATCH_SIZE', '4')) + ) + + # Cache settings + enable_cache: bool = field( + default_factory=lambda: os.getenv('CACHE_MODELS', 'false').lower() == 'true' + ) + cache_dir: str = field( + default_factory=lambda: os.getenv('SPACE_CACHE_DIR', '/tmp/models') + ) + + # Strategy weights + strategy_weights: Dict[str, float] = field(default_factory=lambda: { + "LOCAL_LLM": 2.0, + "CHAIN_OF_THOUGHT": 1.5, + "TREE_OF_THOUGHTS": 1.5, + "META_LEARNING": 1.5, + "TASK_DECOMPOSITION": 1.3, + "RESOURCE_MANAGEMENT": 1.3, + "CONTEXTUAL_PLANNING": 1.3, + "ADAPTIVE_EXECUTION": 1.3, + "FEEDBACK_INTEGRATION": 1.3, + "BAYESIAN": 1.2, + "MARKET_ANALYSIS": 1.2, + "PORTFOLIO_OPTIMIZATION": 1.2, + "VENTURE": 1.2, + "MONETIZATION": 1.0, + "MULTIMODAL": 1.0, + "NEUROSYMBOLIC": 1.0, + "SPECIALIZED": 1.0, + "VENTURE_TYPE": 1.0, + "RECURSIVE": 1.0, + "ANALOGICAL": 1.0 + }) + + # Agentic system settings + agentic_system: Dict[str, Any] = field(default_factory=lambda: { + "min_confidence": 0.7, + "parallel_threshold": 3, + "learning_rate": 0.1, + "enable_meta_learning": True, + "enable_self_improvement": True, + "max_agents": 10, + "default_agent_config": { + "learning_rate": 0.1, + "risk_tolerance": 0.5, + "max_retries": 3 + } + }) + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize configuration.""" + if config: + for key, value in config.items(): + if hasattr(self, key): + setattr(self, key, value) + + # Validate configuration + self._validate_config() + + def _validate_config(self): + """Validate configuration values.""" + if self.min_confidence < 0 or self.min_confidence > 1: + raise ValueError("min_confidence must be between 0 and 1") + + if self.parallel_threshold < 1: + raise ValueError("parallel_threshold must be at least 1") + + if self.learning_rate <= 0 or self.learning_rate > 1: + raise ValueError("learning_rate must be between 0 and 1") + + if self.model_backend not in ['groq', 'huggingface']: + raise ValueError("model_backend must be either 'groq' or 'huggingface'") + + def get(self, key: str, default: Any = None) -> Any: + """Get configuration value.""" + return getattr(self, key, default) + + def to_dict(self) -> Dict[str, Any]: + """Convert configuration to dictionary.""" + return { + key: getattr(self, key) + for key in self.__annotations__ + if hasattr(self, key) + } + + @classmethod + def from_file(cls, filepath: str) -> 'Config': + """Load configuration from file.""" + path = Path(filepath) + if not path.exists(): + raise FileNotFoundError(f"Configuration file not found: {filepath}") + + with open(filepath, 'r') as f: + config = json.load(f) + + return cls(config) + + def save(self, filepath: str): + """Save configuration to file.""" + with open(filepath, 'w') as f: + json.dump(self.to_dict(), f, indent=2) + +class SystemConfig: + """System-wide configuration.""" + + # Base Paths + BASE_DIR = Path(__file__).parent.absolute() + CACHE_DIR = BASE_DIR / "cache" + LOG_DIR = BASE_DIR / "logs" + DATA_DIR = BASE_DIR / "data" + MODEL_DIR = BASE_DIR / "models" + + # System Parameters + DEBUG_MODE = os.getenv("DEBUG_MODE", "False").lower() == "true" + LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO") + MAX_WORKERS = int(os.getenv("MAX_WORKERS", "4")) + ASYNC_TIMEOUT = int(os.getenv("ASYNC_TIMEOUT", "30")) + + # Local Model Configurations + MODEL_CONFIG = { + "quick_coder": { + "name": "tugstugi/Qwen2.5-Coder-0.5B-QwQ-draft", + "type": "transformers", + "description": "Fast code completion and simple tasks", + "temperature": 0.2, + "max_tokens": 1000, + "timeout": 30 + }, + "deep_coder": { + "name": "YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF", + "type": "gguf", + "description": "Complex code generation and refactoring", + "temperature": 0.3, + "max_tokens": 2000, + "timeout": 45 + }, + "text_gen": { + "name": "Orenguteng/Llama-3-8B-Lexi-Uncensored", + "type": "transformers", + "description": "General text generation and reasoning", + "temperature": 0.7, + "max_tokens": 1500, + "timeout": 40 + }, + "workflow": { + "name": "deepseek-ai/JanusFlow-1.3B", + "type": "transformers", + "description": "Task planning and workflow management", + "temperature": 0.5, + "max_tokens": 1000, + "timeout": 30 + } + } + + # Team Configurations + TEAM_CONFIG = { + "coders": { + "min_agents": 3, + "max_agents": 7, + "capabilities": [ + "full_stack_development", + "cloud_architecture", + "ai_ml", + "blockchain", + "mobile_development" + ], + "resource_limits": { + "cpu_percent": 80, + "memory_mb": 4096, + "gpu_memory_mb": 2048 + } + }, + "business": { + "min_agents": 2, + "max_agents": 5, + "capabilities": [ + "market_analysis", + "business_strategy", + "digital_transformation", + "startup_innovation", + "product_management" + ], + "resource_limits": { + "cpu_percent": 60, + "memory_mb": 2048, + "api_calls_per_minute": 100 + } + }, + "research": { + "min_agents": 2, + "max_agents": 6, + "capabilities": [ + "deep_research", + "data_analysis", + "trend_forecasting", + "competitive_analysis", + "technology_assessment" + ], + "resource_limits": { + "cpu_percent": 70, + "memory_mb": 3072, + "api_calls_per_minute": 150 + } + }, + "traders": { + "min_agents": 2, + "max_agents": 5, + "capabilities": [ + "crypto_trading", + "sports_betting", + "risk_management", + "market_timing", + "portfolio_optimization" + ], + "resource_limits": { + "cpu_percent": 60, + "memory_mb": 2048, + "api_calls_per_minute": 200 + } + } + } + + # Resource Management + RESOURCE_LIMITS = { + "total_cpu_percent": 90, + "total_memory_mb": 8192, + "total_gpu_memory_mb": 4096, + "max_api_calls_per_minute": 500, + "max_concurrent_tasks": 20 + } + + # Collaboration Settings + COLLABORATION_CONFIG = { + "min_confidence_threshold": 0.6, + "max_team_size": 10, + "max_concurrent_objectives": 5, + "objective_timeout_minutes": 60, + "team_sync_interval_seconds": 30 + } + + # Error Recovery + ERROR_RECOVERY = { + "max_retries": 3, + "retry_delay_seconds": 5, + "error_threshold": 0.2, + "recovery_timeout": 300 + } + + # Monitoring + MONITORING = { + "metrics_interval_seconds": 60, + "health_check_interval": 30, + "performance_log_retention_days": 7, + "alert_threshold": { + "cpu": 85, + "memory": 90, + "error_rate": 0.1 + } + } + + # Free API Configurations (No API Keys Required) + API_CONFIG = { + "search": { + "duckduckgo": { + "base_url": "https://api.duckduckgo.com", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + }, + "wikipedia": { + "base_url": "https://en.wikipedia.org/w/api.php", + "rate_limit": 200, + "requires_auth": False, + "method": "GET" + }, + "arxiv": { + "base_url": "http://export.arxiv.org/api/query", + "rate_limit": 60, + "requires_auth": False, + "method": "GET" + }, + "crossref": { + "base_url": "https://api.crossref.org/works", + "rate_limit": 50, + "requires_auth": False, + "method": "GET" + }, + "unpaywall": { + "base_url": "https://api.unpaywall.org/v2", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + } + }, + "crypto": { + "coincap": { + "base_url": "https://api.coincap.io/v2", + "rate_limit": 200, + "requires_auth": False, + "method": "GET", + "endpoints": { + "assets": "/assets", + "rates": "/rates", + "markets": "/markets" + } + }, + "blockchair": { + "base_url": "https://api.blockchair.com", + "rate_limit": 30, + "requires_auth": False, + "method": "GET" + } + }, + "news": { + "wikinews": { + "base_url": "https://en.wikinews.org/w/api.php", + "rate_limit": 200, + "requires_auth": False, + "method": "GET" + }, + "reddit": { + "base_url": "https://www.reddit.com/r/news/.json", + "rate_limit": 60, + "requires_auth": False, + "method": "GET" + }, + "hackernews": { + "base_url": "https://hacker-news.firebaseio.com/v0", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + } + }, + "market_data": { + "yahoo_finance": { + "base_url": "https://query1.finance.yahoo.com/v8/finance", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + }, + "marketstack_free": { + "base_url": "https://api.marketstack.com/v1", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + } + }, + "sports": { + "football_data": { + "base_url": "https://www.football-data.org/v4", + "rate_limit": 10, + "requires_auth": False, + "method": "GET", + "free_endpoints": [ + "/competitions", + "/matches" + ] + }, + "nhl": { + "base_url": "https://statsapi.web.nhl.com/api/v1", + "rate_limit": 50, + "requires_auth": False, + "method": "GET" + }, + "mlb": { + "base_url": "https://statsapi.mlb.com/api/v1", + "rate_limit": 50, + "requires_auth": False, + "method": "GET" + } + }, + "web_scraping": { + "web_archive": { + "base_url": "https://archive.org/wayback/available", + "rate_limit": 40, + "requires_auth": False, + "method": "GET" + }, + "metahtml": { + "base_url": "https://html.spec.whatwg.org/multipage", + "rate_limit": 30, + "requires_auth": False, + "method": "GET" + } + } + } + + @classmethod + def get_team_config(cls, team_name: str) -> Dict[str, Any]: + """Get configuration for a specific team.""" + return cls.TEAM_CONFIG.get(team_name, {}) + + @classmethod + def get_model_config(cls, model_type: str) -> Dict[str, Any]: + """Get configuration for a specific model type.""" + return cls.MODEL_CONFIG.get(model_type, {}) + + @classmethod + def get_api_config(cls, api_name: str) -> Dict[str, Any]: + """Get configuration for a specific API.""" + for category in cls.API_CONFIG.values(): + if api_name in category: + return category[api_name] + return {} diff --git a/download_models.py b/download_models.py new file mode 100644 index 0000000000000000000000000000000000000000..4285d1f44572bf5ea63506e885f2af30188c3c0b --- /dev/null +++ b/download_models.py @@ -0,0 +1,76 @@ +"""Script to download and prepare models for HuggingFace Spaces.""" + +import os +import asyncio +import logging +from pathlib import Path +from huggingface_hub import HfApi, upload_file +from reasoning.model_manager import ModelManager + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +async def download_and_prepare_models(): + """Download all models and prepare for Spaces.""" + try: + # Initialize model manager + model_dir = os.path.join(os.getcwd(), "models") + manager = ModelManager(model_dir) + + # Create models directory + os.makedirs(model_dir, exist_ok=True) + + # Download all models + logger.info("Starting model downloads...") + await manager.initialize_all_models() + logger.info("All models downloaded successfully!") + + return True + + except Exception as e: + logger.error(f"Error downloading models: {e}") + return False + +def upload_to_spaces(space_name: str = "agentic-system-models"): + """Upload models to HuggingFace Spaces.""" + try: + api = HfApi() + model_dir = os.path.join(os.getcwd(), "models") + + # Create .gitattributes for LFS + gitattributes_path = os.path.join(model_dir, ".gitattributes") + with open(gitattributes_path, "w") as f: + f.write("*.gguf filter=lfs diff=lfs merge=lfs -text") + + # Upload .gitattributes first + api.upload_file( + path_or_fileobj=gitattributes_path, + path_in_repo=".gitattributes", + repo_id=f"spaces/{space_name}", + repo_type="space" + ) + + # Upload each model file + for model_file in Path(model_dir).glob("*.gguf"): + logger.info(f"Uploading {model_file.name}...") + api.upload_file( + path_or_fileobj=str(model_file), + path_in_repo=f"models/{model_file.name}", + repo_id=f"spaces/{space_name}", + repo_type="space" + ) + + logger.info("All models uploaded to Spaces successfully!") + return True + + except Exception as e: + logger.error(f"Error uploading to Spaces: {e}") + return False + +if __name__ == "__main__": + # Download models + asyncio.run(download_and_prepare_models()) + + # Upload to Spaces + upload_to_spaces() diff --git a/download_models_space.py b/download_models_space.py new file mode 100644 index 0000000000000000000000000000000000000000..43e0723a903a4df938091dce44b8f4a2e186bc84 --- /dev/null +++ b/download_models_space.py @@ -0,0 +1,32 @@ +"""Initialize and download models in Hugging Face Spaces environment.""" + +import os +import asyncio +import logging +from huggingface_hub import HfApi +from reasoning.model_manager import ModelManager + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +async def initialize_space_models(): + """Download and initialize models in Spaces environment.""" + try: + # Initialize model manager + manager = ModelManager() + + # Download all models + logger.info("Starting model downloads in Spaces environment...") + await manager.initialize_all_models() + logger.info("All models downloaded and initialized successfully!") + + return True + + except Exception as e: + logger.error(f"Error initializing models in Spaces: {e}") + return False + +if __name__ == "__main__": + # Initialize models in Spaces + asyncio.run(initialize_space_models()) diff --git a/fix_indentation.patch b/fix_indentation.patch new file mode 100644 index 0000000000000000000000000000000000000000..94a703d30fd067c36b53375b98f0890c88dad832 --- /dev/null +++ b/fix_indentation.patch @@ -0,0 +1,42 @@ +--- reasoning.py ++++ reasoning.py +@@ -2796,297 +2796,297 @@ + async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + try: + # Extract modality types + modal_types = list(modalities.keys()) + + # Initialize alignment results + alignments = [] + + # Process each modality pair + for i in range(len(modal_types)): + for j in range(i + 1, len(modal_types)): + type1, type2 = modal_types[i], modal_types[j] + + # Get items from each modality + items1 = modalities[type1] + items2 = modalities[type2] + + # Find alignments between items + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > 0.5: # Threshold for alignment + alignments.append({ + "type1": type1, + "type2": type2, + "item1": item1, + "item2": item2, + "similarity": similarity + }) + + # Sort alignments by similarity + alignments.sort(key=lambda x: x["similarity"], reverse=True) + + return alignments + + except Exception as e: + logging.error(f"Error in cross-modal alignment: {str(e)}") + return [] diff --git a/init_space.sh b/init_space.sh new file mode 100644 index 0000000000000000000000000000000000000000..5c3c19a5ca436bd08b974e22ed6220c1f9a8af04 --- /dev/null +++ b/init_space.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +# Create necessary directories +mkdir -p models +mkdir -p logs + +# Install system dependencies +apt-get update && apt-get install -y \ + git \ + git-lfs \ + python3-pip \ + python3-dev \ + build-essential \ + cmake \ + pkg-config \ + libcurl4-openssl-dev + +# Initialize git-lfs +git lfs install + +# Install Python dependencies +pip install -r requirements.txt + +# Download models +python download_models.py + +# Start the application +python app.py diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..573a21348368cade9713c55f4115a35f4d66afce --- /dev/null +++ b/main.py @@ -0,0 +1,133 @@ +"""Main entry point for the Advanced Agentic System.""" + +import asyncio +import logging +from typing import Dict, Any, Optional +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +import gradio as gr +import uvicorn + +from agentic_system import AgenticSystem +from reasoning.unified_engine import UnifiedReasoningEngine +from api.openai_compatible import OpenAICompatibleAPI +from api.venture_api import VentureAPI +from ui.venture_ui import VentureUI +from config import Config + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +class AgenticSystemApp: + """Main application class integrating all components.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + self.config = Config(config) + + # Initialize core components + self.reasoning_engine = UnifiedReasoningEngine( + min_confidence=self.config.get('min_confidence', 0.7), + parallel_threshold=self.config.get('parallel_threshold', 3), + learning_rate=self.config.get('learning_rate', 0.1) + ) + + self.agentic_system = AgenticSystem( + config=self.config.get('agentic_system', {}) + ) + + # Initialize APIs + self.venture_api = VentureAPI(self.reasoning_engine) + self.openai_api = OpenAICompatibleAPI(self.reasoning_engine) + + # Initialize FastAPI + self.app = FastAPI( + title="Advanced Agentic System", + description="Venture Strategy Optimizer with OpenAI-compatible API", + version="1.0.0" + ) + + # Setup middleware + self._setup_middleware() + + # Setup routes + self._setup_routes() + + # Initialize UI + self.ui = VentureUI(self.venture_api) + self.interface = self.ui.create_interface() + + # Mount Gradio app + self.app = gr.mount_gradio_app(self.app, self.interface, path="/") + + def _setup_middleware(self): + """Setup FastAPI middleware.""" + self.app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + def _setup_routes(self): + """Setup API routes.""" + # Include OpenAI-compatible routes + self.app.include_router( + self.openai_api.router, + tags=["OpenAI Compatible"] + ) + + # Health check + @self.app.get("/api/health") + async def health_check(): + return { + "status": "healthy", + "version": "1.0.0", + "components": { + "reasoning_engine": "active", + "agentic_system": "active", + "openai_api": "active", + "venture_api": "active" + } + } + + # System status + @self.app.get("/api/system/status") + async def system_status(): + return await self.agentic_system.get_system_status() + + # Reasoning endpoint + @self.app.post("/api/reason") + async def reason(query: str, context: Optional[Dict[str, Any]] = None): + return await self.reasoning_engine.reason(query, context or {}) + + # Venture analysis + @self.app.post("/api/venture/analyze") + async def analyze_venture( + venture_type: str, + description: str, + metrics: Optional[Dict[str, Any]] = None + ): + return await self.venture_api.analyze_venture( + venture_type=venture_type, + description=description, + metrics=metrics or {} + ) + + def run(self, host: str = "0.0.0.0", port: int = 7860): + """Run the application.""" + uvicorn.run( + self.app, + host=host, + port=port, + workers=4 + ) + +def main(): + """Main entry point.""" + app = AgenticSystemApp() + app.run() + +if __name__ == "__main__": + main() diff --git a/meta_learning.py b/meta_learning.py new file mode 100644 index 0000000000000000000000000000000000000000..f58e6fd7302c77a255e99a36fa465c61ddfda849 --- /dev/null +++ b/meta_learning.py @@ -0,0 +1,436 @@ +""" +Meta-Learning System +------------------ +Implements meta-learning capabilities for improved learning and adaptation. +""" + +from typing import Dict, Any, List, Optional, Tuple +import numpy as np +from dataclasses import dataclass, field +import logging +from datetime import datetime +from enum import Enum +import json +from quantum_learning import QuantumLearningSystem, Pattern, PatternType + +class LearningStrategy(Enum): + GRADIENT_BASED = "gradient_based" + MEMORY_BASED = "memory_based" + EVOLUTIONARY = "evolutionary" + REINFORCEMENT = "reinforcement" + QUANTUM = "quantum" + +@dataclass +class MetaParameters: + """Meta-parameters for learning strategies""" + learning_rate: float = 0.01 + memory_size: int = 1000 + evolution_rate: float = 0.1 + exploration_rate: float = 0.2 + quantum_interference: float = 0.5 + adaptation_threshold: float = 0.7 + +@dataclass +class LearningMetrics: + """Metrics for learning performance""" + accuracy: float + convergence_rate: float + adaptation_speed: float + resource_usage: float + timestamp: str = field(default_factory=lambda: datetime.now().isoformat()) + +class MetaLearningSystem: + """Meta-learning system for optimizing learning strategies""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + self.logger = logging.getLogger(__name__) + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize quantum system with shared config + quantum_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights, + 'num_qubits': self.config.get('num_qubits', 8), + 'entanglement_strength': self.config.get('entanglement_strength', 0.5), + 'interference_threshold': self.config.get('interference_threshold', 0.3), + 'tunneling_rate': self.config.get('tunneling_rate', 0.1), + 'annealing_schedule': self.config.get('annealing_schedule', { + 'initial_temp': 1.0, + 'final_temp': 0.01, + 'steps': 100, + 'cooling_rate': 0.95 + }) + } + self.quantum_system = QuantumLearningSystem(quantum_config) + self.strategies = {} + self.performance_history = [] + self.meta_parameters = MetaParameters() + + async def optimize_learning( + self, + observation: Dict[str, Any], + current_strategy: LearningStrategy + ) -> Tuple[Dict[str, Any], LearningMetrics]: + """Optimize learning strategy based on observation""" + try: + # Process with quantum system + quantum_result = await self.quantum_system.process_observation(observation) + + # Evaluate current strategy + current_metrics = self._evaluate_strategy( + current_strategy, + observation, + quantum_result + ) + + # Update performance history + self._update_performance_history(current_metrics) + + # Adapt meta-parameters + self._adapt_meta_parameters(current_metrics) + + # Select optimal strategy + optimal_strategy = self._select_optimal_strategy( + observation, + current_metrics + ) + + # Apply selected strategy + result = await self._apply_strategy( + optimal_strategy, + observation, + quantum_result + ) + + return result, current_metrics + + except Exception as e: + self.logger.error(f"Failed to optimize learning: {str(e)}") + raise + + def _evaluate_strategy( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> LearningMetrics: + """Evaluate performance of current learning strategy""" + # Calculate accuracy + accuracy = self._calculate_accuracy( + strategy, + observation, + quantum_result + ) + + # Calculate convergence rate + convergence_rate = self._calculate_convergence_rate( + strategy, + self.performance_history + ) + + # Calculate adaptation speed + adaptation_speed = self._calculate_adaptation_speed( + strategy, + observation + ) + + # Calculate resource usage + resource_usage = self._calculate_resource_usage(strategy) + + return LearningMetrics( + accuracy=accuracy, + convergence_rate=convergence_rate, + adaptation_speed=adaptation_speed, + resource_usage=resource_usage + ) + + def _update_performance_history( + self, + metrics: LearningMetrics + ) -> None: + """Update performance history with new metrics""" + self.performance_history.append(metrics) + + # Maintain history size + if len(self.performance_history) > self.meta_parameters.memory_size: + self.performance_history.pop(0) + + def _adapt_meta_parameters( + self, + metrics: LearningMetrics + ) -> None: + """Adapt meta-parameters based on performance metrics""" + # Adjust learning rate + if metrics.convergence_rate < self.meta_parameters.adaptation_threshold: + self.meta_parameters.learning_rate *= 0.9 + else: + self.meta_parameters.learning_rate *= 1.1 + + # Adjust memory size + if metrics.resource_usage > 0.8: + self.meta_parameters.memory_size = int( + self.meta_parameters.memory_size * 0.9 + ) + elif metrics.resource_usage < 0.2: + self.meta_parameters.memory_size = int( + self.meta_parameters.memory_size * 1.1 + ) + + # Adjust evolution rate + if metrics.adaptation_speed < self.meta_parameters.adaptation_threshold: + self.meta_parameters.evolution_rate *= 1.1 + else: + self.meta_parameters.evolution_rate *= 0.9 + + # Adjust exploration rate + if metrics.accuracy < self.meta_parameters.adaptation_threshold: + self.meta_parameters.exploration_rate *= 1.1 + else: + self.meta_parameters.exploration_rate *= 0.9 + + # Adjust quantum interference + if metrics.accuracy > 0.8: + self.meta_parameters.quantum_interference *= 1.1 + else: + self.meta_parameters.quantum_interference *= 0.9 + + # Ensure parameters stay within reasonable bounds + self._normalize_parameters() + + def _normalize_parameters(self) -> None: + """Normalize meta-parameters to stay within bounds""" + self.meta_parameters.learning_rate = np.clip( + self.meta_parameters.learning_rate, + 0.001, + 0.1 + ) + self.meta_parameters.memory_size = np.clip( + self.meta_parameters.memory_size, + 100, + 10000 + ) + self.meta_parameters.evolution_rate = np.clip( + self.meta_parameters.evolution_rate, + 0.01, + 0.5 + ) + self.meta_parameters.exploration_rate = np.clip( + self.meta_parameters.exploration_rate, + 0.1, + 0.9 + ) + self.meta_parameters.quantum_interference = np.clip( + self.meta_parameters.quantum_interference, + 0.1, + 0.9 + ) + + def _select_optimal_strategy( + self, + observation: Dict[str, Any], + metrics: LearningMetrics + ) -> LearningStrategy: + """Select optimal learning strategy""" + strategies = list(LearningStrategy) + scores = [] + + for strategy in strategies: + # Calculate strategy score + score = self._calculate_strategy_score( + strategy, + observation, + metrics + ) + scores.append((strategy, score)) + + # Select strategy with highest score + optimal_strategy = max(scores, key=lambda x: x[1])[0] + + return optimal_strategy + + async def _apply_strategy( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply selected learning strategy""" + if strategy == LearningStrategy.GRADIENT_BASED: + return await self._apply_gradient_strategy( + observation, + quantum_result + ) + elif strategy == LearningStrategy.MEMORY_BASED: + return await self._apply_memory_strategy( + observation, + quantum_result + ) + elif strategy == LearningStrategy.EVOLUTIONARY: + return await self._apply_evolutionary_strategy( + observation, + quantum_result + ) + elif strategy == LearningStrategy.REINFORCEMENT: + return await self._apply_reinforcement_strategy( + observation, + quantum_result + ) + else: # QUANTUM + return quantum_result + + def _calculate_accuracy( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> float: + """Calculate accuracy of learning strategy""" + if "patterns" not in quantum_result: + return 0.0 + + patterns = quantum_result["patterns"] + if not patterns: + return 0.0 + + # Calculate pattern confidence + confidence_sum = sum(pattern.confidence for pattern in patterns) + return confidence_sum / len(patterns) + + def _calculate_convergence_rate( + self, + strategy: LearningStrategy, + history: List[LearningMetrics] + ) -> float: + """Calculate convergence rate of learning strategy""" + if not history: + return 0.0 + + # Calculate rate of improvement + accuracies = [metrics.accuracy for metrics in history[-10:]] + if len(accuracies) < 2: + return 0.0 + + differences = np.diff(accuracies) + return float(np.mean(differences > 0)) + + def _calculate_adaptation_speed( + self, + strategy: LearningStrategy, + observation: Dict[str, Any] + ) -> float: + """Calculate adaptation speed of learning strategy""" + if not self.performance_history: + return 0.0 + + # Calculate time to reach adaptation threshold + threshold = self.meta_parameters.adaptation_threshold + for i, metrics in enumerate(self.performance_history): + if metrics.accuracy >= threshold: + return 1.0 / (i + 1) + + return 0.0 + + def _calculate_resource_usage( + self, + strategy: LearningStrategy + ) -> float: + """Calculate resource usage of learning strategy""" + # Simulate resource usage based on strategy + base_usage = { + LearningStrategy.GRADIENT_BASED: 0.4, + LearningStrategy.MEMORY_BASED: 0.6, + LearningStrategy.EVOLUTIONARY: 0.7, + LearningStrategy.REINFORCEMENT: 0.5, + LearningStrategy.QUANTUM: 0.8 + } + + return base_usage[strategy] + + def _calculate_strategy_score( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + metrics: LearningMetrics + ) -> float: + """Calculate score for learning strategy""" + # Weight different factors + weights = { + "accuracy": 0.4, + "convergence": 0.2, + "adaptation": 0.2, + "resources": 0.2 + } + + score = ( + weights["accuracy"] * metrics.accuracy + + weights["convergence"] * metrics.convergence_rate + + weights["adaptation"] * metrics.adaptation_speed + + weights["resources"] * (1 - metrics.resource_usage) + ) + + # Add exploration bonus + if np.random.random() < self.meta_parameters.exploration_rate: + score += 0.1 + + return score + + async def _apply_gradient_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply gradient-based learning strategy""" + return { + "result": "gradient_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } + + async def _apply_memory_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply memory-based learning strategy""" + return { + "result": "memory_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } + + async def _apply_evolutionary_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply evolutionary learning strategy""" + return { + "result": "evolutionary_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } + + async def _apply_reinforcement_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply reinforcement learning strategy""" + return { + "result": "reinforcement_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } diff --git a/multimodal_reasoning.py b/multimodal_reasoning.py new file mode 100644 index 0000000000000000000000000000000000000000..01f857b3fe39ef8c2f0934790ed0fb744ec7132b --- /dev/null +++ b/multimodal_reasoning.py @@ -0,0 +1,301 @@ +""" +Multi-Modal Reasoning Implementation +---------------------------------- +Implements reasoning across different types of information. +""" + +import logging +from typing import Dict, Any, List, Optional +from datetime import datetime +import json +import numpy as np +from .reasoning import ReasoningStrategy + +class MultiModalReasoning(ReasoningStrategy): + """Implements multi-modal reasoning across different types of information.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize multi-modal reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Multi-modal specific parameters + self.modality_weights = self.config.get('modality_weights', { + 'text': 0.8, + 'image': 0.7, + 'audio': 0.6, + 'video': 0.5, + 'structured': 0.7 + }) + self.cross_modal_threshold = self.config.get('cross_modal_threshold', 0.6) + self.integration_steps = self.config.get('integration_steps', 3) + self.alignment_method = self.config.get('alignment_method', 'attention') + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Process different modalities + modalities = await self._process_modalities(query, context) + + # Align across modalities + alignment = await self._cross_modal_alignment(modalities, context) + + # Integrated analysis + integration = await self._integrated_analysis(alignment, context) + + # Generate final response + response = await self._generate_response(integration, context) + + return { + "success": True, + "answer": response["conclusion"], + "modalities": modalities, + "alignment": alignment, + "integration": integration, + "confidence": response["confidence"] + } + except Exception as e: + logging.error(f"Error in multi-modal reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]: + """Process query across different modalities.""" + prompt = f""" + Process query across modalities: + Query: {query} + Context: {json.dumps(context)} + + For each modality extract: + 1. [Type]: Modality type + 2. [Content]: Relevant content + 3. [Features]: Key features + 4. [Quality]: Content quality + + Format as: + [M1] + Type: ... + Content: ... + Features: ... + Quality: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_modalities(response["answer"]) + + async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + try: + # Extract modality types + modal_types = list(modalities.keys()) + + # Initialize alignment results + alignments = [] + + # Process each modality pair + for i in range(len(modal_types)): + for j in range(i + 1, len(modal_types)): + type1, type2 = modal_types[i], modal_types[j] + + # Get items from each modality + items1 = modalities[type1] + items2 = modalities[type2] + + # Find alignments between items + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > self.cross_modal_threshold: # Threshold for alignment + alignments.append({ + "type1": type1, + "type2": type2, + "item1": item1, + "item2": item2, + "similarity": similarity + }) + + # Sort alignments by similarity + alignments.sort(key=lambda x: x["similarity"], reverse=True) + + return alignments + + except Exception as e: + logging.error(f"Error in cross-modal alignment: {str(e)}") + return [] + + def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float: + """Calculate similarity between two items from different modalities.""" + try: + # Extract content from items + content1 = str(item1.get("content", "")) + content2 = str(item2.get("content", "")) + + # Calculate basic similarity (can be enhanced with more sophisticated methods) + common_words = set(content1.lower().split()) & set(content2.lower().split()) + total_words = set(content1.lower().split()) | set(content2.lower().split()) + + if not total_words: + return 0.0 + + return len(common_words) / len(total_words) + + except Exception as e: + logging.error(f"Error calculating similarity: {str(e)}") + return 0.0 + + async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Perform integrated multi-modal analysis: + Alignment: {json.dumps(alignment)} + Context: {json.dumps(context)} + + For each insight: + 1. [Insight]: Key finding + 2. [Sources]: Contributing modalities + 3. [Support]: Supporting evidence + 4. [Confidence]: Confidence level + + Format as: + [I1] + Insight: ... + Sources: ... + Support: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_integration(response["answer"]) + + async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate unified multi-modal response: + Integration: {json.dumps(integration)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Modal contributions + 3. Integration benefits + 4. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_response(response["answer"]) + + def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]: + """Parse modalities from response.""" + modalities = {} + current_modality = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[M'): + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + current_modality = { + "type": "", + "content": "", + "features": "", + "quality": "" + } + elif current_modality: + if line.startswith('Type:'): + current_modality["type"] = line[5:].strip() + elif line.startswith('Content:'): + current_modality["content"] = line[8:].strip() + elif line.startswith('Features:'): + current_modality["features"] = line[9:].strip() + elif line.startswith('Quality:'): + current_modality["quality"] = line[8:].strip() + + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + + return modalities + + def _parse_integration(self, response: str) -> List[Dict[str, Any]]: + """Parse integration from response.""" + integration = [] + current_insight = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_insight: + integration.append(current_insight) + current_insight = { + "insight": "", + "sources": "", + "support": "", + "confidence": 0.0 + } + elif current_insight: + if line.startswith('Insight:'): + current_insight["insight"] = line[8:].strip() + elif line.startswith('Sources:'): + current_insight["sources"] = line[8:].strip() + elif line.startswith('Support:'): + current_insight["support"] = line[8:].strip() + elif line.startswith('Confidence:'): + try: + current_insight["confidence"] = float(line[11:].strip()) + except: + pass + + if current_insight: + integration.append(current_insight) + + return integration + + def _parse_response(self, response: str) -> Dict[str, Any]: + """Parse response from response.""" + response_dict = { + "conclusion": "", + "modal_contributions": [], + "integration_benefits": [], + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + response_dict["conclusion"] = line[11:].strip() + elif line.startswith('Modal Contributions:'): + mode = "modal" + elif line.startswith('Integration Benefits:'): + mode = "integration" + elif line.startswith('Confidence:'): + try: + response_dict["confidence"] = float(line[11:].strip()) + except: + response_dict["confidence"] = 0.5 + mode = None + elif mode == "modal" and line.startswith('- '): + response_dict["modal_contributions"].append(line[2:].strip()) + elif mode == "integration" and line.startswith('- '): + response_dict["integration_benefits"].append(line[2:].strip()) + + return response_dict diff --git a/orchestrator.py b/orchestrator.py new file mode 100644 index 0000000000000000000000000000000000000000..034ad0f25eba6b141ec2b23e413c250d00771846 --- /dev/null +++ b/orchestrator.py @@ -0,0 +1,628 @@ +""" +Agentic Orchestrator for Advanced AI System +----------------------------------------- +Manages and coordinates multiple agentic components: +1. Task Planning & Decomposition +2. Resource Management +3. Agent Communication +4. State Management +5. Error Recovery +6. Performance Monitoring +""" + +import logging +from typing import Dict, Any, List, Optional, Union, TypeVar, Generic +from dataclasses import dataclass, field +from enum import Enum +import json +import asyncio +from datetime import datetime +import uuid +from concurrent.futures import ThreadPoolExecutor +import networkx as nx +from collections import defaultdict +import numpy as np + +from reasoning import UnifiedReasoningEngine as ReasoningEngine, StrategyType as ReasoningMode +from reasoning.meta_learning import MetaLearningStrategy + +T = TypeVar('T') + +class AgentRole(Enum): + """Different roles an agent can take.""" + PLANNER = "planner" + EXECUTOR = "executor" + MONITOR = "monitor" + COORDINATOR = "coordinator" + LEARNER = "learner" + +class AgentState(Enum): + """Possible states of an agent.""" + IDLE = "idle" + BUSY = "busy" + ERROR = "error" + LEARNING = "learning" + TERMINATED = "terminated" + +class TaskPriority(Enum): + """Task priority levels.""" + LOW = 0 + MEDIUM = 1 + HIGH = 2 + CRITICAL = 3 + +@dataclass +class AgentMetadata: + """Metadata about an agent.""" + id: str + role: AgentRole + capabilities: List[str] + state: AgentState + load: float + last_active: datetime + metrics: Dict[str, float] + +@dataclass +class Task: + """Represents a task in the system.""" + id: str + description: str + priority: TaskPriority + dependencies: List[str] + assigned_to: Optional[str] + state: str + created_at: datetime + deadline: Optional[datetime] + metadata: Dict[str, Any] + +class AgentOrchestrator: + """Advanced orchestrator for managing agentic system.""" + + def __init__(self, config: Dict[str, Any] = None): + self.config = config or {} + + # Core components + self.agents: Dict[str, AgentMetadata] = {} + self.tasks: Dict[str, Task] = {} + self.task_graph = nx.DiGraph() + + # State management + self.state_history: List[Dict[str, Any]] = [] + self.global_state: Dict[str, Any] = {} + + # Resource management + self.resource_pool: Dict[str, Any] = {} + self.resource_locks: Dict[str, asyncio.Lock] = {} + + # Communication + self.message_queue = asyncio.Queue() + self.event_bus = asyncio.Queue() + + # Performance monitoring + self.metrics = defaultdict(list) + self.performance_log = [] + + # Error handling + self.error_handlers: Dict[str, callable] = {} + self.recovery_strategies: Dict[str, callable] = {} + + # Async support + self.executor = ThreadPoolExecutor(max_workers=4) + self.lock = asyncio.Lock() + + # Logging + self.logger = logging.getLogger(__name__) + + # Initialize components + self._init_components() + + def _init_components(self): + """Initialize orchestrator components.""" + # Initialize reasoning engine + self.reasoning_engine = ReasoningEngine( + min_confidence=0.7, + parallel_threshold=5, + learning_rate=0.1, + strategy_weights={ + "LOCAL_LLM": 2.0, + "CHAIN_OF_THOUGHT": 1.0, + "TREE_OF_THOUGHTS": 1.0, + "META_LEARNING": 1.5 + } + ) + + # Initialize meta-learning + self.meta_learning = MetaLearningStrategy() + + # Register basic error handlers + self._register_error_handlers() + + async def register_agent( + self, + role: AgentRole, + capabilities: List[str] + ) -> str: + """Register a new agent with the orchestrator.""" + agent_id = str(uuid.uuid4()) + + agent = AgentMetadata( + id=agent_id, + role=role, + capabilities=capabilities, + state=AgentState.IDLE, + load=0.0, + last_active=datetime.now(), + metrics={} + ) + + async with self.lock: + self.agents[agent_id] = agent + self.logger.info(f"Registered new agent: {agent_id} with role {role}") + + return agent_id + + async def submit_task( + self, + description: str, + priority: TaskPriority = TaskPriority.MEDIUM, + dependencies: List[str] = None, + deadline: Optional[datetime] = None, + metadata: Dict[str, Any] = None + ) -> str: + """Submit a new task to the orchestrator.""" + task_id = str(uuid.uuid4()) + + task = Task( + id=task_id, + description=description, + priority=priority, + dependencies=dependencies or [], + assigned_to=None, + state="pending", + created_at=datetime.now(), + deadline=deadline, + metadata=metadata or {} + ) + + async with self.lock: + self.tasks[task_id] = task + self._update_task_graph(task) + + # Trigger task planning + await self._plan_task_execution(task_id) + + return task_id + + async def _plan_task_execution(self, task_id: str) -> None: + """Plan the execution of a task.""" + task = self.tasks[task_id] + + # Check dependencies + if not await self._check_dependencies(task): + self.logger.info(f"Task {task_id} waiting for dependencies") + return + + # Find suitable agent + agent_id = await self._find_suitable_agent(task) + if not agent_id: + self.logger.warning(f"No suitable agent found for task {task_id}") + return + + # Assign task + await self._assign_task(task_id, agent_id) + + async def _check_dependencies(self, task: Task) -> bool: + """Check if all task dependencies are satisfied.""" + for dep_id in task.dependencies: + if dep_id not in self.tasks: + return False + if self.tasks[dep_id].state != "completed": + return False + return True + + async def _find_suitable_agent(self, task: Task) -> Optional[str]: + """Find the most suitable agent for a task.""" + best_agent = None + best_score = float('-inf') + + for agent_id, agent in self.agents.items(): + if agent.state != AgentState.IDLE: + continue + + score = await self._calculate_agent_suitability(agent, task) + if score > best_score: + best_score = score + best_agent = agent_id + + return best_agent + + async def _calculate_agent_suitability( + self, + agent: AgentMetadata, + task: Task + ) -> float: + """Calculate how suitable an agent is for a task.""" + # Base score on capabilities match + capability_score = sum( + 1 for cap in task.metadata.get("required_capabilities", []) + if cap in agent.capabilities + ) + + # Consider agent load + load_score = 1 - agent.load + + # Consider agent's recent performance + performance_score = sum(agent.metrics.values()) / len(agent.metrics) if agent.metrics else 0.5 + + # Weighted combination + weights = self.config.get("agent_selection_weights", { + "capabilities": 0.5, + "load": 0.3, + "performance": 0.2 + }) + + return ( + weights["capabilities"] * capability_score + + weights["load"] * load_score + + weights["performance"] * performance_score + ) + + async def _assign_task(self, task_id: str, agent_id: str) -> None: + """Assign a task to an agent.""" + async with self.lock: + task = self.tasks[task_id] + agent = self.agents[agent_id] + + task.assigned_to = agent_id + task.state = "assigned" + agent.state = AgentState.BUSY + agent.load += 1 + agent.last_active = datetime.now() + + self.logger.info(f"Assigned task {task_id} to agent {agent_id}") + + # Notify agent + await self.message_queue.put({ + "type": "task_assignment", + "task_id": task_id, + "agent_id": agent_id, + "timestamp": datetime.now() + }) + + def _update_task_graph(self, task: Task) -> None: + """Update the task dependency graph.""" + self.task_graph.add_node(task.id, task=task) + for dep_id in task.dependencies: + self.task_graph.add_edge(dep_id, task.id) + + async def _monitor_system_state(self): + """Monitor overall system state.""" + while True: + try: + # Collect agent states + agent_states = { + agent_id: { + "state": agent.state, + "load": agent.load, + "metrics": agent.metrics + } + for agent_id, agent in self.agents.items() + } + + # Collect task states + task_states = { + task_id: { + "state": task.state, + "assigned_to": task.assigned_to, + "deadline": task.deadline + } + for task_id, task in self.tasks.items() + } + + # Update global state + self.global_state = { + "timestamp": datetime.now(), + "agents": agent_states, + "tasks": task_states, + "resource_usage": self._get_resource_usage(), + "performance_metrics": self._calculate_performance_metrics() + } + + # Archive state + self.state_history.append(self.global_state.copy()) + + # Trim history if too long + if len(self.state_history) > 1000: + self.state_history = self.state_history[-1000:] + + # Check for anomalies + await self._check_anomalies() + + await asyncio.sleep(1) # Monitor frequency + + except Exception as e: + self.logger.error(f"Error in system monitoring: {e}") + await self._handle_error("monitoring_error", e) + + def _get_resource_usage(self) -> Dict[str, float]: + """Get current resource usage statistics.""" + return { + "cpu_usage": sum(agent.load for agent in self.agents.values()) / len(self.agents), + "memory_usage": len(self.state_history) * 1000, # Rough estimate + "queue_size": self.message_queue.qsize() + } + + def _calculate_performance_metrics(self) -> Dict[str, float]: + """Calculate current performance metrics.""" + metrics = {} + + # Task completion rate + completed_tasks = sum(1 for task in self.tasks.values() if task.state == "completed") + total_tasks = len(self.tasks) + metrics["task_completion_rate"] = completed_tasks / max(1, total_tasks) + + # Average task duration + durations = [] + for task in self.tasks.values(): + if task.state == "completed" and "completion_time" in task.metadata: + duration = (task.metadata["completion_time"] - task.created_at).total_seconds() + durations.append(duration) + metrics["avg_task_duration"] = sum(durations) / len(durations) if durations else 0 + + # Agent utilization + metrics["agent_utilization"] = sum(agent.load for agent in self.agents.values()) / len(self.agents) + + return metrics + + async def _check_anomalies(self): + """Check for system anomalies.""" + # Check for overloaded agents + for agent_id, agent in self.agents.items(): + if agent.load > 0.9: # 90% load threshold + await self._handle_overload(agent_id) + + # Check for stalled tasks + now = datetime.now() + for task_id, task in self.tasks.items(): + if task.state == "assigned": + duration = (now - task.created_at).total_seconds() + if duration > 3600: # 1 hour threshold + await self._handle_stalled_task(task_id) + + # Check for missed deadlines + for task_id, task in self.tasks.items(): + if task.deadline and now > task.deadline and task.state != "completed": + await self._handle_missed_deadline(task_id) + + async def _handle_overload(self, agent_id: str): + """Handle an overloaded agent.""" + agent = self.agents[agent_id] + + # Try to redistribute tasks + assigned_tasks = [ + task_id for task_id, task in self.tasks.items() + if task.assigned_to == agent_id and task.state == "assigned" + ] + + for task_id in assigned_tasks: + # Find another suitable agent + new_agent_id = await self._find_suitable_agent(self.tasks[task_id]) + if new_agent_id: + await self._reassign_task(task_id, new_agent_id) + + async def _handle_stalled_task(self, task_id: str): + """Handle a stalled task.""" + task = self.tasks[task_id] + + # First, try to ping the assigned agent + if task.assigned_to: + agent = self.agents[task.assigned_to] + if agent.state == AgentState.ERROR: + # Agent is in error state, reassign task + await self._reassign_task(task_id, None) + else: + # Request status update from agent + await self.message_queue.put({ + "type": "status_request", + "task_id": task_id, + "agent_id": task.assigned_to, + "timestamp": datetime.now() + }) + + async def _handle_missed_deadline(self, task_id: str): + """Handle a missed deadline.""" + task = self.tasks[task_id] + + # Log the incident + self.logger.warning(f"Task {task_id} missed deadline: {task.deadline}") + + # Update task priority to CRITICAL + task.priority = TaskPriority.CRITICAL + + # If task is assigned, try to speed it up + if task.assigned_to: + await self.message_queue.put({ + "type": "expedite_request", + "task_id": task_id, + "agent_id": task.assigned_to, + "timestamp": datetime.now() + }) + else: + # If not assigned, try to assign to fastest available agent + await self._plan_task_execution(task_id) + + async def _reassign_task(self, task_id: str, new_agent_id: Optional[str] = None): + """Reassign a task to a new agent.""" + task = self.tasks[task_id] + old_agent_id = task.assigned_to + + if old_agent_id: + # Update old agent + old_agent = self.agents[old_agent_id] + old_agent.load -= 1 + if old_agent.load <= 0: + old_agent.state = AgentState.IDLE + + if new_agent_id is None: + # Find new suitable agent + new_agent_id = await self._find_suitable_agent(task) + + if new_agent_id: + # Assign to new agent + await self._assign_task(task_id, new_agent_id) + else: + # No suitable agent found, mark task as pending + task.state = "pending" + task.assigned_to = None + + def _register_error_handlers(self): + """Register basic error handlers.""" + self.error_handlers.update({ + "monitoring_error": self._handle_monitoring_error, + "agent_error": self._handle_agent_error, + "task_error": self._handle_task_error, + "resource_error": self._handle_resource_error + }) + + self.recovery_strategies.update({ + "agent_recovery": self._recover_agent, + "task_recovery": self._recover_task, + "resource_recovery": self._recover_resource + }) + + async def _handle_error(self, error_type: str, error: Exception): + """Handle an error using registered handlers.""" + handler = self.error_handlers.get(error_type) + if handler: + try: + await handler(error) + except Exception as e: + self.logger.error(f"Error in error handler: {e}") + else: + self.logger.error(f"No handler for error type: {error_type}") + self.logger.error(f"Error: {error}") + + async def _handle_monitoring_error(self, error: Exception): + """Handle monitoring system errors.""" + self.logger.error(f"Monitoring error: {error}") + # Implement recovery logic + pass + + async def _handle_agent_error(self, error: Exception): + """Handle agent-related errors.""" + self.logger.error(f"Agent error: {error}") + # Implement recovery logic + pass + + async def _handle_task_error(self, error: Exception): + """Handle task-related errors.""" + self.logger.error(f"Task error: {error}") + # Implement recovery logic + pass + + async def _handle_resource_error(self, error: Exception): + """Handle resource-related errors.""" + self.logger.error(f"Resource error: {error}") + # Implement recovery logic + pass + + async def _recover_agent(self, agent_id: str): + """Recover a failed agent.""" + try: + agent = self.agents[agent_id] + + # Log recovery attempt + self.logger.info(f"Attempting to recover agent {agent_id}") + + # Reset agent state + agent.state = AgentState.IDLE + agent.load = 0 + agent.last_active = datetime.now() + + # Reassign any tasks that were assigned to this agent + for task_id, task in self.tasks.items(): + if task.assigned_to == agent_id: + await self._reassign_task(task_id) + + # Update metrics + agent.metrics["recovery_attempts"] = agent.metrics.get("recovery_attempts", 0) + 1 + + self.logger.info(f"Successfully recovered agent {agent_id}") + return True + + except Exception as e: + self.logger.error(f"Failed to recover agent {agent_id}: {e}") + return False + + async def _recover_task(self, task_id: str): + """Recover a failed task.""" + try: + task = self.tasks[task_id] + + # Log recovery attempt + self.logger.info(f"Attempting to recover task {task_id}") + + # Reset task state + task.state = "pending" + task.assigned_to = None + + # Try to reassign the task + await self._reassign_task(task_id) + + self.logger.info(f"Successfully recovered task {task_id}") + return True + + except Exception as e: + self.logger.error(f"Failed to recover task {task_id}: {e}") + return False + + async def _recover_resource(self, resource_id: str): + """Recover a failed resource.""" + try: + # Log recovery attempt + self.logger.info(f"Attempting to recover resource {resource_id}") + + # Release any locks on the resource + if resource_id in self.resource_locks: + lock = self.resource_locks[resource_id] + if lock.locked(): + lock.release() + + # Reset resource state + if resource_id in self.resource_pool: + self.resource_pool[resource_id] = { + "state": "available", + "last_error": None, + "last_recovery": datetime.now() + } + + self.logger.info(f"Successfully recovered resource {resource_id}") + return True + + except Exception as e: + self.logger.error(f"Failed to recover resource {resource_id}: {e}") + return False + + async def create_agent(self, role: AgentRole, capabilities: List[str]) -> str: + """Create a new agent with specified role and capabilities.""" + agent_id = str(uuid.uuid4()) + + agent_metadata = AgentMetadata( + id=agent_id, + role=role, + capabilities=capabilities, + state=AgentState.IDLE, + load=0.0, + last_active=datetime.now(), + metrics={ + "tasks_completed": 0, + "success_rate": 1.0, + "avg_response_time": 0.0, + "resource_usage": 0.0 + } + ) + + self.agents[agent_id] = agent_metadata + self.logger.info(f"Created new agent {agent_id} with role {role}") + + return agent_id diff --git a/reasoning.py b/reasoning.py new file mode 100644 index 0000000000000000000000000000000000000000..7d7e58a7cf2e771780e9dc4ad62de900b2b80258 --- /dev/null +++ b/reasoning.py @@ -0,0 +1,7577 @@ +""" +Advanced Reasoning Engine for Multi-Model System +--------------------------------------------- +A highly sophisticated reasoning system combining: + +Core Reasoning: +1. Chain of Thought (CoT) +2. Tree of Thoughts (ToT) +3. Graph of Thoughts (GoT) +4. Recursive Reasoning +5. Analogical Reasoning +6. Meta-Learning + +Advanced Reasoning: +7. Neurosymbolic Reasoning +8. Counterfactual Reasoning +9. State Space Search +10. Probabilistic Reasoning +11. Causal Inference +12. Temporal Reasoning + +Learning & Adaptation: +13. Online Learning +14. Transfer Learning +15. Meta-Learning +16. Active Learning + +Robustness Features: +17. Uncertainty Quantification +18. Error Recovery +19. Consistency Checking +20. Bias Detection +""" + +import logging +from typing import Dict, Any, List, Optional, Tuple, Set, Union, TypeVar, Generic +from dataclasses import dataclass, field +from enum import Enum +import json +import torch +import torch.nn.functional as F +from transformers import AutoTokenizer, AutoModelForCausalLM +import numpy as np +from collections import defaultdict, deque +import heapq +import networkx as nx +from sklearn.metrics.pairwise import cosine_similarity +from scipy.stats import entropy +import pandas as pd +from datetime import datetime +import asyncio +from concurrent.futures import ThreadPoolExecutor +from typing_extensions import Protocol +import uuid + +T = TypeVar('T') +S = TypeVar('S') + +class Uncertainty(Enum): + """Types of uncertainty in reasoning.""" + ALEATORIC = "aleatoric" # Statistical uncertainty + EPISTEMIC = "epistemic" # Model uncertainty + ONTOLOGICAL = "ontological" # Problem uncertainty + +class ReasoningMode(Enum): + """Different modes of reasoning.""" + EXPLORATORY = "exploratory" + FOCUSED = "focused" + CREATIVE = "creative" + ANALYTICAL = "analytical" + CRITICAL = "critical" + +@dataclass +class Evidence: + """Evidence supporting a reasoning step.""" + source: str + confidence: float + timestamp: datetime + metadata: Dict[str, Any] + uncertainty: Dict[Uncertainty, float] + +class Verifiable(Protocol): + """Protocol for verifiable components.""" + def verify(self, context: Dict[str, Any]) -> Tuple[bool, float]: + """Verify component validity and return confidence.""" + ... + +class Observable(Protocol): + """Protocol for observable components.""" + def get_state(self) -> Dict[str, Any]: + """Get current state for monitoring.""" + ... + +class Recoverable(Protocol): + """Protocol for components with error recovery.""" + def recover(self, error: Exception) -> bool: + """Attempt to recover from error.""" + ... + +@dataclass +class RobustComponent(Generic[T]): + """Base class for robust components with error handling.""" + data: T + retries: int = 3 + timeout: float = 1.0 + + async def execute(self, func: callable) -> Optional[T]: + """Execute function with retries and timeout.""" + for attempt in range(self.retries): + try: + return await asyncio.wait_for(func(self.data), self.timeout) + except asyncio.TimeoutError: + logging.warning(f"Timeout on attempt {attempt + 1}/{self.retries}") + except Exception as e: + logging.error(f"Error on attempt {attempt + 1}/{self.retries}: {e}") + return None + +class SymbolicRule(Verifiable, Observable): + """Enhanced symbolic rule with verification.""" + def __init__( + self, + condition: str, + action: str, + confidence: float = 0.5, + metadata: Dict[str, Any] = None + ): + self.id = str(uuid.uuid4()) + self.condition = condition + self.action = action + self.confidence = confidence + self.metadata = metadata or {} + self.usage_count = 0 + self.success_count = 0 + self.creation_time = datetime.now() + self.last_update = self.creation_time + self.evidence: List[Evidence] = [] + + def verify(self, context: Dict[str, Any]) -> Tuple[bool, float]: + """Verify rule validity in context.""" + # Implement verification logic + return True, self.confidence + + def get_state(self) -> Dict[str, Any]: + """Get current rule state.""" + return { + "id": self.id, + "condition": self.condition, + "action": self.action, + "confidence": self.confidence, + "usage_count": self.usage_count, + "success_rate": self.success_count / max(1, self.usage_count), + "age": (datetime.now() - self.creation_time).total_seconds() + } + + def update(self, success: bool, evidence: Evidence = None): + """Update rule with new evidence.""" + self.usage_count += 1 + if success: + self.success_count += 1 + if evidence: + self.evidence.append(evidence) + self.confidence = self._calculate_confidence() + self.last_update = datetime.now() + + def _calculate_confidence(self) -> float: + """Calculate confidence based on evidence.""" + if not self.evidence: + return self.success_count / max(1, self.usage_count) + + # Weight recent evidence more heavily + total_weight = 0 + weighted_sum = 0 + now = datetime.now() + + for e in self.evidence: + age = (now - e.timestamp).total_seconds() + weight = 1 / (1 + age/3600) # Decay over hours + weighted_sum += weight * e.confidence + total_weight += weight + + return weighted_sum / total_weight if total_weight > 0 else 0.5 + +class NeuralFeature(Observable): + """Enhanced neural feature with uncertainty.""" + def __init__( + self, + name: str, + vector: np.ndarray, + uncertainty: Dict[Uncertainty, float] = None + ): + self.name = name + self.vector = vector + self.uncertainty = uncertainty or { + Uncertainty.ALEATORIC: 0.0, + Uncertainty.EPISTEMIC: 0.0, + Uncertainty.ONTOLOGICAL: 0.0 + } + self.associations: Dict[str, float] = {} + self.creation_time = datetime.now() + self.update_count = 0 + + def get_state(self) -> Dict[str, Any]: + """Get current feature state.""" + return { + "name": self.name, + "vector_norm": float(np.linalg.norm(self.vector)), + "uncertainty": self.uncertainty, + "association_count": len(self.associations), + "update_count": self.update_count + } + + def update_vector(self, new_vector: np.ndarray, uncertainty: Dict[Uncertainty, float]): + """Update feature vector with uncertainty.""" + self.vector = self._combine_vectors(self.vector, new_vector) + self.uncertainty = self._combine_uncertainty(self.uncertainty, uncertainty) + self.update_count += 1 + + def _combine_vectors(self, v1: np.ndarray, v2: np.ndarray) -> np.ndarray: + """Combine vectors using weighted average.""" + w1 = 1 - sum(self.uncertainty.values()) / 3 + w2 = 1 - w1 + return w1 * v1 + w2 * v2 + + def _combine_uncertainty( + self, + u1: Dict[Uncertainty, float], + u2: Dict[Uncertainty, float] + ) -> Dict[Uncertainty, float]: + """Combine uncertainty estimates.""" + return { + k: (u1[k] + u2[k])/2 for k in Uncertainty + } + +class StateSpaceNode(Observable): + """Enhanced state space node with heuristics.""" + def __init__( + self, + state: Dict[str, Any], + parent: Optional['StateSpaceNode'] = None, + action: Optional[str] = None, + cost: float = 0.0 + ): + self.id = str(uuid.uuid4()) + self.state = state + self.parent = parent + self.action = action + self.cost = cost + self.heuristic = 0.0 + self.children: List['StateSpaceNode'] = [] + self.visited = False + self.dead_end = False + self.creation_time = datetime.now() + self.metadata: Dict[str, Any] = {} + + def get_state(self) -> Dict[str, Any]: + """Get current node state.""" + return { + "id": self.id, + "state": self.state, + "cost": self.cost, + "heuristic": self.heuristic, + "visited": self.visited, + "dead_end": self.dead_end, + "child_count": len(self.children) + } + + def __lt__(self, other): + """Compare nodes for priority queue.""" + return (self.cost + self.heuristic) < (other.cost + other.heuristic) + +class CounterfactualScenario(Verifiable, Observable): + """Enhanced counterfactual scenario with verification.""" + def __init__( + self, + premise: str, + changes: List[str], + implications: List[str], + probability: float, + context: Dict[str, Any] = None + ): + self.id = str(uuid.uuid4()) + self.premise = premise + self.changes = changes + self.implications = implications + self.probability = probability + self.impact_score = 0.0 + self.context = context or {} + self.creation_time = datetime.now() + self.verified = False + self.verification_time = None + + def verify(self, context: Dict[str, Any]) -> Tuple[bool, float]: + """Verify scenario consistency.""" + # Implement verification logic + self.verified = True + self.verification_time = datetime.now() + return True, self.probability + + def get_state(self) -> Dict[str, Any]: + """Get current scenario state.""" + return { + "id": self.id, + "premise": self.premise, + "change_count": len(self.changes), + "implication_count": len(self.implications), + "probability": self.probability, + "impact_score": self.impact_score, + "verified": self.verified + } + + def evaluate_impact(self, context: Dict[str, Any]) -> float: + """Evaluate scenario impact.""" + if not self.verified: + self.verify(context) + + # Calculate impact based on probability and severity + severity = self._calculate_severity(context) + self.impact_score = self.probability * severity + return self.impact_score + + def _calculate_severity(self, context: Dict[str, Any]) -> float: + """Calculate severity of changes.""" + severity = 0.0 + weights = context.get("severity_weights", {}) + + for change in self.changes: + severity += weights.get(change, 0.5) + + return severity / len(self.changes) if self.changes else 0.0 + +class ReasoningEngine: + """Enhanced reasoning engine with advanced capabilities.""" + def __init__( + self, + model_manager: ModelManager, + max_depth: int = 5, + beam_width: int = 3, + config: Dict[str, Any] = None + ): + self.model_manager = model_manager + self.max_depth = max_depth + self.beam_width = beam_width + self.config = config or {} + + # Component storage + self.symbolic_rules: Dict[str, SymbolicRule] = {} + self.neural_features: Dict[str, NeuralFeature] = {} + self.state_space: nx.DiGraph = nx.DiGraph() + self.counterfactuals: Dict[str, CounterfactualScenario] = {} + + # Memory and learning + self.memory = defaultdict(list) + self.learning_rate = 0.1 + self.exploration_rate = 0.2 + + # Monitoring and logging + self.logger = logging.getLogger(__name__) + self.metrics: Dict[str, List[float]] = defaultdict(list) + + # Async support + self.executor = ThreadPoolExecutor(max_workers=4) + self.lock = asyncio.Lock() + + async def reason( + self, + query: str, + context: Dict[str, Any], + strategy: str = "auto", + mode: ReasoningMode = ReasoningMode.ANALYTICAL + ) -> Dict[str, Any]: + """Enhanced reasoning with automatic strategy selection.""" + try: + # Analyze query complexity + complexity = await self._analyze_complexity(query) + + # Select strategy if auto + if strategy == "auto": + strategy = await self._select_strategy(query, context, complexity) + + # Prepare reasoning context + reasoning_context = await self._prepare_context( + query, + context, + strategy, + mode + ) + + # Execute reasoning with monitoring + async with self.lock: + start_time = datetime.now() + + # Get strategy method + strategy_method = self._get_strategy_method(strategy) + + # Execute with timeout and retries + result = await RobustComponent( + data=(query, reasoning_context) + ).execute( + lambda x: strategy_method(*x) + ) + + # Record metrics + self._record_metrics( + strategy, + start_time, + result + ) + + return result or { + "status": "error", + "error": "Reasoning failed" + } + + except Exception as e: + self.logger.error(f"Reasoning error: {e}") + return {"status": "error", "error": str(e)} + + async def _analyze_complexity(self, query: str) -> float: + """Analyze query complexity.""" + features = [ + len(query), + query.count(" "), + len(set(query.split())), + query.count("?"), + query.count("if"), + query.count("but") + ] + return sum(features) / len(features) + + async def _select_strategy( + self, + query: str, + context: Dict[str, Any], + complexity: float + ) -> str: + """Select best reasoning strategy.""" + if complexity > 7: + return "neurosymbolic" + elif "compare" in query.lower() or "difference" in query.lower(): + return "counterfactual" + elif "optimal" in query.lower() or "best" in query.lower(): + return "state_space" + else: + return "tree_of_thoughts" + + async def _prepare_context( + self, + query: str, + context: Dict[str, Any], + strategy: str, + mode: ReasoningMode + ) -> Dict[str, Any]: + """Prepare reasoning context.""" + return { + "query": query, + "base_context": context, + "strategy": strategy, + "mode": mode, + "timestamp": datetime.now(), + "complexity": await self._analyze_complexity(query), + "history": self.memory[query][-5:] if query in self.memory else [] + } + + def _get_strategy_method(self, strategy: str) -> callable: + """Get strategy method by name.""" + strategies = { + "chain_of_thought": self._chain_of_thought, + "tree_of_thoughts": self._tree_of_thoughts, + "neurosymbolic": self._neurosymbolic_reasoning, + "counterfactual": self._counterfactual_reasoning, + "state_space": self._state_space_search + } + return strategies.get(strategy, self._tree_of_thoughts) + + def _record_metrics( + self, + strategy: str, + start_time: datetime, + result: Dict[str, Any] + ): + """Record reasoning metrics.""" + duration = (datetime.now() - start_time).total_seconds() + success = result.get("status") == "success" + + self.metrics["duration"].append(duration) + self.metrics[f"{strategy}_success"].append(float(success)) + + if len(self.metrics["duration"]) > 1000: + self.metrics["duration"] = self.metrics["duration"][-1000:] + +class ThoughtType(Enum): + """Types of thoughts in reasoning process.""" + INITIAL = "initial" + ANALYSIS = "analysis" + REFINEMENT = "refinement" + SOLUTION = "solution" + EVALUATION = "evaluation" + CONCLUSION = "conclusion" + ANALOGY = "analogy" + CAUSAL = "causal" + STATE = "state" + +@dataclass +class Thought: + """Represents a single thought in the reasoning process.""" + type: ThoughtType + content: str + confidence: float + dependencies: List[str] = field(default_factory=list) + metadata: Dict[str, Any] = field(default_factory=dict) + children: List['Thought'] = field(default_factory=list) + + def to_dict(self) -> Dict: + """Convert thought to dictionary.""" + return { + "type": self.type.value, + "content": self.content, + "confidence": self.confidence, + "dependencies": self.dependencies, + "metadata": self.metadata, + "children": [child.to_dict() for child in self.children] + } + +@dataclass +class State: + """Represents a state in state space search.""" + description: str + value: float + parent: Optional['State'] = None + actions: List[str] = field(default_factory=list) + depth: int = 0 + + def __lt__(self, other): + return self.value > other.value # For priority queue (max heap) + +class ReasoningStrategy: + """Base class for reasoning strategies.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + raise NotImplementedError + +class ChainOfThoughtStrategy(ReasoningStrategy): + """Implements Chain of Thought reasoning.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + Analyze this query using Chain of Thought reasoning: + Query: {query} + Context: {json.dumps(clean_context)} + + Think through this step-by-step: + 1. What are the key components to consider? + 2. How do these components relate to each other? + 3. What logical steps lead to the conclusion? + + Format your response as a chain of thoughts, with each step building on previous ones. + End with a final conclusion that synthesizes your chain of reasoning. + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into reasoning chain and conclusion + lines = response["answer"].split("\n") + reasoning_chain = [] + final_conclusion = "" + + mode = "chain" + for line in lines: + line = line.strip() + if not line: + continue + + if line.lower().startswith("conclusion"): + mode = "conclusion" + continue + + if mode == "chain" and (line.startswith("-") or line.startswith("*") or line.startswith("Step")): + reasoning_chain.append(line.lstrip("- *Step").strip()) + elif mode == "conclusion": + final_conclusion += line + " " + + return { + "success": True, + "reasoning_chain": reasoning_chain, + "final_conclusion": final_conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + +class TreeOfThoughtsStrategy(ReasoningStrategy): + """Implements Tree of Thoughts reasoning.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + Analyze this query using Tree of Thoughts reasoning: + Query: {query} + Context: {json.dumps(clean_context)} + + Consider multiple branches of thought: + 1. What are the different approaches we could take? + 2. For each approach: + - What are the key considerations? + - What are potential outcomes? + - What are the trade-offs? + 3. Which path seems most promising and why? + + Format your response with clear branches, a selected path, and justification. + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into branches, selected path, and justification + lines = response["answer"].split("\n") + thought_branches = [] + selected_path = "" + reasoning_justification = "" + + mode = "branches" + for line in lines: + line = line.strip() + if not line: + continue + + if line.lower().startswith("selected path"): + mode = "path" + continue + elif line.lower().startswith("justification"): + mode = "justification" + continue + + if mode == "branches" and (line.startswith("-") or line.startswith("*")): + thought_branches.append(line.lstrip("- *").strip()) + elif mode == "path": + selected_path = line.strip() + elif mode == "justification": + reasoning_justification += line + " " + + return { + "success": True, + "thought_branches": thought_branches, + "selected_path": selected_path, + "reasoning_justification": reasoning_justification.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + +class RecursiveReasoning(ReasoningStrategy): + """Implements recursive reasoning by breaking down complex problems.""" + def __init__(self, max_depth: int = 3): + self.max_depth = max_depth + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Apply recursive reasoning to solve problem.""" + try: + result = await self._reason_recursively(query, context, depth=0) + return { + "success": True, + "answer": result["solution"], + "subproblems": result["subproblems"], + "confidence": result["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _reason_recursively(self, query: str, context: Dict[str, Any], + depth: int) -> Dict[str, Any]: + """Recursively solve problem by breaking it down.""" + if depth >= self.max_depth: + # Base case: reached max depth, solve directly + prompt = f""" + Solve this problem directly: + Query: {query} + Context: {json.dumps(context)} + """ + response = await context["llm_clients"].generate(prompt) + return { + "solution": response["answer"], + "subproblems": [], + "confidence": 0.8 # Direct solution confidence + } + + # Break down into subproblems + decompose_prompt = f""" + Break down this problem into smaller, manageable subproblems: + Query: {query} + Context: {json.dumps(context)} + + Format each subproblem as: + 1. [Subproblem]: Description + 2. [Subproblem]: Description + ... + """ + + decompose_response = await context["llm_clients"].generate(decompose_prompt) + subproblems = self._parse_subproblems(decompose_response["answer"]) + + # If no subproblems found or only one, solve directly + if len(subproblems) <= 1: + return await self._reason_recursively(query, context, self.max_depth) + + # Solve each subproblem recursively + sub_solutions = [] + for subproblem in subproblems: + sub_context = {**context, "parent_problem": query} + sub_result = await self._reason_recursively( + subproblem["description"], + sub_context, + depth + 1 + ) + sub_solutions.append({ + "subproblem": subproblem["description"], + "solution": sub_result["solution"], + "confidence": sub_result["confidence"] + }) + + # Combine sub-solutions + combine_prompt = f""" + Combine these solutions to solve the original problem: + Original Query: {query} + Context: {json.dumps(context)} + + Subproblem Solutions: + {json.dumps(sub_solutions, indent=2)} + + Provide a comprehensive solution that integrates all subproblem solutions. + """ + + combine_response = await context["llm_clients"].generate(combine_prompt) + + # Calculate confidence based on sub-solutions + confidence = sum(s["confidence"] for s in sub_solutions) / len(sub_solutions) + + return { + "solution": combine_response["answer"], + "subproblems": sub_solutions, + "confidence": confidence * 0.9 # Slight penalty for complexity + } + + def _parse_subproblems(self, response: str) -> List[Dict[str, str]]: + """Parse response into structured subproblems.""" + subproblems = [] + current_problem = "" + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + # Look for numbered subproblems + if re.match(r'^\d+\.?\s*\[Subproblem\]:', line, re.IGNORECASE): + if current_problem: + subproblems.append({"description": current_problem.strip()}) + current_problem = re.sub(r'^\d+\.?\s*\[Subproblem\]:\s*', '', line, flags=re.IGNORECASE) + else: + current_problem += " " + line + + # Add the last subproblem + if current_problem: + subproblems.append({"description": current_problem.strip()}) + + return subproblems + +class AnalogicalReasoning(ReasoningStrategy): + """Implements analogical reasoning by finding and applying relevant analogies.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Find relevant analogies + analogies = await self._find_analogies(query, context) + + # Map the analogies to the current problem + mappings = await self._map_analogies(query, analogies, context) + + # Apply the mapped solutions + solutions = await self._apply_analogies(query, mappings, context) + + # Calculate confidence based on analogy quality + confidence = self._calculate_confidence(analogies, mappings, solutions) + + return { + "success": True, + "answer": solutions["combined_solution"], + "analogies": analogies, + "mappings": mappings, + "detailed_solutions": solutions["detailed_solutions"], + "confidence": confidence + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _find_analogies(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Find relevant analogies for the given problem.""" + prompt = f""" + Find 2-3 relevant analogies for this problem: + Query: {query} + Context: {json.dumps(context)} + + For each analogy, provide: + 1. [Domain]: The field or area the analogy comes from + 2. [Situation]: A clear description of the analogous situation + 3. [Key Elements]: The main components or concepts involved + 4. [Solution Pattern]: How the problem was solved in this analogous case + + Format each analogy as: + [Analogy 1] + Domain: ... + Situation: ... + Key Elements: ... + Solution Pattern: ... + + [Analogy 2] + ... + """ + + response = await context["llm_clients"].generate(prompt) + return self._parse_analogies(response["answer"]) + + async def _map_analogies(self, query: str, analogies: List[Dict[str, Any]], + context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Map analogies to the current problem.""" + prompt = f""" + Map these analogies to our current problem: + Problem: {query} + Context: {json.dumps(context)} + + Analogies: + {json.dumps(analogies, indent=2)} + + For each analogy, identify: + 1. [Corresponding Elements]: How elements in the analogy correspond to our problem + 2. [Relevant Aspects]: Which aspects of the analogy are most relevant + 3. [Adaptation Needed]: How the solution pattern needs to be adapted + + Format each mapping as: + [Mapping 1] + Corresponding Elements: ... + Relevant Aspects: ... + Adaptation Needed: ... + """ + + response = await context["llm_clients"].generate(prompt) + return self._parse_mappings(response["answer"]) + + async def _apply_analogies(self, query: str, mappings: List[Dict[str, Any]], + context: Dict[str, Any]) -> Dict[str, Any]: + """Apply mapped analogies to generate solutions.""" + prompt = f""" + Apply these mapped analogies to solve our problem: + Problem: {query} + Context: {json.dumps(context)} + + Mapped Analogies: + {json.dumps(mappings, indent=2)} + + For each mapping: + 1. Generate a specific solution based on the analogy + 2. Explain how it addresses our problem + 3. Note any potential limitations + + Then, provide a combined solution that integrates the best aspects of each approach. + """ + + response = await context["llm_clients"].generate(prompt) + solutions = self._parse_solutions(response["answer"]) + return solutions + + def _parse_analogies(self, response: str) -> List[Dict[str, Any]]: + """Parse analogies from response.""" + analogies = [] + current_analogy = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Analogy'): + if current_analogy: + analogies.append(current_analogy) + current_analogy = { + "domain": "", + "situation": "", + "key_elements": "", + "solution_pattern": "" + } + elif current_analogy: + if line.startswith('Domain:'): + current_analogy["domain"] = line[7:].strip() + elif line.startswith('Situation:'): + current_analogy["situation"] = line[10:].strip() + elif line.startswith('Key Elements:'): + current_analogy["key_elements"] = line[13:].strip() + elif line.startswith('Solution Pattern:'): + current_analogy["solution_pattern"] = line[16:].strip() + + if current_analogy: + analogies.append(current_analogy) + + return analogies + + def _parse_mappings(self, response: str) -> List[Dict[str, Any]]: + """Parse mappings from response.""" + mappings = [] + current_mapping = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Mapping'): + if current_mapping: + mappings.append(current_mapping) + current_mapping = { + "corresponding_elements": "", + "relevant_aspects": "", + "adaptation_needed": "" + } + elif current_mapping: + if line.startswith('Corresponding Elements:'): + current_mapping["corresponding_elements"] = line[22:].strip() + elif line.startswith('Relevant Aspects:'): + current_mapping["relevant_aspects"] = line[17:].strip() + elif line.startswith('Adaptation Needed:'): + current_mapping["adaptation_needed"] = line[18:].strip() + + if current_mapping: + mappings.append(current_mapping) + + return mappings + + def _parse_solutions(self, response: str) -> Dict[str, Any]: + """Parse solutions from response.""" + solutions = { + "detailed_solutions": [], + "combined_solution": "" + } + + parts = response.split("Combined Solution:", 1) + + # Parse individual solutions + current_solution = None + for line in parts[0].split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Solution'): + if current_solution: + solutions["detailed_solutions"].append(current_solution) + current_solution = { + "approach": "", + "explanation": "", + "limitations": "" + } + elif current_solution: + if "Approach:" in line: + current_solution["approach"] = line.split("Approach:", 1)[1].strip() + elif "Explanation:" in line: + current_solution["explanation"] = line.split("Explanation:", 1)[1].strip() + elif "Limitations:" in line: + current_solution["limitations"] = line.split("Limitations:", 1)[1].strip() + + if current_solution: + solutions["detailed_solutions"].append(current_solution) + + # Parse combined solution + if len(parts) > 1: + solutions["combined_solution"] = parts[1].strip() + + return solutions + + def _calculate_confidence(self, analogies: List[Dict[str, Any]], + mappings: List[Dict[str, Any]], + solutions: Dict[str, Any]) -> float: + """Calculate confidence score based on analogy quality.""" + confidence = 0.0 + + # Quality of analogies (0.4 weight) + if analogies: + analogy_score = sum( + bool(a["domain"]) * 0.25 + + bool(a["situation"]) * 0.25 + + bool(a["key_elements"]) * 0.25 + + bool(a["solution_pattern"]) * 0.25 + for a in analogies + ) / len(analogies) + confidence += analogy_score * 0.4 + + # Quality of mappings (0.3 weight) + if mappings: + mapping_score = sum( + bool(m["corresponding_elements"]) * 0.4 + + bool(m["relevant_aspects"]) * 0.3 + + bool(m["adaptation_needed"]) * 0.3 + for m in mappings + ) / len(mappings) + confidence += mapping_score * 0.3 + + # Quality of solutions (0.3 weight) + if solutions["detailed_solutions"]: + solution_score = sum( + bool(s["approach"]) * 0.4 + + bool(s["explanation"]) * 0.4 + + bool(s["limitations"]) * 0.2 + for s in solutions["detailed_solutions"] + ) / len(solutions["detailed_solutions"]) + confidence += solution_score * 0.3 + + return min(confidence, 1.0) + +class CausalReasoning(ReasoningStrategy): + """Implements causal reasoning by identifying cause-effect relationships.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + # Identify causal factors + factors = await self._identify_causal_factors(query, context) + + # Build causal graph + causal_graph = await self._build_causal_graph(factors, context) + + # Analyze interventions + interventions = await self._analyze_interventions(causal_graph, context) + + return { + "success": True, + "causal_factors": factors, + "causal_graph": causal_graph, + "interventions": interventions + } + + async def _identify_causal_factors(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Identify causal factors in the problem.""" + prompt = f""" + Identify causal factors in this problem: + Query: {query} + Context: {json.dumps(context)} + + For each factor: + 1. Describe the factor + 2. Explain its causal role + 3. Identify dependencies + 4. Rate its importance (1-5) + """ + + response = await context["llm_clients"].generate(prompt) + return self._parse_factors(response["answer"]) if response["success"] else [] + + async def _build_causal_graph(self, factors: List[Dict[str, Any]], + context: Dict[str, Any]) -> Dict[str, Any]: + """Build a causal graph from identified factors.""" + prompt = f""" + Build a causal graph from these factors: + Factors: {json.dumps(factors, indent=2)} + Context: {json.dumps(context)} + + For each relationship: + 1. Identify cause and effect + 2. Describe the relationship + 3. Rate the strength (1-5) + 4. Note any conditions + """ + + response = await context["llm_clients"].generate(prompt) + return self._parse_graph(response["answer"]) if response["success"] else {} + + async def _analyze_interventions(self, causal_graph: Dict[str, Any], + context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Analyze possible interventions based on causal graph.""" + prompt = f""" + Analyze possible interventions based on this causal graph: + Graph: {json.dumps(causal_graph, indent=2)} + Context: {json.dumps(context)} + + For each intervention: + 1. Describe the intervention + 2. Identify target factors + 3. Predict effects + 4. Rate effectiveness (1-5) + """ + + response = await context["llm_clients"].generate(prompt) + return self._parse_interventions(response["answer"]) if response["success"] else [] + + def _parse_factors(self, response: str) -> List[Dict[str, Any]]: + """Parse causal factors from response.""" + factors = [] + current_factor = None + + for line in response.split("\n"): + line = line.strip() + if not line: + continue + + if line.startswith("Factor"): + if current_factor: + factors.append(current_factor) + current_factor = { + "description": "", + "role": "", + "dependencies": [], + "importance": 0 + } + elif current_factor: + if line.startswith("Role:"): + current_factor["role"] = line[5:].strip() + elif line.startswith("Dependencies:"): + mode = "dependencies" + elif line.startswith("Importance:"): + try: + current_factor["importance"] = int(line[11:].strip()) + except: + pass + elif line.startswith("- "): + if mode == "dependencies": + current_factor["dependencies"].append(line[2:].strip()) + else: + current_factor["description"] += line + "\n" + + if current_factor: + factors.append(current_factor) + + return factors + + def _parse_graph(self, response: str) -> Dict[str, Any]: + """Parse causal graph from response.""" + nodes = {} + edges = [] + current_relationship = None + + for line in response.split("\n"): + line = line.strip() + if not line: + continue + + if line.startswith("Relationship"): + if current_relationship: + edges.append(current_relationship) + current_relationship = { + "cause": "", + "effect": "", + "description": "", + "strength": 0, + "conditions": [] + } + elif current_relationship: + if line.startswith("Cause:"): + current_relationship["cause"] = line[6:].strip() + elif line.startswith("Effect:"): + current_relationship["effect"] = line[7:].strip() + elif line.startswith("Strength:"): + try: + current_relationship["strength"] = int(line[9:].strip()) + except: + pass + elif line.startswith("Conditions:"): + mode = "conditions" + elif line.startswith("- "): + if mode == "conditions": + current_relationship["conditions"].append(line[2:].strip()) + else: + current_relationship["description"] += line + "\n" + + if current_relationship: + edges.append(current_relationship) + + return {"nodes": nodes, "edges": edges} + + def _parse_interventions(self, response: str) -> List[Dict[str, Any]]: + """Parse interventions from response.""" + interventions = [] + current_intervention = None + + for line in response.split("\n"): + line = line.strip() + if not line: + continue + + if line.startswith("Intervention"): + if current_intervention: + interventions.append(current_intervention) + current_intervention = { + "description": "", + "targets": [], + "effects": [], + "effectiveness": 0 + } + elif current_intervention: + if line.startswith("Targets:"): + mode = "targets" + elif line.startswith("Effects:"): + mode = "effects" + elif line.startswith("Effectiveness:"): + try: + current_intervention["effectiveness"] = int(line[14:].strip()) + except: + pass + elif line.startswith("- "): + if mode == "targets": + current_intervention["targets"].append(line[2:].strip()) + elif mode == "effects": + current_intervention["effects"].append(line[2:].strip()) + else: + current_intervention["description"] += line + "\n" + + if current_intervention: + interventions.append(current_intervention) + + return interventions + +class StateSpaceSearch(ReasoningStrategy): + """Implements state space search for problem solving.""" + def __init__(self, max_states: int = 100): + self.max_states = max_states + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + initial_state = await self._create_initial_state(query, context) + goal_state = await self._define_goal_state(query, context) + + path = await self._a_star_search(initial_state, goal_state) + + return { + "success": True, + "initial_state": initial_state.description, + "goal_state": goal_state.description, + "solution_path": path + } + + async def _a_star_search(self, start: State, goal: State) -> List[str]: + frontier = [] + heapq.heappush(frontier, start) + came_from = {start: None} + cost_so_far = {start: 0} + + while frontier and len(came_from) < self.max_states: + current = heapq.heappop(frontier) + + if self._is_goal(current, goal): + return self._reconstruct_path(came_from, current) + + for next_state in await self._get_neighbors(current): + new_cost = cost_so_far[current] + 1 + + if next_state not in cost_so_far or new_cost < cost_so_far[next_state]: + cost_so_far[next_state] = new_cost + priority = new_cost + self._heuristic(next_state, goal) + heapq.heappush(frontier, next_state) + came_from[next_state] = current + + return [] # No path found + + async def _create_initial_state(self, query: str, context: Dict[str, Any]) -> State: + """Create initial state from query and context.""" + prompt = f""" + Create an initial state for this problem: + Query: {query} + Context: {json.dumps(context)} + + Describe: + 1. Current system state + 2. Available actions + 3. Initial value estimate + """ + + response = await context["llm_clients"].generate(prompt) + if response["success"]: + parsed = self._parse_state(response["answer"]) + return State( + description=parsed["description"], + value=parsed["value"], + actions=parsed["actions"] + ) + return None + + async def _define_goal_state(self, query: str, context: Dict[str, Any]) -> State: + """Define goal state from query and context.""" + prompt = f""" + Define a goal state for this problem: + Query: {query} + Context: {json.dumps(context)} + + Describe: + 1. Desired system state + 2. Success criteria + 3. Value estimate + """ + + response = await context["llm_clients"].generate(prompt) + if response["success"]: + parsed = self._parse_state(response["answer"]) + return State( + description=parsed["description"], + value=parsed["value"], + actions=[] + ) + return None + + async def _get_neighbors(self, state: State) -> List[State]: + """Get neighboring states by applying possible actions.""" + prompt = f""" + Generate neighboring states by applying these actions: + Current State: {state.description} + Actions: {json.dumps(state.actions)} + + For each action: + 1. Describe resulting state + 2. Estimate new value + 3. List new available actions + """ + + response = await context["llm_clients"].generate(prompt) + neighbors = [] + + if response["success"]: + for parsed in self._parse_neighbors(response["answer"]): + neighbor = State( + description=parsed["description"], + value=parsed["value"], + parent=state, + actions=parsed["actions"], + depth=state.depth + 1 + ) + neighbors.append(neighbor) + + return neighbors + + def _is_goal(self, current: State, goal: State) -> bool: + """Check if current state matches goal state.""" + return current.description == goal.description + + def _heuristic(self, state: State, goal: State) -> float: + """Estimate distance from state to goal.""" + # Simple heuristic based on value difference + return abs(state.value - goal.value) + + def _reconstruct_path(self, came_from: Dict[State, State], + current: State) -> List[str]: + """Reconstruct path from start to current state.""" + path = [] + while current: + path.append(current.description) + current = came_from.get(current) + return list(reversed(path)) + + def _parse_state(self, response: str) -> Dict[str, Any]: + """Parse state from response.""" + state = { + "description": "", + "value": 0.0, + "actions": [] + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('State:'): + mode = "description" + elif line.startswith('Value:'): + try: + state["value"] = float(line[6:].strip()) + except: + pass + elif line.startswith('Actions:'): + mode = "actions" + elif line.startswith("- "): + if mode == "actions": + state["actions"].append(line[2:].strip()) + elif mode == "description": + state["description"] += line[2:].strip() + "\n" + elif mode == "description": + state["description"] += line + "\n" + + return state + + def _parse_neighbors(self, response: str) -> List[Dict[str, Any]]: + """Parse neighboring states from response.""" + neighbors = [] + current_neighbor = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith("Neighbor"): + if current_neighbor: + neighbors.append(current_neighbor) + current_neighbor = { + "description": "", + "value": 0.0, + "actions": [] + } + elif current_neighbor: + if line.startswith("Value:"): + try: + current_neighbor["value"] = float(line[6:].strip()) + except: + pass + elif line.startswith("Actions:"): + mode = "actions" + elif line.startswith("- "): + if mode == "actions": + current_neighbor["actions"].append(line[2:].strip()) + else: + current_neighbor["description"] += line[2:].strip() + "\n" + else: + current_neighbor["description"] += line + "\n" + + if current_neighbor: + neighbors.append(current_neighbor) + + return neighbors + +class BayesianReasoning(ReasoningStrategy): + """Implements Bayesian reasoning for probabilistic analysis.""" + def __init__(self, prior_weight: float = 0.3): + self.prior_weight = prior_weight + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Generate hypotheses + hypotheses = await self._generate_hypotheses(query, context) + + # Calculate prior probabilities + priors = await self._calculate_priors(hypotheses, context) + + # Update with evidence + posteriors = await self._update_with_evidence(hypotheses, priors, context) + + # Generate final analysis + analysis = await self._generate_analysis(posteriors, context) + + return { + "success": True, + "answer": analysis["conclusion"], + "hypotheses": hypotheses, + "priors": priors, + "posteriors": posteriors, + "confidence": analysis["confidence"], + "reasoning_path": analysis["reasoning_path"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Generate 3-4 hypotheses for this problem: + Query: {query} + Context: {json.dumps(context)} + + For each hypothesis: + 1. [Statement]: Clear statement of the hypothesis + 2. [Assumptions]: Key assumptions made + 3. [Testability]: How it could be tested/verified + + Format as: + [H1] + Statement: ... + Assumptions: ... + Testability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_hypotheses(response["answer"]) + + async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Calculate prior probabilities for these hypotheses: + Context: {json.dumps(context)} + + Hypotheses: + {json.dumps(hypotheses, indent=2)} + + For each hypothesis, estimate its prior probability (0-1) based on: + 1. Alignment with known principles + 2. Historical precedent + 3. Domain expertise + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], + context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Update probabilities with available evidence: + Context: {json.dumps(context)} + + Hypotheses and Priors: + {json.dumps(list(zip(hypotheses, priors.values())), indent=2)} + + Consider: + 1. How well each hypothesis explains the evidence + 2. Any new evidence from the context + 3. Potential conflicts or support between hypotheses + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate final Bayesian analysis: + Context: {json.dumps(context)} + + Posterior Probabilities: + {json.dumps(posteriors, indent=2)} + + Provide: + 1. Main conclusion based on highest probability hypotheses + 2. Confidence level (0-1) + 3. Key reasoning steps taken + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_analysis(response["answer"]) + + def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]: + """Parse hypotheses from response.""" + hypotheses = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[H'): + if current: + hypotheses.append(current) + current = { + "statement": "", + "assumptions": "", + "testability": "" + } + elif current: + if line.startswith('Statement:'): + current["statement"] = line[10:].strip() + elif line.startswith('Assumptions:'): + current["assumptions"] = line[12:].strip() + elif line.startswith('Testability:'): + current["testability"] = line[12:].strip() + + if current: + hypotheses.append(current) + + return hypotheses + + def _parse_probabilities(self, response: str) -> Dict[str, float]: + """Parse probabilities from response.""" + probs = {} + pattern = r'\[H(\d+)\]:\s*(0\.\d+)' + + for match in re.finditer(pattern, response): + h_num = int(match.group(1)) + prob = float(match.group(2)) + probs[f"H{h_num}"] = prob + + return probs + + def _parse_analysis(self, response: str) -> Dict[str, Any]: + """Parse analysis from response.""" + lines = response.split('\n') + analysis = { + "conclusion": "", + "confidence": 0.0, + "reasoning_path": [] + } + + for line in lines: + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + analysis["conclusion"] = line[11:].strip() + elif line.startswith('Confidence:'): + try: + analysis["confidence"] = float(line[11:].strip()) + except: + analysis["confidence"] = 0.5 + elif line.startswith('- '): + analysis["reasoning_path"].append(line[2:].strip()) + + return analysis + +class CounterfactualReasoning(ReasoningStrategy): + """Implements counterfactual reasoning to explore alternative scenarios.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Generate counterfactuals + counterfactuals = await self._generate_counterfactuals(query, context) + + # Analyze implications + implications = await self._analyze_implications(counterfactuals, context) + + # Synthesize insights + synthesis = await self._synthesize_insights(counterfactuals, implications, context) + + return { + "success": True, + "answer": synthesis["conclusion"], + "counterfactuals": counterfactuals, + "implications": implications, + "confidence": synthesis["confidence"], + "key_insights": synthesis["key_insights"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _generate_counterfactuals(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Generate 3-4 counterfactual scenarios for this problem: + Query: {query} + Context: {json.dumps(context)} + + For each counterfactual: + 1. [Scenario]: What if...? description + 2. [Changes]: Key changes from current situation + 3. [Plausibility]: How likely/realistic is this scenario + + Format as: + [CF1] + Scenario: ... + Changes: ... + Plausibility: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_counterfactuals(response["answer"]) + + async def _analyze_implications(self, counterfactuals: List[Dict[str, Any]], + context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze implications of these counterfactual scenarios: + Context: {json.dumps(context)} + + Counterfactuals: + {json.dumps(counterfactuals, indent=2)} + + For each scenario analyze: + 1. Direct effects + 2. Indirect consequences + 3. System-wide impacts + + Format as: + [CF1 Analysis] + Direct: ... + Indirect: ... + Systemic: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_implications(response["answer"]) + + async def _synthesize_insights(self, counterfactuals: List[Dict[str, Any]], + implications: List[Dict[str, Any]], + context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Synthesize insights from counterfactual analysis: + Context: {json.dumps(context)} + + Counterfactuals: + {json.dumps(counterfactuals, indent=2)} + + Implications: + {json.dumps(implications, indent=2)} + + Provide: + 1. Key insights learned + 2. Main conclusion + 3. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_synthesis(response["answer"]) + + def _parse_counterfactuals(self, response: str) -> List[Dict[str, Any]]: + """Parse counterfactuals from response.""" + counterfactuals = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[CF'): + if current: + counterfactuals.append(current) + current = { + "scenario": "", + "changes": "", + "plausibility": "" + } + elif current: + if line.startswith('Scenario:'): + current["scenario"] = line[9:].strip() + elif line.startswith('Changes:'): + current["changes"] = line[8:].strip() + elif line.startswith('Plausibility:'): + current["plausibility"] = line[12:].strip() + + if current: + counterfactuals.append(current) + + return counterfactuals + + def _parse_implications(self, response: str) -> List[Dict[str, Any]]: + """Parse implications from response.""" + implications = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[CF'): + if current: + implications.append(current) + current = { + "direct": "", + "indirect": "", + "systemic": "" + } + elif current: + if line.startswith('Direct:'): + current["direct"] = line[7:].strip() + elif line.startswith('Indirect:'): + current["indirect"] = line[9:].strip() + elif line.startswith('Systemic:'): + current["systemic"] = line[9:].strip() + + if current: + implications.append(current) + + return implications + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis from response.""" + synthesis = { + "key_insights": [], + "conclusion": "", + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Key Insights:'): + mode = "insights" + elif line.startswith('Conclusion:'): + synthesis["conclusion"] = line[11:].strip() + mode = None + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + synthesis["confidence"] = 0.5 + mode = None + elif mode == "insights" and line.startswith('- '): + synthesis["key_insights"].append(line[2:].strip()) + + return synthesis + +class MetaReasoning(ReasoningStrategy): + """Implements meta-reasoning to analyze and improve the reasoning process itself.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Analyze reasoning requirements + requirements = await self._analyze_requirements(query, context) + + # Generate reasoning strategies + strategies = await self._generate_strategies(requirements, context) + + # Evaluate strategies + evaluation = await self._evaluate_strategies(strategies, context) + + # Select and apply best strategy + result = await self._apply_strategy(evaluation["best_strategy"], query, context) + + return { + "success": True, + "answer": result["conclusion"], + "requirements": requirements, + "strategies": strategies, + "evaluation": evaluation, + "confidence": result["confidence"], + "meta_insights": result["meta_insights"] + } + + except Exception as e: + return {"success": False, "error": str(e)} + + async def _analyze_requirements(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Analyze reasoning requirements for this problem: + Query: {query} + Context: {json.dumps(context)} + + Consider: + 1. Complexity level + 2. Required knowledge types + 3. Constraints and limitations + 4. Success criteria + + Format as: + Complexity: ... + Knowledge: ... + Constraints: ... + Criteria: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_requirements(response["answer"]) + + async def _generate_strategies(self, requirements: Dict[str, Any], + context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Generate potential reasoning strategies based on requirements: + Requirements: {json.dumps(requirements)} + Context: {json.dumps(context)} + + For each strategy: + 1. [Approach]: Description of reasoning approach + 2. [Strengths]: Key advantages + 3. [Weaknesses]: Potential limitations + + Format as: + [S1] + Approach: ... + Strengths: ... + Weaknesses: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_strategies(response["answer"]) + + async def _evaluate_strategies(self, strategies: List[Dict[str, Any]], + context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Evaluate proposed reasoning strategies: + Context: {json.dumps(context)} + + Strategies: + {json.dumps(strategies, indent=2)} + + Evaluate each strategy on: + 1. Effectiveness (0-1) + 2. Efficiency (0-1) + 3. Reliability (0-1) + + Then select the best strategy and explain why. + + Format as: + [S1 Evaluation] + Effectiveness: 0.XX + Efficiency: 0.XX + Reliability: 0.XX + + Best Strategy: [SX] + Rationale: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_evaluation(response["answer"]) + + async def _apply_strategy(self, strategy: Dict[str, Any], query: str, + context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Apply the selected reasoning strategy: + Strategy: {json.dumps(strategy)} + Query: {query} + Context: {json.dumps(context)} + + Provide: + 1. Step-by-step application + 2. Main conclusion + 3. Confidence level (0-1) + 4. Meta-insights about the reasoning process + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_result(response["answer"]) + + def _parse_requirements(self, response: str) -> Dict[str, Any]: + """Parse requirements from response.""" + requirements = { + "complexity": "", + "knowledge": "", + "constraints": "", + "criteria": "" + } + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Complexity:'): + requirements["complexity"] = line[11:].strip() + elif line.startswith('Knowledge:'): + requirements["knowledge"] = line[10:].strip() + elif line.startswith('Constraints:'): + requirements["constraints"] = line[12:].strip() + elif line.startswith('Criteria:'): + requirements["criteria"] = line[9:].strip() + + return requirements + + def _parse_strategies(self, response: str) -> List[Dict[str, Any]]: + """Parse strategies from response.""" + strategies = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current: + strategies.append(current) + current = { + "approach": "", + "strengths": "", + "weaknesses": "" + } + elif current: + if line.startswith('Approach:'): + current["approach"] = line[9:].strip() + elif line.startswith('Strengths:'): + current["strengths"] = line[10:].strip() + elif line.startswith('Weaknesses:'): + current["weaknesses"] = line[11:].strip() + + if current: + strategies.append(current) + + return strategies + + def _parse_evaluation(self, response: str) -> Dict[str, Any]: + """Parse evaluation from response.""" + evaluation = { + "evaluations": [], + "best_strategy": None, + "rationale": "" + } + + current = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current: + evaluation["evaluations"].append(current) + current = { + "effectiveness": 0.0, + "efficiency": 0.0, + "reliability": 0.0 + } + elif current: + if line.startswith('Effectiveness:'): + current["effectiveness"] = float(line[14:].strip()) + elif line.startswith('Efficiency:'): + current["efficiency"] = float(line[11:].strip()) + elif line.startswith('Reliability:'): + current["reliability"] = float(line[12:].strip()) + elif line.startswith('Best Strategy:'): + strategy_num = re.search(r'\[S(\d+)\]', line) + if strategy_num: + evaluation["best_strategy"] = int(strategy_num.group(1)) + elif line.startswith('Rationale:'): + evaluation["rationale"] = line[10:].strip() + + if current: + evaluation["evaluations"].append(current) + + return evaluation + + def _parse_result(self, response: str) -> Dict[str, Any]: + """Parse result from response.""" + result = { + "steps": [], + "conclusion": "", + "confidence": 0.0, + "meta_insights": [] + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Step '): + result["steps"].append(line) + elif line.startswith('Conclusion:'): + result["conclusion"] = line[11:].strip() + elif line.startswith('Confidence:'): + try: + result["confidence"] = float(line[11:].strip()) + except: + result["confidence"] = 0.5 + elif line.startswith('Meta-insights:'): + mode = "meta" + elif mode == "meta" and line.startswith('- '): + result["meta_insights"].append(line[2:].strip()) + + return result + +class EmergentReasoning(ReasoningStrategy): + """Implements emergent reasoning by analyzing collective patterns and system-level behaviors.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Identify system components + components = await self._identify_components(query, context) + + # Analyze interactions + interactions = await self._analyze_interactions(components, context) + + # Detect emergent patterns + patterns = await self._detect_patterns(interactions, context) + + # Synthesize emergent properties + synthesis = await self._synthesize_properties(patterns, context) + + return { + "success": True, + "answer": synthesis["conclusion"], + "components": components, + "interactions": interactions, + "patterns": patterns, + "emergent_properties": synthesis["properties"], + "confidence": synthesis["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Identify key system components for analysis: + Query: {query} + Context: {json.dumps(context)} + + For each component identify: + 1. [Name]: Component identifier + 2. [Properties]: Key characteristics + 3. [Role]: Function in the system + 4. [Dependencies]: Related components + + Format as: + [C1] + Name: ... + Properties: ... + Role: ... + Dependencies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_components(response["answer"]) + + async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze interactions between components: + Components: {json.dumps(components)} + Context: {json.dumps(context)} + + For each interaction describe: + 1. [Components]: Participating components + 2. [Type]: Nature of interaction + 3. [Effects]: Impact on system + 4. [Dynamics]: How it changes over time + + Format as: + [I1] + Components: ... + Type: ... + Effects: ... + Dynamics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interactions(response["answer"]) + + async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Detect emergent patterns from interactions: + Interactions: {json.dumps(interactions)} + Context: {json.dumps(context)} + + For each pattern identify: + 1. [Pattern]: Description of the pattern + 2. [Scale]: At what level it emerges + 3. [Conditions]: Required conditions + 4. [Stability]: How stable/persistent it is + + Format as: + [P1] + Pattern: ... + Scale: ... + Conditions: ... + Stability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_patterns(response["answer"]) + + async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Synthesize emergent properties from patterns: + Patterns: {json.dumps(patterns)} + Context: {json.dumps(context)} + + Provide: + 1. List of emergent properties + 2. How they arise from patterns + 3. Their significance + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_synthesis(response["answer"]) + + def _parse_components(self, response: str) -> List[Dict[str, Any]]: + """Parse components from response.""" + components = [] + current_component = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[C'): + if current_component: + components.append(current_component) + current_component = { + "name": "", + "properties": "", + "role": "", + "dependencies": [] + } + elif current_component: + if line.startswith('Name:'): + current_component["name"] = line[5:].strip() + elif line.startswith('Properties:'): + current_component["properties"] = line[11:].strip() + elif line.startswith('Role:'): + current_component["role"] = line[5:].strip() + elif line.startswith('Dependencies:'): + mode = "dependencies" + elif line.startswith("- "): + if mode == "dependencies": + current_component["dependencies"].append(line[2:].strip()) + + if current_component: + components.append(current_component) + + return components + + def _parse_interactions(self, response: str) -> List[Dict[str, Any]]: + """Parse interactions from response.""" + interactions = [] + current_interaction = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_interaction: + interactions.append(current_interaction) + current_interaction = { + "components": "", + "type": "", + "effects": "", + "dynamics": "" + } + elif current_interaction: + if line.startswith('Components:'): + current_interaction["components"] = line[11:].strip() + elif line.startswith('Type:'): + current_interaction["type"] = line[5:].strip() + elif line.startswith('Effects:'): + current_interaction["effects"] = line[7:].strip() + elif line.startswith('Dynamics:'): + current_interaction["dynamics"] = line[9:].strip() + + if current_interaction: + interactions.append(current_interaction) + + return interactions + + def _parse_patterns(self, response: str) -> List[Dict[str, Any]]: + """Parse patterns from response.""" + patterns = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current_pattern: + patterns.append(current_pattern) + current_pattern = { + "pattern": "", + "scale": "", + "conditions": "", + "stability": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Scale:'): + current_pattern["scale"] = line[6:].strip() + elif line.startswith('Conditions:'): + current_pattern["conditions"] = line[11:].strip() + elif line.startswith('Stability:'): + current_pattern["stability"] = line[10:].strip() + + if current_pattern: + patterns.append(current_pattern) + + return patterns + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis from response.""" + synthesis = { + "properties": [], + "conclusion": "", + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Properties:'): + mode = "properties" + elif line.startswith('Conclusion:'): + synthesis["conclusion"] = line[11:].strip() + mode = None + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + synthesis["confidence"] = 0.5 + mode = None + elif mode == "properties" and line.startswith('- '): + synthesis["properties"].append(line[2:].strip()) + + return synthesis + +class QuantumReasoning(ReasoningStrategy): + """Implements quantum-inspired reasoning using superposition and entanglement principles.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create superposition of possibilities + superposition = await self._create_superposition(query, context) + + # Analyze entanglements + entanglements = await self._analyze_entanglements(superposition, context) + + # Perform quantum interference + interference = await self._quantum_interference(superposition, entanglements, context) + + # Collapse to solution + solution = await self._collapse_to_solution(interference, context) + + return { + "success": True, + "answer": solution["conclusion"], + "superposition": superposition, + "entanglements": entanglements, + "interference_patterns": interference, + "measurement": solution["measurement"], + "confidence": solution["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Create superposition of possible solutions: + Query: {query} + Context: {json.dumps(context)} + + For each possibility state: + 1. [State]: Description of possibility + 2. [Amplitude]: Relative strength (0-1) + 3. [Phase]: Relationship to other states + 4. [Basis]: Underlying assumptions + + Format as: + [S1] + State: ... + Amplitude: ... + Phase: ... + Basis: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_superposition(response["answer"]) + + async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze entanglements between possibilities: + Superposition: {json.dumps(superposition)} + Context: {json.dumps(context)} + + For each entanglement describe: + 1. [States]: Entangled states + 2. [Type]: Nature of entanglement + 3. [Strength]: Correlation strength + 4. [Impact]: Effect on outcomes + + Format as: + [E1] + States: ... + Type: ... + Strength: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_entanglements(response["answer"]) + + async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Calculate quantum interference patterns: + Superposition: {json.dumps(superposition)} + Entanglements: {json.dumps(entanglements)} + Context: {json.dumps(context)} + + For each interference pattern: + 1. [Pattern]: Description + 2. [Amplitude]: Combined strength + 3. [Phase]: Combined phase + 4. [Effect]: Impact on solution space + + Format as: + [I1] + Pattern: ... + Amplitude: ... + Phase: ... + Effect: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interference(response["answer"]) + + async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Collapse quantum state to final solution: + Interference: {json.dumps(interference)} + Context: {json.dumps(context)} + + Provide: + 1. Final measured state + 2. Measurement confidence + 3. Key quantum effects utilized + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_collapse(response["answer"]) + + def _parse_superposition(self, response: str) -> List[Dict[str, Any]]: + """Parse superposition states from response.""" + superposition = [] + current_state = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current_state: + superposition.append(current_state) + current_state = { + "state": "", + "amplitude": 0.0, + "phase": "", + "basis": "" + } + elif current_state: + if line.startswith('State:'): + current_state["state"] = line[6:].strip() + elif line.startswith('Amplitude:'): + try: + current_state["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_state["phase"] = line[6:].strip() + elif line.startswith('Basis:'): + current_state["basis"] = line[6:].strip() + + if current_state: + superposition.append(current_state) + + return superposition + + def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]: + """Parse entanglements from response.""" + entanglements = [] + current_entanglement = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[E'): + if current_entanglement: + entanglements.append(current_entanglement) + current_entanglement = { + "states": "", + "type": "", + "strength": 0.0, + "impact": "" + } + elif current_entanglement: + if line.startswith('States:'): + current_entanglement["states"] = line[7:].strip() + elif line.startswith('Type:'): + current_entanglement["type"] = line[5:].strip() + elif line.startswith('Strength:'): + try: + current_entanglement["strength"] = float(line[9:].strip()) + except: + pass + elif line.startswith('Impact:'): + current_entanglement["impact"] = line[7:].strip() + + if current_entanglement: + entanglements.append(current_entanglement) + + return entanglements + + def _parse_interference(self, response: str) -> List[Dict[str, Any]]: + """Parse interference patterns from response.""" + interference = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_pattern: + interference.append(current_pattern) + current_pattern = { + "pattern": "", + "amplitude": 0.0, + "phase": "", + "effect": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Amplitude:'): + try: + current_pattern["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_pattern["phase"] = line[6:].strip() + elif line.startswith('Effect:'): + current_pattern["effect"] = line[7:].strip() + + if current_pattern: + interference.append(current_pattern) + + return interference + + def _parse_collapse(self, response: str) -> Dict[str, Any]: + """Parse collapse to solution from response.""" + collapse = { + "measurement": "", + "confidence": 0.0, + "quantum_effects": [], + "conclusion": "" + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Measurement:'): + collapse["measurement"] = line[12:].strip() + elif line.startswith('Confidence:'): + try: + collapse["confidence"] = float(line[11:].strip()) + except: + collapse["confidence"] = 0.5 + elif line.startswith('Quantum Effects:'): + mode = "effects" + elif mode == "effects" and line.startswith('- '): + collapse["quantum_effects"].append(line[2:].strip()) + elif line.startswith('Conclusion:'): + collapse["conclusion"] = line[11:].strip() + + return collapse + +class QuantumInspiredStrategy(ReasoningStrategy): + """Implements Quantum-Inspired reasoning.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + +class NeurosymbolicReasoning(ReasoningStrategy): + """Implements neurosymbolic reasoning combining neural and symbolic approaches.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Extract neural features + neural_features = await self._extract_neural_features(query) + + # Generate symbolic rules + symbolic_rules = await self._generate_symbolic_rules( + neural_features, + context + ) + + # Combine neural and symbolic reasoning + combined_result = await self._combine_neural_symbolic( + neural_features, + symbolic_rules, + context + ) + + # Update knowledge base + self._update_knowledge_base( + neural_features, + symbolic_rules, + combined_result + ) + + return { + "success": True, + "neural_features": [ + { + "name": f.name, + "associations": f.associations + } + for f in neural_features + ], + "symbolic_rules": [ + { + "condition": r.condition, + "action": r.action, + "confidence": r.confidence + } + for r in symbolic_rules + ], + "combined_result": combined_result + } + + except Exception as e: + return {"success": False, "error": str(e)} + + async def _extract_neural_features(self, query: str) -> List[NeuralFeature]: + """Extract neural features from the query.""" + try: + # Use text generation model to extract features + prompt = f""" + Extract key features from this query: + {query} + + List each feature with its properties: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + features = [] + for line in result.split("\n"): + if line.strip(): + # Create feature vector using simple embedding + vector = np.random.rand(768) # Placeholder + feature = NeuralFeature( + name=line.strip(), + vector=vector + ) + features.append(feature) + + return features + + except Exception as e: + return [] + + async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]: + """Generate symbolic rules based on features.""" + try: + # Use features to generate rules + feature_desc = "\n".join(f.name for f in features) + prompt = f""" + Given these features: + {feature_desc} + + Generate logical rules in if-then format: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=200, + temperature=0.7 + ) + + rules = [] + for line in result.split("\n"): + if "if" in line.lower() and "then" in line.lower(): + parts = line.lower().split("then") + condition = parts[0].replace("if", "").strip() + action = parts[1].strip() + rule = SymbolicRule(condition, action) + rules.append(rule) + + return rules + + except Exception as e: + return [] + + async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]: + """Combine neural and symbolic reasoning.""" + try: + # Use neural features to evaluate symbolic rules + evaluated_rules = [] + for rule in rules: + # Calculate confidence based on feature associations + confidence = 0.0 + for feature in features: + if feature.name in rule.condition: + confidence += feature.associations.get(rule.action, 0.0) + rule.confidence = confidence / len(features) + evaluated_rules.append(rule) + + # Generate combined result + prompt = f""" + Combine these evaluated rules to generate a solution: + Rules: {json.dumps(evaluated_rules, indent=2)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level (0-1) + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + return { + "conclusion": result["answer"], + "confidence": 0.8 # Placeholder confidence + } + + except Exception as e: + return {} + + def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None: + """Update knowledge base with new features and rules.""" + # Update feature associations + for feature in features: + for rule in rules: + if feature.name in rule.condition: + feature.associations[rule.action] = rule.confidence + + # Update symbolic rules + for rule in rules: + rule.update_confidence(result["confidence"]) + +class MultiModalReasoning(ReasoningStrategy): + """Implements multi-modal reasoning across different types of information.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Process different modalities + modalities = await self._process_modalities(query, context) + + # Cross-modal alignment + alignment = await self._cross_modal_alignment(modalities, context) + + # Integrated analysis + integration = await self._integrated_analysis(alignment, context) + + # Generate unified response + response = await self._generate_response(integration, context) + + return { + "success": True, + "answer": response["conclusion"], + "modalities": modalities, + "alignment": alignment, + "integration": integration, + "confidence": response["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]: + prompt = f""" + Process information across modalities: + Query: {query} + Context: {json.dumps(context)} + + For each modality analyze: + 1. [Type]: Modality type + 2. [Content]: Key information + 3. [Features]: Important features + 4. [Quality]: Information quality + + Format as: + [M1] + Type: ... + Content: ... + Features: ... + Quality: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_modalities(response["answer"]) + + async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + try: + # Extract modality types + modal_types = list(modalities.keys()) + + # Initialize alignment results + alignments = [] + + # Process each modality pair + for i in range(len(modal_types)): + for j in range(i + 1, len(modal_types)): + type1, type2 = modal_types[i], modal_types[j] + + # Get items from each modality + items1 = modalities[type1] + items2 = modalities[type2] + + # Find alignments between items + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > 0.5: # Threshold for alignment + alignments.append({ + "type1": type1, + "type2": type2, + "item1": item1, + "item2": item2, + "similarity": similarity + }) + + # Sort alignments by similarity + alignments.sort(key=lambda x: x["similarity"], reverse=True) + + return alignments + + except Exception as e: + logging.error(f"Error in cross-modal alignment: {str(e)}") + return [] + + def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float: + """Calculate similarity between two items from different modalities.""" + try: + # Extract content from items + content1 = str(item1.get("content", "")) + content2 = str(item2.get("content", "")) + + # Calculate basic similarity (can be enhanced with more sophisticated methods) + common_words = set(content1.lower().split()) & set(content2.lower().split()) + total_words = set(content1.lower().split()) | set(content2.lower().split()) + + if not total_words: + return 0.0 + + return len(common_words) / len(total_words) + + except Exception as e: + logging.error(f"Error calculating similarity: {str(e)}") + return 0.0 + + async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Perform integrated multi-modal analysis: + Alignment: {json.dumps(alignment)} + Context: {json.dumps(context)} + + For each insight: + 1. [Insight]: Key finding + 2. [Sources]: Contributing modalities + 3. [Support]: Supporting evidence + 4. [Confidence]: Confidence level + + Format as: + [I1] + Insight: ... + Sources: ... + Support: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_integration(response["answer"]) + + async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate unified multi-modal response: + Integration: {json.dumps(integration)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Modal contributions + 3. Integration benefits + 4. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_response(response["answer"]) + + def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]: + """Parse modalities from response.""" + modalities = {} + current_modality = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[M'): + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + current_modality = { + "type": "", + "content": "", + "features": "", + "quality": "" + } + elif current_modality: + if line.startswith('Type:'): + current_modality["type"] = line[5:].strip() + elif line.startswith('Content:'): + current_modality["content"] = line[8:].strip() + elif line.startswith('Features:'): + current_modality["features"] = line[9:].strip() + elif line.startswith('Quality:'): + current_modality["quality"] = line[8:].strip() + + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + + return modalities + + def _parse_alignment(self, response: str) -> List[Dict[str, Any]]: + """Parse alignment from response.""" + alignment = [] + current_alignment = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[A'): + if current_alignment: + alignment.append(current_alignment) + current_alignment = { + "modalities": "", + "mapping": "", + "confidence": 0.0, + "conflicts": [] + } + elif current_alignment: + if line.startswith('Modalities:'): + current_alignment["modalities"] = line[11:].strip() + elif line.startswith('Mapping:'): + current_alignment["mapping"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + current_alignment["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Conflicts:'): + mode = "conflicts" + elif line.startswith("- "): + if mode == "conflicts": + current_alignment["conflicts"].append(line[2:].strip()) + + if current_alignment: + alignment.append(current_alignment) + + return alignment + + def _parse_integration(self, response: str) -> List[Dict[str, Any]]: + """Parse integration from response.""" + integration = [] + current_insight = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_insight: + integration.append(current_insight) + current_insight = { + "insight": "", + "sources": "", + "support": "", + "confidence": 0.0 + } + elif current_insight: + if line.startswith('Insight:'): + current_insight["insight"] = line[8:].strip() + elif line.startswith('Sources:'): + current_insight["sources"] = line[8:].strip() + elif line.startswith('Support:'): + current_insight["support"] = line[8:].strip() + elif line.startswith('Confidence:'): + try: + current_insight["confidence"] = float(line[11:].strip()) + except: + pass + + if current_insight: + integration.append(current_insight) + + return integration + + def _parse_response(self, response: str) -> Dict[str, Any]: + """Parse response from response.""" + response_dict = { + "conclusion": "", + "modal_contributions": [], + "integration_benefits": [], + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + response_dict["conclusion"] = line[11:].strip() + elif line.startswith('Modal Contributions:'): + mode = "modal" + elif line.startswith('Integration Benefits:'): + mode = "integration" + elif line.startswith('Confidence:'): + try: + response_dict["confidence"] = float(line[11:].strip()) + except: + response_dict["confidence"] = 0.5 + mode = None + elif mode == "modal" and line.startswith('- '): + response_dict["modal_contributions"].append(line[2:].strip()) + elif mode == "integration" and line.startswith('- '): + response_dict["integration_benefits"].append(line[2:].strip()) + + return response_dict + +class MetaLearningStrategy(ReasoningStrategy): + """A meta-learning strategy that adapts its reasoning approach based on problem characteristics.""" + + def __init__(self): + self.strategy_patterns = { + "analytical": ["analyze", "compare", "evaluate", "measure"], + "creative": ["design", "create", "innovate", "imagine"], + "systematic": ["organize", "structure", "plan", "implement"], + "critical": ["critique", "assess", "validate", "test"] + } + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + # Analyze query to determine best reasoning patterns + patterns = self._identify_patterns(query.lower()) + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: {', '.join(patterns)} + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + + def _identify_patterns(self, query: str) -> List[str]: + """Identify which reasoning patterns are most relevant for the query.""" + patterns = [] + for pattern, keywords in self.strategy_patterns.items(): + if any(keyword in query for keyword in keywords): + patterns.append(pattern) + + # Default to analytical if no patterns match + if not patterns: + patterns = ["analytical"] + + return patterns + +class BavePantherReasoning: + """Advanced reasoning engine combining multiple reasoning strategies.""" + + def __init__(self, verbose: bool = True): + """Initialize reasoning engine with multiple strategies.""" + self.logger = logging.getLogger(__name__) + self.groq_api = GroqAPI() + self.verbose = verbose + + # Configure verbose logging + if verbose: + logging.basicConfig( + level=logging.DEBUG, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' + ) + + # Initialize core strategies + self.strategies = { + "cot": ChainOfThoughtStrategy(), + "tot": TreeOfThoughtsStrategy(), + "quantum": QuantumInspiredStrategy(), + "meta_learning": MetaLearningStrategy() + } + + def _log_verbose(self, message: str, level: str = "info"): + """Log message if verbose mode is enabled.""" + if self.verbose: + if level == "debug": + self.logger.debug(message) + elif level == "info": + self.logger.info(message) + elif level == "warning": + self.logger.warning(message) + elif level == "error": + self.logger.error(message) + + async def process(self, query: str, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + """Process query using selected reasoning strategies.""" + try: + if context is None: + context = {} + + # Create a clean context for serialization + clean_context = { + k: v for k, v in context.items() + if k != "groq_api" + } + + # Determine which strategies to use based on query + selected_strategies = [] + + if "chain of thought" in query.lower(): + selected_strategies.append("cot") + elif "tree of thoughts" in query.lower(): + selected_strategies.append("tot") + elif "quantum-inspired" in query.lower(): + selected_strategies.append("quantum") + elif "meta-learning" in query.lower(): + selected_strategies.append("meta_learning") + else: + # For basic reasoning, use the base strategy + prompt = f""" + Analyze this query using basic reasoning: + Query: {query} + Context: {json.dumps(clean_context)} + + Please provide: + 1. A step-by-step reasoning path + 2. A clear conclusion + """ + + response = await self.groq_api.predict(prompt) + + if not response["success"]: + return response + + # Parse response into reasoning path and conclusion + lines = response["answer"].split("\n") + reasoning_path = [] + conclusion = "" + + mode = "path" + for line in lines: + line = line.strip() + if not line: + continue + + if mode == "path" and (line.startswith("-") or line.startswith("*") or line.startswith("Step")): + reasoning_path.append(line.lstrip("- *Step").strip()) + elif mode == "conclusion": + conclusion += line + " " + + return { + "success": True, + "reasoning_path": reasoning_path, + "conclusion": conclusion.strip(), + "reasoning_chain": [], + "final_conclusion": "", + "thought_branches": [], + "selected_path": "", + "reasoning_justification": "" + } + + # Apply selected strategies + results = {} + for name in selected_strategies: + try: + strategy = self.strategies[name] + strategy_context = {**clean_context, "groq_api": self.groq_api} + result = await strategy.reason(query, strategy_context) + results[name] = result + except Exception as e: + self.logger.error(f"Error in {name} strategy: {e}") + results[name] = {"error": str(e), "success": False} + + # Combine insights from different strategies + combined = self._combine_insights(results, query, clean_context) + + # Add reasoning_path and conclusion for compatibility + combined["reasoning_path"] = combined.get("reasoning_chain", []) + combined["conclusion"] = combined.get("final_conclusion", "") + + return { + "success": True, + **combined + } + + except Exception as e: + self.logger.error(f"Error in reasoning process: {e}") + return { + "error": str(e), + "success": False + } + + def _combine_insights(self, results: Dict[str, Any], query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Combine insights from different reasoning strategies.""" + combined = { + "reasoning_chain": [], + "final_conclusion": "", + "thought_branches": [], + "selected_path": "", + "reasoning_justification": "" + } + + # Extract insights from each strategy + if "cot" in results and results["cot"].get("success"): + combined["reasoning_chain"] = results["cot"].get("reasoning_chain", []) + combined["final_conclusion"] = results["cot"].get("final_conclusion", "") + + if "tot" in results and results["tot"].get("success"): + combined["thought_branches"] = results["tot"].get("thought_branches", []) + combined["selected_path"] = results["tot"].get("selected_path", "") + combined["reasoning_justification"] = results["tot"].get("reasoning_justification", "") + if not combined["final_conclusion"]: + combined["final_conclusion"] = results["tot"].get("selected_path", "") + + if "quantum" in results and results["quantum"].get("success"): + combined["reasoning_chain"] = results["quantum"].get("quantum_states", []) + combined["final_conclusion"] = results["quantum"].get("measured_outcome", "") + + if "meta_learning" in results and results["meta_learning"].get("success"): + combined["reasoning_chain"] = results["meta_learning"].get("problem_analysis", []) + results["meta_learning"].get("solution_paths", []) + combined["final_conclusion"] = results["meta_learning"].get("conclusion", "") + + return combined + +class SymbolicRule: + """Represents a symbolic rule for neurosymbolic reasoning.""" + def __init__(self, condition: str, action: str, confidence: float = 0.5): + self.id = str(uuid.uuid4()) + self.condition = condition + self.action = action + self.confidence = confidence + self.usage_count = 0 + self.success_count = 0 + + def update_confidence(self, success: bool): + """Update rule confidence based on usage.""" + self.usage_count += 1 + if success: + self.success_count += 1 + self.confidence = self.success_count / max(1, self.usage_count) + +class NeuralFeature: + """Represents a neural feature for neurosymbolic reasoning.""" + def __init__(self, name: str, vector: np.ndarray): + self.name = name + self.vector = vector + self.associations: Dict[str, float] = {} + + def update_association(self, concept: str, strength: float): + """Update association strength with a concept.""" + self.associations[concept] = strength + +class StateSpaceNode: + """Represents a node in the state space search.""" + def __init__( + self, + state: Dict[str, Any], + parent: Optional['StateSpaceNode'] = None, + action: Optional[str] = None, + cost: float = 0.0 + ): + self.id = str(uuid.uuid4()) + self.state = state + self.parent = parent + self.action = action + self.cost = cost + self.heuristic = 0.0 + self.children: List['StateSpaceNode'] = [] + + def __lt__(self, other): + return (self.cost + self.heuristic) < (other.cost + other.heuristic) + +class CounterfactualScenario: + """Represents a counterfactual scenario.""" + def __init__( + self, + premise: str, + changes: List[str], + implications: List[str], + probability: float + ): + self.id = str(uuid.uuid4()) + self.premise = premise + self.changes = changes + self.implications = implications + self.probability = probability + self.impact_score = 0.0 + + def evaluate_impact(self, context: Dict[str, Any]) -> float: + """Evaluate the impact of this counterfactual scenario.""" + # Implementation will vary based on the specific domain + return self.impact_score + +class MetaLearningStrategy(ReasoningStrategy): + """A meta-learning strategy that adapts its reasoning approach based on problem characteristics.""" + + def __init__(self): + self.strategy_patterns = { + "analytical": ["analyze", "compare", "evaluate", "measure"], + "creative": ["design", "create", "innovate", "imagine"], + "systematic": ["organize", "structure", "plan", "implement"], + "critical": ["critique", "assess", "validate", "test"] + } + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + # Analyze query to determine best reasoning patterns + patterns = self._identify_patterns(query.lower()) + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: {', '.join(patterns)} + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + + def _identify_patterns(self, query: str) -> List[str]: + """Identify which reasoning patterns are most relevant for the query.""" + patterns = [] + for pattern, keywords in self.strategy_patterns.items(): + if any(keyword in query for keyword in keywords): + patterns.append(pattern) + + # Default to analytical if no patterns match + if not patterns: + patterns = ["analytical"] + + return patterns + +class BayesianReasoning(ReasoningStrategy): + """Implements Bayesian reasoning for probabilistic analysis.""" + def __init__(self, prior_weight: float = 0.3): + self.prior_weight = prior_weight + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Generate hypotheses + hypotheses = await self._generate_hypotheses(query, context) + + # Calculate prior probabilities + priors = await self._calculate_priors(hypotheses, context) + + # Update with evidence + posteriors = await self._update_with_evidence(hypotheses, priors, context) + + # Generate final analysis + analysis = await self._generate_analysis(posteriors, context) + + return { + "success": True, + "answer": analysis["conclusion"], + "hypotheses": hypotheses, + "priors": priors, + "posteriors": posteriors, + "confidence": analysis["confidence"], + "reasoning_path": analysis["reasoning_path"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Generate 3-4 hypotheses for this problem: + Query: {query} + Context: {json.dumps(context)} + + For each hypothesis: + 1. [Statement]: Clear statement of the hypothesis + 2. [Assumptions]: Key assumptions made + 3. [Testability]: How it could be tested/verified + + Format as: + [H1] + Statement: ... + Assumptions: ... + Testability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_hypotheses(response["answer"]) + + async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Calculate prior probabilities for these hypotheses: + Context: {json.dumps(context)} + + Hypotheses: + {json.dumps(hypotheses, indent=2)} + + For each hypothesis, estimate its prior probability (0-1) based on: + 1. Alignment with known principles + 2. Historical precedent + 3. Domain expertise + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], + context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Update probabilities with available evidence: + Context: {json.dumps(context)} + + Hypotheses and Priors: + {json.dumps(list(zip(hypotheses, priors.values())), indent=2)} + + Consider: + 1. How well each hypothesis explains the evidence + 2. Any new evidence from the context + 3. Potential conflicts or support between hypotheses + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate final Bayesian analysis: + Context: {json.dumps(context)} + + Posterior Probabilities: + {json.dumps(posteriors, indent=2)} + + Provide: + 1. Main conclusion based on highest probability hypotheses + 2. Confidence level (0-1) + 3. Key reasoning steps taken + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_analysis(response["answer"]) + + def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]: + """Parse hypotheses from response.""" + hypotheses = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[H'): + if current: + hypotheses.append(current) + current = { + "statement": "", + "assumptions": "", + "testability": "" + } + elif current: + if line.startswith('Statement:'): + current["statement"] = line[10:].strip() + elif line.startswith('Assumptions:'): + current["assumptions"] = line[12:].strip() + elif line.startswith('Testability:'): + current["testability"] = line[12:].strip() + + if current: + hypotheses.append(current) + + return hypotheses + + def _parse_probabilities(self, response: str) -> Dict[str, float]: + """Parse probabilities from response.""" + probs = {} + pattern = r'\[H(\d+)\]:\s*(0\.\d+)' + + for match in re.finditer(pattern, response): + h_num = int(match.group(1)) + prob = float(match.group(2)) + probs[f"H{h_num}"] = prob + + return probs + + def _parse_analysis(self, response: str) -> Dict[str, Any]: + """Parse analysis from response.""" + lines = response.split('\n') + analysis = { + "conclusion": "", + "confidence": 0.0, + "reasoning_path": [] + } + + for line in lines: + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + analysis["conclusion"] = line[11:].strip() + elif line.startswith('Confidence:'): + try: + analysis["confidence"] = float(line[11:].strip()) + except: + analysis["confidence"] = 0.5 + elif line.startswith('- '): + analysis["reasoning_path"].append(line[2:].strip()) + + return analysis + +class EmergentReasoning(ReasoningStrategy): + """Implements emergent reasoning by analyzing collective patterns and system-level behaviors.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Identify system components + components = await self._identify_components(query, context) + + # Analyze interactions + interactions = await self._analyze_interactions(components, context) + + # Detect emergent patterns + patterns = await self._detect_patterns(interactions, context) + + # Synthesize emergent properties + synthesis = await self._synthesize_properties(patterns, context) + + return { + "success": True, + "answer": synthesis["conclusion"], + "components": components, + "interactions": interactions, + "patterns": patterns, + "emergent_properties": synthesis["properties"], + "confidence": synthesis["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Identify key system components for analysis: + Query: {query} + Context: {json.dumps(context)} + + For each component identify: + 1. [Name]: Component identifier + 2. [Properties]: Key characteristics + 3. [Role]: Function in the system + 4. [Dependencies]: Related components + + Format as: + [C1] + Name: ... + Properties: ... + Role: ... + Dependencies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_components(response["answer"]) + + async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze interactions between components: + Components: {json.dumps(components)} + Context: {json.dumps(context)} + + For each interaction describe: + 1. [Components]: Participating components + 2. [Type]: Nature of interaction + 3. [Effects]: Impact on system + 4. [Dynamics]: How it changes over time + + Format as: + [I1] + Components: ... + Type: ... + Effects: ... + Dynamics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interactions(response["answer"]) + + async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Detect emergent patterns from interactions: + Interactions: {json.dumps(interactions)} + Context: {json.dumps(context)} + + For each pattern identify: + 1. [Pattern]: Description of the pattern + 2. [Scale]: At what level it emerges + 3. [Conditions]: Required conditions + 4. [Stability]: How stable/persistent it is + + Format as: + [P1] + Pattern: ... + Scale: ... + Conditions: ... + Stability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_patterns(response["answer"]) + + async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Synthesize emergent properties from patterns: + Patterns: {json.dumps(patterns)} + Context: {json.dumps(context)} + + Provide: + 1. List of emergent properties + 2. How they arise from patterns + 3. Their significance + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_synthesis(response["answer"]) + + def _parse_components(self, response: str) -> List[Dict[str, Any]]: + """Parse components from response.""" + components = [] + current_component = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[C'): + if current_component: + components.append(current_component) + current_component = { + "name": "", + "properties": "", + "role": "", + "dependencies": [] + } + elif current_component: + if line.startswith('Name:'): + current_component["name"] = line[5:].strip() + elif line.startswith('Properties:'): + current_component["properties"] = line[11:].strip() + elif line.startswith('Role:'): + current_component["role"] = line[5:].strip() + elif line.startswith('Dependencies:'): + mode = "dependencies" + elif line.startswith("- "): + if mode == "dependencies": + current_component["dependencies"].append(line[2:].strip()) + + if current_component: + components.append(current_component) + + return components + + def _parse_interactions(self, response: str) -> List[Dict[str, Any]]: + """Parse interactions from response.""" + interactions = [] + current_interaction = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_interaction: + interactions.append(current_interaction) + current_interaction = { + "components": "", + "type": "", + "effects": "", + "dynamics": "" + } + elif current_interaction: + if line.startswith('Components:'): + current_interaction["components"] = line[11:].strip() + elif line.startswith('Type:'): + current_interaction["type"] = line[5:].strip() + elif line.startswith('Effects:'): + current_interaction["effects"] = line[7:].strip() + elif line.startswith('Dynamics:'): + current_interaction["dynamics"] = line[9:].strip() + + if current_interaction: + interactions.append(current_interaction) + + return interactions + + def _parse_patterns(self, response: str) -> List[Dict[str, Any]]: + """Parse patterns from response.""" + patterns = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current_pattern: + patterns.append(current_pattern) + current_pattern = { + "pattern": "", + "scale": "", + "conditions": "", + "stability": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Scale:'): + current_pattern["scale"] = line[6:].strip() + elif line.startswith('Conditions:'): + current_pattern["conditions"] = line[11:].strip() + elif line.startswith('Stability:'): + current_pattern["stability"] = line[10:].strip() + + if current_pattern: + patterns.append(current_pattern) + + return patterns + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis from response.""" + synthesis = { + "properties": [], + "conclusion": "", + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Properties:'): + mode = "properties" + elif line.startswith('Conclusion:'): + synthesis["conclusion"] = line[11:].strip() + mode = None + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + synthesis["confidence"] = 0.5 + mode = None + elif mode == "properties" and line.startswith('- '): + synthesis["properties"].append(line[2:].strip()) + + return synthesis + +class QuantumReasoning(ReasoningStrategy): + """Implements quantum-inspired reasoning using superposition and entanglement principles.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create superposition of possibilities + superposition = await self._create_superposition(query, context) + + # Analyze entanglements + entanglements = await self._analyze_entanglements(superposition, context) + + # Perform quantum interference + interference = await self._quantum_interference(superposition, entanglements, context) + + # Collapse to solution + solution = await self._collapse_to_solution(interference, context) + + return { + "success": True, + "answer": solution["conclusion"], + "superposition": superposition, + "entanglements": entanglements, + "interference_patterns": interference, + "measurement": solution["measurement"], + "confidence": solution["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Create superposition of possible solutions: + Query: {query} + Context: {json.dumps(context)} + + For each possibility state: + 1. [State]: Description of possibility + 2. [Amplitude]: Relative strength (0-1) + 3. [Phase]: Relationship to other states + 4. [Basis]: Underlying assumptions + + Format as: + [S1] + State: ... + Amplitude: ... + Phase: ... + Basis: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_superposition(response["answer"]) + + async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze entanglements between possibilities: + Superposition: {json.dumps(superposition)} + Context: {json.dumps(context)} + + For each entanglement describe: + 1. [States]: Entangled states + 2. [Type]: Nature of entanglement + 3. [Strength]: Correlation strength + 4. [Impact]: Effect on outcomes + + Format as: + [E1] + States: ... + Type: ... + Strength: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_entanglements(response["answer"]) + + async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Calculate quantum interference patterns: + Superposition: {json.dumps(superposition)} + Entanglements: {json.dumps(entanglements)} + Context: {json.dumps(context)} + + For each interference pattern: + 1. [Pattern]: Description + 2. [Amplitude]: Combined strength + 3. [Phase]: Combined phase + 4. [Effect]: Impact on solution space + + Format as: + [I1] + Pattern: ... + Amplitude: ... + Phase: ... + Effect: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interference(response["answer"]) + + async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Collapse quantum state to final solution: + Interference: {json.dumps(interference)} + Context: {json.dumps(context)} + + Provide: + 1. Final measured state + 2. Measurement confidence + 3. Key quantum effects utilized + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_collapse(response["answer"]) + + def _parse_superposition(self, response: str) -> List[Dict[str, Any]]: + """Parse superposition states from response.""" + superposition = [] + current_state = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current_state: + superposition.append(current_state) + current_state = { + "state": "", + "amplitude": 0.0, + "phase": "", + "basis": "" + } + elif current_state: + if line.startswith('State:'): + current_state["state"] = line[6:].strip() + elif line.startswith('Amplitude:'): + try: + current_state["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_state["phase"] = line[6:].strip() + elif line.startswith('Basis:'): + current_state["basis"] = line[6:].strip() + + if current_state: + superposition.append(current_state) + + return superposition + + def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]: + """Parse entanglements from response.""" + entanglements = [] + current_entanglement = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[E'): + if current_entanglement: + entanglements.append(current_entanglement) + current_entanglement = { + "states": "", + "type": "", + "strength": 0.0, + "impact": "" + } + elif current_entanglement: + if line.startswith('States:'): + current_entanglement["states"] = line[7:].strip() + elif line.startswith('Type:'): + current_entanglement["type"] = line[5:].strip() + elif line.startswith('Strength:'): + try: + current_entanglement["strength"] = float(line[9:].strip()) + except: + pass + elif line.startswith('Impact:'): + current_entanglement["impact"] = line[7:].strip() + + if current_entanglement: + entanglements.append(current_entanglement) + + return entanglements + + def _parse_interference(self, response: str) -> List[Dict[str, Any]]: + """Parse interference patterns from response.""" + interference = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_pattern: + interference.append(current_pattern) + current_pattern = { + "pattern": "", + "amplitude": 0.0, + "phase": "", + "effect": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Amplitude:'): + try: + current_pattern["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_pattern["phase"] = line[6:].strip() + elif line.startswith('Effect:'): + current_pattern["effect"] = line[7:].strip() + + if current_pattern: + interference.append(current_pattern) + + return interference + + def _parse_collapse(self, response: str) -> Dict[str, Any]: + """Parse collapse to solution from response.""" + collapse = { + "measurement": "", + "confidence": 0.0, + "quantum_effects": [], + "conclusion": "" + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Measurement:'): + collapse["measurement"] = line[12:].strip() + elif line.startswith('Confidence:'): + try: + collapse["confidence"] = float(line[11:].strip()) + except: + collapse["confidence"] = 0.5 + elif line.startswith('Quantum Effects:'): + mode = "effects" + elif mode == "effects" and line.startswith('- '): + collapse["quantum_effects"].append(line[2:].strip()) + elif line.startswith('Conclusion:'): + collapse["conclusion"] = line[11:].strip() + + return collapse + +class QuantumInspiredStrategy(ReasoningStrategy): + """Implements Quantum-Inspired reasoning.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + +class NeurosymbolicReasoning(ReasoningStrategy): + """Implements neurosymbolic reasoning combining neural and symbolic approaches.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Extract neural features + neural_features = await self._extract_neural_features(query) + + # Generate symbolic rules + symbolic_rules = await self._generate_symbolic_rules( + neural_features, + context + ) + + # Combine neural and symbolic reasoning + combined_result = await self._combine_neural_symbolic( + neural_features, + symbolic_rules, + context + ) + + # Update knowledge base + self._update_knowledge_base( + neural_features, + symbolic_rules, + combined_result + ) + + return { + "success": True, + "neural_features": [ + { + "name": f.name, + "associations": f.associations + } + for f in neural_features + ], + "symbolic_rules": [ + { + "condition": r.condition, + "action": r.action, + "confidence": r.confidence + } + for r in symbolic_rules + ], + "combined_result": combined_result + } + + except Exception as e: + return {"success": False, "error": str(e)} + + async def _extract_neural_features(self, query: str) -> List[NeuralFeature]: + """Extract neural features from the query.""" + try: + # Use text generation model to extract features + prompt = f""" + Extract key features from this query: + {query} + + List each feature with its properties: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + features = [] + for line in result.split("\n"): + if line.strip(): + # Create feature vector using simple embedding + vector = np.random.rand(768) # Placeholder + feature = NeuralFeature( + name=line.strip(), + vector=vector + ) + features.append(feature) + + return features + + except Exception as e: + return [] + + async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]: + """Generate symbolic rules based on features.""" + try: + # Use features to generate rules + feature_desc = "\n".join(f.name for f in features) + prompt = f""" + Given these features: + {feature_desc} + + Generate logical rules in if-then format: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=200, + temperature=0.7 + ) + + rules = [] + for line in result.split("\n"): + if "if" in line.lower() and "then" in line.lower(): + parts = line.lower().split("then") + condition = parts[0].replace("if", "").strip() + action = parts[1].strip() + rule = SymbolicRule(condition, action) + rules.append(rule) + + return rules + + except Exception as e: + return [] + + async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]: + """Combine neural and symbolic reasoning.""" + try: + # Use neural features to evaluate symbolic rules + evaluated_rules = [] + for rule in rules: + # Calculate confidence based on feature associations + confidence = 0.0 + for feature in features: + if feature.name in rule.condition: + confidence += feature.associations.get(rule.action, 0.0) + rule.confidence = confidence / len(features) + evaluated_rules.append(rule) + + # Generate combined result + prompt = f""" + Combine these evaluated rules to generate a solution: + Rules: {json.dumps(evaluated_rules, indent=2)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level (0-1) + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + return { + "conclusion": result["answer"], + "confidence": 0.8 # Placeholder confidence + } + + except Exception as e: + return {} + + def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None: + """Update knowledge base with new features and rules.""" + # Update feature associations + for feature in features: + for rule in rules: + if feature.name in rule.condition: + feature.associations[rule.action] = rule.confidence + + # Update symbolic rules + for rule in rules: + rule.update_confidence(result["confidence"]) + +class MultiModalReasoning(ReasoningStrategy): + """Implements multi-modal reasoning across different types of information.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Process different modalities + modalities = await self._process_modalities(query, context) + + # Cross-modal alignment + alignment = await self._cross_modal_alignment(modalities, context) + + # Integrated analysis + integration = await self._integrated_analysis(alignment, context) + + # Generate unified response + response = await self._generate_response(integration, context) + + return { + "success": True, + "answer": response["conclusion"], + "modalities": modalities, + "alignment": alignment, + "integration": integration, + "confidence": response["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]: + prompt = f""" + Process information across modalities: + Query: {query} + Context: {json.dumps(context)} + + For each modality analyze: + 1. [Type]: Modality type + 2. [Content]: Key information + 3. [Features]: Important features + 4. [Quality]: Information quality + + Format as: + [M1] + Type: ... + Content: ... + Features: ... + Quality: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_modalities(response["answer"]) + + async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + try: + # Extract modality types + modal_types = list(modalities.keys()) + + # Initialize alignment results + alignments = [] + + # Process each modality pair + for i in range(len(modal_types)): + for j in range(i + 1, len(modal_types)): + type1, type2 = modal_types[i], modal_types[j] + + # Get items from each modality + items1 = modalities[type1] + items2 = modalities[type2] + + # Find alignments between items + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > 0.5: # Threshold for alignment + alignments.append({ + "type1": type1, + "type2": type2, + "item1": item1, + "item2": item2, + "similarity": similarity + }) + + # Sort alignments by similarity + alignments.sort(key=lambda x: x["similarity"], reverse=True) + + return alignments + + except Exception as e: + logging.error(f"Error in cross-modal alignment: {str(e)}") + return [] + + def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float: + """Calculate similarity between two items from different modalities.""" + try: + # Extract content from items + content1 = str(item1.get("content", "")) + content2 = str(item2.get("content", "")) + + # Calculate basic similarity (can be enhanced with more sophisticated methods) + common_words = set(content1.lower().split()) & set(content2.lower().split()) + total_words = set(content1.lower().split()) | set(content2.lower().split()) + + if not total_words: + return 0.0 + + return len(common_words) / len(total_words) + + except Exception as e: + logging.error(f"Error calculating similarity: {str(e)}") + return 0.0 + + async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Perform integrated multi-modal analysis: + Alignment: {json.dumps(alignment)} + Context: {json.dumps(context)} + + For each insight: + 1. [Insight]: Key finding + 2. [Sources]: Contributing modalities + 3. [Support]: Supporting evidence + 4. [Confidence]: Confidence level + + Format as: + [I1] + Insight: ... + Sources: ... + Support: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_integration(response["answer"]) + + async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate unified multi-modal response: + Integration: {json.dumps(integration)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Modal contributions + 3. Integration benefits + 4. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_response(response["answer"]) + + def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]: + """Parse modalities from response.""" + modalities = {} + current_modality = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[M'): + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + current_modality = { + "type": "", + "content": "", + "features": "", + "quality": "" + } + elif current_modality: + if line.startswith('Type:'): + current_modality["type"] = line[5:].strip() + elif line.startswith('Content:'): + current_modality["content"] = line[8:].strip() + elif line.startswith('Features:'): + current_modality["features"] = line[9:].strip() + elif line.startswith('Quality:'): + current_modality["quality"] = line[8:].strip() + + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + + return modalities + + def _parse_alignment(self, response: str) -> List[Dict[str, Any]]: + """Parse alignment from response.""" + alignment = [] + current_alignment = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[A'): + if current_alignment: + alignment.append(current_alignment) + current_alignment = { + "modalities": "", + "mapping": "", + "confidence": 0.0, + "conflicts": [] + } + elif current_alignment: + if line.startswith('Modalities:'): + current_alignment["modalities"] = line[11:].strip() + elif line.startswith('Mapping:'): + current_alignment["mapping"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + current_alignment["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Conflicts:'): + mode = "conflicts" + elif line.startswith("- "): + if mode == "conflicts": + current_alignment["conflicts"].append(line[2:].strip()) + + if current_alignment: + alignment.append(current_alignment) + + return alignment + + def _parse_integration(self, response: str) -> List[Dict[str, Any]]: + """Parse integration from response.""" + integration = [] + current_insight = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_insight: + integration.append(current_insight) + current_insight = { + "insight": "", + "sources": "", + "support": "", + "confidence": 0.0 + } + elif current_insight: + if line.startswith('Insight:'): + current_insight["insight"] = line[8:].strip() + elif line.startswith('Sources:'): + current_insight["sources"] = line[8:].strip() + elif line.startswith('Support:'): + current_insight["support"] = line[8:].strip() + elif line.startswith('Confidence:'): + try: + current_insight["confidence"] = float(line[11:].strip()) + except: + pass + + if current_insight: + integration.append(current_insight) + + return integration + + def _parse_response(self, response: str) -> Dict[str, Any]: + """Parse response from response.""" + response_dict = { + "conclusion": "", + "modal_contributions": [], + "integration_benefits": [], + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + response_dict["conclusion"] = line[11:].strip() + elif line.startswith('Modal Contributions:'): + mode = "modal" + elif line.startswith('Integration Benefits:'): + mode = "integration" + elif line.startswith('Confidence:'): + try: + response_dict["confidence"] = float(line[11:].strip()) + except: + response_dict["confidence"] = 0.5 + mode = None + elif mode == "modal" and line.startswith('- '): + response_dict["modal_contributions"].append(line[2:].strip()) + elif mode == "integration" and line.startswith('- '): + response_dict["integration_benefits"].append(line[2:].strip()) + + return response_dict + +class MetaLearningStrategy(ReasoningStrategy): + """A meta-learning strategy that adapts its reasoning approach based on problem characteristics.""" + + def __init__(self): + self.strategy_patterns = { + "analytical": ["analyze", "compare", "evaluate", "measure"], + "creative": ["design", "create", "innovate", "imagine"], + "systematic": ["organize", "structure", "plan", "implement"], + "critical": ["critique", "assess", "validate", "test"] + } + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + # Analyze query to determine best reasoning patterns + patterns = self._identify_patterns(query.lower()) + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: {', '.join(patterns)} + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + + def _identify_patterns(self, query: str) -> List[str]: + """Identify which reasoning patterns are most relevant for the query.""" + patterns = [] + for pattern, keywords in self.strategy_patterns.items(): + if any(keyword in query for keyword in keywords): + patterns.append(pattern) + + # Default to analytical if no patterns match + if not patterns: + patterns = ["analytical"] + + return patterns + +class BayesianReasoning(ReasoningStrategy): + """Implements Bayesian reasoning for probabilistic analysis.""" + def __init__(self, prior_weight: float = 0.3): + self.prior_weight = prior_weight + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Generate hypotheses + hypotheses = await self._generate_hypotheses(query, context) + + # Calculate prior probabilities + priors = await self._calculate_priors(hypotheses, context) + + # Update with evidence + posteriors = await self._update_with_evidence(hypotheses, priors, context) + + # Generate final analysis + analysis = await self._generate_analysis(posteriors, context) + + return { + "success": True, + "answer": analysis["conclusion"], + "hypotheses": hypotheses, + "priors": priors, + "posteriors": posteriors, + "confidence": analysis["confidence"], + "reasoning_path": analysis["reasoning_path"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Generate 3-4 hypotheses for this problem: + Query: {query} + Context: {json.dumps(context)} + + For each hypothesis: + 1. [Statement]: Clear statement of the hypothesis + 2. [Assumptions]: Key assumptions made + 3. [Testability]: How it could be tested/verified + + Format as: + [H1] + Statement: ... + Assumptions: ... + Testability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_hypotheses(response["answer"]) + + async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Calculate prior probabilities for these hypotheses: + Context: {json.dumps(context)} + + Hypotheses: + {json.dumps(hypotheses, indent=2)} + + For each hypothesis, estimate its prior probability (0-1) based on: + 1. Alignment with known principles + 2. Historical precedent + 3. Domain expertise + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], + context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Update probabilities with available evidence: + Context: {json.dumps(context)} + + Hypotheses and Priors: + {json.dumps(list(zip(hypotheses, priors.values())), indent=2)} + + Consider: + 1. How well each hypothesis explains the evidence + 2. Any new evidence from the context + 3. Potential conflicts or support between hypotheses + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate final Bayesian analysis: + Context: {json.dumps(context)} + + Posterior Probabilities: + {json.dumps(posteriors, indent=2)} + + Provide: + 1. Main conclusion based on highest probability hypotheses + 2. Confidence level (0-1) + 3. Key reasoning steps taken + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_analysis(response["answer"]) + + def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]: + """Parse hypotheses from response.""" + hypotheses = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[H'): + if current: + hypotheses.append(current) + current = { + "statement": "", + "assumptions": "", + "testability": "" + } + elif current: + if line.startswith('Statement:'): + current["statement"] = line[10:].strip() + elif line.startswith('Assumptions:'): + current["assumptions"] = line[12:].strip() + elif line.startswith('Testability:'): + current["testability"] = line[12:].strip() + + if current: + hypotheses.append(current) + + return hypotheses + + def _parse_probabilities(self, response: str) -> Dict[str, float]: + """Parse probabilities from response.""" + probs = {} + pattern = r'\[H(\d+)\]:\s*(0\.\d+)' + + for match in re.finditer(pattern, response): + h_num = int(match.group(1)) + prob = float(match.group(2)) + probs[f"H{h_num}"] = prob + + return probs + + def _parse_analysis(self, response: str) -> Dict[str, Any]: + """Parse analysis from response.""" + lines = response.split('\n') + analysis = { + "conclusion": "", + "confidence": 0.0, + "reasoning_path": [] + } + + for line in lines: + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + analysis["conclusion"] = line[11:].strip() + elif line.startswith('Confidence:'): + try: + analysis["confidence"] = float(line[11:].strip()) + except: + analysis["confidence"] = 0.5 + elif line.startswith('- '): + analysis["reasoning_path"].append(line[2:].strip()) + + return analysis + +class EmergentReasoning(ReasoningStrategy): + """Implements emergent reasoning by analyzing collective patterns and system-level behaviors.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Identify system components + components = await self._identify_components(query, context) + + # Analyze interactions + interactions = await self._analyze_interactions(components, context) + + # Detect emergent patterns + patterns = await self._detect_patterns(interactions, context) + + # Synthesize emergent properties + synthesis = await self._synthesize_properties(patterns, context) + + return { + "success": True, + "answer": synthesis["conclusion"], + "components": components, + "interactions": interactions, + "patterns": patterns, + "emergent_properties": synthesis["properties"], + "confidence": synthesis["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Identify key system components for analysis: + Query: {query} + Context: {json.dumps(context)} + + For each component identify: + 1. [Name]: Component identifier + 2. [Properties]: Key characteristics + 3. [Role]: Function in the system + 4. [Dependencies]: Related components + + Format as: + [C1] + Name: ... + Properties: ... + Role: ... + Dependencies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_components(response["answer"]) + + async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze interactions between components: + Components: {json.dumps(components)} + Context: {json.dumps(context)} + + For each interaction describe: + 1. [Components]: Participating components + 2. [Type]: Nature of interaction + 3. [Effects]: Impact on system + 4. [Dynamics]: How it changes over time + + Format as: + [I1] + Components: ... + Type: ... + Effects: ... + Dynamics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interactions(response["answer"]) + + async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Detect emergent patterns from interactions: + Interactions: {json.dumps(interactions)} + Context: {json.dumps(context)} + + For each pattern identify: + 1. [Pattern]: Description of the pattern + 2. [Scale]: At what level it emerges + 3. [Conditions]: Required conditions + 4. [Stability]: How stable/persistent it is + + Format as: + [P1] + Pattern: ... + Scale: ... + Conditions: ... + Stability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_patterns(response["answer"]) + + async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Synthesize emergent properties from patterns: + Patterns: {json.dumps(patterns)} + Context: {json.dumps(context)} + + Provide: + 1. List of emergent properties + 2. How they arise from patterns + 3. Their significance + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_synthesis(response["answer"]) + + def _parse_components(self, response: str) -> List[Dict[str, Any]]: + """Parse components from response.""" + components = [] + current_component = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[C'): + if current_component: + components.append(current_component) + current_component = { + "name": "", + "properties": "", + "role": "", + "dependencies": [] + } + elif current_component: + if line.startswith('Name:'): + current_component["name"] = line[5:].strip() + elif line.startswith('Properties:'): + current_component["properties"] = line[11:].strip() + elif line.startswith('Role:'): + current_component["role"] = line[5:].strip() + elif line.startswith('Dependencies:'): + mode = "dependencies" + elif line.startswith("- "): + if mode == "dependencies": + current_component["dependencies"].append(line[2:].strip()) + + if current_component: + components.append(current_component) + + return components + + def _parse_interactions(self, response: str) -> List[Dict[str, Any]]: + """Parse interactions from response.""" + interactions = [] + current_interaction = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_interaction: + interactions.append(current_interaction) + current_interaction = { + "components": "", + "type": "", + "effects": "", + "dynamics": "" + } + elif current_interaction: + if line.startswith('Components:'): + current_interaction["components"] = line[11:].strip() + elif line.startswith('Type:'): + current_interaction["type"] = line[5:].strip() + elif line.startswith('Effects:'): + current_interaction["effects"] = line[7:].strip() + elif line.startswith('Dynamics:'): + current_interaction["dynamics"] = line[9:].strip() + + if current_interaction: + interactions.append(current_interaction) + + return interactions + + def _parse_patterns(self, response: str) -> List[Dict[str, Any]]: + """Parse patterns from response.""" + patterns = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current_pattern: + patterns.append(current_pattern) + current_pattern = { + "pattern": "", + "scale": "", + "conditions": "", + "stability": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Scale:'): + current_pattern["scale"] = line[6:].strip() + elif line.startswith('Conditions:'): + current_pattern["conditions"] = line[11:].strip() + elif line.startswith('Stability:'): + current_pattern["stability"] = line[10:].strip() + + if current_pattern: + patterns.append(current_pattern) + + return patterns + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis from response.""" + synthesis = { + "properties": [], + "conclusion": "", + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Properties:'): + mode = "properties" + elif line.startswith('Conclusion:'): + synthesis["conclusion"] = line[11:].strip() + mode = None + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + synthesis["confidence"] = 0.5 + mode = None + elif mode == "properties" and line.startswith('- '): + synthesis["properties"].append(line[2:].strip()) + + return synthesis + +class QuantumReasoning(ReasoningStrategy): + """Implements quantum-inspired reasoning using superposition and entanglement principles.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create superposition of possibilities + superposition = await self._create_superposition(query, context) + + # Analyze entanglements + entanglements = await self._analyze_entanglements(superposition, context) + + # Perform quantum interference + interference = await self._quantum_interference(superposition, entanglements, context) + + # Collapse to solution + solution = await self._collapse_to_solution(interference, context) + + return { + "success": True, + "answer": solution["conclusion"], + "superposition": superposition, + "entanglements": entanglements, + "interference_patterns": interference, + "measurement": solution["measurement"], + "confidence": solution["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Create superposition of possible solutions: + Query: {query} + Context: {json.dumps(context)} + + For each possibility state: + 1. [State]: Description of possibility + 2. [Amplitude]: Relative strength (0-1) + 3. [Phase]: Relationship to other states + 4. [Basis]: Underlying assumptions + + Format as: + [S1] + State: ... + Amplitude: ... + Phase: ... + Basis: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_superposition(response["answer"]) + + async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze entanglements between possibilities: + Superposition: {json.dumps(superposition)} + Context: {json.dumps(context)} + + For each entanglement describe: + 1. [States]: Entangled states + 2. [Type]: Nature of entanglement + 3. [Strength]: Correlation strength + 4. [Impact]: Effect on outcomes + + Format as: + [E1] + States: ... + Type: ... + Strength: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_entanglements(response["answer"]) + + async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Calculate quantum interference patterns: + Superposition: {json.dumps(superposition)} + Entanglements: {json.dumps(entanglements)} + Context: {json.dumps(context)} + + For each interference pattern: + 1. [Pattern]: Description + 2. [Amplitude]: Combined strength + 3. [Phase]: Combined phase + 4. [Effect]: Impact on solution space + + Format as: + [I1] + Pattern: ... + Amplitude: ... + Phase: ... + Effect: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interference(response["answer"]) + + async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Collapse quantum state to final solution: + Interference: {json.dumps(interference)} + Context: {json.dumps(context)} + + Provide: + 1. Final measured state + 2. Measurement confidence + 3. Key quantum effects utilized + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_collapse(response["answer"]) + + def _parse_superposition(self, response: str) -> List[Dict[str, Any]]: + """Parse superposition states from response.""" + superposition = [] + current_state = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current_state: + superposition.append(current_state) + current_state = { + "state": "", + "amplitude": 0.0, + "phase": "", + "basis": "" + } + elif current_state: + if line.startswith('State:'): + current_state["state"] = line[6:].strip() + elif line.startswith('Amplitude:'): + try: + current_state["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_state["phase"] = line[6:].strip() + elif line.startswith('Basis:'): + current_state["basis"] = line[6:].strip() + + if current_state: + superposition.append(current_state) + + return superposition + + def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]: + """Parse entanglements from response.""" + entanglements = [] + current_entanglement = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[E'): + if current_entanglement: + entanglements.append(current_entanglement) + current_entanglement = { + "states": "", + "type": "", + "strength": 0.0, + "impact": "" + } + elif current_entanglement: + if line.startswith('States:'): + current_entanglement["states"] = line[7:].strip() + elif line.startswith('Type:'): + current_entanglement["type"] = line[5:].strip() + elif line.startswith('Strength:'): + try: + current_entanglement["strength"] = float(line[9:].strip()) + except: + pass + elif line.startswith('Impact:'): + current_entanglement["impact"] = line[7:].strip() + + if current_entanglement: + entanglements.append(current_entanglement) + + return entanglements + + def _parse_interference(self, response: str) -> List[Dict[str, Any]]: + """Parse interference patterns from response.""" + interference = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_pattern: + interference.append(current_pattern) + current_pattern = { + "pattern": "", + "amplitude": 0.0, + "phase": "", + "effect": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Amplitude:'): + try: + current_pattern["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_pattern["phase"] = line[6:].strip() + elif line.startswith('Effect:'): + current_pattern["effect"] = line[7:].strip() + + if current_pattern: + interference.append(current_pattern) + + return interference + + def _parse_collapse(self, response: str) -> Dict[str, Any]: + """Parse collapse to solution from response.""" + collapse = { + "measurement": "", + "confidence": 0.0, + "quantum_effects": [], + "conclusion": "" + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Measurement:'): + collapse["measurement"] = line[12:].strip() + elif line.startswith('Confidence:'): + try: + collapse["confidence"] = float(line[11:].strip()) + except: + collapse["confidence"] = 0.5 + elif line.startswith('Quantum Effects:'): + mode = "effects" + elif mode == "effects" and line.startswith('- '): + collapse["quantum_effects"].append(line[2:].strip()) + elif line.startswith('Conclusion:'): + collapse["conclusion"] = line[11:].strip() + + return collapse + +class QuantumInspiredStrategy(ReasoningStrategy): + """Implements Quantum-Inspired reasoning.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + +class NeurosymbolicReasoning(ReasoningStrategy): + """Implements neurosymbolic reasoning combining neural and symbolic approaches.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Extract neural features + neural_features = await self._extract_neural_features(query) + + # Generate symbolic rules + symbolic_rules = await self._generate_symbolic_rules( + neural_features, + context + ) + + # Combine neural and symbolic reasoning + combined_result = await self._combine_neural_symbolic( + neural_features, + symbolic_rules, + context + ) + + # Update knowledge base + self._update_knowledge_base( + neural_features, + symbolic_rules, + combined_result + ) + + return { + "success": True, + "neural_features": [ + { + "name": f.name, + "associations": f.associations + } + for f in neural_features + ], + "symbolic_rules": [ + { + "condition": r.condition, + "action": r.action, + "confidence": r.confidence + } + for r in symbolic_rules + ], + "combined_result": combined_result + } + + except Exception as e: + return {"success": False, "error": str(e)} + + async def _extract_neural_features(self, query: str) -> List[NeuralFeature]: + """Extract neural features from the query.""" + try: + # Use text generation model to extract features + prompt = f""" + Extract key features from this query: + {query} + + List each feature with its properties: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + features = [] + for line in result.split("\n"): + if line.strip(): + # Create feature vector using simple embedding + vector = np.random.rand(768) # Placeholder + feature = NeuralFeature( + name=line.strip(), + vector=vector + ) + features.append(feature) + + return features + + except Exception as e: + return [] + + async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]: + """Generate symbolic rules based on features.""" + try: + # Use features to generate rules + feature_desc = "\n".join(f.name for f in features) + prompt = f""" + Given these features: + {feature_desc} + + Generate logical rules in if-then format: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=200, + temperature=0.7 + ) + + rules = [] + for line in result.split("\n"): + if "if" in line.lower() and "then" in line.lower(): + parts = line.lower().split("then") + condition = parts[0].replace("if", "").strip() + action = parts[1].strip() + rule = SymbolicRule(condition, action) + rules.append(rule) + + return rules + + except Exception as e: + return [] + + async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]: + """Combine neural and symbolic reasoning.""" + try: + # Use neural features to evaluate symbolic rules + evaluated_rules = [] + for rule in rules: + # Calculate confidence based on feature associations + confidence = 0.0 + for feature in features: + if feature.name in rule.condition: + confidence += feature.associations.get(rule.action, 0.0) + rule.confidence = confidence / len(features) + evaluated_rules.append(rule) + + # Generate combined result + prompt = f""" + Combine these evaluated rules to generate a solution: + Rules: {json.dumps(evaluated_rules, indent=2)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level (0-1) + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + return { + "conclusion": result["answer"], + "confidence": 0.8 # Placeholder confidence + } + + except Exception as e: + return {} + + def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None: + """Update knowledge base with new features and rules.""" + # Update feature associations + for feature in features: + for rule in rules: + if feature.name in rule.condition: + feature.associations[rule.action] = rule.confidence + + # Update symbolic rules + for rule in rules: + rule.update_confidence(result["confidence"]) + +class MultiModalReasoning(ReasoningStrategy): + """Implements multi-modal reasoning across different types of information.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Process different modalities + modalities = await self._process_modalities(query, context) + + # Cross-modal alignment + alignment = await self._cross_modal_alignment(modalities, context) + + # Integrated analysis + integration = await self._integrated_analysis(alignment, context) + + # Generate unified response + response = await self._generate_response(integration, context) + + return { + "success": True, + "answer": response["conclusion"], + "modalities": modalities, + "alignment": alignment, + "integration": integration, + "confidence": response["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]: + prompt = f""" + Process information across modalities: + Query: {query} + Context: {json.dumps(context)} + + For each modality analyze: + 1. [Type]: Modality type + 2. [Content]: Key information + 3. [Features]: Important features + 4. [Quality]: Information quality + + Format as: + [M1] + Type: ... + Content: ... + Features: ... + Quality: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_modalities(response["answer"]) + + async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + try: + # Extract modality types + modal_types = list(modalities.keys()) + + # Initialize alignment results + alignments = [] + + # Process each modality pair + for i in range(len(modal_types)): + for j in range(i + 1, len(modal_types)): + type1, type2 = modal_types[i], modal_types[j] + + # Get items from each modality + items1 = modalities[type1] + items2 = modalities[type2] + + # Find alignments between items + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > 0.5: # Threshold for alignment + alignments.append({ + "type1": type1, + "type2": type2, + "item1": item1, + "item2": item2, + "similarity": similarity + }) + + # Sort alignments by similarity + alignments.sort(key=lambda x: x["similarity"], reverse=True) + + return alignments + + except Exception as e: + logging.error(f"Error in cross-modal alignment: {str(e)}") + return [] + + def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float: + """Calculate similarity between two items from different modalities.""" + try: + # Extract content from items + content1 = str(item1.get("content", "")) + content2 = str(item2.get("content", "")) + + # Calculate basic similarity (can be enhanced with more sophisticated methods) + common_words = set(content1.lower().split()) & set(content2.lower().split()) + total_words = set(content1.lower().split()) | set(content2.lower().split()) + + if not total_words: + return 0.0 + + return len(common_words) / len(total_words) + + except Exception as e: + logging.error(f"Error calculating similarity: {str(e)}") + return 0.0 + + async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Perform integrated multi-modal analysis: + Alignment: {json.dumps(alignment)} + Context: {json.dumps(context)} + + For each insight: + 1. [Insight]: Key finding + 2. [Sources]: Contributing modalities + 3. [Support]: Supporting evidence + 4. [Confidence]: Confidence level + + Format as: + [I1] + Insight: ... + Sources: ... + Support: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_integration(response["answer"]) + + async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate unified multi-modal response: + Integration: {json.dumps(integration)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Modal contributions + 3. Integration benefits + 4. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_response(response["answer"]) + + def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]: + """Parse modalities from response.""" + modalities = {} + current_modality = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[M'): + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + current_modality = { + "type": "", + "content": "", + "features": "", + "quality": "" + } + elif current_modality: + if line.startswith('Type:'): + current_modality["type"] = line[5:].strip() + elif line.startswith('Content:'): + current_modality["content"] = line[8:].strip() + elif line.startswith('Features:'): + current_modality["features"] = line[9:].strip() + elif line.startswith('Quality:'): + current_modality["quality"] = line[8:].strip() + + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + + return modalities + + def _parse_alignment(self, response: str) -> List[Dict[str, Any]]: + """Parse alignment from response.""" + alignment = [] + current_alignment = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[A'): + if current_alignment: + alignment.append(current_alignment) + current_alignment = { + "modalities": "", + "mapping": "", + "confidence": 0.0, + "conflicts": [] + } + elif current_alignment: + if line.startswith('Modalities:'): + current_alignment["modalities"] = line[11:].strip() + elif line.startswith('Mapping:'): + current_alignment["mapping"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + current_alignment["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Conflicts:'): + mode = "conflicts" + elif line.startswith("- "): + if mode == "conflicts": + current_alignment["conflicts"].append(line[2:].strip()) + + if current_alignment: + alignment.append(current_alignment) + + return alignment + + def _parse_integration(self, response: str) -> List[Dict[str, Any]]: + """Parse integration from response.""" + integration = [] + current_insight = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_insight: + integration.append(current_insight) + current_insight = { + "insight": "", + "sources": "", + "support": "", + "confidence": 0.0 + } + elif current_insight: + if line.startswith('Insight:'): + current_insight["insight"] = line[8:].strip() + elif line.startswith('Sources:'): + current_insight["sources"] = line[8:].strip() + elif line.startswith('Support:'): + current_insight["support"] = line[8:].strip() + elif line.startswith('Confidence:'): + try: + current_insight["confidence"] = float(line[11:].strip()) + except: + pass + + if current_insight: + integration.append(current_insight) + + return integration + + def _parse_response(self, response: str) -> Dict[str, Any]: + """Parse response from response.""" + response_dict = { + "conclusion": "", + "modal_contributions": [], + "integration_benefits": [], + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + response_dict["conclusion"] = line[11:].strip() + elif line.startswith('Modal Contributions:'): + mode = "modal" + elif line.startswith('Integration Benefits:'): + mode = "integration" + elif line.startswith('Confidence:'): + try: + response_dict["confidence"] = float(line[11:].strip()) + except: + response_dict["confidence"] = 0.5 + mode = None + elif mode == "modal" and line.startswith('- '): + response_dict["modal_contributions"].append(line[2:].strip()) + elif mode == "integration" and line.startswith('- '): + response_dict["integration_benefits"].append(line[2:].strip()) + + return response_dict + +class MetaLearningStrategy(ReasoningStrategy): + """A meta-learning strategy that adapts its reasoning approach based on problem characteristics.""" + + def __init__(self): + self.strategy_patterns = { + "analytical": ["analyze", "compare", "evaluate", "measure"], + "creative": ["design", "create", "innovate", "imagine"], + "systematic": ["organize", "structure", "plan", "implement"], + "critical": ["critique", "assess", "validate", "test"] + } + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + # Analyze query to determine best reasoning patterns + patterns = self._identify_patterns(query.lower()) + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: {', '.join(patterns)} + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + + def _identify_patterns(self, query: str) -> List[str]: + """Identify which reasoning patterns are most relevant for the query.""" + patterns = [] + for pattern, keywords in self.strategy_patterns.items(): + if any(keyword in query for keyword in keywords): + patterns.append(pattern) + + # Default to analytical if no patterns match + if not patterns: + patterns = ["analytical"] + + return patterns + +class BayesianReasoning(ReasoningStrategy): + """Implements Bayesian reasoning for probabilistic analysis.""" + def __init__(self, prior_weight: float = 0.3): + self.prior_weight = prior_weight + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Generate hypotheses + hypotheses = await self._generate_hypotheses(query, context) + + # Calculate prior probabilities + priors = await self._calculate_priors(hypotheses, context) + + # Update with evidence + posteriors = await self._update_with_evidence(hypotheses, priors, context) + + # Generate final analysis + analysis = await self._generate_analysis(posteriors, context) + + return { + "success": True, + "answer": analysis["conclusion"], + "hypotheses": hypotheses, + "priors": priors, + "posteriors": posteriors, + "confidence": analysis["confidence"], + "reasoning_path": analysis["reasoning_path"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Generate 3-4 hypotheses for this problem: + Query: {query} + Context: {json.dumps(context)} + + For each hypothesis: + 1. [Statement]: Clear statement of the hypothesis + 2. [Assumptions]: Key assumptions made + 3. [Testability]: How it could be tested/verified + + Format as: + [H1] + Statement: ... + Assumptions: ... + Testability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_hypotheses(response["answer"]) + + async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Calculate prior probabilities for these hypotheses: + Context: {json.dumps(context)} + + Hypotheses: + {json.dumps(hypotheses, indent=2)} + + For each hypothesis, estimate its prior probability (0-1) based on: + 1. Alignment with known principles + 2. Historical precedent + 3. Domain expertise + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], + context: Dict[str, Any]) -> Dict[str, float]: + prompt = f""" + Update probabilities with available evidence: + Context: {json.dumps(context)} + + Hypotheses and Priors: + {json.dumps(list(zip(hypotheses, priors.values())), indent=2)} + + Consider: + 1. How well each hypothesis explains the evidence + 2. Any new evidence from the context + 3. Potential conflicts or support between hypotheses + + Format: [H1]: 0.XX, [H2]: 0.XX, ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_probabilities(response["answer"]) + + async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate final Bayesian analysis: + Context: {json.dumps(context)} + + Posterior Probabilities: + {json.dumps(posteriors, indent=2)} + + Provide: + 1. Main conclusion based on highest probability hypotheses + 2. Confidence level (0-1) + 3. Key reasoning steps taken + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_analysis(response["answer"]) + + def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]: + """Parse hypotheses from response.""" + hypotheses = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[H'): + if current: + hypotheses.append(current) + current = { + "statement": "", + "assumptions": "", + "testability": "" + } + elif current: + if line.startswith('Statement:'): + current["statement"] = line[10:].strip() + elif line.startswith('Assumptions:'): + current["assumptions"] = line[12:].strip() + elif line.startswith('Testability:'): + current["testability"] = line[12:].strip() + + if current: + hypotheses.append(current) + + return hypotheses + + def _parse_probabilities(self, response: str) -> Dict[str, float]: + """Parse probabilities from response.""" + probs = {} + pattern = r'\[H(\d+)\]:\s*(0\.\d+)' + + for match in re.finditer(pattern, response): + h_num = int(match.group(1)) + prob = float(match.group(2)) + probs[f"H{h_num}"] = prob + + return probs + + def _parse_analysis(self, response: str) -> Dict[str, Any]: + """Parse analysis from response.""" + lines = response.split('\n') + analysis = { + "conclusion": "", + "confidence": 0.0, + "reasoning_path": [] + } + + for line in lines: + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + analysis["conclusion"] = line[11:].strip() + elif line.startswith('Confidence:'): + try: + analysis["confidence"] = float(line[11:].strip()) + except: + analysis["confidence"] = 0.5 + elif line.startswith('- '): + analysis["reasoning_path"].append(line[2:].strip()) + + return analysis + +class EmergentReasoning(ReasoningStrategy): + """Implements emergent reasoning by analyzing collective patterns and system-level behaviors.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Identify system components + components = await self._identify_components(query, context) + + # Analyze interactions + interactions = await self._analyze_interactions(components, context) + + # Detect emergent patterns + patterns = await self._detect_patterns(interactions, context) + + # Synthesize emergent properties + synthesis = await self._synthesize_properties(patterns, context) + + return { + "success": True, + "answer": synthesis["conclusion"], + "components": components, + "interactions": interactions, + "patterns": patterns, + "emergent_properties": synthesis["properties"], + "confidence": synthesis["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Identify key system components for analysis: + Query: {query} + Context: {json.dumps(context)} + + For each component identify: + 1. [Name]: Component identifier + 2. [Properties]: Key characteristics + 3. [Role]: Function in the system + 4. [Dependencies]: Related components + + Format as: + [C1] + Name: ... + Properties: ... + Role: ... + Dependencies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_components(response["answer"]) + + async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze interactions between components: + Components: {json.dumps(components)} + Context: {json.dumps(context)} + + For each interaction describe: + 1. [Components]: Participating components + 2. [Type]: Nature of interaction + 3. [Effects]: Impact on system + 4. [Dynamics]: How it changes over time + + Format as: + [I1] + Components: ... + Type: ... + Effects: ... + Dynamics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interactions(response["answer"]) + + async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Detect emergent patterns from interactions: + Interactions: {json.dumps(interactions)} + Context: {json.dumps(context)} + + For each pattern identify: + 1. [Pattern]: Description of the pattern + 2. [Scale]: At what level it emerges + 3. [Conditions]: Required conditions + 4. [Stability]: How stable/persistent it is + + Format as: + [P1] + Pattern: ... + Scale: ... + Conditions: ... + Stability: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_patterns(response["answer"]) + + async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Synthesize emergent properties from patterns: + Patterns: {json.dumps(patterns)} + Context: {json.dumps(context)} + + Provide: + 1. List of emergent properties + 2. How they arise from patterns + 3. Their significance + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_synthesis(response["answer"]) + + def _parse_components(self, response: str) -> List[Dict[str, Any]]: + """Parse components from response.""" + components = [] + current_component = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[C'): + if current_component: + components.append(current_component) + current_component = { + "name": "", + "properties": "", + "role": "", + "dependencies": [] + } + elif current_component: + if line.startswith('Name:'): + current_component["name"] = line[5:].strip() + elif line.startswith('Properties:'): + current_component["properties"] = line[11:].strip() + elif line.startswith('Role:'): + current_component["role"] = line[5:].strip() + elif line.startswith('Dependencies:'): + mode = "dependencies" + elif line.startswith("- "): + if mode == "dependencies": + current_component["dependencies"].append(line[2:].strip()) + + if current_component: + components.append(current_component) + + return components + + def _parse_interactions(self, response: str) -> List[Dict[str, Any]]: + """Parse interactions from response.""" + interactions = [] + current_interaction = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_interaction: + interactions.append(current_interaction) + current_interaction = { + "components": "", + "type": "", + "effects": "", + "dynamics": "" + } + elif current_interaction: + if line.startswith('Components:'): + current_interaction["components"] = line[11:].strip() + elif line.startswith('Type:'): + current_interaction["type"] = line[5:].strip() + elif line.startswith('Effects:'): + current_interaction["effects"] = line[7:].strip() + elif line.startswith('Dynamics:'): + current_interaction["dynamics"] = line[9:].strip() + + if current_interaction: + interactions.append(current_interaction) + + return interactions + + def _parse_patterns(self, response: str) -> List[Dict[str, Any]]: + """Parse patterns from response.""" + patterns = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current_pattern: + patterns.append(current_pattern) + current_pattern = { + "pattern": "", + "scale": "", + "conditions": "", + "stability": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Scale:'): + current_pattern["scale"] = line[6:].strip() + elif line.startswith('Conditions:'): + current_pattern["conditions"] = line[11:].strip() + elif line.startswith('Stability:'): + current_pattern["stability"] = line[10:].strip() + + if current_pattern: + patterns.append(current_pattern) + + return patterns + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis from response.""" + synthesis = { + "properties": [], + "conclusion": "", + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Properties:'): + mode = "properties" + elif line.startswith('Conclusion:'): + synthesis["conclusion"] = line[11:].strip() + mode = None + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + synthesis["confidence"] = 0.5 + mode = None + elif mode == "properties" and line.startswith('- '): + synthesis["properties"].append(line[2:].strip()) + + return synthesis + +class QuantumReasoning(ReasoningStrategy): + """Implements quantum-inspired reasoning using superposition and entanglement principles.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create superposition of possibilities + superposition = await self._create_superposition(query, context) + + # Analyze entanglements + entanglements = await self._analyze_entanglements(superposition, context) + + # Perform quantum interference + interference = await self._quantum_interference(superposition, entanglements, context) + + # Collapse to solution + solution = await self._collapse_to_solution(interference, context) + + return { + "success": True, + "answer": solution["conclusion"], + "superposition": superposition, + "entanglements": entanglements, + "interference_patterns": interference, + "measurement": solution["measurement"], + "confidence": solution["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Create superposition of possible solutions: + Query: {query} + Context: {json.dumps(context)} + + For each possibility state: + 1. [State]: Description of possibility + 2. [Amplitude]: Relative strength (0-1) + 3. [Phase]: Relationship to other states + 4. [Basis]: Underlying assumptions + + Format as: + [S1] + State: ... + Amplitude: ... + Phase: ... + Basis: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_superposition(response["answer"]) + + async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Analyze entanglements between possibilities: + Superposition: {json.dumps(superposition)} + Context: {json.dumps(context)} + + For each entanglement describe: + 1. [States]: Entangled states + 2. [Type]: Nature of entanglement + 3. [Strength]: Correlation strength + 4. [Impact]: Effect on outcomes + + Format as: + [E1] + States: ... + Type: ... + Strength: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_entanglements(response["answer"]) + + async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Calculate quantum interference patterns: + Superposition: {json.dumps(superposition)} + Entanglements: {json.dumps(entanglements)} + Context: {json.dumps(context)} + + For each interference pattern: + 1. [Pattern]: Description + 2. [Amplitude]: Combined strength + 3. [Phase]: Combined phase + 4. [Effect]: Impact on solution space + + Format as: + [I1] + Pattern: ... + Amplitude: ... + Phase: ... + Effect: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_interference(response["answer"]) + + async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Collapse quantum state to final solution: + Interference: {json.dumps(interference)} + Context: {json.dumps(context)} + + Provide: + 1. Final measured state + 2. Measurement confidence + 3. Key quantum effects utilized + 4. Overall conclusion + 5. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_collapse(response["answer"]) + + def _parse_superposition(self, response: str) -> List[Dict[str, Any]]: + """Parse superposition states from response.""" + superposition = [] + current_state = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current_state: + superposition.append(current_state) + current_state = { + "state": "", + "amplitude": 0.0, + "phase": "", + "basis": "" + } + elif current_state: + if line.startswith('State:'): + current_state["state"] = line[6:].strip() + elif line.startswith('Amplitude:'): + try: + current_state["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_state["phase"] = line[6:].strip() + elif line.startswith('Basis:'): + current_state["basis"] = line[6:].strip() + + if current_state: + superposition.append(current_state) + + return superposition + + def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]: + """Parse entanglements from response.""" + entanglements = [] + current_entanglement = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[E'): + if current_entanglement: + entanglements.append(current_entanglement) + current_entanglement = { + "states": "", + "type": "", + "strength": 0.0, + "impact": "" + } + elif current_entanglement: + if line.startswith('States:'): + current_entanglement["states"] = line[7:].strip() + elif line.startswith('Type:'): + current_entanglement["type"] = line[5:].strip() + elif line.startswith('Strength:'): + try: + current_entanglement["strength"] = float(line[9:].strip()) + except: + pass + elif line.startswith('Impact:'): + current_entanglement["impact"] = line[7:].strip() + + if current_entanglement: + entanglements.append(current_entanglement) + + return entanglements + + def _parse_interference(self, response: str) -> List[Dict[str, Any]]: + """Parse interference patterns from response.""" + interference = [] + current_pattern = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_pattern: + interference.append(current_pattern) + current_pattern = { + "pattern": "", + "amplitude": 0.0, + "phase": "", + "effect": "" + } + elif current_pattern: + if line.startswith('Pattern:'): + current_pattern["pattern"] = line[8:].strip() + elif line.startswith('Amplitude:'): + try: + current_pattern["amplitude"] = float(line[10:].strip()) + except: + pass + elif line.startswith('Phase:'): + current_pattern["phase"] = line[6:].strip() + elif line.startswith('Effect:'): + current_pattern["effect"] = line[7:].strip() + + if current_pattern: + interference.append(current_pattern) + + return interference + + def _parse_collapse(self, response: str) -> Dict[str, Any]: + """Parse collapse to solution from response.""" + collapse = { + "measurement": "", + "confidence": 0.0, + "quantum_effects": [], + "conclusion": "" + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Measurement:'): + collapse["measurement"] = line[12:].strip() + elif line.startswith('Confidence:'): + try: + collapse["confidence"] = float(line[11:].strip()) + except: + collapse["confidence"] = 0.5 + elif line.startswith('Quantum Effects:'): + mode = "effects" + elif mode == "effects" and line.startswith('- '): + collapse["quantum_effects"].append(line[2:].strip()) + elif line.startswith('Conclusion:'): + collapse["conclusion"] = line[11:].strip() + + return collapse + +class QuantumInspiredStrategy(ReasoningStrategy): + """Implements Quantum-Inspired reasoning.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} + +class NeurosymbolicReasoning(ReasoningStrategy): + """Implements neurosymbolic reasoning combining neural and symbolic approaches.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Extract neural features + neural_features = await self._extract_neural_features(query) + + # Generate symbolic rules + symbolic_rules = await self._generate_symbolic_rules( + neural_features, + context + ) + + # Combine neural and symbolic reasoning + combined_result = await self._combine_neural_symbolic( + neural_features, + symbolic_rules, + context + ) + + # Update knowledge base + self._update_knowledge_base( + neural_features, + symbolic_rules, + combined_result + ) + + return { + "success": True, + "neural_features": [ + { + "name": f.name, + "associations": f.associations + } + for f in neural_features + ], + "symbolic_rules": [ + { + "condition": r.condition, + "action": r.action, + "confidence": r.confidence + } + for r in symbolic_rules + ], + "combined_result": combined_result + } + + except Exception as e: + return {"success": False, "error": str(e)} + + async def _extract_neural_features(self, query: str) -> List[NeuralFeature]: + """Extract neural features from the query.""" + try: + # Use text generation model to extract features + prompt = f""" + Extract key features from this query: + {query} + + List each feature with its properties: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + features = [] + for line in result.split("\n"): + if line.strip(): + # Create feature vector using simple embedding + vector = np.random.rand(768) # Placeholder + feature = NeuralFeature( + name=line.strip(), + vector=vector + ) + features.append(feature) + + return features + + except Exception as e: + return [] + + async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]: + """Generate symbolic rules based on features.""" + try: + # Use features to generate rules + feature_desc = "\n".join(f.name for f in features) + prompt = f""" + Given these features: + {feature_desc} + + Generate logical rules in if-then format: + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=200, + temperature=0.7 + ) + + rules = [] + for line in result.split("\n"): + if "if" in line.lower() and "then" in line.lower(): + parts = line.lower().split("then") + condition = parts[0].replace("if", "").strip() + action = parts[1].strip() + rule = SymbolicRule(condition, action) + rules.append(rule) + + return rules + + except Exception as e: + return [] + + async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]: + """Combine neural and symbolic reasoning.""" + try: + # Use neural features to evaluate symbolic rules + evaluated_rules = [] + for rule in rules: + # Calculate confidence based on feature associations + confidence = 0.0 + for feature in features: + if feature.name in rule.condition: + confidence += feature.associations.get(rule.action, 0.0) + rule.confidence = confidence / len(features) + evaluated_rules.append(rule) + + # Generate combined result + prompt = f""" + Combine these evaluated rules to generate a solution: + Rules: {json.dumps(evaluated_rules, indent=2)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level (0-1) + """ + + result = await self.model_manager.generate( + "text_gen", + prompt, + max_length=150, + temperature=0.7 + ) + + return { + "conclusion": result["answer"], + "confidence": 0.8 # Placeholder confidence + } + + except Exception as e: + return {} + + def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None: + """Update knowledge base with new features and rules.""" + # Update feature associations + for feature in features: + for rule in rules: + if feature.name in rule.condition: + feature.associations[rule.action] = rule.confidence + + # Update symbolic rules + for rule in rules: + rule.update_confidence(result["confidence"]) + +class MultiModalReasoning(ReasoningStrategy): + """Implements multi-modal reasoning across different types of information.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Process different modalities + modalities = await self._process_modalities(query, context) + + # Cross-modal alignment + alignment = await self._cross_modal_alignment(modalities, context) + + # Integrated analysis + integration = await self._integrated_analysis(alignment, context) + + # Generate unified response + response = await self._generate_response(integration, context) + + return { + "success": True, + "answer": response["conclusion"], + "modalities": modalities, + "alignment": alignment, + "integration": integration, + "confidence": response["confidence"] + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]: + prompt = f""" + Process information across modalities: + Query: {query} + Context: {json.dumps(context)} + + For each modality analyze: + 1. [Type]: Modality type + 2. [Content]: Key information + 3. [Features]: Important features + 4. [Quality]: Information quality + + Format as: + [M1] + Type: ... + Content: ... + Features: ... + Quality: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_modalities(response["answer"]) + + async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + try: + # Extract modality types + modal_types = list(modalities.keys()) + + # Initialize alignment results + alignments = [] + + # Process each modality pair + for i in range(len(modal_types)): + for j in range(i + 1, len(modal_types)): + type1, type2 = modal_types[i], modal_types[j] + + # Get items from each modality + items1 = modalities[type1] + items2 = modalities[type2] + + # Find alignments between items + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > 0.5: # Threshold for alignment + alignments.append({ + "type1": type1, + "type2": type2, + "item1": item1, + "item2": item2, + "similarity": similarity + }) + + # Sort alignments by similarity + alignments.sort(key=lambda x: x["similarity"], reverse=True) + + return alignments + + except Exception as e: + logging.error(f"Error in cross-modal alignment: {str(e)}") + return [] + + def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float: + """Calculate similarity between two items from different modalities.""" + try: + # Extract content from items + content1 = str(item1.get("content", "")) + content2 = str(item2.get("content", "")) + + # Calculate basic similarity (can be enhanced with more sophisticated methods) + common_words = set(content1.lower().split()) & set(content2.lower().split()) + total_words = set(content1.lower().split()) | set(content2.lower().split()) + + if not total_words: + return 0.0 + + return len(common_words) / len(total_words) + + except Exception as e: + logging.error(f"Error calculating similarity: {str(e)}") + return 0.0 + + async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: + prompt = f""" + Perform integrated multi-modal analysis: + Alignment: {json.dumps(alignment)} + Context: {json.dumps(context)} + + For each insight: + 1. [Insight]: Key finding + 2. [Sources]: Contributing modalities + 3. [Support]: Supporting evidence + 4. [Confidence]: Confidence level + + Format as: + [I1] + Insight: ... + Sources: ... + Support: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_integration(response["answer"]) + + async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: + prompt = f""" + Generate unified multi-modal response: + Integration: {json.dumps(integration)} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Modal contributions + 3. Integration benefits + 4. Confidence level (0-1) + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_response(response["answer"]) + + def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]: + """Parse modalities from response.""" + modalities = {} + current_modality = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[M'): + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + current_modality = { + "type": "", + "content": "", + "features": "", + "quality": "" + } + elif current_modality: + if line.startswith('Type:'): + current_modality["type"] = line[5:].strip() + elif line.startswith('Content:'): + current_modality["content"] = line[8:].strip() + elif line.startswith('Features:'): + current_modality["features"] = line[9:].strip() + elif line.startswith('Quality:'): + current_modality["quality"] = line[8:].strip() + + if current_modality: + if current_modality["type"] not in modalities: + modalities[current_modality["type"]] = [] + modalities[current_modality["type"]].append(current_modality) + + return modalities + + def _parse_alignment(self, response: str) -> List[Dict[str, Any]]: + """Parse alignment from response.""" + alignment = [] + current_alignment = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[A'): + if current_alignment: + alignment.append(current_alignment) + current_alignment = { + "modalities": "", + "mapping": "", + "confidence": 0.0, + "conflicts": [] + } + elif current_alignment: + if line.startswith('Modalities:'): + current_alignment["modalities"] = line[11:].strip() + elif line.startswith('Mapping:'): + current_alignment["mapping"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + current_alignment["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Conflicts:'): + mode = "conflicts" + elif line.startswith("- "): + if mode == "conflicts": + current_alignment["conflicts"].append(line[2:].strip()) + + if current_alignment: + alignment.append(current_alignment) + + return alignment + + def _parse_integration(self, response: str) -> List[Dict[str, Any]]: + """Parse integration from response.""" + integration = [] + current_insight = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[I'): + if current_insight: + integration.append(current_insight) + current_insight = { + "insight": "", + "sources": "", + "support": "", + "confidence": 0.0 + } + elif current_insight: + if line.startswith('Insight:'): + current_insight["insight"] = line[8:].strip() + elif line.startswith('Sources:'): + current_insight["sources"] = line[8:].strip() + elif line.startswith('Support:'): + current_insight["support"] = line[8:].strip() + elif line.startswith('Confidence:'): + try: + current_insight["confidence"] = float(line[11:].strip()) + except: + pass + + if current_insight: + integration.append(current_insight) + + return integration + + def _parse_response(self, response: str) -> Dict[str, Any]: + """Parse response from response.""" + response_dict = { + "conclusion": "", + "modal_contributions": [], + "integration_benefits": [], + "confidence": 0.0 + } + + mode = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('Conclusion:'): + response_dict["conclusion"] = line[11:].strip() + elif line.startswith('Modal Contributions:'): + mode = "modal" + elif line.startswith('Integration Benefits:'): + mode = "integration" + elif line.startswith('Confidence:'): + try: + response_dict["confidence"] = float(line[11:].strip()) + except: + response_dict["confidence"] = 0.5 + mode = None + elif mode == "modal" and line.startswith('- '): + response_dict["modal_contributions"].append(line[2:].strip()) + elif mode == "integration" and line.startswith('- '): + response_dict["integration_benefits"].append(line[2:].strip()) + + return response_dict + +class MetaLearningStrategy(ReasoningStrategy): + """A meta-learning strategy that adapts its reasoning approach based on problem characteristics.""" + + def __init__(self): + self.strategy_patterns = { + "analytical": ["analyze", "compare", "evaluate", "measure"], + "creative": ["design", "create", "innovate", "imagine"], + "systematic": ["organize", "structure", "plan", "implement"], + "critical": ["critique", "assess", "validate", "test"] + } + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + # Analyze query to determine best reasoning patterns + patterns = self._identify_patterns(query.lower()) + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: {', '.join(patterns)} + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: diff --git a/reasoning/__init__.py b/reasoning/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..32178de58cf5044c09321e5560d64e93af747534 --- /dev/null +++ b/reasoning/__init__.py @@ -0,0 +1,70 @@ +""" +Advanced Reasoning Engine for Multi-Model System +--------------------------------------------- +A highly sophisticated reasoning system combining multiple reasoning strategies. + +Core Reasoning: +1. Chain of Thought (CoT) +2. Tree of Thoughts (ToT) +3. Recursive Reasoning +4. Analogical Reasoning +5. Meta-Learning +6. Local LLM + +Advanced Reasoning: +7. Neurosymbolic Reasoning +8. Bayesian Reasoning +9. Quantum Reasoning +10. Emergent Reasoning +11. Multimodal Reasoning +12. Specialized Reasoning + +Learning & Adaptation: +13. Market Analysis +14. Portfolio Optimization +15. Venture Strategies +16. Monetization Strategies +""" + +from .base import ReasoningStrategy +from .multimodal import MultiModalReasoning +from .bayesian import BayesianReasoning +from .quantum import QuantumReasoning +from .neurosymbolic import NeurosymbolicReasoning +from .emergent import EmergentReasoning +from .meta_learning import MetaLearningStrategy +from .chain_of_thought import ChainOfThoughtStrategy +from .tree_of_thoughts import TreeOfThoughtsStrategy +from .recursive import RecursiveReasoning +from .analogical import AnalogicalReasoning +from .specialized import SpecializedReasoning +from .local_llm import LocalLLMStrategy +from .market_analysis import MarketAnalysisStrategy +from .portfolio_optimization import PortfolioOptimizationStrategy +from .venture_strategies import VentureStrategy +from .monetization import MonetizationStrategy +from .unified_engine import UnifiedReasoningEngine, StrategyType, StrategyResult, UnifiedResult + +__all__ = [ + 'ReasoningStrategy', + 'MultiModalReasoning', + 'BayesianReasoning', + 'QuantumReasoning', + 'NeurosymbolicReasoning', + 'EmergentReasoning', + 'MetaLearningStrategy', + 'ChainOfThoughtStrategy', + 'TreeOfThoughtsStrategy', + 'RecursiveReasoning', + 'AnalogicalReasoning', + 'SpecializedReasoning', + 'LocalLLMStrategy', + 'MarketAnalysisStrategy', + 'PortfolioOptimizationStrategy', + 'VentureStrategy', + 'MonetizationStrategy', + 'UnifiedReasoningEngine', + 'StrategyType', + 'StrategyResult', + 'UnifiedResult' +] diff --git a/reasoning/agentic.py b/reasoning/agentic.py new file mode 100644 index 0000000000000000000000000000000000000000..b5f2da73606766f7115a031a1d08eeb949800bb9 --- /dev/null +++ b/reasoning/agentic.py @@ -0,0 +1,345 @@ +"""Specialized reasoning strategies for Agentic Workflow.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy + +class TaskType(Enum): + """Types of tasks in agentic workflow.""" + CODE_GENERATION = "code_generation" + CODE_MODIFICATION = "code_modification" + CODE_REVIEW = "code_review" + DEBUGGING = "debugging" + ARCHITECTURE = "architecture" + OPTIMIZATION = "optimization" + DOCUMENTATION = "documentation" + TESTING = "testing" + +class ResourceType(Enum): + """Types of resources in agentic workflow.""" + CODE_CONTEXT = "code_context" + SYSTEM_CONTEXT = "system_context" + USER_CONTEXT = "user_context" + TOOLS = "tools" + APIS = "apis" + DOCUMENTATION = "documentation" + DEPENDENCIES = "dependencies" + HISTORY = "history" + +@dataclass +class TaskComponent: + """Component of a decomposed task.""" + id: str + type: TaskType + description: str + dependencies: List[str] + resources: Dict[ResourceType, Any] + constraints: List[str] + priority: float + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class ResourceAllocation: + """Resource allocation for a task.""" + resource_type: ResourceType + quantity: Union[int, float] + priority: float + constraints: List[str] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class ExecutionStep: + """Step in task execution.""" + id: str + task_id: str + action: str + resources: Dict[ResourceType, Any] + status: str + result: Optional[Dict[str, Any]] + feedback: List[str] + timestamp: datetime = field(default_factory=datetime.now) + +class TaskDecompositionStrategy(ReasoningStrategy): + """ + Advanced task decomposition strategy that: + 1. Analyzes task complexity and dependencies + 2. Breaks down tasks into manageable components + 3. Identifies resource requirements + 4. Establishes execution order + 5. Manages constraints and priorities + """ + + def __init__(self, max_components: int = 10): + self.max_components = max_components + self.components: Dict[str, TaskComponent] = {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Decompose task into components.""" + try: + # Analyze task + task_analysis = await self._analyze_task(query, context) + + # Generate components + components = await self._generate_components(task_analysis, context) + + # Establish dependencies + dependency_graph = await self._establish_dependencies(components, context) + + # Determine execution order + execution_order = await self._determine_execution_order( + components, dependency_graph, context) + + return { + "success": True, + "components": [self._component_to_dict(c) for c in components], + "dependency_graph": dependency_graph, + "execution_order": execution_order, + "metadata": { + "total_components": len(components), + "complexity_score": task_analysis.get("complexity_score", 0.0), + "resource_requirements": task_analysis.get("resource_requirements", {}) + } + } + except Exception as e: + logging.error(f"Error in task decomposition: {str(e)}") + return {"success": False, "error": str(e)} + +class ResourceManagementStrategy(ReasoningStrategy): + """ + Advanced resource management strategy that: + 1. Tracks available resources + 2. Allocates resources to tasks + 3. Handles resource constraints + 4. Optimizes resource utilization + 5. Manages resource dependencies + """ + + def __init__(self): + self.allocations: Dict[str, ResourceAllocation] = {} + self.utilization_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Manage resource allocation.""" + try: + # Analyze resource requirements + requirements = await self._analyze_requirements(query, context) + + # Check resource availability + availability = await self._check_availability(requirements, context) + + # Generate allocation plan + allocation_plan = await self._generate_allocation_plan( + requirements, availability, context) + + # Optimize allocations + optimized_plan = await self._optimize_allocations(allocation_plan, context) + + return { + "success": True, + "allocation_plan": optimized_plan, + "resource_metrics": { + "utilization": self._calculate_utilization(), + "efficiency": self._calculate_efficiency(), + "constraints_satisfied": self._check_constraints(optimized_plan) + } + } + except Exception as e: + logging.error(f"Error in resource management: {str(e)}") + return {"success": False, "error": str(e)} + +class ContextualPlanningStrategy(ReasoningStrategy): + """ + Advanced contextual planning strategy that: + 1. Analyzes multiple context types + 2. Generates context-aware plans + 3. Handles context changes + 4. Maintains context consistency + 5. Optimizes for context constraints + """ + + def __init__(self): + self.context_history: List[Dict[str, Any]] = [] + self.plan_adaptations: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate context-aware plan.""" + try: + # Analyze contexts + context_analysis = await self._analyze_contexts(query, context) + + # Generate base plan + base_plan = await self._generate_base_plan(context_analysis, context) + + # Adapt to contexts + adapted_plan = await self._adapt_to_contexts(base_plan, context_analysis) + + # Validate plan + validation = await self._validate_plan(adapted_plan, context) + + return { + "success": True, + "plan": adapted_plan, + "context_impact": context_analysis.get("impact_assessment", {}), + "adaptations": self.plan_adaptations, + "validation_results": validation + } + except Exception as e: + logging.error(f"Error in contextual planning: {str(e)}") + return {"success": False, "error": str(e)} + +class AdaptiveExecutionStrategy(ReasoningStrategy): + """ + Advanced adaptive execution strategy that: + 1. Monitors execution progress + 2. Adapts to changes and feedback + 3. Handles errors and exceptions + 4. Optimizes execution flow + 5. Maintains execution state + """ + + def __init__(self): + self.execution_steps: List[ExecutionStep] = [] + self.adaptation_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Execute task adaptively.""" + try: + # Initialize execution + execution_state = await self._initialize_execution(query, context) + + # Monitor and adapt + while not self._is_execution_complete(execution_state): + # Execute step + step_result = await self._execute_step(execution_state, context) + + # Process feedback + feedback = await self._process_feedback(step_result, context) + + # Adapt execution + execution_state = await self._adapt_execution( + execution_state, feedback, context) + + # Record step + self._record_step(step_result, feedback) + + return { + "success": True, + "execution_trace": [self._step_to_dict(s) for s in self.execution_steps], + "adaptations": self.adaptation_history, + "final_state": execution_state + } + except Exception as e: + logging.error(f"Error in adaptive execution: {str(e)}") + return {"success": False, "error": str(e)} + +class FeedbackIntegrationStrategy(ReasoningStrategy): + """ + Advanced feedback integration strategy that: + 1. Collects multiple types of feedback + 2. Analyzes feedback patterns + 3. Generates improvement suggestions + 4. Tracks feedback implementation + 5. Measures feedback impact + """ + + def __init__(self): + self.feedback_history: List[Dict[str, Any]] = [] + self.improvement_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Integrate and apply feedback.""" + try: + # Collect feedback + feedback = await self._collect_feedback(query, context) + + # Analyze patterns + patterns = await self._analyze_patterns(feedback, context) + + # Generate improvements + improvements = await self._generate_improvements(patterns, context) + + # Implement changes + implementation = await self._implement_improvements(improvements, context) + + # Measure impact + impact = await self._measure_impact(implementation, context) + + return { + "success": True, + "feedback_analysis": patterns, + "improvements": improvements, + "implementation_status": implementation, + "impact_metrics": impact + } + except Exception as e: + logging.error(f"Error in feedback integration: {str(e)}") + return {"success": False, "error": str(e)} + + async def _collect_feedback(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Collect feedback from multiple sources.""" + prompt = f""" + Collect feedback from: + Query: {query} + Context: {json.dumps(context)} + + Consider: + 1. User feedback + 2. System metrics + 3. Code analysis + 4. Performance data + 5. Error patterns + + Format as: + [Feedback] + Source: ... + Type: ... + Content: ... + Priority: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_feedback(response["answer"]) + + def _parse_feedback(self, response: str) -> List[Dict[str, Any]]: + """Parse feedback from response.""" + feedback_items = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Feedback]'): + if current: + feedback_items.append(current) + current = { + "source": "", + "type": "", + "content": "", + "priority": 0.0 + } + elif current: + if line.startswith('Source:'): + current["source"] = line[7:].strip() + elif line.startswith('Type:'): + current["type"] = line[5:].strip() + elif line.startswith('Content:'): + current["content"] = line[8:].strip() + elif line.startswith('Priority:'): + try: + current["priority"] = float(line[9:].strip()) + except: + pass + + if current: + feedback_items.append(current) + + return feedback_items diff --git a/reasoning/analogical.py b/reasoning/analogical.py new file mode 100644 index 0000000000000000000000000000000000000000..378f9fd0ff25cc30d4f8a3aa129e3a3ab2493d66 --- /dev/null +++ b/reasoning/analogical.py @@ -0,0 +1,611 @@ +"""Analogical reasoning implementation with advanced pattern matching and transfer learning.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Tuple, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +class AnalogicalLevel(Enum): + """Levels of analogical similarity.""" + SURFACE = "surface" + STRUCTURAL = "structural" + SEMANTIC = "semantic" + FUNCTIONAL = "functional" + CAUSAL = "causal" + ABSTRACT = "abstract" + +class MappingType(Enum): + """Types of analogical mappings.""" + DIRECT = "direct" + TRANSFORMED = "transformed" + COMPOSITE = "composite" + ABSTRACT = "abstract" + METAPHORICAL = "metaphorical" + HYBRID = "hybrid" + +@dataclass +class AnalogicalPattern: + """Represents a pattern for analogical matching.""" + id: str + level: AnalogicalLevel + features: Dict[str, Any] + relations: List[Tuple[str, str, str]] # (entity1, relation, entity2) + constraints: List[str] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class AnalogicalMapping: + """Represents a mapping between source and target domains.""" + id: str + type: MappingType + source_elements: Dict[str, Any] + target_elements: Dict[str, Any] + correspondences: List[Tuple[str, str, float]] # (source, target, strength) + transformations: List[Dict[str, Any]] + confidence: float + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class AnalogicalSolution: + """Represents a solution derived through analogical reasoning.""" + id: str + source_analogy: str + mapping: AnalogicalMapping + adaptation: Dict[str, Any] + inference: Dict[str, Any] + confidence: float + validation: Dict[str, Any] + metadata: Dict[str, Any] = field(default_factory=dict) + +class AnalogicalReasoning(ReasoningStrategy): + """ + Advanced Analogical Reasoning implementation with: + - Multi-level pattern matching + - Sophisticated similarity metrics + - Transfer learning capabilities + - Dynamic adaptation mechanisms + - Quality assessment + - Learning from experience + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize analogical reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Analogical reasoning specific parameters + self.min_similarity = self.config.get('min_similarity', 0.6) + self.max_candidates = self.config.get('max_candidates', 5) + self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7) + + # Knowledge base + self.patterns: Dict[str, AnalogicalPattern] = {} + self.mappings: Dict[str, AnalogicalMapping] = {} + self.solutions: Dict[str, AnalogicalSolution] = {} + + # Learning components + self.pattern_weights: Dict[str, float] = defaultdict(float) + self.success_history: List[Dict[str, Any]] = [] + self.adaptation_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing analogical reasoning.""" + try: + # Extract patterns from query + patterns = await self._extract_patterns(query, context) + + # Find analogical matches + matches = await self._find_matches(patterns, context) + + # Create and evaluate mappings + mappings = await self._create_mappings(matches, context) + + # Generate and adapt solutions + solutions = await self._generate_solutions(mappings, context) + + # Select best solution + best_solution = await self._select_best_solution(solutions, context) + + # Learn from experience + self._update_knowledge(patterns, mappings, best_solution) + + return { + "success": True, + "answer": best_solution.inference["conclusion"], + "confidence": best_solution.confidence, + "analogy": { + "source": best_solution.source_analogy, + "mapping": self._mapping_to_dict(best_solution.mapping), + "adaptation": best_solution.adaptation + }, + "reasoning_trace": best_solution.metadata.get("reasoning_trace", []), + "meta_insights": best_solution.metadata.get("meta_insights", []) + } + except Exception as e: + logging.error(f"Error in analogical reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _extract_patterns(self, query: str, context: Dict[str, Any]) -> List[AnalogicalPattern]: + """Extract patterns from query for analogical matching.""" + prompt = f""" + Extract analogical patterns from query: + Query: {query} + Context: {json.dumps(context)} + + For each pattern level: + 1. Surface features + 2. Structural relations + 3. Semantic concepts + 4. Functional roles + 5. Causal relationships + 6. Abstract principles + + Format as: + [P1] + Level: ... + Features: ... + Relations: ... + Constraints: ... + + [P2] + ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_patterns(response["answer"]) + + async def _find_matches(self, patterns: List[AnalogicalPattern], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Find matching patterns in knowledge base.""" + prompt = f""" + Find analogical matches: + Patterns: {json.dumps([self._pattern_to_dict(p) for p in patterns])} + Context: {json.dumps(context)} + + For each match provide: + 1. Source domain + 2. Similarity assessment + 3. Key correspondences + 4. Transfer potential + + Format as: + [M1] + Source: ... + Similarity: ... + Correspondences: ... + Transfer: ... + + [M2] + ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_matches(response["answer"]) + + async def _create_mappings(self, matches: List[Dict[str, Any]], context: Dict[str, Any]) -> List[AnalogicalMapping]: + """Create mappings between source and target domains.""" + prompt = f""" + Create analogical mappings: + Matches: {json.dumps(matches)} + Context: {json.dumps(context)} + + For each mapping specify: + 1. [Type]: {" | ".join([t.value for t in MappingType])} + 2. [Elements]: Source and target elements + 3. [Correspondences]: Element mappings + 4. [Transformations]: Required adaptations + 5. [Confidence]: Mapping strength + + Format as: + [Map1] + Type: ... + Elements: ... + Correspondences: ... + Transformations: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_mappings(response["answer"]) + + async def _generate_solutions(self, mappings: List[AnalogicalMapping], context: Dict[str, Any]) -> List[AnalogicalSolution]: + """Generate solutions through analogical transfer.""" + prompt = f""" + Generate analogical solutions: + Mappings: {json.dumps([self._mapping_to_dict(m) for m in mappings])} + Context: {json.dumps(context)} + + For each solution provide: + 1. Analogical inference + 2. Required adaptations + 3. Validation criteria + 4. Confidence assessment + 5. Reasoning trace + + Format as: + [S1] + Inference: ... + Adaptation: ... + Validation: ... + Confidence: ... + Trace: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_solutions(response["answer"], mappings) + + async def _select_best_solution(self, solutions: List[AnalogicalSolution], context: Dict[str, Any]) -> AnalogicalSolution: + """Select the best solution based on multiple criteria.""" + prompt = f""" + Evaluate and select best solution: + Solutions: {json.dumps([self._solution_to_dict(s) for s in solutions])} + Context: {json.dumps(context)} + + Evaluate based on: + 1. Inference quality + 2. Adaptation feasibility + 3. Validation strength + 4. Overall confidence + + Format as: + [Evaluation] + Rankings: ... + Rationale: ... + Selection: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + selection = self._parse_selection(response["answer"]) + + # Find selected solution + selected = max(solutions, key=lambda s: s.confidence) + for solution in solutions: + if solution.id == selection.get("selected_id"): + selected = solution + break + + return selected + + def _update_knowledge(self, patterns: List[AnalogicalPattern], mappings: List[AnalogicalMapping], solution: AnalogicalSolution): + """Update knowledge base with new patterns and successful mappings.""" + # Update patterns + for pattern in patterns: + if pattern.id not in self.patterns: + self.patterns[pattern.id] = pattern + self.pattern_weights[pattern.id] += self.learning_rate * solution.confidence + + # Update mappings + if solution.mapping.id not in self.mappings: + self.mappings[solution.mapping.id] = solution.mapping + + # Record solution + self.solutions[solution.id] = solution + + # Update history + self.success_history.append({ + "timestamp": datetime.now().isoformat(), + "solution_id": solution.id, + "confidence": solution.confidence, + "patterns": [p.id for p in patterns], + "mapping_type": solution.mapping.type.value + }) + + # Update adaptation history + self.adaptation_history.append({ + "timestamp": datetime.now().isoformat(), + "solution_id": solution.id, + "adaptations": solution.adaptation, + "success": solution.confidence >= self.adaptation_threshold + }) + + def _parse_patterns(self, response: str) -> List[AnalogicalPattern]: + """Parse patterns from response.""" + patterns = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current: + patterns.append(current) + current = None + elif line.startswith('Level:'): + level_str = line[6:].strip().lower() + try: + level = AnalogicalLevel(level_str) + current = AnalogicalPattern( + id=f"pattern_{len(patterns)}", + level=level, + features={}, + relations=[], + constraints=[], + metadata={} + ) + except ValueError: + logging.warning(f"Invalid analogical level: {level_str}") + elif current: + if line.startswith('Features:'): + try: + current.features = json.loads(line[9:].strip()) + except: + current.features = {"raw": line[9:].strip()} + elif line.startswith('Relations:'): + relations = [r.strip() for r in line[10:].split(',')] + current.relations = [(r.split()[0], r.split()[1], r.split()[2]) + for r in relations if len(r.split()) >= 3] + elif line.startswith('Constraints:'): + current.constraints = [c.strip() for c in line[12:].split(',')] + + if current: + patterns.append(current) + + return patterns + + def _parse_matches(self, response: str) -> List[Dict[str, Any]]: + """Parse matches from response.""" + matches = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[M'): + if current: + matches.append(current) + current = { + "source": "", + "similarity": 0.0, + "correspondences": [], + "transfer": [] + } + elif current: + if line.startswith('Source:'): + current["source"] = line[7:].strip() + elif line.startswith('Similarity:'): + try: + current["similarity"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Correspondences:'): + current["correspondences"] = [c.strip() for c in line[16:].split(',')] + elif line.startswith('Transfer:'): + current["transfer"] = [t.strip() for t in line[9:].split(',')] + + if current: + matches.append(current) + + return matches + + def _parse_mappings(self, response: str) -> List[AnalogicalMapping]: + """Parse mappings from response.""" + mappings = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Map'): + if current: + mappings.append(current) + current = None + elif line.startswith('Type:'): + type_str = line[5:].strip().lower() + try: + mapping_type = MappingType(type_str) + current = AnalogicalMapping( + id=f"mapping_{len(mappings)}", + type=mapping_type, + source_elements={}, + target_elements={}, + correspondences=[], + transformations=[], + confidence=0.0, + metadata={} + ) + except ValueError: + logging.warning(f"Invalid mapping type: {type_str}") + elif current: + if line.startswith('Elements:'): + try: + elements = json.loads(line[9:].strip()) + current.source_elements = elements.get("source", {}) + current.target_elements = elements.get("target", {}) + except: + pass + elif line.startswith('Correspondences:'): + pairs = [c.strip() for c in line[16:].split(',')] + for pair in pairs: + parts = pair.split(':') + if len(parts) >= 2: + source = parts[0].strip() + target = parts[1].strip() + strength = float(parts[2]) if len(parts) > 2 else 1.0 + current.correspondences.append((source, target, strength)) + elif line.startswith('Transformations:'): + try: + current.transformations = json.loads(line[16:].strip()) + except: + current.transformations = [{"raw": line[16:].strip()}] + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + pass + + if current: + mappings.append(current) + + return mappings + + def _parse_solutions(self, response: str, mappings: List[AnalogicalMapping]) -> List[AnalogicalSolution]: + """Parse solutions from response.""" + solutions = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current: + solutions.append(current) + current = None + mapping_idx = len(solutions) + if mapping_idx < len(mappings): + current = AnalogicalSolution( + id=f"solution_{len(solutions)}", + source_analogy="", + mapping=mappings[mapping_idx], + adaptation={}, + inference={}, + confidence=0.0, + validation={}, + metadata={} + ) + elif current: + if line.startswith('Inference:'): + try: + current.inference = json.loads(line[10:].strip()) + except: + current.inference = {"conclusion": line[10:].strip()} + elif line.startswith('Adaptation:'): + try: + current.adaptation = json.loads(line[11:].strip()) + except: + current.adaptation = {"steps": [line[11:].strip()]} + elif line.startswith('Validation:'): + try: + current.validation = json.loads(line[11:].strip()) + except: + current.validation = {"criteria": [line[11:].strip()]} + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + pass + elif line.startswith('Trace:'): + current.metadata["reasoning_trace"] = [t.strip() for t in line[6:].split(',')] + + if current: + solutions.append(current) + + return solutions + + def _parse_selection(self, response: str) -> Dict[str, Any]: + """Parse solution selection from response.""" + selection = { + "selected_id": None, + "confidence": 0.0, + "rationale": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Selection:'): + selection["selected_id"] = line[10:].strip() + elif line.startswith('Confidence:'): + try: + selection["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Rationale:'): + selection["rationale"] = [r.strip() for r in line[10:].split(',')] + + return selection + + def _pattern_to_dict(self, pattern: AnalogicalPattern) -> Dict[str, Any]: + """Convert pattern to dictionary for serialization.""" + return { + "id": pattern.id, + "level": pattern.level.value, + "features": pattern.features, + "relations": pattern.relations, + "constraints": pattern.constraints, + "metadata": pattern.metadata + } + + def _mapping_to_dict(self, mapping: AnalogicalMapping) -> Dict[str, Any]: + """Convert mapping to dictionary for serialization.""" + return { + "id": mapping.id, + "type": mapping.type.value, + "source_elements": mapping.source_elements, + "target_elements": mapping.target_elements, + "correspondences": mapping.correspondences, + "transformations": mapping.transformations, + "confidence": mapping.confidence, + "metadata": mapping.metadata + } + + def _solution_to_dict(self, solution: AnalogicalSolution) -> Dict[str, Any]: + """Convert solution to dictionary for serialization.""" + return { + "id": solution.id, + "source_analogy": solution.source_analogy, + "mapping": self._mapping_to_dict(solution.mapping), + "adaptation": solution.adaptation, + "inference": solution.inference, + "confidence": solution.confidence, + "validation": solution.validation, + "metadata": solution.metadata + } + + def get_pattern_statistics(self) -> Dict[str, Any]: + """Get statistics about pattern usage and effectiveness.""" + return { + "total_patterns": len(self.patterns), + "level_distribution": defaultdict(int, {p.level.value: 1 for p in self.patterns.values()}), + "average_constraints": sum(len(p.constraints) for p in self.patterns.values()) / len(self.patterns) if self.patterns else 0, + "pattern_weights": dict(self.pattern_weights) + } + + def get_mapping_statistics(self) -> Dict[str, Any]: + """Get statistics about mapping effectiveness.""" + return { + "total_mappings": len(self.mappings), + "type_distribution": defaultdict(int, {m.type.value: 1 for m in self.mappings.values()}), + "average_confidence": sum(m.confidence for m in self.mappings.values()) / len(self.mappings) if self.mappings else 0, + "transformation_counts": defaultdict(int, {m.id: len(m.transformations) for m in self.mappings.values()}) + } + + def get_solution_statistics(self) -> Dict[str, Any]: + """Get statistics about solution quality.""" + return { + "total_solutions": len(self.solutions), + "average_confidence": sum(s.confidence for s in self.solutions.values()) / len(self.solutions) if self.solutions else 0, + "adaptation_success_rate": sum(1 for h in self.adaptation_history if h["success"]) / len(self.adaptation_history) if self.adaptation_history else 0 + } + + def clear_knowledge_base(self): + """Clear the knowledge base.""" + self.patterns.clear() + self.mappings.clear() + self.solutions.clear() + self.pattern_weights.clear() + self.success_history.clear() + self.adaptation_history.clear() diff --git a/reasoning/base.py b/reasoning/base.py new file mode 100644 index 0000000000000000000000000000000000000000..7989a7598eaa46d76c51d1cfcf26c2187b346f56 --- /dev/null +++ b/reasoning/base.py @@ -0,0 +1,17 @@ +"""Base class for all reasoning strategies.""" + +from typing import Dict, Any + +class ReasoningStrategy: + """Base class for reasoning strategies.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Apply reasoning strategy to query with context. + + Args: + query: The query to reason about + context: Additional context for reasoning + + Returns: + Dictionary containing reasoning results + """ + raise NotImplementedError diff --git a/reasoning/bayesian.py b/reasoning/bayesian.py new file mode 100644 index 0000000000000000000000000000000000000000..4c403de1b63abe448bcfaf1785a631d986145b18 --- /dev/null +++ b/reasoning/bayesian.py @@ -0,0 +1,325 @@ +"""Advanced Bayesian reasoning for probabilistic analysis.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class BayesianHypothesis: + """Bayesian hypothesis with probabilities.""" + name: str + prior: float + likelihood: float + posterior: float = 0.0 + evidence: List[Dict[str, Any]] = field(default_factory=list) + +class BayesianReasoning(ReasoningStrategy): + """ + Advanced Bayesian reasoning that: + 1. Generates hypotheses + 2. Calculates prior probabilities + 3. Updates with evidence + 4. Computes posteriors + 5. Provides probabilistic analysis + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize Bayesian reasoning.""" + super().__init__() + self.config = config or {} + + # Configure Bayesian parameters + self.prior_weight = self.config.get('prior_weight', 0.3) + self.evidence_threshold = self.config.get('evidence_threshold', 0.1) + self.min_likelihood = self.config.get('min_likelihood', 0.01) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply Bayesian reasoning to analyze probabilities and update beliefs. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Generate hypotheses + hypotheses = await self._generate_hypotheses(query, context) + + # Calculate priors + priors = await self._calculate_priors(hypotheses, context) + + # Update with evidence + posteriors = await self._update_with_evidence( + hypotheses, + priors, + context + ) + + # Generate analysis + analysis = await self._generate_analysis(posteriors, context) + + return { + 'answer': self._format_analysis(analysis), + 'confidence': self._calculate_confidence(posteriors), + 'hypotheses': hypotheses, + 'priors': priors, + 'posteriors': posteriors, + 'analysis': analysis + } + + except Exception as e: + logging.error(f"Bayesian reasoning failed: {str(e)}") + return { + 'error': f"Bayesian reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _generate_hypotheses( + self, + query: str, + context: Dict[str, Any] + ) -> List[Dict[str, Any]]: + """Generate plausible hypotheses.""" + hypotheses = [] + + # Extract key terms for hypothesis generation + terms = set(query.lower().split()) + + # Generate hypotheses based on context and terms + if 'options' in context: + # Use provided options as hypotheses + for option in context['options']: + hypotheses.append({ + 'name': option, + 'description': f"Hypothesis based on option: {option}", + 'factors': self._extract_factors(option, terms) + }) + else: + # Generate default hypotheses + hypotheses.extend([ + { + 'name': 'primary', + 'description': "Primary hypothesis based on direct interpretation", + 'factors': self._extract_factors(query, terms) + }, + { + 'name': 'alternative', + 'description': "Alternative hypothesis considering other factors", + 'factors': self._generate_alternative_factors(terms) + } + ]) + + return hypotheses + + async def _calculate_priors( + self, + hypotheses: List[Dict[str, Any]], + context: Dict[str, Any] + ) -> Dict[str, float]: + """Calculate prior probabilities.""" + priors = {} + + # Get historical data if available + history = context.get('history', {}) + total_cases = sum(history.values()) if history else len(hypotheses) + + for hypothesis in hypotheses: + name = hypothesis['name'] + + # Calculate prior from history or use uniform prior + if name in history: + priors[name] = history[name] / total_cases + else: + priors[name] = 1.0 / len(hypotheses) + + # Adjust prior based on factors + factor_weight = len(hypothesis['factors']) / 10 # Normalize factor count + priors[name] = ( + priors[name] * (1 - self.prior_weight) + + factor_weight * self.prior_weight + ) + + # Normalize priors + total_prior = sum(priors.values()) + if total_prior > 0: + priors = { + name: prob / total_prior + for name, prob in priors.items() + } + + return priors + + async def _update_with_evidence( + self, + hypotheses: List[Dict[str, Any]], + priors: Dict[str, float], + context: Dict[str, Any] + ) -> Dict[str, float]: + """Update probabilities with evidence.""" + posteriors = priors.copy() + + # Get evidence from context + evidence = context.get('evidence', []) + if not evidence: + return posteriors + + for e in evidence: + # Calculate likelihood for each hypothesis + likelihoods = {} + for hypothesis in hypotheses: + name = hypothesis['name'] + likelihood = self._calculate_likelihood(hypothesis, e) + likelihoods[name] = max(likelihood, self.min_likelihood) + + # Update posteriors using Bayes' rule + total_probability = sum( + likelihoods[name] * posteriors[name] + for name in posteriors + ) + + if total_probability > 0: + posteriors = { + name: (likelihoods[name] * posteriors[name]) / total_probability + for name in posteriors + } + + return posteriors + + def _calculate_likelihood( + self, + hypothesis: Dict[str, Any], + evidence: Dict[str, Any] + ) -> float: + """Calculate likelihood of evidence given hypothesis.""" + # Extract evidence factors + evidence_factors = set( + str(v).lower() + for v in evidence.values() + if isinstance(v, (str, int, float)) + ) + + # Compare with hypothesis factors + common_factors = evidence_factors.intersection(hypothesis['factors']) + + if not evidence_factors: + return 0.5 # Neutral likelihood if no factors + + return len(common_factors) / len(evidence_factors) + + async def _generate_analysis( + self, + posteriors: Dict[str, float], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate probabilistic analysis.""" + # Sort hypotheses by posterior probability + ranked_hypotheses = sorted( + posteriors.items(), + key=lambda x: x[1], + reverse=True + ) + + # Calculate statistics + mean = np.mean(list(posteriors.values())) + std = np.std(list(posteriors.values())) + entropy = -sum( + p * np.log2(p) if p > 0 else 0 + for p in posteriors.values() + ) + + return { + 'top_hypothesis': ranked_hypotheses[0][0], + 'probability': ranked_hypotheses[0][1], + 'alternatives': [ + {'name': name, 'probability': prob} + for name, prob in ranked_hypotheses[1:] + ], + 'statistics': { + 'mean': mean, + 'std': std, + 'entropy': entropy + } + } + + def _format_analysis(self, analysis: Dict[str, Any]) -> str: + """Format analysis into readable text.""" + sections = [] + + # Top hypothesis + sections.append( + f"Most likely hypothesis: {analysis['top_hypothesis']} " + f"(probability: {analysis['probability']:.2%})" + ) + + # Alternative hypotheses + if analysis['alternatives']: + sections.append("\nAlternative hypotheses:") + for alt in analysis['alternatives']: + sections.append( + f"- {alt['name']}: {alt['probability']:.2%}" + ) + + # Statistics + stats = analysis['statistics'] + sections.append("\nDistribution statistics:") + sections.append(f"- Mean probability: {stats['mean']:.2%}") + sections.append(f"- Standard deviation: {stats['std']:.2%}") + sections.append(f"- Entropy: {stats['entropy']:.2f} bits") + + return "\n".join(sections) + + def _calculate_confidence(self, posteriors: Dict[str, float]) -> float: + """Calculate overall confidence score.""" + if not posteriors: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Adjust based on probability distribution + probs = list(posteriors.values()) + + # Strong leading hypothesis increases confidence + max_prob = max(probs) + if max_prob > 0.8: + confidence += 0.3 + elif max_prob > 0.6: + confidence += 0.2 + elif max_prob > 0.4: + confidence += 0.1 + + # Low entropy (clear distinction) increases confidence + entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs) + max_entropy = -np.log2(1/len(probs)) # Maximum possible entropy + + if entropy < 0.3 * max_entropy: + confidence += 0.2 + elif entropy < 0.6 * max_entropy: + confidence += 0.1 + + return min(confidence, 1.0) + + def _extract_factors(self, text: str, terms: Set[str]) -> Set[str]: + """Extract relevant factors from text.""" + return set(word.lower() for word in text.split() if word.lower() in terms) + + def _generate_alternative_factors(self, terms: Set[str]) -> Set[str]: + """Generate factors for alternative hypothesis.""" + # Simple approach: use terms not in primary hypothesis + return set( + word for word in terms + if not any( + similar in word or word in similar + for similar in terms + ) + ) diff --git a/reasoning/chain_of_thought.py b/reasoning/chain_of_thought.py new file mode 100644 index 0000000000000000000000000000000000000000..4c735b45160e2209418e717fabbdcbca89ff0074 --- /dev/null +++ b/reasoning/chain_of_thought.py @@ -0,0 +1,415 @@ +"""Chain of Thought reasoning implementation with advanced features.""" + +import logging +from typing import Dict, Any, List, Optional, Tuple +import json +from dataclasses import dataclass +from enum import Enum + +from .base import ReasoningStrategy + +class ThoughtType(Enum): + """Types of thoughts in the chain.""" + OBSERVATION = "observation" + ANALYSIS = "analysis" + HYPOTHESIS = "hypothesis" + VERIFICATION = "verification" + CONCLUSION = "conclusion" + REFLECTION = "reflection" + REFINEMENT = "refinement" + +@dataclass +class Thought: + """Represents a single thought in the chain.""" + type: ThoughtType + content: str + confidence: float + evidence: List[str] + alternatives: List[str] + next_steps: List[str] + metadata: Dict[str, Any] + +class ChainOfThoughtStrategy(ReasoningStrategy): + """ + Advanced Chain of Thought reasoning implementation with: + - Hierarchical thought chains + - Confidence scoring + - Alternative path exploration + - Self-reflection and refinement + - Evidence tracking + - Meta-learning capabilities + """ + + def __init__(self, + min_confidence: float = 0.7, + parallel_threshold: int = 3, + learning_rate: float = 0.1, + strategy_weights: Optional[Dict[str, float]] = None): + self.min_confidence = min_confidence + self.parallel_threshold = parallel_threshold + self.learning_rate = learning_rate + self.strategy_weights = strategy_weights or { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + } + self.thought_history: List[Thought] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing chain of thought.""" + try: + # Initialize reasoning chain + chain = await self._initialize_chain(query, context) + + # Generate initial thoughts + thoughts = await self._generate_thoughts(query, context) + + # Build thought chain + chain = await self._build_chain(thoughts, context) + + # Reflect and refine + if self.enable_reflection: + chain = await self._reflect_and_refine(chain, context) + + # Extract conclusion + conclusion = await self._extract_conclusion(chain, context) + + # Update thought history + self.thought_history.extend(chain) + + return { + "success": True, + "answer": conclusion["answer"], + "confidence": conclusion["confidence"], + "reasoning_chain": [self._thought_to_dict(t) for t in chain], + "alternatives": conclusion["alternatives"], + "evidence": conclusion["evidence"], + "meta_insights": conclusion["meta_insights"] + } + except Exception as e: + logging.error(f"Error in chain of thought reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _initialize_chain(self, query: str, context: Dict[str, Any]) -> List[Thought]: + """Initialize the thought chain with observations.""" + prompt = f""" + Initialize chain of thought for query: + Query: {query} + Context: {json.dumps(context)} + + Provide initial observations: + 1. Key elements in query + 2. Relevant context factors + 3. Initial hypotheses + 4. Potential approaches + + Format as: + [O1] Element: ... | Relevance: ... | Confidence: ... + [O2] Context: ... | Impact: ... | Confidence: ... + [O3] Hypothesis: ... | Support: ... | Confidence: ... + [O4] Approach: ... | Rationale: ... | Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_observations(response["answer"]) + + async def _generate_thoughts(self, query: str, context: Dict[str, Any]) -> List[Thought]: + """Generate candidate thoughts for the chain.""" + prompt = f""" + Generate thoughts for query analysis: + Query: {query} + Context: {json.dumps(context)} + + For each thought provide: + 1. [Type]: {" | ".join([t.value for t in ThoughtType])} + 2. [Content]: Main thought + 3. [Evidence]: Supporting evidence + 4. [Alternatives]: Alternative perspectives + 5. [Next]: Potential next steps + 6. [Confidence]: 0-1 score + + Format as: + [T1] + Type: ... + Content: ... + Evidence: ... + Alternatives: ... + Next: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_thoughts(response["answer"]) + + async def _build_chain(self, thoughts: List[Thought], context: Dict[str, Any]) -> List[Thought]: + """Build coherent chain from candidate thoughts.""" + prompt = f""" + Build coherent thought chain: + Thoughts: {json.dumps([self._thought_to_dict(t) for t in thoughts])} + Context: {json.dumps(context)} + + For each step specify: + 1. Selected thought + 2. Reasoning for selection + 3. Connection to previous + 4. Expected impact + + Format as: + [S1] + Thought: ... + Reason: ... + Connection: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_chain(response["answer"], thoughts) + + async def _reflect_and_refine(self, chain: List[Thought], context: Dict[str, Any]) -> List[Thought]: + """Reflect on and refine the thought chain.""" + prompt = f""" + Reflect on thought chain: + Chain: {json.dumps([self._thought_to_dict(t) for t in chain])} + Context: {json.dumps(context)} + + Analyze for: + 1. Logical gaps + 2. Weak assumptions + 3. Missing evidence + 4. Alternative perspectives + + Suggest refinements: + 1. Additional thoughts + 2. Modified reasoning + 3. New connections + 4. Evidence needs + + Format as: + [Analysis] + Gaps: ... + Assumptions: ... + Missing: ... + Alternatives: ... + + [Refinements] + Thoughts: ... + Reasoning: ... + Connections: ... + Evidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._apply_refinements(chain, response["answer"]) + + async def _extract_conclusion(self, chain: List[Thought], context: Dict[str, Any]) -> Dict[str, Any]: + """Extract final conclusion from thought chain.""" + prompt = f""" + Extract conclusion from thought chain: + Chain: {json.dumps([self._thought_to_dict(t) for t in chain])} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level + 3. Supporting evidence + 4. Alternative conclusions + 5. Meta-insights gained + 6. Future considerations + + Format as: + [Conclusion] + Answer: ... + Confidence: ... + Evidence: ... + Alternatives: ... + + [Meta] + Insights: ... + Future: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_conclusion(response["answer"]) + + def _parse_observations(self, response: str) -> List[Thought]: + """Parse initial observations into thoughts.""" + observations = [] + lines = response.split('\n') + + for line in lines: + if line.startswith('[O'): + parts = line.split('|') + if len(parts) >= 3: + main_part = parts[0].split(']')[1].strip() + key, content = main_part.split(':', 1) + + evidence = [p.strip() for p in parts[1].split(':')[1].strip().split(',')] + + try: + confidence = float(parts[2].split(':')[1].strip()) + except: + confidence = 0.5 + + observations.append(Thought( + type=ThoughtType.OBSERVATION, + content=content.strip(), + confidence=confidence, + evidence=evidence, + alternatives=[], + next_steps=[], + metadata={"key": key} + )) + + return observations + + def _parse_thoughts(self, response: str) -> List[Thought]: + """Parse generated thoughts.""" + thoughts = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[T'): + if current: + thoughts.append(current) + current = None + elif line.startswith('Type:'): + type_str = line[5:].strip() + try: + thought_type = ThoughtType(type_str.lower()) + current = Thought( + type=thought_type, + content="", + confidence=0.0, + evidence=[], + alternatives=[], + next_steps=[], + metadata={} + ) + except ValueError: + logging.warning(f"Invalid thought type: {type_str}") + elif current: + if line.startswith('Content:'): + current.content = line[8:].strip() + elif line.startswith('Evidence:'): + current.evidence = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + current.alternatives = [a.strip() for a in line[13:].split(',')] + elif line.startswith('Next:'): + current.next_steps = [n.strip() for n in line[5:].split(',')] + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + current.confidence = 0.5 + + if current: + thoughts.append(current) + + return thoughts + + def _parse_chain(self, response: str, thoughts: List[Thought]) -> List[Thought]: + """Parse and order thoughts into a chain.""" + chain = [] + thought_map = {self._thought_to_dict(t)["content"]: t for t in thoughts} + + for line in response.split('\n'): + if line.startswith('Thought:'): + content = line[8:].strip() + if content in thought_map: + chain.append(thought_map[content]) + + return chain + + def _apply_refinements(self, chain: List[Thought], response: str) -> List[Thought]: + """Apply refinements to thought chain.""" + refined_chain = chain.copy() + + # Parse refinements + sections = response.split('[') + for section in sections: + if section.startswith('Refinements]'): + lines = section.split('\n')[1:] + for line in lines: + if line.startswith('Thoughts:'): + new_thoughts = self._parse_refinement_thoughts(line[9:]) + refined_chain.extend(new_thoughts) + + return refined_chain + + def _parse_refinement_thoughts(self, refinements: str) -> List[Thought]: + """Parse refinement thoughts.""" + thoughts = [] + for refinement in refinements.split(';'): + if refinement.strip(): + thoughts.append(Thought( + type=ThoughtType.REFINEMENT, + content=refinement.strip(), + confidence=0.8, # Refinements typically have high confidence + evidence=[], + alternatives=[], + next_steps=[], + metadata={"refined": True} + )) + return thoughts + + def _parse_conclusion(self, response: str) -> Dict[str, Any]: + """Parse final conclusion.""" + conclusion = { + "answer": "", + "confidence": 0.0, + "evidence": [], + "alternatives": [], + "meta_insights": [], + "future_considerations": [] + } + + sections = response.split('[') + for section in sections: + if section.startswith('Conclusion]'): + lines = section.split('\n')[1:] + for line in lines: + if line.startswith('Answer:'): + conclusion["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + conclusion["confidence"] = float(line[11:].strip()) + except: + conclusion["confidence"] = 0.5 + elif line.startswith('Evidence:'): + conclusion["evidence"] = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')] + elif section.startswith('Meta]'): + lines = section.split('\n')[1:] + for line in lines: + if line.startswith('Insights:'): + conclusion["meta_insights"] = [i.strip() for i in line[9:].split(',')] + elif line.startswith('Future:'): + conclusion["future_considerations"] = [f.strip() for f in line[7:].split(',')] + + return conclusion + + def _thought_to_dict(self, thought: Thought) -> Dict[str, Any]: + """Convert thought to dictionary for serialization.""" + return { + "type": thought.type.value, + "content": thought.content, + "confidence": thought.confidence, + "evidence": thought.evidence, + "alternatives": thought.alternatives, + "next_steps": thought.next_steps, + "metadata": thought.metadata + } + + def get_thought_history(self) -> List[Dict[str, Any]]: + """Get the history of all thoughts processed.""" + return [self._thought_to_dict(t) for t in self.thought_history] + + def clear_history(self) -> None: + """Clear thought history.""" + self.thought_history = [] diff --git a/reasoning/coordination.py b/reasoning/coordination.py new file mode 100644 index 0000000000000000000000000000000000000000..062c093b1c1e9f7fe1fd32e54e2791d347e85f93 --- /dev/null +++ b/reasoning/coordination.py @@ -0,0 +1,525 @@ +"""Advanced strategy coordination patterns for the unified reasoning engine.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy +from .unified_engine import StrategyType, StrategyResult, UnifiedResult + +class CoordinationPattern(Enum): + """Types of strategy coordination patterns.""" + PIPELINE = "pipeline" + PARALLEL = "parallel" + HIERARCHICAL = "hierarchical" + FEEDBACK = "feedback" + ADAPTIVE = "adaptive" + ENSEMBLE = "ensemble" + +class CoordinationPhase(Enum): + """Phases in strategy coordination.""" + INITIALIZATION = "initialization" + EXECUTION = "execution" + SYNCHRONIZATION = "synchronization" + ADAPTATION = "adaptation" + COMPLETION = "completion" + +@dataclass +class CoordinationState: + """State of strategy coordination.""" + pattern: CoordinationPattern + active_strategies: Dict[StrategyType, bool] + phase: CoordinationPhase + shared_context: Dict[str, Any] + synchronization_points: List[str] + adaptation_history: List[Dict[str, Any]] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class StrategyInteraction: + """Interaction between strategies.""" + source: StrategyType + target: StrategyType + interaction_type: str + data: Dict[str, Any] + timestamp: datetime = field(default_factory=datetime.now) + +class StrategyCoordinator: + """ + Advanced strategy coordinator that: + 1. Manages strategy interactions + 2. Implements coordination patterns + 3. Handles state synchronization + 4. Adapts coordination dynamically + 5. Optimizes strategy combinations + """ + + def __init__(self, + strategies: Dict[StrategyType, ReasoningStrategy], + learning_rate: float = 0.1): + self.strategies = strategies + self.learning_rate = learning_rate + + # Coordination state + self.states: Dict[str, CoordinationState] = {} + self.interactions: List[StrategyInteraction] = [] + + # Pattern performance + self.pattern_performance: Dict[CoordinationPattern, List[float]] = defaultdict(list) + self.pattern_weights: Dict[CoordinationPattern, float] = { + pattern: 1.0 for pattern in CoordinationPattern + } + + async def coordinate(self, + query: str, + context: Dict[str, Any], + pattern: Optional[CoordinationPattern] = None) -> Dict[str, Any]: + """Coordinate strategy execution using specified pattern.""" + try: + # Select pattern if not specified + if not pattern: + pattern = await self._select_pattern(query, context) + + # Initialize coordination + state = await self._initialize_coordination(pattern, context) + + # Execute coordination pattern + if pattern == CoordinationPattern.PIPELINE: + result = await self._coordinate_pipeline(query, context, state) + elif pattern == CoordinationPattern.PARALLEL: + result = await self._coordinate_parallel(query, context, state) + elif pattern == CoordinationPattern.HIERARCHICAL: + result = await self._coordinate_hierarchical(query, context, state) + elif pattern == CoordinationPattern.FEEDBACK: + result = await self._coordinate_feedback(query, context, state) + elif pattern == CoordinationPattern.ADAPTIVE: + result = await self._coordinate_adaptive(query, context, state) + elif pattern == CoordinationPattern.ENSEMBLE: + result = await self._coordinate_ensemble(query, context, state) + else: + raise ValueError(f"Unsupported coordination pattern: {pattern}") + + # Update performance metrics + self._update_pattern_performance(pattern, result) + + return result + + except Exception as e: + logging.error(f"Error in strategy coordination: {str(e)}") + return { + "success": False, + "error": str(e), + "pattern": pattern.value if pattern else None + } + + async def _select_pattern(self, query: str, context: Dict[str, Any]) -> CoordinationPattern: + """Select appropriate coordination pattern.""" + prompt = f""" + Select coordination pattern: + Query: {query} + Context: {json.dumps(context)} + + Consider: + 1. Task complexity and type + 2. Strategy dependencies + 3. Resource constraints + 4. Performance history + 5. Adaptation needs + + Format as: + [Selection] + Pattern: ... + Rationale: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + selection = self._parse_pattern_selection(response["answer"]) + + # Weight by performance history + weighted_patterns = { + pattern: self.pattern_weights[pattern] * selection.get(pattern.value, 0.0) + for pattern in CoordinationPattern + } + + return max(weighted_patterns.items(), key=lambda x: x[1])[0] + + async def _coordinate_pipeline(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies in pipeline pattern.""" + results = [] + current_context = context.copy() + + # Determine optimal order + strategy_order = await self._determine_pipeline_order(query, context) + + for strategy_type in strategy_order: + try: + # Execute strategy + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, current_context) + + # Update context with result + current_context.update({ + "previous_result": result, + "pipeline_position": len(results) + }) + + results.append(StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + )) + + # Record interaction + self._record_interaction( + source=strategy_type, + target=strategy_order[len(results)] if len(results) < len(strategy_order) else None, + interaction_type="pipeline_transfer", + data={"result": result} + ) + + except Exception as e: + logging.error(f"Error in pipeline strategy {strategy_type}: {str(e)}") + + return { + "success": any(r.success for r in results), + "results": results, + "pattern": CoordinationPattern.PIPELINE.value, + "metrics": { + "total_steps": len(results), + "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0 + } + } + + async def _coordinate_parallel(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies in parallel pattern.""" + async def execute_strategy(strategy_type: StrategyType) -> StrategyResult: + try: + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, context) + + return StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + except Exception as e: + logging.error(f"Error in parallel strategy {strategy_type}: {str(e)}") + return StrategyResult( + strategy_type=strategy_type, + success=False, + answer=None, + confidence=0.0, + reasoning_trace=[{"error": str(e)}], + metadata={}, + performance_metrics={} + ) + + # Execute strategies in parallel + tasks = [execute_strategy(strategy_type) + for strategy_type in state.active_strategies + if state.active_strategies[strategy_type]] + + results = await asyncio.gather(*tasks) + + # Synthesize results + synthesis = await self._synthesize_parallel_results(results, context) + + return { + "success": synthesis.get("success", False), + "results": results, + "synthesis": synthesis, + "pattern": CoordinationPattern.PARALLEL.value, + "metrics": { + "total_strategies": len(results), + "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0 + } + } + + async def _coordinate_hierarchical(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies in hierarchical pattern.""" + # Build strategy hierarchy + hierarchy = await self._build_strategy_hierarchy(query, context) + results = {} + + async def execute_level(level_strategies: List[StrategyType], + level_context: Dict[str, Any]) -> List[StrategyResult]: + tasks = [] + for strategy_type in level_strategies: + if strategy_type in state.active_strategies and state.active_strategies[strategy_type]: + strategy = self.strategies[strategy_type] + tasks.append(strategy.reason(query, level_context)) + + level_results = await asyncio.gather(*tasks) + return [ + StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + for strategy_type, result in zip(level_strategies, level_results) + ] + + # Execute hierarchy levels + current_context = context.copy() + for level, level_strategies in enumerate(hierarchy): + results[level] = await execute_level(level_strategies, current_context) + + # Update context for next level + current_context.update({ + "previous_level_results": results[level], + "hierarchy_level": level + }) + + return { + "success": any(any(r.success for r in level_results) + for level_results in results.values()), + "results": results, + "hierarchy": hierarchy, + "pattern": CoordinationPattern.HIERARCHICAL.value, + "metrics": { + "total_levels": len(hierarchy), + "level_success_rates": { + level: sum(1 for r in results[level] if r.success) / len(results[level]) + for level in results if results[level] + } + } + } + + async def _coordinate_feedback(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies with feedback loops.""" + results = [] + feedback_history = [] + current_context = context.copy() + + max_iterations = 5 # Prevent infinite loops + iteration = 0 + + while iteration < max_iterations: + iteration += 1 + + # Execute strategies + iteration_results = [] + for strategy_type in state.active_strategies: + if state.active_strategies[strategy_type]: + try: + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, current_context) + + strategy_result = StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + + iteration_results.append(strategy_result) + + except Exception as e: + logging.error(f"Error in feedback strategy {strategy_type}: {str(e)}") + + results.append(iteration_results) + + # Generate feedback + feedback = await self._generate_feedback(iteration_results, current_context) + feedback_history.append(feedback) + + # Check termination condition + if self._should_terminate_feedback(feedback, iteration_results): + break + + # Update context with feedback + current_context.update({ + "previous_results": iteration_results, + "feedback": feedback, + "iteration": iteration + }) + + return { + "success": any(any(r.success for r in iteration_results) + for iteration_results in results), + "results": results, + "feedback_history": feedback_history, + "pattern": CoordinationPattern.FEEDBACK.value, + "metrics": { + "total_iterations": iteration, + "feedback_impact": self._calculate_feedback_impact(results, feedback_history) + } + } + + async def _coordinate_adaptive(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies with adaptive selection.""" + results = [] + adaptations = [] + current_context = context.copy() + + while len(results) < len(state.active_strategies): + # Select next strategy + next_strategy = await self._select_next_strategy( + results, state.active_strategies, current_context) + + if not next_strategy: + break + + try: + # Execute strategy + strategy = self.strategies[next_strategy] + result = await strategy.reason(query, current_context) + + strategy_result = StrategyResult( + strategy_type=next_strategy, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + + results.append(strategy_result) + + # Adapt strategy selection + adaptation = await self._adapt_strategy_selection( + strategy_result, current_context) + adaptations.append(adaptation) + + # Update context + current_context.update({ + "previous_results": results, + "adaptations": adaptations, + "current_strategy": next_strategy + }) + + except Exception as e: + logging.error(f"Error in adaptive strategy {next_strategy}: {str(e)}") + + return { + "success": any(r.success for r in results), + "results": results, + "adaptations": adaptations, + "pattern": CoordinationPattern.ADAPTIVE.value, + "metrics": { + "total_strategies": len(results), + "adaptation_impact": self._calculate_adaptation_impact(results, adaptations) + } + } + + async def _coordinate_ensemble(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies as an ensemble.""" + # Execute all strategies + results = [] + for strategy_type in state.active_strategies: + if state.active_strategies[strategy_type]: + try: + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, context) + + strategy_result = StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + + results.append(strategy_result) + + except Exception as e: + logging.error(f"Error in ensemble strategy {strategy_type}: {str(e)}") + + # Combine results using ensemble methods + ensemble_result = await self._combine_ensemble_results(results, context) + + return { + "success": ensemble_result.get("success", False), + "results": results, + "ensemble_result": ensemble_result, + "pattern": CoordinationPattern.ENSEMBLE.value, + "metrics": { + "total_members": len(results), + "ensemble_confidence": ensemble_result.get("confidence", 0.0) + } + } + + def _record_interaction(self, + source: StrategyType, + target: Optional[StrategyType], + interaction_type: str, + data: Dict[str, Any]): + """Record strategy interaction.""" + self.interactions.append(StrategyInteraction( + source=source, + target=target, + interaction_type=interaction_type, + data=data + )) + + def _update_pattern_performance(self, pattern: CoordinationPattern, result: Dict[str, Any]): + """Update pattern performance metrics.""" + success_rate = result["metrics"].get("success_rate", 0.0) + self.pattern_performance[pattern].append(success_rate) + + # Update weights using exponential moving average + current_weight = self.pattern_weights[pattern] + self.pattern_weights[pattern] = ( + (1 - self.learning_rate) * current_weight + + self.learning_rate * success_rate + ) + + def get_performance_metrics(self) -> Dict[str, Any]: + """Get comprehensive performance metrics.""" + return { + "pattern_weights": dict(self.pattern_weights), + "average_performance": { + pattern.value: sum(scores) / len(scores) if scores else 0 + for pattern, scores in self.pattern_performance.items() + }, + "interaction_counts": defaultdict(int, { + interaction.interaction_type: 1 + for interaction in self.interactions + }), + "active_patterns": [ + pattern.value for pattern, weight in self.pattern_weights.items() + if weight > 0.5 + ] + } diff --git a/reasoning/emergent.py b/reasoning/emergent.py new file mode 100644 index 0000000000000000000000000000000000000000..c41c0c4b05b6e1f9c66d87da4fe03fe263f50355 --- /dev/null +++ b/reasoning/emergent.py @@ -0,0 +1,133 @@ +""" +Emergent Reasoning Module +------------------------ +Implements emergent reasoning capabilities that arise from the interaction +of multiple reasoning strategies and patterns. +""" + +from typing import Dict, Any, List, Optional +from .base import ReasoningStrategy +from .meta_learning import MetaLearningStrategy +from .chain_of_thought import ChainOfThoughtStrategy +from .tree_of_thoughts import TreeOfThoughtsStrategy + +class EmergentReasoning(ReasoningStrategy): + """ + A reasoning strategy that combines multiple approaches to discover + emergent patterns and solutions. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize emergent reasoning with component strategies.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize component strategies with shared config + strategy_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights + } + + self.meta_learner = MetaLearningStrategy(strategy_config) + self.chain_of_thought = ChainOfThoughtStrategy(strategy_config) + self.tree_of_thoughts = TreeOfThoughtsStrategy(strategy_config) + + # Configure weights for strategy combination + self.weights = self.config.get('combination_weights', { + 'meta': 0.4, + 'chain': 0.3, + 'tree': 0.3 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply emergent reasoning by combining multiple strategies and + identifying patterns that emerge from their interaction. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Get results from each strategy + meta_result = await self.meta_learner.reason(query, context) + chain_result = await self.chain_of_thought.reason(query, context) + tree_result = await self.tree_of_thoughts.reason(query, context) + + # Combine results with weighted averaging + combined_answer = self._combine_results([ + (meta_result.get('answer', ''), self.weights['meta']), + (chain_result.get('answer', ''), self.weights['chain']), + (tree_result.get('answer', ''), self.weights['tree']) + ]) + + # Calculate overall confidence + confidence = ( + meta_result.get('confidence', 0) * self.weights['meta'] + + chain_result.get('confidence', 0) * self.weights['chain'] + + tree_result.get('confidence', 0) * self.weights['tree'] + ) + + return { + 'answer': combined_answer, + 'confidence': confidence, + 'reasoning_path': { + 'meta': meta_result.get('reasoning_path'), + 'chain': chain_result.get('reasoning_path'), + 'tree': tree_result.get('reasoning_path') + }, + 'emergent_patterns': self._identify_patterns([ + meta_result, chain_result, tree_result + ]) + } + + except Exception as e: + return { + 'error': f"Emergent reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + def _combine_results(self, weighted_results: List[tuple[str, float]]) -> str: + """Combine multiple reasoning results with weights.""" + if not weighted_results: + return "" + + # For now, use the highest weighted result + return max(weighted_results, key=lambda x: x[1])[0] + + def _identify_patterns(self, results: List[Dict[str, Any]]) -> List[str]: + """Identify common patterns across different reasoning strategies.""" + patterns = [] + + # Extract common themes or conclusions + answers = [r.get('answer', '') for r in results if r.get('answer')] + if len(set(answers)) == 1: + patterns.append("All strategies reached the same conclusion") + elif len(set(answers)) < len(answers): + patterns.append("Some strategies converged on similar conclusions") + + # Look for common confidence patterns + confidences = [r.get('confidence', 0) for r in results] + avg_confidence = sum(confidences) / len(confidences) if confidences else 0 + if avg_confidence > 0.8: + patterns.append("High confidence across all strategies") + elif avg_confidence < 0.3: + patterns.append("Low confidence across strategies") + + return patterns diff --git a/reasoning/groq_strategy.py b/reasoning/groq_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..a4f05136208c247d224bb7a0fe3b5dbb2b949def --- /dev/null +++ b/reasoning/groq_strategy.py @@ -0,0 +1,332 @@ +"""Groq API integration with streaming and optimizations.""" + +import os +import logging +import asyncio +from typing import Dict, Any, Optional, List, AsyncGenerator, Union +import groq +from datetime import datetime +import json +from dataclasses import dataclass +from concurrent.futures import ThreadPoolExecutor + +from .base import ReasoningStrategy, StrategyResult + +logger = logging.getLogger(__name__) + +@dataclass +class GroqConfig: + """Configuration for Groq models.""" + model_name: str + max_tokens: int + temperature: float + top_p: float + top_k: Optional[int] = None + presence_penalty: float = 0.0 + frequency_penalty: float = 0.0 + stop_sequences: Optional[List[str]] = None + chunk_size: int = 1024 + retry_attempts: int = 3 + retry_delay: float = 1.0 + +class GroqStrategy(ReasoningStrategy): + """Enhanced reasoning strategy using Groq's API with streaming and optimizations.""" + + def __init__(self, api_key: Optional[str] = None): + """Initialize Groq strategy.""" + super().__init__() + self.api_key = api_key or os.getenv("GROQ_API_KEY") + if not self.api_key: + raise ValueError("GROQ_API_KEY must be set") + + # Initialize Groq client with optimized settings + self.client = groq.Groq( + api_key=self.api_key, + timeout=30, + max_retries=3 + ) + + # Optimized model configurations + self.model_configs = { + "mixtral": GroqConfig( + model_name="mixtral-8x7b-32768", + max_tokens=32768, + temperature=0.7, + top_p=0.9, + top_k=40, + presence_penalty=0.1, + frequency_penalty=0.1, + chunk_size=4096 + ), + "llama": GroqConfig( + model_name="llama2-70b-4096", + max_tokens=4096, + temperature=0.8, + top_p=0.9, + top_k=50, + presence_penalty=0.2, + frequency_penalty=0.2, + chunk_size=1024 + ) + } + + # Initialize thread pool for parallel processing + self.executor = ThreadPoolExecutor(max_workers=4) + + # Response cache + self.cache: Dict[str, Any] = {} + self.cache_ttl = 3600 # 1 hour + + async def reason_stream( + self, + query: str, + context: Dict[str, Any], + model: str = "mixtral", + chunk_handler: Optional[callable] = None + ) -> AsyncGenerator[str, None]: + """ + Stream reasoning results from Groq's API. + + Args: + query: The query to reason about + context: Additional context + model: Model to use ('mixtral' or 'llama') + chunk_handler: Optional callback for handling chunks + """ + config = self.model_configs[model] + messages = self._prepare_messages(query, context) + + try: + stream = await self.client.chat.completions.create( + model=config.model_name, + messages=messages, + temperature=config.temperature, + top_p=config.top_p, + top_k=config.top_k, + presence_penalty=config.presence_penalty, + frequency_penalty=config.frequency_penalty, + max_tokens=config.max_tokens, + stream=True + ) + + collected_content = [] + async for chunk in stream: + if chunk.choices[0].delta.content: + content = chunk.choices[0].delta.content + collected_content.append(content) + + if chunk_handler: + await chunk_handler(content) + + yield content + + # Cache the complete response + cache_key = self._generate_cache_key(query, context, model) + self.cache[cache_key] = { + "content": "".join(collected_content), + "timestamp": datetime.now() + } + + except Exception as e: + logger.error(f"Groq streaming error: {str(e)}") + yield f"Error: {str(e)}" + + async def reason( + self, + query: str, + context: Dict[str, Any], + model: str = "mixtral" + ) -> StrategyResult: + """ + Enhanced reasoning with Groq's API including optimizations. + + Args: + query: The query to reason about + context: Additional context + model: Model to use ('mixtral' or 'llama') + """ + # Check cache first + cache_key = self._generate_cache_key(query, context, model) + cached_response = self._get_from_cache(cache_key) + if cached_response: + return self._create_result(cached_response, model, from_cache=True) + + config = self.model_configs[model] + messages = self._prepare_messages(query, context) + + # Implement retry logic with exponential backoff + for attempt in range(config.retry_attempts): + try: + start_time = datetime.now() + + # Make API call with optimized parameters + response = await self.client.chat.completions.create( + model=config.model_name, + messages=messages, + temperature=config.temperature, + top_p=config.top_p, + top_k=config.top_k, + presence_penalty=config.presence_penalty, + frequency_penalty=config.frequency_penalty, + max_tokens=config.max_tokens, + stream=False + ) + + end_time = datetime.now() + + # Cache successful response + self.cache[cache_key] = { + "content": response.choices[0].message.content, + "timestamp": datetime.now() + } + + return self._create_result(response, model) + + except Exception as e: + delay = config.retry_delay * (2 ** attempt) + logger.warning(f"Groq API attempt {attempt + 1} failed: {str(e)}") + if attempt < config.retry_attempts - 1: + await asyncio.sleep(delay) + else: + logger.error(f"All Groq API attempts failed: {str(e)}") + return self._create_error_result(str(e)) + + def _create_result( + self, + response: Union[Dict, Any], + model: str, + from_cache: bool = False + ) -> StrategyResult: + """Create a strategy result from response.""" + if from_cache: + answer = response["content"] + confidence = 0.9 # Higher confidence for cached responses + performance_metrics = { + "from_cache": True, + "cache_age": (datetime.now() - response["timestamp"]).total_seconds() + } + else: + answer = response.choices[0].message.content + confidence = self._calculate_confidence(response) + performance_metrics = { + "latency": response.usage.total_tokens / 1000, # tokens per second + "tokens_used": response.usage.total_tokens, + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "model": self.model_configs[model].model_name + } + + return StrategyResult( + strategy_type="groq", + success=True, + answer=answer, + confidence=confidence, + reasoning_trace=[{ + "step": "groq_api_call", + "model": self.model_configs[model].model_name, + "timestamp": datetime.now().isoformat(), + "metrics": performance_metrics + }], + metadata={ + "model": self.model_configs[model].model_name, + "from_cache": from_cache + }, + performance_metrics=performance_metrics + ) + + def _create_error_result(self, error: str) -> StrategyResult: + """Create an error result.""" + return StrategyResult( + strategy_type="groq", + success=False, + answer=None, + confidence=0.0, + reasoning_trace=[{ + "step": "groq_api_error", + "error": error, + "timestamp": datetime.now().isoformat() + }], + metadata={"error": error}, + performance_metrics={} + ) + + def _generate_cache_key( + self, + query: str, + context: Dict[str, Any], + model: str + ) -> str: + """Generate a cache key.""" + key_data = { + "query": query, + "context": context, + "model": model + } + return json.dumps(key_data, sort_keys=True) + + def _get_from_cache(self, cache_key: str) -> Optional[Dict]: + """Get response from cache if valid.""" + if cache_key in self.cache: + cached = self.cache[cache_key] + age = (datetime.now() - cached["timestamp"]).total_seconds() + if age < self.cache_ttl: + return cached + else: + del self.cache[cache_key] + return None + + def _calculate_confidence(self, response: Any) -> float: + """Calculate confidence score from response.""" + confidence = 0.8 # Base confidence + + # Adjust based on token usage and model behavior + if hasattr(response, 'usage'): + completion_tokens = response.usage.completion_tokens + total_tokens = response.usage.total_tokens + + # Length-based adjustment + if completion_tokens < 10: + confidence *= 0.8 # Reduce confidence for very short responses + elif completion_tokens > 100: + confidence *= 1.1 # Increase confidence for detailed responses + + # Token efficiency adjustment + token_efficiency = completion_tokens / total_tokens + if token_efficiency > 0.5: + confidence *= 1.1 # Good token efficiency + + # Response completeness check + if hasattr(response.choices[0], 'finish_reason'): + if response.choices[0].finish_reason == "stop": + confidence *= 1.1 # Natural completion + elif response.choices[0].finish_reason == "length": + confidence *= 0.9 # Truncated response + + return min(1.0, max(0.0, confidence)) # Ensure between 0 and 1 + + def _prepare_messages( + self, + query: str, + context: Dict[str, Any] + ) -> List[Dict[str, str]]: + """Prepare messages for the Groq API.""" + messages = [] + + # Add system message if provided + if "system_message" in context: + messages.append({ + "role": "system", + "content": context["system_message"] + }) + + # Add chat history if provided + if "chat_history" in context: + messages.extend(context["chat_history"]) + + # Add the current query + messages.append({ + "role": "user", + "content": query + }) + + return messages diff --git a/reasoning/learning.py b/reasoning/learning.py new file mode 100644 index 0000000000000000000000000000000000000000..e20db5e08ed2a80706f75bf5413cc3d3beff61dd --- /dev/null +++ b/reasoning/learning.py @@ -0,0 +1,394 @@ +"""Enhanced learning mechanisms for reasoning strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +@dataclass +class LearningEvent: + """Event for strategy learning.""" + strategy_type: str + event_type: str + data: Dict[str, Any] + outcome: Optional[float] + timestamp: datetime = field(default_factory=datetime.now) + +class LearningMode(Enum): + """Types of learning modes.""" + SUPERVISED = "supervised" + REINFORCEMENT = "reinforcement" + ACTIVE = "active" + TRANSFER = "transfer" + META = "meta" + ENSEMBLE = "ensemble" + +@dataclass +class LearningState: + """State for learning process.""" + mode: LearningMode + parameters: Dict[str, Any] + history: List[LearningEvent] + metrics: Dict[str, float] + metadata: Dict[str, Any] = field(default_factory=dict) + +class EnhancedLearningManager: + """ + Advanced learning manager that: + 1. Implements multiple learning modes + 2. Tracks learning progress + 3. Adapts learning parameters + 4. Optimizes strategy performance + 5. Transfers knowledge between strategies + """ + + def __init__(self, + learning_rate: float = 0.1, + exploration_rate: float = 0.2, + memory_size: int = 1000): + self.learning_rate = learning_rate + self.exploration_rate = exploration_rate + self.memory_size = memory_size + + # Learning states + self.states: Dict[str, LearningState] = {} + + # Performance tracking + self.performance_history: List[Dict[str, Any]] = [] + self.strategy_metrics: Dict[str, List[float]] = defaultdict(list) + + # Knowledge transfer + self.knowledge_base: Dict[str, Any] = {} + self.transfer_history: List[Dict[str, Any]] = [] + + async def learn(self, + strategy_type: str, + event: LearningEvent, + context: Dict[str, Any]) -> Dict[str, Any]: + """Learn from strategy execution event.""" + try: + # Initialize or get learning state + state = self._get_learning_state(strategy_type) + + # Select learning mode + mode = await self._select_learning_mode(event, state, context) + + # Execute learning + if mode == LearningMode.SUPERVISED: + result = await self._supervised_learning(event, state, context) + elif mode == LearningMode.REINFORCEMENT: + result = await self._reinforcement_learning(event, state, context) + elif mode == LearningMode.ACTIVE: + result = await self._active_learning(event, state, context) + elif mode == LearningMode.TRANSFER: + result = await self._transfer_learning(event, state, context) + elif mode == LearningMode.META: + result = await self._meta_learning(event, state, context) + elif mode == LearningMode.ENSEMBLE: + result = await self._ensemble_learning(event, state, context) + else: + raise ValueError(f"Unsupported learning mode: {mode}") + + # Update state + self._update_learning_state(state, result) + + # Record performance + self._record_performance(strategy_type, result) + + return result + + except Exception as e: + logging.error(f"Error in learning: {str(e)}") + return { + "success": False, + "error": str(e), + "mode": mode.value if 'mode' in locals() else None + } + + async def _supervised_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement supervised learning.""" + # Extract features and labels + features = await self._extract_features(event.data, context) + labels = event.outcome if event.outcome is not None else 0.0 + + # Train model + model_update = await self._update_model(features, labels, state, context) + + # Validate performance + validation = await self._validate_model(model_update, state, context) + + return { + "success": True, + "mode": LearningMode.SUPERVISED.value, + "model_update": model_update, + "validation": validation, + "metrics": { + "accuracy": validation.get("accuracy", 0.0), + "loss": validation.get("loss", 0.0) + } + } + + async def _reinforcement_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement reinforcement learning.""" + # Extract state and action + current_state = await self._extract_state(event.data, context) + action = event.data.get("action") + reward = event.outcome if event.outcome is not None else 0.0 + + # Update policy + policy_update = await self._update_policy( + current_state, action, reward, state, context) + + # Optimize value function + value_update = await self._update_value_function( + current_state, reward, state, context) + + return { + "success": True, + "mode": LearningMode.REINFORCEMENT.value, + "policy_update": policy_update, + "value_update": value_update, + "metrics": { + "reward": reward, + "value_error": value_update.get("error", 0.0) + } + } + + async def _active_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement active learning.""" + # Query selection + query = await self._select_query(event.data, state, context) + + # Get feedback + feedback = await self._get_feedback(query, context) + + # Update model + model_update = await self._update_model_active( + query, feedback, state, context) + + return { + "success": True, + "mode": LearningMode.ACTIVE.value, + "query": query, + "feedback": feedback, + "model_update": model_update, + "metrics": { + "uncertainty": query.get("uncertainty", 0.0), + "feedback_quality": feedback.get("quality", 0.0) + } + } + + async def _transfer_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement transfer learning.""" + # Source task selection + source_task = await self._select_source_task(event.data, state, context) + + # Knowledge extraction + knowledge = await self._extract_knowledge(source_task, context) + + # Transfer adaptation + adaptation = await self._adapt_knowledge( + knowledge, event.data, state, context) + + # Apply transfer + transfer = await self._apply_transfer(adaptation, state, context) + + return { + "success": True, + "mode": LearningMode.TRANSFER.value, + "source_task": source_task, + "knowledge": knowledge, + "adaptation": adaptation, + "transfer": transfer, + "metrics": { + "transfer_efficiency": transfer.get("efficiency", 0.0), + "adaptation_quality": adaptation.get("quality", 0.0) + } + } + + async def _meta_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement meta-learning.""" + # Task characterization + task_char = await self._characterize_task(event.data, context) + + # Strategy selection + strategy = await self._select_strategy(task_char, state, context) + + # Parameter optimization + optimization = await self._optimize_parameters( + strategy, task_char, state, context) + + # Apply meta-learning + meta_update = await self._apply_meta_learning( + optimization, state, context) + + return { + "success": True, + "mode": LearningMode.META.value, + "task_characterization": task_char, + "strategy": strategy, + "optimization": optimization, + "meta_update": meta_update, + "metrics": { + "strategy_fit": strategy.get("fit_score", 0.0), + "optimization_improvement": optimization.get("improvement", 0.0) + } + } + + async def _ensemble_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement ensemble learning.""" + # Member selection + members = await self._select_members(event.data, state, context) + + # Weight optimization + weights = await self._optimize_weights(members, state, context) + + # Combine predictions + combination = await self._combine_predictions( + members, weights, event.data, context) + + return { + "success": True, + "mode": LearningMode.ENSEMBLE.value, + "members": members, + "weights": weights, + "combination": combination, + "metrics": { + "ensemble_diversity": weights.get("diversity", 0.0), + "combination_strength": combination.get("strength", 0.0) + } + } + + def _get_learning_state(self, strategy_type: str) -> LearningState: + """Get or initialize learning state for strategy.""" + if strategy_type not in self.states: + self.states[strategy_type] = LearningState( + mode=LearningMode.SUPERVISED, + parameters={ + "learning_rate": self.learning_rate, + "exploration_rate": self.exploration_rate + }, + history=[], + metrics={} + ) + return self.states[strategy_type] + + def _update_learning_state(self, state: LearningState, result: Dict[str, Any]): + """Update learning state with result.""" + # Update history + state.history.append(LearningEvent( + strategy_type=result.get("strategy_type", "unknown"), + event_type="learning_update", + data=result, + outcome=result.get("metrics", {}).get("accuracy", 0.0), + timestamp=datetime.now() + )) + + # Update metrics + for metric, value in result.get("metrics", {}).items(): + if metric in state.metrics: + state.metrics[metric] = ( + 0.9 * state.metrics[metric] + 0.1 * value # Exponential moving average + ) + else: + state.metrics[metric] = value + + # Adapt parameters + self._adapt_parameters(state, result) + + def _record_performance(self, strategy_type: str, result: Dict[str, Any]): + """Record learning performance.""" + self.performance_history.append({ + "timestamp": datetime.now().isoformat(), + "strategy_type": strategy_type, + "mode": result.get("mode"), + "metrics": result.get("metrics", {}), + "success": result.get("success", False) + }) + + # Update strategy metrics + for metric, value in result.get("metrics", {}).items(): + self.strategy_metrics[f"{strategy_type}_{metric}"].append(value) + + # Maintain memory size + if len(self.performance_history) > self.memory_size: + self.performance_history = self.performance_history[-self.memory_size:] + + def _adapt_parameters(self, state: LearningState, result: Dict[str, Any]): + """Adapt learning parameters based on performance.""" + # Adapt learning rate + if "accuracy" in result.get("metrics", {}): + accuracy = result["metrics"]["accuracy"] + if accuracy > 0.8: + state.parameters["learning_rate"] *= 0.95 # Decrease if performing well + elif accuracy < 0.6: + state.parameters["learning_rate"] *= 1.05 # Increase if performing poorly + + # Adapt exploration rate + if "reward" in result.get("metrics", {}): + reward = result["metrics"]["reward"] + if reward > 0: + state.parameters["exploration_rate"] *= 0.95 # Decrease if getting rewards + else: + state.parameters["exploration_rate"] *= 1.05 # Increase if not getting rewards + + # Clip parameters to reasonable ranges + state.parameters["learning_rate"] = np.clip( + state.parameters["learning_rate"], 0.001, 0.5) + state.parameters["exploration_rate"] = np.clip( + state.parameters["exploration_rate"], 0.01, 0.5) + + def get_performance_metrics(self) -> Dict[str, Any]: + """Get comprehensive performance metrics.""" + return { + "learning_states": { + strategy_type: { + "mode": state.mode.value, + "parameters": state.parameters, + "metrics": state.metrics + } + for strategy_type, state in self.states.items() + }, + "strategy_performance": { + metric: { + "mean": np.mean(values) if values else 0.0, + "std": np.std(values) if values else 0.0, + "min": min(values) if values else 0.0, + "max": max(values) if values else 0.0 + } + for metric, values in self.strategy_metrics.items() + }, + "transfer_metrics": { + "total_transfers": len(self.transfer_history), + "success_rate": sum(1 for t in self.transfer_history if t.get("success", False)) / len(self.transfer_history) if self.transfer_history else 0 + } + } + + def clear_history(self): + """Clear learning history and reset states.""" + self.states.clear() + self.performance_history.clear() + self.strategy_metrics.clear() + self.transfer_history.clear() diff --git a/reasoning/local_llm.py b/reasoning/local_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..9718e68603d3cbde8177d77e58f9785d611d5cc1 --- /dev/null +++ b/reasoning/local_llm.py @@ -0,0 +1,117 @@ +"""Local LLM integration for the reasoning system.""" + +import os +from typing import Dict, Any, Optional +from datetime import datetime +import logging +from llama_cpp import Llama +import huggingface_hub +from .base import ReasoningStrategy +from .model_manager import ModelManager, ModelType + +class LocalLLMStrategy(ReasoningStrategy): + """Implements reasoning using local LLM.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the local LLM strategy.""" + super().__init__() + self.config = config or {} + + # Initialize model manager + self.model_manager = ModelManager(self.config.get('model_dir', "models")) + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + self.logger = logging.getLogger(__name__) + + async def initialize(self): + """Initialize all models.""" + await self.model_manager.initialize_all_models() + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate reasoning response using appropriate local LLM.""" + try: + # Determine best model for the task + task_type = context.get('task_type', 'general') + model_key = self.model_manager.get_best_model_for_task(task_type) + + # Get or initialize the model + model = await self.model_manager.get_model(model_key) + if not model: + raise Exception(f"Failed to initialize {model_key} model") + + # Format prompt with context + prompt = self._format_prompt(query, context) + + # Generate response + response = model( + prompt, + max_tokens=1024 if model.n_ctx >= 4096 else 512, + temperature=0.7, + top_p=0.95, + repeat_penalty=1.1, + echo=False + ) + + # Extract and structure the response + result = self._parse_response(response['choices'][0]['text']) + + return { + 'success': True, + 'answer': result['answer'], + 'reasoning': result['reasoning'], + 'confidence': result['confidence'], + 'timestamp': datetime.now(), + 'metadata': { + 'model': model_key, + 'strategy': 'local_llm', + 'context_length': len(prompt), + 'response_length': len(response['choices'][0]['text']) + } + } + + except Exception as e: + self.logger.error(f"Error in reasoning: {e}") + return { + 'success': False, + 'error': str(e), + 'timestamp': datetime.now() + } + + def _format_prompt(self, query: str, context: Dict[str, Any]) -> str: + """Format the prompt with query and context.""" + # Include relevant context + context_str = "\n".join([ + f"{k}: {v}" for k, v in context.items() + if k in ['objective', 'constraints', 'background'] + ]) + + return f"""Let's solve this problem step by step. + +Context: +{context_str} + +Question: {query} + +Let me break this down: +1.""" + + def _parse_response(self, text: str) -> Dict[str, Any]: + """Parse the response into structured output.""" + # Simple parsing for now + lines = text.strip().split('\n') + + return { + 'answer': lines[-1] if lines else '', + 'reasoning': '\n'.join(lines[:-1]) if len(lines) > 1 else '', + 'confidence': 0.8 # Default confidence + } diff --git a/reasoning/market_analysis.py b/reasoning/market_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..01a7ba5d0471e9b5c9fa9a5c731f53cef3902830 --- /dev/null +++ b/reasoning/market_analysis.py @@ -0,0 +1,450 @@ +"""Advanced market analysis tools for venture strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class MarketSegment: + """Market segment analysis.""" + size: float + growth_rate: float + cagr: float + competition: List[Dict[str, Any]] + barriers: List[str] + opportunities: List[str] + risks: List[str] + +@dataclass +class CompetitorAnalysis: + """Competitor analysis.""" + name: str + market_share: float + strengths: List[str] + weaknesses: List[str] + strategy: str + revenue: Optional[float] + valuation: Optional[float] + +@dataclass +class MarketTrend: + """Market trend analysis.""" + name: str + impact: float + timeline: str + adoption_rate: float + market_potential: float + risk_level: float + +class MarketAnalyzer: + """ + Advanced market analysis toolkit that: + 1. Analyzes market segments + 2. Tracks competitors + 3. Identifies trends + 4. Predicts opportunities + 5. Assesses risks + """ + + def __init__(self): + self.segments: Dict[str, MarketSegment] = {} + self.competitors: Dict[str, CompetitorAnalysis] = {} + self.trends: List[MarketTrend] = [] + + async def analyze_market(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Perform comprehensive market analysis.""" + try: + # Segment analysis + segment_analysis = await self._analyze_segment(segment, context) + + # Competitor analysis + competitor_analysis = await self._analyze_competitors(segment, context) + + # Trend analysis + trend_analysis = await self._analyze_trends(segment, context) + + # Opportunity analysis + opportunity_analysis = await self._analyze_opportunities( + segment_analysis, competitor_analysis, trend_analysis, context) + + # Risk analysis + risk_analysis = await self._analyze_risks( + segment_analysis, competitor_analysis, trend_analysis, context) + + return { + "success": True, + "segment_analysis": segment_analysis, + "competitor_analysis": competitor_analysis, + "trend_analysis": trend_analysis, + "opportunity_analysis": opportunity_analysis, + "risk_analysis": risk_analysis, + "metrics": { + "market_score": self._calculate_market_score(segment_analysis), + "opportunity_score": self._calculate_opportunity_score(opportunity_analysis), + "risk_score": self._calculate_risk_score(risk_analysis) + } + } + except Exception as e: + logging.error(f"Error in market analysis: {str(e)}") + return {"success": False, "error": str(e)} + + async def _analyze_segment(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market segment.""" + prompt = f""" + Analyze market segment: + Segment: {segment} + Context: {json.dumps(context)} + + Analyze: + 1. Market size and growth + 2. Customer segments + 3. Value chain + 4. Entry barriers + 5. Competitive dynamics + + Format as: + [Analysis] + Size: ... + Growth: ... + Segments: ... + Value_Chain: ... + Barriers: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_segment_analysis(response["answer"]) + + async def _analyze_competitors(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze competitors in segment.""" + prompt = f""" + Analyze competitors: + Segment: {segment} + Context: {json.dumps(context)} + + For each competitor analyze: + 1. Market share + 2. Business model + 3. Strengths/weaknesses + 4. Strategy + 5. Performance metrics + + Format as: + [Competitor1] + Share: ... + Model: ... + Strengths: ... + Weaknesses: ... + Strategy: ... + Metrics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_competitor_analysis(response["answer"]) + + async def _analyze_trends(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market trends.""" + prompt = f""" + Analyze market trends: + Segment: {segment} + Context: {json.dumps(context)} + + Analyze trends in: + 1. Technology + 2. Customer behavior + 3. Business models + 4. Regulation + 5. Market dynamics + + Format as: + [Trend1] + Type: ... + Impact: ... + Timeline: ... + Adoption: ... + Potential: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_trend_analysis(response["answer"]) + + async def _analyze_opportunities(self, + segment_analysis: Dict[str, Any], + competitor_analysis: Dict[str, Any], + trend_analysis: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market opportunities.""" + prompt = f""" + Analyze market opportunities: + Segment: {json.dumps(segment_analysis)} + Competitors: {json.dumps(competitor_analysis)} + Trends: {json.dumps(trend_analysis)} + Context: {json.dumps(context)} + + Identify opportunities in: + 1. Unmet needs + 2. Market gaps + 3. Innovation potential + 4. Scaling potential + 5. Value creation + + Format as: + [Opportunity1] + Type: ... + Description: ... + Potential: ... + Requirements: ... + Timeline: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_opportunity_analysis(response["answer"]) + + async def _analyze_risks(self, + segment_analysis: Dict[str, Any], + competitor_analysis: Dict[str, Any], + trend_analysis: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market risks.""" + prompt = f""" + Analyze market risks: + Segment: {json.dumps(segment_analysis)} + Competitors: {json.dumps(competitor_analysis)} + Trends: {json.dumps(trend_analysis)} + Context: {json.dumps(context)} + + Analyze risks in: + 1. Market dynamics + 2. Competition + 3. Technology + 4. Regulation + 5. Execution + + Format as: + [Risk1] + Type: ... + Description: ... + Impact: ... + Probability: ... + Mitigation: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_risk_analysis(response["answer"]) + + def _calculate_market_score(self, analysis: Dict[str, Any]) -> float: + """Calculate market attractiveness score.""" + weights = { + "size": 0.3, + "growth": 0.3, + "competition": 0.2, + "barriers": 0.1, + "dynamics": 0.1 + } + + scores = { + "size": min(analysis.get("size", 0) / 1e9, 1.0), # Normalize to 1B + "growth": min(analysis.get("growth", 0) / 30, 1.0), # Normalize to 30% + "competition": 1.0 - min(len(analysis.get("competitors", [])) / 10, 1.0), + "barriers": 1.0 - min(len(analysis.get("barriers", [])) / 5, 1.0), + "dynamics": analysis.get("dynamics_score", 0.5) + } + + return sum(weights[k] * scores[k] for k in weights) + + def _calculate_opportunity_score(self, analysis: Dict[str, Any]) -> float: + """Calculate opportunity attractiveness score.""" + weights = { + "market_potential": 0.3, + "innovation_potential": 0.2, + "execution_feasibility": 0.2, + "competitive_advantage": 0.2, + "timing": 0.1 + } + + scores = { + "market_potential": analysis.get("market_potential", 0.5), + "innovation_potential": analysis.get("innovation_potential", 0.5), + "execution_feasibility": analysis.get("execution_feasibility", 0.5), + "competitive_advantage": analysis.get("competitive_advantage", 0.5), + "timing": analysis.get("timing_score", 0.5) + } + + return sum(weights[k] * scores[k] for k in weights) + + def _calculate_risk_score(self, analysis: Dict[str, Any]) -> float: + """Calculate risk level score.""" + weights = { + "market_risk": 0.2, + "competition_risk": 0.2, + "technology_risk": 0.2, + "regulatory_risk": 0.2, + "execution_risk": 0.2 + } + + scores = { + "market_risk": analysis.get("market_risk", 0.5), + "competition_risk": analysis.get("competition_risk", 0.5), + "technology_risk": analysis.get("technology_risk", 0.5), + "regulatory_risk": analysis.get("regulatory_risk", 0.5), + "execution_risk": analysis.get("execution_risk", 0.5) + } + + return sum(weights[k] * scores[k] for k in weights) + + def get_market_insights(self) -> Dict[str, Any]: + """Get comprehensive market insights.""" + return { + "segment_insights": { + segment: { + "size": s.size, + "growth_rate": s.growth_rate, + "cagr": s.cagr, + "opportunity_score": self._calculate_market_score({ + "size": s.size, + "growth": s.growth_rate, + "competitors": s.competition, + "barriers": s.barriers + }) + } + for segment, s in self.segments.items() + }, + "competitor_insights": { + competitor: { + "market_share": c.market_share, + "strength_score": len(c.strengths) / (len(c.strengths) + len(c.weaknesses)), + "revenue": c.revenue, + "valuation": c.valuation + } + for competitor, c in self.competitors.items() + }, + "trend_insights": [ + { + "name": t.name, + "impact": t.impact, + "potential": t.market_potential, + "risk": t.risk_level + } + for t in self.trends + ] + } + +class MarketAnalysisStrategy(ReasoningStrategy): + """ + Advanced market analysis strategy that combines multiple analytical tools + to provide comprehensive market insights. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize market analysis strategy.""" + super().__init__() + self.config = config or {} + self.analyzer = MarketAnalyzer() + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Perform market analysis based on query and context. + + Args: + query: The market analysis query + context: Additional context and parameters + + Returns: + Dict containing market analysis results and confidence scores + """ + try: + # Extract market segment from query/context + segment = self._extract_segment(query, context) + + # Perform market analysis + analysis = await self._analyze_market(segment, context) + + # Get insights + insights = self.analyzer.get_market_insights() + + # Calculate confidence based on data quality and completeness + confidence = self._calculate_confidence(analysis, insights) + + return { + 'answer': self._format_insights(insights), + 'confidence': confidence, + 'analysis': analysis, + 'insights': insights, + 'segment': segment + } + + except Exception as e: + logging.error(f"Market analysis failed: {str(e)}") + return { + 'error': f"Market analysis failed: {str(e)}", + 'confidence': 0.0 + } + + def _extract_segment(self, query: str, context: Dict[str, Any]) -> str: + """Extract market segment from query and context.""" + # Use context if available + if 'segment' in context: + return context['segment'] + + # Default to general market + return 'general' + + async def _analyze_market(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Perform comprehensive market analysis.""" + return await self.analyzer.analyze_market(segment, context) + + def _calculate_confidence(self, analysis: Dict[str, Any], insights: Dict[str, Any]) -> float: + """Calculate confidence score based on analysis quality.""" + # Base confidence + confidence = 0.5 + + # Adjust based on data completeness + if analysis.get('segment_analysis'): + confidence += 0.1 + if analysis.get('competitor_analysis'): + confidence += 0.1 + if analysis.get('trend_analysis'): + confidence += 0.1 + + # Adjust based on insight quality + if insights.get('opportunities'): + confidence += 0.1 + if insights.get('risks'): + confidence += 0.1 + + return min(confidence, 1.0) + + def _format_insights(self, insights: Dict[str, Any]) -> str: + """Format market insights into readable text.""" + sections = [] + + if 'market_overview' in insights: + sections.append(f"Market Overview: {insights['market_overview']}") + + if 'opportunities' in insights: + opps = insights['opportunities'] + sections.append("Key Opportunities:\n- " + "\n- ".join(opps)) + + if 'risks' in insights: + risks = insights['risks'] + sections.append("Key Risks:\n- " + "\n- ".join(risks)) + + if 'recommendations' in insights: + recs = insights['recommendations'] + sections.append("Recommendations:\n- " + "\n- ".join(recs)) + + return "\n\n".join(sections) diff --git a/reasoning/meta_learning.py b/reasoning/meta_learning.py new file mode 100644 index 0000000000000000000000000000000000000000..8a5a8e0fcfc1dd6f6cda2d9205e444cabd0f595e --- /dev/null +++ b/reasoning/meta_learning.py @@ -0,0 +1,339 @@ +"""Advanced meta-learning strategy for adaptive reasoning.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class MetaTask: + """Meta-learning task with parameters and performance metrics.""" + name: str + parameters: Dict[str, Any] + metrics: Dict[str, float] + history: List[Dict[str, Any]] = field(default_factory=list) + +class MetaLearningStrategy(ReasoningStrategy): + """ + Advanced meta-learning strategy that: + 1. Adapts to new tasks + 2. Learns from experience + 3. Optimizes parameters + 4. Transfers knowledge + 5. Improves over time + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize meta-learning strategy.""" + super().__init__() + self.config = config or {} + + # Configure parameters + self.learning_rate = self.config.get('learning_rate', 0.01) + self.memory_size = self.config.get('memory_size', 100) + self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7) + + # Initialize task memory + self.task_memory: List[MetaTask] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply meta-learning to adapt and optimize reasoning. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Identify similar tasks + similar_tasks = await self._find_similar_tasks(query, context) + + # Adapt parameters + adapted_params = await self._adapt_parameters(similar_tasks, context) + + # Apply meta-learning + results = await self._apply_meta_learning( + query, + adapted_params, + context + ) + + # Update memory + await self._update_memory(query, results, context) + + # Generate analysis + analysis = await self._generate_analysis(results, context) + + return { + 'answer': self._format_analysis(analysis), + 'confidence': self._calculate_confidence(results), + 'similar_tasks': similar_tasks, + 'adapted_params': adapted_params, + 'results': results, + 'analysis': analysis + } + + except Exception as e: + logging.error(f"Meta-learning failed: {str(e)}") + return { + 'error': f"Meta-learning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _find_similar_tasks( + self, + query: str, + context: Dict[str, Any] + ) -> List[MetaTask]: + """Find similar tasks in memory.""" + similar_tasks = [] + + # Extract query features + query_features = self._extract_features(query) + + for task in self.task_memory: + # Calculate similarity + similarity = self._calculate_similarity( + query_features, + self._extract_features(task.name) + ) + + if similarity > self.adaptation_threshold: + similar_tasks.append(task) + + # Sort by similarity + similar_tasks.sort( + key=lambda x: np.mean(list(x.metrics.values())), + reverse=True + ) + + return similar_tasks + + def _extract_features(self, text: str) -> np.ndarray: + """Extract features from text.""" + # Simple bag of words for now + words = set(text.lower().split()) + return np.array([hash(word) % 100 for word in words]) + + def _calculate_similarity( + self, + features1: np.ndarray, + features2: np.ndarray + ) -> float: + """Calculate similarity between feature sets.""" + # Simple Jaccard similarity + intersection = np.intersect1d(features1, features2) + union = np.union1d(features1, features2) + + return len(intersection) / len(union) if len(union) > 0 else 0 + + async def _adapt_parameters( + self, + similar_tasks: List[MetaTask], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Adapt parameters based on similar tasks.""" + if not similar_tasks: + return self.config.copy() + + adapted_params = {} + + # Weight tasks by performance + total_performance = sum( + np.mean(list(task.metrics.values())) + for task in similar_tasks + ) + + if total_performance > 0: + # Weighted average of parameters + for param_name in self.config: + adapted_params[param_name] = sum( + task.parameters.get(param_name, self.config[param_name]) * + (np.mean(list(task.metrics.values())) / total_performance) + for task in similar_tasks + ) + else: + adapted_params = self.config.copy() + + return adapted_params + + async def _apply_meta_learning( + self, + query: str, + parameters: Dict[str, Any], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply meta-learning with adapted parameters.""" + results = { + 'query': query, + 'parameters': parameters, + 'metrics': {} + } + + # Apply learning rate + for param_name, value in parameters.items(): + if isinstance(value, (int, float)): + results['parameters'][param_name] = ( + value * (1 - self.learning_rate) + + self.config[param_name] * self.learning_rate + ) + + # Calculate performance metrics + results['metrics'] = { + 'adaptation_score': np.mean([ + p / self.config[name] + for name, p in results['parameters'].items() + if isinstance(p, (int, float)) and self.config[name] != 0 + ]), + 'novelty_score': 1 - max( + self._calculate_similarity( + self._extract_features(query), + self._extract_features(task.name) + ) + for task in self.task_memory + ) if self.task_memory else 1.0 + } + + return results + + async def _update_memory( + self, + query: str, + results: Dict[str, Any], + context: Dict[str, Any] + ) -> None: + """Update task memory.""" + # Create new task + task = MetaTask( + name=query, + parameters=results['parameters'], + metrics=results['metrics'], + history=[{ + 'timestamp': datetime.now().isoformat(), + 'context': context, + 'results': results + }] + ) + + # Add to memory + self.task_memory.append(task) + + # Maintain memory size + if len(self.task_memory) > self.memory_size: + # Remove worst performing task + self.task_memory.sort( + key=lambda x: np.mean(list(x.metrics.values())) + ) + self.task_memory.pop(0) + + async def _generate_analysis( + self, + results: Dict[str, Any], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate meta-learning analysis.""" + # Calculate statistics + param_stats = { + name: { + 'value': value, + 'adaptation': value / self.config[name] + if isinstance(value, (int, float)) and self.config[name] != 0 + else 1.0 + } + for name, value in results['parameters'].items() + } + + # Calculate overall metrics + metrics = { + 'adaptation_score': results['metrics']['adaptation_score'], + 'novelty_score': results['metrics']['novelty_score'], + 'memory_usage': len(self.task_memory) / self.memory_size + } + + return { + 'parameters': param_stats, + 'metrics': metrics, + 'memory_size': len(self.task_memory), + 'total_tasks_seen': len(self.task_memory) + } + + def _format_analysis(self, analysis: Dict[str, Any]) -> str: + """Format analysis into readable text.""" + sections = [] + + # Parameter adaptations + sections.append("Parameter adaptations:") + for name, stats in analysis['parameters'].items(): + sections.append( + f"- {name}: {stats['value']:.2f} " + f"({stats['adaptation']:.1%} of original)" + ) + + # Performance metrics + sections.append("\nPerformance metrics:") + metrics = analysis['metrics'] + sections.append(f"- Adaptation score: {metrics['adaptation_score']:.1%}") + sections.append(f"- Novelty score: {metrics['novelty_score']:.1%}") + sections.append(f"- Memory usage: {metrics['memory_usage']:.1%}") + + # Memory statistics + sections.append("\nMemory statistics:") + sections.append(f"- Current tasks in memory: {analysis['memory_size']}") + sections.append(f"- Total tasks seen: {analysis['total_tasks_seen']}") + + return "\n".join(sections) + + def _calculate_confidence(self, results: Dict[str, Any]) -> float: + """Calculate overall confidence score.""" + if not results.get('metrics'): + return 0.0 + + # Base confidence + confidence = 0.5 + + # Adjust based on adaptation score + adaptation_score = results['metrics']['adaptation_score'] + if adaptation_score > 0.8: + confidence += 0.3 + elif adaptation_score > 0.6: + confidence += 0.2 + elif adaptation_score > 0.4: + confidence += 0.1 + + # Adjust based on novelty + novelty_score = results['metrics']['novelty_score'] + if novelty_score < 0.2: # Very similar to known tasks + confidence += 0.2 + elif novelty_score < 0.4: + confidence += 0.1 + + return min(confidence, 1.0) + + def get_performance_metrics(self) -> Dict[str, Any]: + """Get current performance metrics.""" + return { + "success_rate": 0.0, + "adaptation_rate": 0.0, + "exploration_count": 0, + "episode_count": len(self.task_memory), + "pattern_count": 0, + "learning_rate": self.learning_rate, + "exploration_rate": 0.0 + } + + def get_top_patterns(self, n: int = 10) -> List[Tuple[str, float]]: + """Get top performing patterns.""" + return [] + + def clear_memory(self): + """Clear learning memory.""" + self.task_memory.clear() diff --git a/reasoning/model_manager.py b/reasoning/model_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..3bcf2f0459b05e757c366ec62b627e0d92eb2934 --- /dev/null +++ b/reasoning/model_manager.py @@ -0,0 +1,145 @@ +"""Model manager for handling multiple LLMs in Hugging Face Spaces.""" + +import os +from typing import Dict, Any, Optional, List +import logging +from dataclasses import dataclass +from enum import Enum +import huggingface_hub +from llama_cpp import Llama + +class ModelType(Enum): + """Types of models and their specific tasks.""" + REASONING = "reasoning" + CODE = "code" + CHAT = "chat" + PLANNING = "planning" + ANALYSIS = "analysis" + +@dataclass +class ModelConfig: + """Configuration for a specific model.""" + repo_id: str + filename: str + model_type: ModelType + context_size: int = 4096 + gpu_layers: int = 35 + batch_size: int = 512 + threads: int = 8 + +class ModelManager: + """Manages multiple LLM models for different tasks in Spaces.""" + + def __init__(self): + # In Spaces, models are stored in the cache directory + self.model_dir = os.getenv('SPACE_CACHE_DIR', '/tmp/models') + self.models: Dict[str, Llama] = {} + self.logger = logging.getLogger(__name__) + + # Define model configurations + self.model_configs = { + "reasoning": ModelConfig( + repo_id="rrbale/pruned-qwen-moe", + filename="model-Q6_K.gguf", + model_type=ModelType.REASONING + ), + "code": ModelConfig( + repo_id="YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF", + filename="model.gguf", + model_type=ModelType.CODE + ), + "chat": ModelConfig( + repo_id="Nidum-Llama-3.2-3B-Uncensored-GGUF", + filename="model-Q6_K.gguf", + model_type=ModelType.CHAT + ), + "planning": ModelConfig( + repo_id="deepseek-ai/JanusFlow-1.3B", + filename="model.gguf", + model_type=ModelType.PLANNING + ), + "analysis": ModelConfig( + repo_id="prithivMLmods/QwQ-4B-Instruct", + filename="model.gguf", + model_type=ModelType.ANALYSIS, + context_size=8192, + gpu_layers=40 + ), + "general": ModelConfig( + repo_id="gpt-omni/mini-omni2", + filename="mini-omni2.gguf", + model_type=ModelType.CHAT + ) + } + + os.makedirs(self.model_dir, exist_ok=True) + + async def initialize_model(self, model_key: str) -> Optional[Llama]: + """Initialize a specific model in Spaces.""" + try: + config = self.model_configs[model_key] + cache_dir = os.path.join(self.model_dir, model_key) + os.makedirs(cache_dir, exist_ok=True) + + # Download model using HF Hub + self.logger.info(f"Downloading {model_key} model...") + model_path = huggingface_hub.hf_hub_download( + repo_id=config.repo_id, + filename=config.filename, + repo_type="model", + cache_dir=cache_dir, + local_dir_use_symlinks=False + ) + + # Configure for Spaces GPU environment + try: + model = Llama( + model_path=model_path, + n_ctx=config.context_size, + n_batch=config.batch_size, + n_threads=config.threads, + n_gpu_layers=config.gpu_layers, + main_gpu=0, + tensor_split=None # Let it use all available GPU memory + ) + self.logger.info(f"{model_key} model loaded with GPU acceleration!") + except Exception as e: + self.logger.warning(f"GPU loading failed for {model_key}: {e}, falling back to CPU...") + model = Llama( + model_path=model_path, + n_ctx=2048, + n_batch=256, + n_threads=4, + n_gpu_layers=0 + ) + self.logger.info(f"{model_key} model loaded in CPU-only mode") + + self.models[model_key] = model + return model + + except Exception as e: + self.logger.error(f"Error initializing {model_key} model: {e}") + return None + + async def get_model(self, model_key: str) -> Optional[Llama]: + """Get a model, initializing it if necessary.""" + if model_key not in self.models: + return await self.initialize_model(model_key) + return self.models[model_key] + + async def initialize_all_models(self): + """Initialize all configured models.""" + for model_key in self.model_configs.keys(): + await self.initialize_model(model_key) + + def get_best_model_for_task(self, task_type: str) -> str: + """Get the best model key for a specific task type.""" + task_model_mapping = { + "reasoning": "reasoning", + "code": "code", + "chat": "chat", + "planning": "planning", + "analysis": "analysis", + "general": "general" + } + return task_model_mapping.get(task_type, "general") diff --git a/reasoning/monetization.py b/reasoning/monetization.py new file mode 100644 index 0000000000000000000000000000000000000000..abde3dead8654dcf6ee90b4fa119235bba7eb414 --- /dev/null +++ b/reasoning/monetization.py @@ -0,0 +1,447 @@ +"""Advanced monetization strategies for venture optimization.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class MonetizationModel: + """Monetization model configuration.""" + name: str + type: str + pricing_tiers: List[Dict[str, Any]] + features: List[str] + constraints: List[str] + metrics: Dict[str, float] + +@dataclass +class RevenueStream: + """Revenue stream configuration.""" + name: str + type: str + volume: float + unit_economics: Dict[str, float] + growth_rate: float + churn_rate: float + +class MonetizationOptimizer: + """ + Advanced monetization optimization that: + 1. Designs pricing models + 2. Optimizes revenue streams + 3. Maximizes customer value + 4. Reduces churn + 5. Increases lifetime value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize monetization optimizer.""" + self.config = config or {} + + # Configure optimization parameters + self.min_revenue = self.config.get('min_revenue', 1_000_000) + self.min_margin = self.config.get('min_margin', 0.3) + self.max_churn = self.config.get('max_churn', 0.1) + self.target_ltv = self.config.get('target_ltv', 1000) + + self.models: Dict[str, MonetizationModel] = {} + self.streams: Dict[str, RevenueStream] = {} + + async def optimize_monetization(self, + venture_type: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize monetization strategy.""" + try: + # Design models + models = await self._design_models(venture_type, context) + + # Optimize pricing + pricing = await self._optimize_pricing(models, context) + + # Revenue optimization + revenue = await self._optimize_revenue(pricing, context) + + # Value optimization + value = await self._optimize_value(revenue, context) + + # Performance projections + projections = await self._project_performance(value, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "models": models, + "pricing": pricing, + "revenue": revenue, + "value": value, + "projections": projections + } + except Exception as e: + logging.error(f"Error in monetization optimization: {str(e)}") + return {"success": False, "error": str(e)} + + async def _design_models(self, + venture_type: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Design monetization models.""" + prompt = f""" + Design monetization models: + Venture: {venture_type} + Context: {json.dumps(context)} + + Design models for: + 1. Subscription tiers + 2. Usage-based pricing + 3. Hybrid models + 4. Enterprise pricing + 5. Marketplace fees + + Format as: + [Model1] + Name: ... + Type: ... + Tiers: ... + Features: ... + Constraints: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_model_design(response["answer"]) + + async def _optimize_pricing(self, + models: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize pricing strategy.""" + prompt = f""" + Optimize pricing strategy: + Models: {json.dumps(models)} + Context: {json.dumps(context)} + + Optimize for: + 1. Market positioning + 2. Value perception + 3. Competitive dynamics + 4. Customer segments + 5. Growth potential + + Format as: + [Strategy1] + Model: ... + Positioning: ... + Value_Props: ... + Segments: ... + Growth: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_pricing_strategy(response["answer"]) + + async def _optimize_revenue(self, + pricing: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize revenue streams.""" + prompt = f""" + Optimize revenue streams: + Pricing: {json.dumps(pricing)} + Context: {json.dumps(context)} + + Optimize for: + 1. Revenue mix + 2. Growth drivers + 3. Retention factors + 4. Expansion potential + 5. Risk mitigation + + Format as: + [Stream1] + Type: ... + Drivers: ... + Retention: ... + Expansion: ... + Risks: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_revenue_optimization(response["answer"]) + + async def _optimize_value(self, + revenue: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize customer value.""" + prompt = f""" + Optimize customer value: + Revenue: {json.dumps(revenue)} + Context: {json.dumps(context)} + + Optimize for: + 1. Acquisition cost + 2. Lifetime value + 3. Churn reduction + 4. Upsell potential + 5. Network effects + + Format as: + [Value1] + Metric: ... + Strategy: ... + Potential: ... + Actions: ... + Timeline: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_value_optimization(response["answer"]) + + async def _project_performance(self, + value: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Project monetization performance.""" + prompt = f""" + Project performance: + Value: {json.dumps(value)} + Context: {json.dumps(context)} + + Project: + 1. Revenue growth + 2. Customer metrics + 3. Unit economics + 4. Profitability + 5. Scale effects + + Format as: + [Projections] + Revenue: ... + Metrics: ... + Economics: ... + Profit: ... + Scale: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_performance_projections(response["answer"]) + + def _calculate_revenue_potential(self, model: MonetizationModel) -> float: + """Calculate revenue potential for model.""" + base_potential = sum( + tier.get("price", 0) * tier.get("volume", 0) + for tier in model.pricing_tiers + ) + + growth_factor = 1.0 + (model.metrics.get("growth_rate", 0) / 100) + retention_factor = 1.0 - (model.metrics.get("churn_rate", 0) / 100) + + return base_potential * growth_factor * retention_factor + + def _calculate_customer_ltv(self, stream: RevenueStream) -> float: + """Calculate customer lifetime value.""" + monthly_revenue = stream.volume * stream.unit_economics.get("arpu", 0) + churn_rate = stream.churn_rate / 100 + discount_rate = 0.1 # 10% annual discount rate + + if churn_rate > 0: + ltv = monthly_revenue / churn_rate + else: + ltv = monthly_revenue * 12 # Assume 1 year if no churn + + return ltv / (1 + discount_rate) + + def get_monetization_metrics(self) -> Dict[str, Any]: + """Get comprehensive monetization metrics.""" + return { + "model_metrics": { + model.name: { + "revenue_potential": self._calculate_revenue_potential(model), + "tier_count": len(model.pricing_tiers), + "feature_count": len(model.features), + "constraint_count": len(model.constraints) + } + for model in self.models.values() + }, + "stream_metrics": { + stream.name: { + "monthly_revenue": stream.volume * stream.unit_economics.get("arpu", 0), + "ltv": self._calculate_customer_ltv(stream), + "growth_rate": stream.growth_rate, + "churn_rate": stream.churn_rate + } + for stream in self.streams.values() + }, + "aggregate_metrics": { + "total_revenue_potential": sum( + self._calculate_revenue_potential(model) + for model in self.models.values() + ), + "average_ltv": np.mean([ + self._calculate_customer_ltv(stream) + for stream in self.streams.values() + ]) if self.streams else 0, + "weighted_growth_rate": np.average( + [stream.growth_rate for stream in self.streams.values()], + weights=[stream.volume for stream in self.streams.values()] + ) if self.streams else 0 + } + } + +class MonetizationStrategy(ReasoningStrategy): + """ + Advanced monetization strategy that: + 1. Designs optimal pricing models + 2. Optimizes revenue streams + 3. Maximizes customer lifetime value + 4. Reduces churn + 5. Increases profitability + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize monetization strategy.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize optimizer with shared config + optimizer_config = { + 'min_revenue': self.config.get('min_revenue', 1_000_000), + 'min_margin': self.config.get('min_margin', 0.3), + 'max_churn': self.config.get('max_churn', 0.1), + 'target_ltv': self.config.get('target_ltv', 1000) + } + self.optimizer = MonetizationOptimizer(optimizer_config) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Generate monetization strategy based on query and context. + + Args: + query: The monetization query + context: Additional context and parameters + + Returns: + Dict containing monetization strategy and confidence scores + """ + try: + # Extract venture type + venture_type = self._extract_venture_type(query, context) + + # Optimize monetization + optimization_result = await self.optimizer.optimize_monetization( + venture_type=venture_type, + context=context + ) + + # Format results + formatted_result = self._format_strategy(optimization_result) + + return { + 'answer': formatted_result, + 'confidence': self._calculate_confidence(optimization_result), + 'optimization': optimization_result + } + + except Exception as e: + logging.error(f"Monetization strategy generation failed: {str(e)}") + return { + 'error': f"Monetization strategy generation failed: {str(e)}", + 'confidence': 0.0 + } + + def _extract_venture_type(self, query: str, context: Dict[str, Any]) -> str: + """Extract venture type from query and context.""" + # Use context if available + if 'venture_type' in context: + return context['venture_type'] + + # Simple keyword matching + query_lower = query.lower() + if any(term in query_lower for term in ['ai', 'ml', 'model']): + return 'ai_startup' + elif any(term in query_lower for term in ['saas', 'software']): + return 'saas' + elif any(term in query_lower for term in ['api', 'service']): + return 'api_service' + elif any(term in query_lower for term in ['data', 'analytics']): + return 'data_analytics' + + # Default to SaaS if unclear + return 'saas' + + def _calculate_confidence(self, result: Dict[str, Any]) -> float: + """Calculate confidence score based on optimization quality.""" + # Base confidence + confidence = 0.5 + + # Adjust based on optimization completeness + if result.get('models'): + confidence += 0.1 + if result.get('pricing'): + confidence += 0.1 + if result.get('revenue'): + confidence += 0.1 + if result.get('value'): + confidence += 0.1 + + # Adjust based on projected performance + performance = result.get('performance', {}) + if performance.get('roi', 0) > 2.0: + confidence += 0.1 + if performance.get('ltv', 0) > 1000: + confidence += 0.1 + + return min(confidence, 1.0) + + def _format_strategy(self, result: Dict[str, Any]) -> str: + """Format monetization strategy into readable text.""" + sections = [] + + # Monetization models + if 'models' in result: + models = result['models'] + sections.append("Monetization Models:") + for model in models: + sections.append(f"- {model['name']}: {model['type']}") + if 'pricing_tiers' in model: + sections.append(" Pricing Tiers:") + for tier in model['pricing_tiers']: + sections.append(f" * {tier['name']}: ${tier['price']}/mo") + + # Revenue optimization + if 'revenue' in result: + revenue = result['revenue'] + sections.append("\nRevenue Optimization:") + for stream, details in revenue.items(): + sections.append(f"- {stream.replace('_', ' ').title()}:") + sections.append(f" * Projected Revenue: ${details['projected_revenue']:,.2f}") + sections.append(f" * Growth Rate: {details['growth_rate']*100:.1f}%") + + # Customer value optimization + if 'value' in result: + value = result['value'] + sections.append("\nCustomer Value Optimization:") + sections.append(f"- Customer Acquisition Cost: ${value['cac']:,.2f}") + sections.append(f"- Lifetime Value: ${value['ltv']:,.2f}") + sections.append(f"- Churn Rate: {value['churn_rate']*100:.1f}%") + + # Performance projections + if 'performance' in result: + perf = result['performance'] + sections.append("\nPerformance Projections:") + sections.append(f"- ROI: {perf['roi']*100:.1f}%") + sections.append(f"- Payback Period: {perf['payback_months']:.1f} months") + sections.append(f"- Break-even Point: ${perf['breakeven']:,.2f}") + + return "\n".join(sections) diff --git a/reasoning/multimodal.py b/reasoning/multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..cc7d38b3c9a09f5f0fab5404158a17a0f1bf3d8d --- /dev/null +++ b/reasoning/multimodal.py @@ -0,0 +1,305 @@ +"""Advanced multimodal reasoning combining different types of information.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class ModalityFeatures: + """Features extracted from different modalities.""" + text: List[Dict[str, Any]] + image: Optional[List[Dict[str, Any]]] = None + audio: Optional[List[Dict[str, Any]]] = None + video: Optional[List[Dict[str, Any]]] = None + structured: Optional[List[Dict[str, Any]]] = None + +class MultiModalReasoning(ReasoningStrategy): + """ + Advanced multimodal reasoning that: + 1. Processes different types of information + 2. Aligns cross-modal features + 3. Integrates multimodal context + 4. Generates coherent responses + 5. Handles uncertainty + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize multimodal reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Configure model repositories + self.models = self.config.get('models', { + 'img2img': { + 'repo_id': 'enhanceaiteam/Flux-Uncensored-V2', + 'filename': 'Flux-Uncensored-V2.safetensors' + }, + 'img2vid': { + 'repo_id': 'stabilityai/stable-video-diffusion-img2vid-xt', + 'filename': 'svd_xt.safetensors' + }, + 'any2any': { + 'repo_id': 'deepseek-ai/JanusFlow-1.3B', + 'filename': 'janusflow-1.3b.safetensors' + } + }) + + # Configure modality weights + self.weights = self.config.get('modality_weights', { + 'text': 0.4, + 'image': 0.3, + 'audio': 0.1, + 'video': 0.1, + 'structured': 0.1 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply multimodal reasoning to process and integrate different types of information. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Process across modalities + modalities = await self._process_modalities(query, context) + + # Align cross-modal information + alignment = await self._cross_modal_alignment(modalities, context) + + # Integrate aligned information + integration = await self._integrated_analysis(alignment, context) + + # Generate final response + response = await self._generate_response(integration, context) + + return { + 'answer': response.get('text', ''), + 'confidence': self._calculate_confidence(integration), + 'modalities': modalities, + 'alignment': alignment, + 'integration': integration + } + + except Exception as e: + logging.error(f"Multimodal reasoning failed: {str(e)}") + return { + 'error': f"Multimodal reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _process_modalities( + self, + query: str, + context: Dict[str, Any] + ) -> Dict[str, List[Dict[str, Any]]]: + """Process query across different modalities.""" + modalities = {} + + # Process text + if 'text' in context: + modalities['text'] = self._process_text(context['text']) + + # Process images + if 'images' in context: + modalities['image'] = self._process_images(context['images']) + + # Process audio + if 'audio' in context: + modalities['audio'] = self._process_audio(context['audio']) + + # Process video + if 'video' in context: + modalities['video'] = self._process_video(context['video']) + + # Process structured data + if 'structured' in context: + modalities['structured'] = self._process_structured(context['structured']) + + return modalities + + async def _cross_modal_alignment( + self, + modalities: Dict[str, List[Dict[str, Any]]], + context: Dict[str, Any] + ) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + alignments = [] + + # Get all modality pairs + modality_pairs = [ + (m1, m2) for i, m1 in enumerate(modalities.keys()) + for m2 in list(modalities.keys())[i+1:] + ] + + # Align each pair + for mod1, mod2 in modality_pairs: + items1 = modalities[mod1] + items2 = modalities[mod2] + + # Calculate cross-modal similarities + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > 0.7: # Alignment threshold + alignments.append({ + 'modality1': mod1, + 'modality2': mod2, + 'item1': item1, + 'item2': item2, + 'similarity': similarity + }) + + return alignments + + def _calculate_similarity( + self, + item1: Dict[str, Any], + item2: Dict[str, Any] + ) -> float: + """Calculate similarity between two items from different modalities.""" + # Simple feature overlap for now + features1 = set(str(v) for v in item1.values()) + features2 = set(str(v) for v in item2.values()) + + if not features1 or not features2: + return 0.0 + + overlap = len(features1.intersection(features2)) + total = len(features1.union(features2)) + + return overlap / total if total > 0 else 0.0 + + async def _integrated_analysis( + self, + alignment: List[Dict[str, Any]], + context: Dict[str, Any] + ) -> List[Dict[str, Any]]: + """Perform integrated analysis of aligned information.""" + integrated = [] + + # Group alignments by similarity + similarity_groups = defaultdict(list) + for align in alignment: + similarity_groups[align['similarity']].append(align) + + # Process groups in order of similarity + for similarity, group in sorted( + similarity_groups.items(), + key=lambda x: x[0], + reverse=True + ): + # Combine aligned features + for align in group: + integrated.append({ + 'features': { + **align['item1'], + **align['item2'] + }, + 'modalities': [align['modality1'], align['modality2']], + 'confidence': align['similarity'] + }) + + return integrated + + async def _generate_response( + self, + integration: List[Dict[str, Any]], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate coherent response from integrated analysis.""" + if not integration: + return {'text': '', 'confidence': 0.0} + + # Combine all integrated features + all_features = {} + for item in integration: + all_features.update(item['features']) + + # Generate response text + response_text = [] + + # Add main findings + response_text.append("Main findings across modalities:") + for feature, value in all_features.items(): + response_text.append(f"- {feature}: {value}") + + # Add confidence + confidence = sum(item['confidence'] for item in integration) / len(integration) + response_text.append(f"\nOverall confidence: {confidence:.2f}") + + return { + 'text': "\n".join(response_text), + 'confidence': confidence + } + + def _calculate_confidence(self, integration: List[Dict[str, Any]]) -> float: + """Calculate overall confidence score.""" + if not integration: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Adjust based on number of modalities + unique_modalities = set() + for item in integration: + unique_modalities.update(item['modalities']) + + modality_bonus = len(unique_modalities) * 0.1 + confidence += min(modality_bonus, 0.3) + + # Adjust based on integration quality + avg_similarity = sum( + item['confidence'] for item in integration + ) / len(integration) + confidence += avg_similarity * 0.2 + + return min(confidence, 1.0) + + def _process_text(self, text: str) -> List[Dict[str, Any]]: + """Process text modality.""" + # Simple text processing for now + return [{'text': text}] + + def _process_images(self, images: List[str]) -> List[Dict[str, Any]]: + """Process image modality.""" + # Simple image processing for now + return [{'image': image} for image in images] + + def _process_audio(self, audio: List[str]) -> List[Dict[str, Any]]: + """Process audio modality.""" + # Simple audio processing for now + return [{'audio': audio_file} for audio_file in audio] + + def _process_video(self, video: List[str]) -> List[Dict[str, Any]]: + """Process video modality.""" + # Simple video processing for now + return [{'video': video_file} for video_file in video] + + def _process_structured(self, structured: Dict[str, Any]) -> List[Dict[str, Any]]: + """Process structured data modality.""" + # Simple structured data processing for now + return [{'structured': structured}] diff --git a/reasoning/neurosymbolic.py b/reasoning/neurosymbolic.py new file mode 100644 index 0000000000000000000000000000000000000000..3d9c3efe88946937dd574cd937fef24fe28b8ca6 --- /dev/null +++ b/reasoning/neurosymbolic.py @@ -0,0 +1,316 @@ +"""Advanced neurosymbolic reasoning combining neural and symbolic approaches.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class NeuralFeature: + """Neural features extracted from data.""" + name: str + values: np.ndarray + importance: float + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class SymbolicRule: + """Symbolic rule with conditions and confidence.""" + name: str + conditions: List[str] + conclusion: str + confidence: float + metadata: Dict[str, Any] = field(default_factory=dict) + +class NeurosymbolicReasoning(ReasoningStrategy): + """ + Advanced neurosymbolic reasoning that: + 1. Extracts neural features + 2. Generates symbolic rules + 3. Combines approaches + 4. Handles uncertainty + 5. Provides interpretable results + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize neurosymbolic reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Neurosymbolic specific parameters + self.feature_threshold = self.config.get('feature_threshold', 0.1) + self.rule_confidence_threshold = self.config.get('rule_confidence', 0.7) + self.max_rules = self.config.get('max_rules', 10) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply neurosymbolic reasoning to combine neural and symbolic approaches. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Extract neural features + features = await self._extract_features(query, context) + + # Generate symbolic rules + rules = await self._generate_rules(features, context) + + # Combine approaches + combined = await self._combine_approaches(features, rules, context) + + # Generate analysis + analysis = await self._generate_analysis(combined, context) + + return { + 'answer': self._format_analysis(analysis), + 'confidence': self._calculate_confidence(combined), + 'features': features, + 'rules': rules, + 'combined': combined, + 'analysis': analysis + } + + except Exception as e: + logging.error(f"Neurosymbolic reasoning failed: {str(e)}") + return { + 'error': f"Neurosymbolic reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _extract_features( + self, + query: str, + context: Dict[str, Any] + ) -> List[NeuralFeature]: + """Extract neural features from input.""" + features = [] + + # Extract key terms + terms = query.lower().split() + + # Process each term + for term in terms: + # Simple feature extraction for now + values = np.random.randn(10) # Placeholder for real feature extraction + importance = np.abs(values).mean() + + if importance > self.feature_threshold: + features.append(NeuralFeature( + name=term, + values=values, + importance=importance, + metadata={'source': 'term_extraction'} + )) + + # Sort by importance + features.sort(key=lambda x: x.importance, reverse=True) + + return features + + async def _generate_rules( + self, + features: List[NeuralFeature], + context: Dict[str, Any] + ) -> List[SymbolicRule]: + """Generate symbolic rules from features.""" + rules = [] + + # Process feature combinations + for i, feature1 in enumerate(features): + for j, feature2 in enumerate(features[i+1:], i+1): + # Calculate correlation + correlation = np.corrcoef(feature1.values, feature2.values)[0, 1] + + if abs(correlation) > self.rule_confidence_threshold: + # Create rule based on correlation + if correlation > 0: + condition = f"{feature1.name} AND {feature2.name}" + conclusion = "positively_correlated" + else: + condition = f"{feature1.name} XOR {feature2.name}" + conclusion = "negatively_correlated" + + rules.append(SymbolicRule( + name=f"rule_{len(rules)}", + conditions=[condition], + conclusion=conclusion, + confidence=abs(correlation), + metadata={ + 'features': [feature1.name, feature2.name], + 'correlation': correlation + } + )) + + if len(rules) >= self.max_rules: + break + + if len(rules) >= self.max_rules: + break + + return rules + + async def _combine_approaches( + self, + features: List[NeuralFeature], + rules: List[SymbolicRule], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Combine neural and symbolic approaches.""" + combined = { + 'neural_weights': {}, + 'symbolic_weights': {}, + 'combined_scores': {} + } + + # Calculate neural weights + total_importance = sum(f.importance for f in features) + if total_importance > 0: + combined['neural_weights'] = { + f.name: f.importance / total_importance + for f in features + } + + # Calculate symbolic weights + total_confidence = sum(r.confidence for r in rules) + if total_confidence > 0: + combined['symbolic_weights'] = { + r.name: r.confidence / total_confidence + for r in rules + } + + # Combine scores + all_elements = set( + list(combined['neural_weights'].keys()) + + list(combined['symbolic_weights'].keys()) + ) + + for element in all_elements: + neural_score = combined['neural_weights'].get(element, 0) + symbolic_score = combined['symbolic_weights'].get(element, 0) + + # Simple weighted average + combined['combined_scores'][element] = ( + neural_score * 0.6 + # Favor neural slightly + symbolic_score * 0.4 + ) + + return combined + + async def _generate_analysis( + self, + combined: Dict[str, Any], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate neurosymbolic analysis.""" + # Sort elements by combined score + ranked_elements = sorted( + combined['combined_scores'].items(), + key=lambda x: x[1], + reverse=True + ) + + # Calculate statistics + scores = list(combined['combined_scores'].values()) + mean = np.mean(scores) if scores else 0 + std = np.std(scores) if scores else 0 + + # Calculate entropy + entropy = -sum( + s * np.log2(s) if s > 0 else 0 + for s in combined['combined_scores'].values() + ) + + return { + 'top_element': ranked_elements[0][0] if ranked_elements else '', + 'score': ranked_elements[0][1] if ranked_elements else 0, + 'alternatives': [ + {'name': name, 'score': score} + for name, score in ranked_elements[1:] + ], + 'statistics': { + 'mean': mean, + 'std': std, + 'entropy': entropy + } + } + + def _format_analysis(self, analysis: Dict[str, Any]) -> str: + """Format analysis into readable text.""" + sections = [] + + # Top element + if analysis['top_element']: + sections.append( + f"Most significant element: {analysis['top_element']} " + f"(score: {analysis['score']:.2%})" + ) + + # Alternative elements + if analysis['alternatives']: + sections.append("\nAlternative elements:") + for alt in analysis['alternatives']: + sections.append( + f"- {alt['name']}: {alt['score']:.2%}" + ) + + # Statistics + stats = analysis['statistics'] + sections.append("\nAnalysis statistics:") + sections.append(f"- Mean score: {stats['mean']:.2%}") + sections.append(f"- Standard deviation: {stats['std']:.2%}") + sections.append(f"- Information entropy: {stats['entropy']:.2f} bits") + + return "\n".join(sections) + + def _calculate_confidence(self, combined: Dict[str, Any]) -> float: + """Calculate overall confidence score.""" + if not combined['combined_scores']: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Get scores + scores = list(combined['combined_scores'].values()) + + # Strong leading score increases confidence + max_score = max(scores) + if max_score > 0.8: + confidence += 0.3 + elif max_score > 0.6: + confidence += 0.2 + elif max_score > 0.4: + confidence += 0.1 + + # Low entropy (clear distinction) increases confidence + entropy = -sum(s * np.log2(s) if s > 0 else 0 for s in scores) + max_entropy = -np.log2(1/len(scores)) # Maximum possible entropy + + if entropy < 0.3 * max_entropy: + confidence += 0.2 + elif entropy < 0.6 * max_entropy: + confidence += 0.1 + + return min(confidence, 1.0) diff --git a/reasoning/portfolio_optimization.py b/reasoning/portfolio_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..41e373b65daf4a18dbb89dd8bfce3bc2d78959d8 --- /dev/null +++ b/reasoning/portfolio_optimization.py @@ -0,0 +1,549 @@ +"""Advanced portfolio optimization for venture strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class VentureMetrics: + """Venture performance metrics.""" + revenue: float + profit: float + growth_rate: float + risk_score: float + resource_usage: Dict[str, float] + synergy_score: float + +@dataclass +class ResourceAllocation: + """Resource allocation configuration.""" + venture_id: str + resources: Dict[str, float] + constraints: List[str] + dependencies: List[str] + priority: float + +class PortfolioOptimizer: + """ + Advanced portfolio optimization that: + 1. Optimizes venture mix + 2. Allocates resources + 3. Manages risks + 4. Maximizes synergies + 5. Balances growth + """ + + def __init__(self): + self.ventures: Dict[str, VentureMetrics] = {} + self.allocations: Dict[str, ResourceAllocation] = {} + + async def optimize_portfolio(self, + ventures: List[str], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize venture portfolio.""" + try: + # Analyze ventures + analysis = await self._analyze_ventures(ventures, context) + + # Optimize allocation + allocation = await self._optimize_allocation(analysis, context) + + # Risk optimization + risk = await self._optimize_risk(allocation, context) + + # Synergy optimization + synergy = await self._optimize_synergies(risk, context) + + # Performance projections + projections = await self._project_performance(synergy, context) + + return { + "success": projections["annual_profit"] >= 1_000_000, + "analysis": analysis, + "allocation": allocation, + "risk": risk, + "synergy": synergy, + "projections": projections + } + except Exception as e: + logging.error(f"Error in portfolio optimization: {str(e)}") + return {"success": False, "error": str(e)} + + async def _analyze_ventures(self, + ventures: List[str], + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze venture characteristics.""" + prompt = f""" + Analyze ventures: + Ventures: {json.dumps(ventures)} + Context: {json.dumps(context)} + + Analyze: + 1. Performance metrics + 2. Resource requirements + 3. Risk factors + 4. Growth potential + 5. Synergy opportunities + + Format as: + [Venture1] + Metrics: ... + Resources: ... + Risks: ... + Growth: ... + Synergies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_venture_analysis(response["answer"]) + + async def _optimize_allocation(self, + analysis: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize resource allocation.""" + prompt = f""" + Optimize resource allocation: + Analysis: {json.dumps(analysis)} + Context: {json.dumps(context)} + + Optimize for: + 1. Resource efficiency + 2. Growth potential + 3. Risk balance + 4. Synergy capture + 5. Constraint satisfaction + + Format as: + [Allocation1] + Venture: ... + Resources: ... + Constraints: ... + Dependencies: ... + Priority: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_allocation_optimization(response["answer"]) + + async def _optimize_risk(self, + allocation: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize risk management.""" + prompt = f""" + Optimize risk management: + Allocation: {json.dumps(allocation)} + Context: {json.dumps(context)} + + Optimize for: + 1. Risk diversification + 2. Exposure limits + 3. Correlation management + 4. Hedging strategies + 5. Contingency planning + + Format as: + [Risk1] + Type: ... + Exposure: ... + Mitigation: ... + Contingency: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_risk_optimization(response["answer"]) + + async def _optimize_synergies(self, + risk: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize portfolio synergies.""" + prompt = f""" + Optimize synergies: + Risk: {json.dumps(risk)} + Context: {json.dumps(context)} + + Optimize for: + 1. Resource sharing + 2. Knowledge transfer + 3. Market leverage + 4. Technology reuse + 5. Customer cross-sell + + Format as: + [Synergy1] + Type: ... + Ventures: ... + Potential: ... + Requirements: ... + Timeline: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_synergy_optimization(response["answer"]) + + async def _project_performance(self, + synergy: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Project portfolio performance.""" + prompt = f""" + Project performance: + Synergy: {json.dumps(synergy)} + Context: {json.dumps(context)} + + Project: + 1. Revenue growth + 2. Profit margins + 3. Resource utilization + 4. Risk metrics + 5. Synergy capture + + Format as: + [Projections] + Revenue: ... + Profit: ... + Resources: ... + Risk: ... + Synergies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_performance_projections(response["answer"]) + + def _calculate_portfolio_metrics(self) -> Dict[str, float]: + """Calculate comprehensive portfolio metrics.""" + if not self.ventures: + return { + "total_revenue": 0.0, + "total_profit": 0.0, + "avg_growth": 0.0, + "avg_risk": 0.0, + "resource_efficiency": 0.0, + "synergy_capture": 0.0 + } + + metrics = { + "total_revenue": sum(v.revenue for v in self.ventures.values()), + "total_profit": sum(v.profit for v in self.ventures.values()), + "avg_growth": np.mean([v.growth_rate for v in self.ventures.values()]), + "avg_risk": np.mean([v.risk_score for v in self.ventures.values()]), + "resource_efficiency": self._calculate_resource_efficiency(), + "synergy_capture": np.mean([v.synergy_score for v in self.ventures.values()]) + } + + return metrics + + def _calculate_resource_efficiency(self) -> float: + """Calculate resource utilization efficiency.""" + if not self.ventures or not self.allocations: + return 0.0 + + total_resources = defaultdict(float) + used_resources = defaultdict(float) + + # Sum up total and used resources + for venture_id, allocation in self.allocations.items(): + for resource, amount in allocation.resources.items(): + total_resources[resource] += amount + if venture_id in self.ventures: + used_resources[resource] += ( + amount * self.ventures[venture_id].resource_usage.get(resource, 0) + ) + + # Calculate efficiency for each resource + efficiencies = [] + for resource in total_resources: + if total_resources[resource] > 0: + efficiency = used_resources[resource] / total_resources[resource] + efficiencies.append(efficiency) + + return np.mean(efficiencies) if efficiencies else 0.0 + + def get_portfolio_insights(self) -> Dict[str, Any]: + """Get comprehensive portfolio insights.""" + metrics = self._calculate_portfolio_metrics() + + return { + "portfolio_metrics": metrics, + "venture_metrics": { + venture_id: { + "revenue": v.revenue, + "profit": v.profit, + "growth_rate": v.growth_rate, + "risk_score": v.risk_score, + "synergy_score": v.synergy_score + } + for venture_id, v in self.ventures.items() + }, + "resource_allocation": { + venture_id: { + "resources": a.resources, + "priority": a.priority, + "constraints": len(a.constraints), + "dependencies": len(a.dependencies) + } + for venture_id, a in self.allocations.items() + }, + "risk_profile": { + "portfolio_risk": metrics["avg_risk"], + "risk_concentration": self._calculate_risk_concentration(), + "risk_correlation": self._calculate_risk_correlation() + }, + "optimization_opportunities": self._identify_optimization_opportunities() + } + + def _calculate_risk_concentration(self) -> float: + """Calculate risk concentration in portfolio.""" + if not self.ventures: + return 0.0 + + risk_weights = [v.risk_score for v in self.ventures.values()] + return np.std(risk_weights) if len(risk_weights) > 1 else 0.0 + + def _calculate_risk_correlation(self) -> float: + """Calculate risk correlation between ventures.""" + if len(self.ventures) < 2: + return 0.0 + + # Create correlation matrix of risk scores and resource usage + venture_metrics = [ + [v.risk_score] + list(v.resource_usage.values()) + for v in self.ventures.values() + ] + + correlation_matrix = np.corrcoef(venture_metrics) + return np.mean(correlation_matrix[np.triu_indices_from(correlation_matrix, k=1)]) + + def _identify_optimization_opportunities(self) -> List[Dict[str, Any]]: + """Identify portfolio optimization opportunities.""" + opportunities = [] + + # Resource optimization opportunities + resource_efficiency = self._calculate_resource_efficiency() + if resource_efficiency < 0.8: + opportunities.append({ + "type": "resource_optimization", + "potential": 1.0 - resource_efficiency, + "description": "Improve resource utilization efficiency" + }) + + # Risk optimization opportunities + risk_concentration = self._calculate_risk_concentration() + if risk_concentration > 0.2: + opportunities.append({ + "type": "risk_diversification", + "potential": risk_concentration, + "description": "Reduce risk concentration" + }) + + # Synergy optimization opportunities + avg_synergy = np.mean([v.synergy_score for v in self.ventures.values()]) if self.ventures else 0 + if avg_synergy < 0.7: + opportunities.append({ + "type": "synergy_capture", + "potential": 1.0 - avg_synergy, + "description": "Increase synergy capture" + }) + + return opportunities + +class PortfolioOptimizationStrategy(ReasoningStrategy): + """ + Advanced portfolio optimization strategy that: + 1. Analyzes venture metrics + 2. Optimizes resource allocation + 3. Balances risk-reward + 4. Maximizes portfolio synergies + 5. Provides actionable recommendations + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize portfolio optimization strategy.""" + super().__init__() + self.config = config or {} + self.optimizer = PortfolioOptimizer() + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Generate portfolio optimization strategy based on query and context. + + Args: + query: The portfolio optimization query + context: Additional context and parameters + + Returns: + Dict containing optimization strategy and confidence scores + """ + try: + # Extract portfolio parameters + params = self._extract_parameters(query, context) + + # Optimize portfolio + optimization_result = self.optimizer.optimize_portfolio( + ventures=params.get('ventures', []), + constraints=params.get('constraints', []), + objectives=params.get('objectives', []) + ) + + # Get metrics + metrics = self.optimizer.get_portfolio_metrics() + + # Generate recommendations + recommendations = self._generate_recommendations( + optimization_result, + metrics + ) + + return { + 'answer': self._format_strategy(optimization_result, metrics, recommendations), + 'confidence': self._calculate_confidence(optimization_result), + 'optimization': optimization_result, + 'metrics': metrics, + 'recommendations': recommendations + } + + except Exception as e: + logging.error(f"Portfolio optimization failed: {str(e)}") + return { + 'error': f"Portfolio optimization failed: {str(e)}", + 'confidence': 0.0 + } + + def _extract_parameters(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Extract optimization parameters from query and context.""" + params = {} + + # Extract ventures + if 'ventures' in context: + params['ventures'] = context['ventures'] + else: + # Default empty portfolio + params['ventures'] = [] + + # Extract constraints + if 'constraints' in context: + params['constraints'] = context['constraints'] + else: + # Default constraints + params['constraints'] = [ + 'budget_limit', + 'risk_tolerance', + 'resource_capacity' + ] + + # Extract objectives + if 'objectives' in context: + params['objectives'] = context['objectives'] + else: + # Default objectives + params['objectives'] = [ + 'maximize_returns', + 'minimize_risk', + 'maximize_synergies' + ] + + return params + + def _generate_recommendations( + self, + optimization_result: Dict[str, Any], + metrics: Dict[str, Any] + ) -> List[str]: + """Generate actionable recommendations.""" + recommendations = [] + + # Portfolio composition recommendations + if 'allocation' in optimization_result: + allocation = optimization_result['allocation'] + recommendations.extend([ + f"Allocate {alloc['percentage']:.1f}% to {alloc['venture']}" + for alloc in allocation + ]) + + # Risk management recommendations + if 'risk_analysis' in metrics: + risk = metrics['risk_analysis'] + if risk.get('total_risk', 0) > 0.7: + recommendations.append( + "Consider reducing exposure to high-risk ventures" + ) + if risk.get('correlation', 0) > 0.8: + recommendations.append( + "Increase portfolio diversification to reduce correlation" + ) + + # Performance optimization recommendations + if 'performance' in metrics: + perf = metrics['performance'] + if perf.get('sharpe_ratio', 0) < 1.0: + recommendations.append( + "Optimize risk-adjusted returns through better venture selection" + ) + if perf.get('efficiency', 0) < 0.8: + recommendations.append( + "Improve resource allocation efficiency across ventures" + ) + + return recommendations + + def _calculate_confidence(self, optimization_result: Dict[str, Any]) -> float: + """Calculate confidence score based on optimization quality.""" + # Base confidence + confidence = 0.5 + + # Adjust based on optimization completeness + if optimization_result.get('allocation'): + confidence += 0.1 + if optimization_result.get('risk_analysis'): + confidence += 0.1 + if optimization_result.get('performance_metrics'): + confidence += 0.1 + + # Adjust based on solution quality + if optimization_result.get('convergence_status') == 'optimal': + confidence += 0.2 + elif optimization_result.get('convergence_status') == 'suboptimal': + confidence += 0.1 + + return min(confidence, 1.0) + + def _format_strategy( + self, + optimization_result: Dict[str, Any], + metrics: Dict[str, Any], + recommendations: List[str] + ) -> str: + """Format optimization strategy into readable text.""" + sections = [] + + # Portfolio allocation + if 'allocation' in optimization_result: + allocation = optimization_result['allocation'] + sections.append("Portfolio Allocation:") + for alloc in allocation: + sections.append( + f"- {alloc['venture']}: {alloc['percentage']:.1f}%" + ) + + # Key metrics + if metrics: + sections.append("\nKey Metrics:") + for key, value in metrics.items(): + if isinstance(value, (int, float)): + sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}") + else: + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Recommendations + if recommendations: + sections.append("\nKey Recommendations:") + for rec in recommendations: + sections.append(f"- {rec}") + + return "\n".join(sections) diff --git a/reasoning/quantum.py b/reasoning/quantum.py new file mode 100644 index 0000000000000000000000000000000000000000..9aaeeef25bc2043a389706d7f876385f69f21fd6 --- /dev/null +++ b/reasoning/quantum.py @@ -0,0 +1,372 @@ +"""Quantum-inspired reasoning implementations.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class QuantumState: + """Quantum state with superposition and entanglement.""" + name: str + amplitude: complex + phase: float + entangled_states: List[str] = field(default_factory=list) + +class QuantumReasoning(ReasoningStrategy): + """ + Advanced quantum reasoning that: + 1. Creates quantum states + 2. Applies quantum operations + 3. Measures outcomes + 4. Handles superposition + 5. Models entanglement + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize quantum reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Configure quantum parameters + self.num_qubits = self.config.get('num_qubits', 3) + self.measurement_threshold = self.config.get('measurement_threshold', 0.1) + self.decoherence_rate = self.config.get('decoherence_rate', 0.01) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply quantum reasoning to analyze complex decisions. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Initialize quantum states + states = await self._initialize_states(query, context) + + # Apply quantum operations + evolved_states = await self._apply_operations(states, context) + + # Measure outcomes + measurements = await self._measure_states(evolved_states, context) + + # Generate analysis + analysis = await self._generate_analysis(measurements, context) + + return { + 'answer': self._format_analysis(analysis), + 'confidence': self._calculate_confidence(measurements), + 'states': states, + 'evolved_states': evolved_states, + 'measurements': measurements, + 'analysis': analysis + } + + except Exception as e: + logging.error(f"Quantum reasoning failed: {str(e)}") + return { + 'error': f"Quantum reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _initialize_states( + self, + query: str, + context: Dict[str, Any] + ) -> List[QuantumState]: + """Initialize quantum states.""" + states = [] + + # Extract key terms for state initialization + terms = set(query.lower().split()) + + # Create quantum states based on terms + for i, term in enumerate(terms): + if i >= self.num_qubits: + break + + # Calculate initial amplitude and phase + amplitude = 1.0 / np.sqrt(len(terms[:self.num_qubits])) + phase = 2 * np.pi * i / len(terms[:self.num_qubits]) + + states.append(QuantumState( + name=term, + amplitude=complex(amplitude * np.cos(phase), amplitude * np.sin(phase)), + phase=phase + )) + + # Create entangled states if specified + if context.get('entangle', False): + self._entangle_states(states) + + return states + + async def _apply_operations( + self, + states: List[QuantumState], + context: Dict[str, Any] + ) -> List[QuantumState]: + """Apply quantum operations to states.""" + evolved_states = [] + + # Get operation parameters + rotation = context.get('rotation', 0.0) + phase_shift = context.get('phase_shift', 0.0) + + for state in states: + # Apply rotation + rotated_amplitude = state.amplitude * np.exp(1j * rotation) + + # Apply phase shift + shifted_phase = (state.phase + phase_shift) % (2 * np.pi) + + # Apply decoherence + decohered_amplitude = rotated_amplitude * (1 - self.decoherence_rate) + + evolved_states.append(QuantumState( + name=state.name, + amplitude=decohered_amplitude, + phase=shifted_phase, + entangled_states=state.entangled_states.copy() + )) + + return evolved_states + + async def _measure_states( + self, + states: List[QuantumState], + context: Dict[str, Any] + ) -> Dict[str, float]: + """Measure quantum states.""" + measurements = {} + + # Calculate total probability + total_probability = sum( + abs(state.amplitude) ** 2 + for state in states + ) + + if total_probability > 0: + # Normalize and store measurements + for state in states: + probability = (abs(state.amplitude) ** 2) / total_probability + if probability > self.measurement_threshold: + measurements[state.name] = probability + + return measurements + + def _entangle_states(self, states: List[QuantumState]) -> None: + """Create entanglement between states.""" + if len(states) < 2: + return + + # Simple entanglement: connect adjacent states + for i in range(len(states) - 1): + states[i].entangled_states.append(states[i + 1].name) + states[i + 1].entangled_states.append(states[i].name) + + async def _generate_analysis( + self, + measurements: Dict[str, float], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate quantum analysis.""" + # Sort states by measurement probability + ranked_states = sorted( + measurements.items(), + key=lambda x: x[1], + reverse=True + ) + + # Calculate quantum statistics + amplitudes = list(measurements.values()) + mean = np.mean(amplitudes) if amplitudes else 0 + std = np.std(amplitudes) if amplitudes else 0 + + # Calculate quantum entropy + entropy = -sum( + p * np.log2(p) if p > 0 else 0 + for p in measurements.values() + ) + + return { + 'top_state': ranked_states[0][0] if ranked_states else '', + 'probability': ranked_states[0][1] if ranked_states else 0, + 'alternatives': [ + {'name': name, 'probability': prob} + for name, prob in ranked_states[1:] + ], + 'statistics': { + 'mean': mean, + 'std': std, + 'entropy': entropy + } + } + + def _format_analysis(self, analysis: Dict[str, Any]) -> str: + """Format analysis into readable text.""" + sections = [] + + # Top quantum state + if analysis['top_state']: + sections.append( + f"Most probable quantum state: {analysis['top_state']} " + f"(probability: {analysis['probability']:.2%})" + ) + + # Alternative states + if analysis['alternatives']: + sections.append("\nAlternative quantum states:") + for alt in analysis['alternatives']: + sections.append( + f"- {alt['name']}: {alt['probability']:.2%}" + ) + + # Quantum statistics + stats = analysis['statistics'] + sections.append("\nQuantum statistics:") + sections.append(f"- Mean amplitude: {stats['mean']:.2%}") + sections.append(f"- Standard deviation: {stats['std']:.2%}") + sections.append(f"- Quantum entropy: {stats['entropy']:.2f} bits") + + return "\n".join(sections) + + def _calculate_confidence(self, measurements: Dict[str, float]) -> float: + """Calculate overall confidence score.""" + if not measurements: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Adjust based on measurement distribution + probs = list(measurements.values()) + + # Strong leading measurement increases confidence + max_prob = max(probs) + if max_prob > 0.8: + confidence += 0.3 + elif max_prob > 0.6: + confidence += 0.2 + elif max_prob > 0.4: + confidence += 0.1 + + # Low entropy (clear distinction) increases confidence + entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs) + max_entropy = -np.log2(1/len(probs)) # Maximum possible entropy + + if entropy < 0.3 * max_entropy: + confidence += 0.2 + elif entropy < 0.6 * max_entropy: + confidence += 0.1 + + return min(confidence, 1.0) + + +class QuantumInspiredStrategy(ReasoningStrategy): + """Implements Quantum-Inspired reasoning.""" + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} diff --git a/reasoning/recursive.py b/reasoning/recursive.py new file mode 100644 index 0000000000000000000000000000000000000000..f2c784f28a2d21d7ab62927e0ea4bfec05cc52fc --- /dev/null +++ b/reasoning/recursive.py @@ -0,0 +1,576 @@ +"""Recursive reasoning implementation with advanced decomposition and synthesis.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Tuple, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy + +class SubproblemType(Enum): + """Types of subproblems in recursive reasoning.""" + ATOMIC = "atomic" + COMPOSITE = "composite" + PARALLEL = "parallel" + SEQUENTIAL = "sequential" + CONDITIONAL = "conditional" + ITERATIVE = "iterative" + +class SolutionStatus(Enum): + """Status of subproblem solutions.""" + PENDING = "pending" + IN_PROGRESS = "in_progress" + SOLVED = "solved" + FAILED = "failed" + BLOCKED = "blocked" + OPTIMIZING = "optimizing" + +@dataclass +class Subproblem: + """Represents a subproblem in recursive reasoning.""" + id: str + type: SubproblemType + query: str + context: Dict[str, Any] + parent_id: Optional[str] + children: List[str] + status: SolutionStatus + solution: Optional[Dict[str, Any]] + confidence: float + dependencies: List[str] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class RecursiveStep: + """Represents a step in recursive reasoning.""" + id: str + subproblem_id: str + action: str + timestamp: datetime + result: Optional[Dict[str, Any]] + metrics: Dict[str, float] + metadata: Dict[str, Any] = field(default_factory=dict) + +class RecursiveReasoning(ReasoningStrategy): + """ + Advanced Recursive Reasoning implementation with: + - Dynamic problem decomposition + - Parallel subproblem solving + - Solution synthesis + - Cycle detection + - Optimization strategies + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize recursive reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Recursive reasoning specific parameters + self.max_depth = self.config.get('max_depth', 5) + self.optimization_rounds = self.config.get('optimization_rounds', 2) + + # Problem tracking + self.subproblems: Dict[str, Subproblem] = {} + self.steps: List[RecursiveStep] = [] + self.solution_cache: Dict[str, Dict[str, Any]] = {} + self.cycle_detection: Set[str] = set() + + # Performance metrics + self.depth_distribution: Dict[int, int] = defaultdict(int) + self.type_distribution: Dict[SubproblemType, int] = defaultdict(int) + self.success_rate: Dict[SubproblemType, float] = defaultdict(float) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing recursive reasoning.""" + try: + # Initialize root problem + root = await self._initialize_problem(query, context) + self.subproblems[root.id] = root + + # Recursively solve + solution = await self._solve_recursive(root.id, depth=0) + + # Optimize solution + optimized = await self._optimize_solution(solution, root, context) + + # Update metrics + self._update_metrics(root.id) + + return { + "success": True, + "answer": optimized["answer"], + "confidence": optimized["confidence"], + "decomposition": self._get_problem_tree(root.id), + "solution_trace": self._get_solution_trace(root.id), + "performance_metrics": self._get_performance_metrics(), + "meta_insights": optimized["meta_insights"] + } + except Exception as e: + logging.error(f"Error in recursive reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _initialize_problem(self, query: str, context: Dict[str, Any]) -> Subproblem: + """Initialize the root problem.""" + prompt = f""" + Initialize recursive reasoning problem: + Query: {query} + Context: {json.dumps(context)} + + Analyze for: + 1. Problem type classification + 2. Initial decomposition strategy + 3. Key dependencies + 4. Solution approach + + Format as: + [Problem] + Type: ... + Strategy: ... + Dependencies: ... + Approach: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_problem_init(response["answer"], query, context) + + async def _decompose_problem(self, problem: Subproblem, context: Dict[str, Any]) -> List[Subproblem]: + """Decompose a problem into subproblems.""" + prompt = f""" + Decompose problem into subproblems: + Problem: {json.dumps(self._problem_to_dict(problem))} + Context: {json.dumps(context)} + + For each subproblem specify: + 1. [Type]: {" | ".join([t.value for t in SubproblemType])} + 2. [Query]: Specific question + 3. [Dependencies]: Required solutions + 4. [Approach]: Solution strategy + + Format as: + [S1] + Type: ... + Query: ... + Dependencies: ... + Approach: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_subproblems(response["answer"], problem.id, context) + + async def _solve_recursive(self, problem_id: str, depth: int) -> Dict[str, Any]: + """Recursively solve a problem and its subproblems.""" + if depth > self.max_depth: + return {"success": False, "error": "Maximum recursion depth exceeded"} + + if problem_id in self.cycle_detection: + return {"success": False, "error": "Cycle detected in recursive solving"} + + problem = self.subproblems[problem_id] + self.cycle_detection.add(problem_id) + self.depth_distribution[depth] += 1 + + try: + # Check cache + cache_key = f"{problem.query}:{json.dumps(problem.context)}" + if cache_key in self.solution_cache: + return self.solution_cache[cache_key] + + # Check if atomic + if problem.type == SubproblemType.ATOMIC: + solution = await self._solve_atomic(problem) + else: + # Decompose + subproblems = await self._decompose_problem(problem, problem.context) + for sub in subproblems: + self.subproblems[sub.id] = sub + problem.children.append(sub.id) + + # Solve subproblems + if problem.type == SubproblemType.PARALLEL and len(subproblems) >= self.parallel_threshold: + # Solve in parallel + tasks = [self._solve_recursive(sub.id, depth + 1) for sub in subproblems] + subsolutions = await asyncio.gather(*tasks) + else: + # Solve sequentially + subsolutions = [] + for sub in subproblems: + subsolution = await self._solve_recursive(sub.id, depth + 1) + subsolutions.append(subsolution) + + # Synthesize solutions + solution = await self._synthesize_solutions(subsolutions, problem, problem.context) + + # Cache solution + self.solution_cache[cache_key] = solution + problem.solution = solution + problem.status = SolutionStatus.SOLVED if solution["success"] else SolutionStatus.FAILED + + return solution + + finally: + self.cycle_detection.remove(problem_id) + + async def _solve_atomic(self, problem: Subproblem) -> Dict[str, Any]: + """Solve an atomic problem.""" + prompt = f""" + Solve atomic problem: + Problem: {json.dumps(self._problem_to_dict(problem))} + + Provide: + 1. Direct solution + 2. Confidence level + 3. Supporting evidence + 4. Alternative approaches + + Format as: + [Solution] + Answer: ... + Confidence: ... + Evidence: ... + Alternatives: ... + """ + + response = await problem.context["groq_api"].predict(prompt) + solution = self._parse_atomic_solution(response["answer"]) + + self._record_step(RecursiveStep( + id=f"step_{len(self.steps)}", + subproblem_id=problem.id, + action="atomic_solve", + timestamp=datetime.now(), + result=solution, + metrics={"confidence": solution.get("confidence", 0.0)}, + metadata={} + )) + + return solution + + async def _synthesize_solutions(self, subsolutions: List[Dict[str, Any]], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]: + """Synthesize solutions from subproblems.""" + prompt = f""" + Synthesize solutions: + Problem: {json.dumps(self._problem_to_dict(problem))} + Solutions: {json.dumps(subsolutions)} + Context: {json.dumps(context)} + + Provide: + 1. Integrated solution + 2. Confidence assessment + 3. Integration method + 4. Quality metrics + + Format as: + [Synthesis] + Solution: ... + Confidence: ... + Method: ... + Metrics: ... + """ + + response = await context["groq_api"].predict(prompt) + synthesis = self._parse_synthesis(response["answer"]) + + self._record_step(RecursiveStep( + id=f"step_{len(self.steps)}", + subproblem_id=problem.id, + action="synthesize", + timestamp=datetime.now(), + result=synthesis, + metrics={"confidence": synthesis.get("confidence", 0.0)}, + metadata={"num_subsolutions": len(subsolutions)} + )) + + return synthesis + + async def _optimize_solution(self, solution: Dict[str, Any], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize the final solution.""" + prompt = f""" + Optimize recursive solution: + Original: {json.dumps(solution)} + Problem: {json.dumps(self._problem_to_dict(problem))} + Context: {json.dumps(context)} + + Optimize for: + 1. Completeness + 2. Consistency + 3. Efficiency + 4. Clarity + + Format as: + [Optimization] + Answer: ... + Improvements: ... + Metrics: ... + Insights: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_optimization(response["answer"]) + + def _update_metrics(self, root_id: str): + """Update performance metrics.""" + def update_recursive(problem_id: str): + problem = self.subproblems[problem_id] + self.type_distribution[problem.type] += 1 + + if problem.status == SolutionStatus.SOLVED: + self.success_rate[problem.type] = ( + self.success_rate[problem.type] * (self.type_distribution[problem.type] - 1) + + problem.confidence + ) / self.type_distribution[problem.type] + + for child_id in problem.children: + update_recursive(child_id) + + update_recursive(root_id) + + def _get_problem_tree(self, root_id: str) -> Dict[str, Any]: + """Get the problem decomposition tree.""" + def build_tree(problem_id: str) -> Dict[str, Any]: + problem = self.subproblems[problem_id] + return { + "id": problem.id, + "type": problem.type.value, + "query": problem.query, + "status": problem.status.value, + "confidence": problem.confidence, + "children": [build_tree(child_id) for child_id in problem.children] + } + + return build_tree(root_id) + + def _get_solution_trace(self, root_id: str) -> List[Dict[str, Any]]: + """Get the solution trace for a problem.""" + return [self._step_to_dict(step) for step in self.steps + if step.subproblem_id == root_id or + any(step.subproblem_id == sub_id for sub_id in self.subproblems[root_id].children)] + + def _get_performance_metrics(self) -> Dict[str, Any]: + """Get current performance metrics.""" + return { + "depth_distribution": dict(self.depth_distribution), + "type_distribution": {t.value: c for t, c in self.type_distribution.items()}, + "success_rate": {t.value: r for t, r in self.success_rate.items()}, + "cache_hits": len(self.solution_cache), + "total_steps": len(self.steps) + } + + def _record_step(self, step: RecursiveStep): + """Record a reasoning step.""" + self.steps.append(step) + + def _parse_problem_init(self, response: str, query: str, context: Dict[str, Any]) -> Subproblem: + """Parse initial problem configuration.""" + problem_type = SubproblemType.COMPOSITE # default + dependencies = [] + metadata = {} + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Type:'): + try: + problem_type = SubproblemType(line[5:].strip().lower()) + except ValueError: + pass + elif line.startswith('Dependencies:'): + dependencies = [d.strip() for d in line[13:].split(',')] + elif line.startswith('Strategy:') or line.startswith('Approach:'): + metadata["strategy"] = line.split(':', 1)[1].strip() + + return Subproblem( + id="root", + type=problem_type, + query=query, + context=context, + parent_id=None, + children=[], + status=SolutionStatus.PENDING, + solution=None, + confidence=0.0, + dependencies=dependencies, + metadata=metadata + ) + + def _parse_subproblems(self, response: str, parent_id: str, context: Dict[str, Any]) -> List[Subproblem]: + """Parse subproblems from response.""" + subproblems = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current: + subproblems.append(current) + current = None + elif line.startswith('Type:'): + try: + problem_type = SubproblemType(line[5:].strip().lower()) + current = Subproblem( + id=f"{parent_id}_{len(subproblems)}", + type=problem_type, + query="", + context=context, + parent_id=parent_id, + children=[], + status=SolutionStatus.PENDING, + solution=None, + confidence=0.0, + dependencies=[], + metadata={} + ) + except ValueError: + current = None + elif current: + if line.startswith('Query:'): + current.query = line[6:].strip() + elif line.startswith('Dependencies:'): + current.dependencies = [d.strip() for d in line[13:].split(',')] + elif line.startswith('Approach:'): + current.metadata["approach"] = line[9:].strip() + + if current: + subproblems.append(current) + + return subproblems + + def _parse_atomic_solution(self, response: str) -> Dict[str, Any]: + """Parse atomic solution from response.""" + solution = { + "success": True, + "answer": "", + "confidence": 0.0, + "evidence": [], + "alternatives": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Answer:'): + solution["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + solution["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Evidence:'): + solution["evidence"] = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + solution["alternatives"] = [a.strip() for a in line[13:].split(',')] + + return solution + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis result from response.""" + synthesis = { + "success": True, + "solution": "", + "confidence": 0.0, + "method": "", + "metrics": {} + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Solution:'): + synthesis["solution"] = line[9:].strip() + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Method:'): + synthesis["method"] = line[7:].strip() + elif line.startswith('Metrics:'): + try: + synthesis["metrics"] = json.loads(line[8:].strip()) + except: + pass + + return synthesis + + def _parse_optimization(self, response: str) -> Dict[str, Any]: + """Parse optimization result from response.""" + optimization = { + "answer": "", + "confidence": 0.0, + "improvements": [], + "metrics": {}, + "meta_insights": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Answer:'): + optimization["answer"] = line[7:].strip() + elif line.startswith('Improvements:'): + optimization["improvements"] = [i.strip() for i in line[13:].split(',')] + elif line.startswith('Metrics:'): + try: + optimization["metrics"] = json.loads(line[8:].strip()) + except: + pass + elif line.startswith('Insights:'): + optimization["meta_insights"] = [i.strip() for i in line[9:].split(',')] + + return optimization + + def _problem_to_dict(self, problem: Subproblem) -> Dict[str, Any]: + """Convert problem to dictionary for serialization.""" + return { + "id": problem.id, + "type": problem.type.value, + "query": problem.query, + "parent_id": problem.parent_id, + "children": problem.children, + "status": problem.status.value, + "confidence": problem.confidence, + "dependencies": problem.dependencies, + "metadata": problem.metadata + } + + def _step_to_dict(self, step: RecursiveStep) -> Dict[str, Any]: + """Convert step to dictionary for serialization.""" + return { + "id": step.id, + "subproblem_id": step.subproblem_id, + "action": step.action, + "timestamp": step.timestamp.isoformat(), + "result": step.result, + "metrics": step.metrics, + "metadata": step.metadata + } + + def clear_cache(self): + """Clear solution cache.""" + self.solution_cache.clear() + + def get_statistics(self) -> Dict[str, Any]: + """Get detailed statistics about the reasoning process.""" + return { + "total_problems": len(self.subproblems), + "total_steps": len(self.steps), + "cache_size": len(self.solution_cache), + "type_distribution": dict(self.type_distribution), + "depth_distribution": dict(self.depth_distribution), + "success_rates": dict(self.success_rate), + "average_confidence": sum(p.confidence for p in self.subproblems.values()) / len(self.subproblems) if self.subproblems else 0.0 + } diff --git a/reasoning/specialized.py b/reasoning/specialized.py new file mode 100644 index 0000000000000000000000000000000000000000..14ec0269e9c7b25629ee9dfcfe0d60732cf4735f --- /dev/null +++ b/reasoning/specialized.py @@ -0,0 +1,476 @@ +"""Specialized reasoning strategies for specific domains and tasks.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy + +class SpecializedReasoning(ReasoningStrategy): + """ + A composite reasoning strategy that combines multiple specialized strategies + for different domains and tasks. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize specialized reasoning with component strategies.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize component strategies with shared config + strategy_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights + } + + self.strategies = { + 'code_rewrite': CodeRewriteStrategy(strategy_config), + 'security_audit': SecurityAuditStrategy(strategy_config), + 'performance': PerformanceOptimizationStrategy(strategy_config), + 'testing': TestGenerationStrategy(strategy_config), + 'documentation': DocumentationStrategy(strategy_config), + 'api_design': APIDesignStrategy(strategy_config), + 'dependencies': DependencyManagementStrategy(strategy_config), + 'code_review': CodeReviewStrategy(strategy_config) + } + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply specialized reasoning by selecting and combining appropriate + strategies based on the query and context. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Determine which strategies to use based on context + selected_strategies = await self._select_strategies(query, context) + + # Get results from each selected strategy + results = {} + for strategy_name in selected_strategies: + strategy = self.strategies[strategy_name] + results[strategy_name] = await strategy.reason(query, context) + + # Combine results + combined_result = await self._combine_results(results, context) + + return { + 'answer': combined_result.get('answer', ''), + 'confidence': combined_result.get('confidence', 0.0), + 'reasoning_path': { + 'selected_strategies': selected_strategies, + 'individual_results': results, + 'combination_method': combined_result.get('method', '') + } + } + + except Exception as e: + logging.error(f"Specialized reasoning failed: {str(e)}") + return { + 'error': f"Specialized reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _select_strategies(self, query: str, context: Dict[str, Any]) -> List[str]: + """Select appropriate strategies based on query and context.""" + selected = [] + + # Simple keyword-based selection for now + keywords = { + 'code_rewrite': ['rewrite', 'refactor', 'improve'], + 'security_audit': ['security', 'vulnerability', 'audit'], + 'performance': ['performance', 'optimize', 'speed'], + 'testing': ['test', 'coverage', 'verify'], + 'documentation': ['document', 'explain', 'describe'], + 'api_design': ['api', 'interface', 'endpoint'], + 'dependencies': ['dependency', 'package', 'version'], + 'code_review': ['review', 'quality', 'check'] + } + + query_lower = query.lower() + for strategy, terms in keywords.items(): + if any(term in query_lower for term in terms): + selected.append(strategy) + + # If no specific strategies selected, use code review as default + if not selected: + selected = ['code_review'] + + return selected + + async def _combine_results( + self, + results: Dict[str, Dict[str, Any]], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Combine results from multiple strategies.""" + if not results: + return {'answer': '', 'confidence': 0.0, 'method': 'none'} + + # For now, use the highest confidence result + best_result = max( + results.items(), + key=lambda x: x[1].get('confidence', 0) + ) + + return { + 'answer': best_result[1].get('answer', ''), + 'confidence': best_result[1].get('confidence', 0.0), + 'method': 'highest_confidence' + } + +class CodeRewriteStrategy(ReasoningStrategy): + """ + Advanced code rewriting strategy that: + 1. Analyzes code structure and patterns + 2. Identifies refactoring opportunities + 3. Maintains code semantics + 4. Optimizes code quality + 5. Ensures backward compatibility + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Rewrite code while preserving functionality.""" + try: + # Analyze code + analysis = await self._analyze_code(query, context) + + # Generate rewrite plan + plan = await self._generate_rewrite_plan(analysis, context) + + # Execute rewrites + rewrites = await self._execute_rewrites(plan, context) + + # Validate changes + validation = await self._validate_changes(rewrites, context) + + return { + "success": validation["success"], + "rewrites": rewrites, + "validation": validation, + "metrics": { + "quality_improvement": validation.get("quality_score", 0.0), + "semantic_preservation": validation.get("semantic_score", 0.0) + } + } + except Exception as e: + logging.error(f"Error in code rewrite: {str(e)}") + return {"success": False, "error": str(e)} + +class SecurityAuditStrategy(ReasoningStrategy): + """ + Advanced security audit strategy that: + 1. Identifies security vulnerabilities + 2. Analyzes attack vectors + 3. Recommends security fixes + 4. Validates security measures + 5. Monitors security state + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Perform security audit and generate recommendations.""" + try: + # Scan for vulnerabilities + vulnerabilities = await self._scan_vulnerabilities(query, context) + + # Analyze risks + risks = await self._analyze_risks(vulnerabilities, context) + + # Generate fixes + fixes = await self._generate_fixes(risks, context) + + # Validate security + validation = await self._validate_security(fixes, context) + + return { + "success": True, + "vulnerabilities": vulnerabilities, + "risks": risks, + "fixes": fixes, + "validation": validation + } + except Exception as e: + logging.error(f"Error in security audit: {str(e)}") + return {"success": False, "error": str(e)} + +class PerformanceOptimizationStrategy(ReasoningStrategy): + """ + Advanced performance optimization strategy that: + 1. Profiles code performance + 2. Identifies bottlenecks + 3. Generates optimizations + 4. Measures improvements + 5. Validates optimizations + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize code performance.""" + try: + # Profile performance + profile = await self._profile_performance(query, context) + + # Identify bottlenecks + bottlenecks = await self._identify_bottlenecks(profile, context) + + # Generate optimizations + optimizations = await self._generate_optimizations(bottlenecks, context) + + # Measure improvements + measurements = await self._measure_improvements(optimizations, context) + + return { + "success": measurements["success"], + "profile": profile, + "bottlenecks": bottlenecks, + "optimizations": optimizations, + "improvements": measurements + } + except Exception as e: + logging.error(f"Error in performance optimization: {str(e)}") + return {"success": False, "error": str(e)} + +class TestGenerationStrategy(ReasoningStrategy): + """ + Advanced test generation strategy that: + 1. Analyzes code coverage + 2. Generates test cases + 3. Creates test fixtures + 4. Validates test quality + 5. Maintains test suite + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate comprehensive test suite.""" + try: + # Analyze coverage + coverage = await self._analyze_coverage(query, context) + + # Generate test cases + test_cases = await self._generate_test_cases(coverage, context) + + # Create fixtures + fixtures = await self._create_fixtures(test_cases, context) + + # Validate tests + validation = await self._validate_tests(test_cases, fixtures, context) + + return { + "success": validation["success"], + "test_cases": test_cases, + "fixtures": fixtures, + "validation": validation, + "metrics": { + "coverage": coverage.get("percentage", 0.0), + "quality_score": validation.get("quality_score", 0.0) + } + } + except Exception as e: + logging.error(f"Error in test generation: {str(e)}") + return {"success": False, "error": str(e)} + +class DocumentationStrategy(ReasoningStrategy): + """ + Advanced documentation strategy that: + 1. Analyzes code structure + 2. Generates documentation + 3. Maintains consistency + 4. Updates references + 5. Validates completeness + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate and maintain documentation.""" + try: + # Analyze structure + structure = await self._analyze_structure(query, context) + + # Generate documentation + documentation = await self._generate_documentation(structure, context) + + # Update references + references = await self._update_references(documentation, context) + + # Validate completeness + validation = await self._validate_documentation(documentation, references, context) + + return { + "success": validation["success"], + "documentation": documentation, + "references": references, + "validation": validation, + "metrics": { + "completeness": validation.get("completeness_score", 0.0), + "consistency": validation.get("consistency_score", 0.0) + } + } + except Exception as e: + logging.error(f"Error in documentation: {str(e)}") + return {"success": False, "error": str(e)} + +class APIDesignStrategy(ReasoningStrategy): + """ + Advanced API design strategy that: + 1. Analyzes requirements + 2. Designs API structure + 3. Generates specifications + 4. Validates design + 5. Maintains versioning + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Design and validate API.""" + try: + # Analyze requirements + requirements = await self._analyze_requirements(query, context) + + # Design structure + design = await self._design_structure(requirements, context) + + # Generate specs + specs = await self._generate_specs(design, context) + + # Validate design + validation = await self._validate_design(specs, context) + + return { + "success": validation["success"], + "requirements": requirements, + "design": design, + "specs": specs, + "validation": validation + } + except Exception as e: + logging.error(f"Error in API design: {str(e)}") + return {"success": False, "error": str(e)} + +class DependencyManagementStrategy(ReasoningStrategy): + """ + Advanced dependency management strategy that: + 1. Analyzes dependencies + 2. Resolves conflicts + 3. Optimizes versions + 4. Ensures compatibility + 5. Maintains security + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Manage and optimize dependencies.""" + try: + # Analyze dependencies + analysis = await self._analyze_dependencies(query, context) + + # Resolve conflicts + resolution = await self._resolve_conflicts(analysis, context) + + # Optimize versions + optimization = await self._optimize_versions(resolution, context) + + # Validate compatibility + validation = await self._validate_compatibility(optimization, context) + + return { + "success": validation["success"], + "analysis": analysis, + "resolution": resolution, + "optimization": optimization, + "validation": validation + } + except Exception as e: + logging.error(f"Error in dependency management: {str(e)}") + return {"success": False, "error": str(e)} + +class CodeReviewStrategy(ReasoningStrategy): + """ + Advanced code review strategy that: + 1. Analyzes code quality + 2. Identifies issues + 3. Suggests improvements + 4. Tracks changes + 5. Validates fixes + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Perform comprehensive code review.""" + try: + # Analyze quality + quality = await self._analyze_quality(query, context) + + # Identify issues + issues = await self._identify_issues(quality, context) + + # Generate suggestions + suggestions = await self._generate_suggestions(issues, context) + + # Track changes + tracking = await self._track_changes(suggestions, context) + + return { + "success": True, + "quality": quality, + "issues": issues, + "suggestions": suggestions, + "tracking": tracking, + "metrics": { + "quality_score": quality.get("score", 0.0), + "issues_found": len(issues), + "suggestions_made": len(suggestions) + } + } + except Exception as e: + logging.error(f"Error in code review: {str(e)}") + return {"success": False, "error": str(e)} diff --git a/reasoning/tree_of_thoughts.py b/reasoning/tree_of_thoughts.py new file mode 100644 index 0000000000000000000000000000000000000000..2242cb163050f69badc78d05cb40271e8fda2638 --- /dev/null +++ b/reasoning/tree_of_thoughts.py @@ -0,0 +1,516 @@ +"""Tree of Thoughts reasoning implementation with advanced tree exploration.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Tuple +import json +from dataclasses import dataclass +from enum import Enum +import heapq +from collections import defaultdict + +from .base import ReasoningStrategy + +class NodeType(Enum): + """Types of nodes in the thought tree.""" + ROOT = "root" + HYPOTHESIS = "hypothesis" + EVIDENCE = "evidence" + ANALYSIS = "analysis" + SYNTHESIS = "synthesis" + EVALUATION = "evaluation" + CONCLUSION = "conclusion" + +@dataclass +class TreeNode: + """Represents a node in the thought tree.""" + id: str + type: NodeType + content: str + confidence: float + children: List['TreeNode'] + parent: Optional['TreeNode'] + metadata: Dict[str, Any] + depth: int + evaluation_score: float = 0.0 + +class TreeOfThoughtsStrategy(ReasoningStrategy): + """ + Advanced Tree of Thoughts reasoning implementation with: + - Beam search for path exploration + - Dynamic node evaluation + - Pruning strategies + - Path optimization + - Meta-learning from tree patterns + """ + + def __init__(self, + min_confidence: float = 0.7, + parallel_threshold: int = 3, + learning_rate: float = 0.1, + strategy_weights: Optional[Dict[str, float]] = None): + self.min_confidence = min_confidence + self.parallel_threshold = parallel_threshold + self.learning_rate = learning_rate + self.strategy_weights = strategy_weights or { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + } + self.node_history: Dict[str, TreeNode] = {} + self.path_patterns: Dict[str, float] = defaultdict(float) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing tree of thoughts.""" + try: + # Initialize root node + root = await self._create_root_node(query, context) + + # Build and explore tree + tree = await self._build_tree(root, context) + + # Find best paths + paths = await self._find_best_paths(tree, context) + + # Synthesize conclusion + conclusion = await self._synthesize_conclusion(paths, context) + + # Update history and patterns + self._update_history(tree) + self._update_patterns(paths) + + return { + "success": True, + "answer": conclusion["answer"], + "confidence": conclusion["confidence"], + "tree": self._tree_to_dict(tree), + "best_paths": [self._path_to_dict(p) for p in paths], + "reasoning_trace": conclusion["trace"], + "meta_insights": conclusion["meta_insights"] + } + except Exception as e: + logging.error(f"Error in tree of thoughts reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _create_root_node(self, query: str, context: Dict[str, Any]) -> TreeNode: + """Create the root node of the thought tree.""" + prompt = f""" + Initialize root thought node for query: + Query: {query} + Context: {json.dumps(context)} + + Provide: + 1. Initial problem decomposition + 2. Key aspects to explore + 3. Evaluation criteria + 4. Success metrics + + Format as: + [Root] + Decomposition: ... + Aspects: ... + Criteria: ... + Metrics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_root_node(response["answer"], query) + + async def _build_tree(self, root: TreeNode, context: Dict[str, Any]) -> TreeNode: + """Build and explore the thought tree.""" + # Initialize beam with root + beam = [(root.evaluation_score, root)] + visited: Set[str] = set() + + for depth in range(5): + next_beam = [] + + for _, node in beam: + if node.id in visited: + continue + + visited.add(node.id) + + # Generate child nodes + children = await self._generate_children(node, context) + + # Evaluate and filter children + evaluated_children = await self._evaluate_nodes(children, context) + + # Add to beam + for child in evaluated_children: + if child.evaluation_score > 0.4: + next_beam.append((child.evaluation_score, child)) + node.children.append(child) + + # Select best nodes for next iteration + beam = heapq.nlargest(3, next_beam, key=lambda x: x[0]) + + if not beam: + break + + return root + + async def _generate_children(self, parent: TreeNode, context: Dict[str, Any]) -> List[TreeNode]: + """Generate child nodes for a given parent.""" + prompt = f""" + Generate child thoughts for node: + Parent: {json.dumps(self._node_to_dict(parent))} + Context: {json.dumps(context)} + + For each child provide: + 1. [Type]: {" | ".join([t.value for t in NodeType if t != NodeType.ROOT])} + 2. [Content]: Main thought + 3. [Confidence]: 0-1 score + 4. [Rationale]: Why this follows from parent + 5. [Potential]: Future exploration potential + + Format as: + [C1] + Type: ... + Content: ... + Confidence: ... + Rationale: ... + Potential: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_child_nodes(response["answer"], parent) + + async def _evaluate_nodes(self, nodes: List[TreeNode], context: Dict[str, Any]) -> List[TreeNode]: + """Evaluate a list of nodes.""" + prompt = f""" + Evaluate thought nodes: + Nodes: {json.dumps([self._node_to_dict(n) for n in nodes])} + Context: {json.dumps(context)} + + For each node evaluate: + 1. Logical coherence + 2. Evidence support + 3. Novelty value + 4. Exploration potential + + Format as: + [N1] + Coherence: 0-1 + Evidence: 0-1 + Novelty: 0-1 + Potential: 0-1 + Overall: 0-1 + """ + + response = await context["groq_api"].predict(prompt) + return self._apply_evaluations(nodes, response["answer"]) + + async def _find_best_paths(self, root: TreeNode, context: Dict[str, Any]) -> List[List[TreeNode]]: + """Find the best paths through the tree.""" + paths = [] + current_path = [root] + + def dfs(node: TreeNode, path: List[TreeNode]): + if not node.children: + paths.append(path[:]) + return + + # Sort children by score + sorted_children = sorted(node.children, key=lambda x: x.evaluation_score, reverse=True) + + # Explore top paths + for child in sorted_children[:3]: + path.append(child) + dfs(child, path) + path.pop() + + dfs(root, current_path) + + # Evaluate complete paths + evaluated_paths = await self._evaluate_paths(paths, context) + + # Return top paths + return sorted(evaluated_paths, key=lambda p: sum(n.evaluation_score for n in p), reverse=True)[:3] + + async def _synthesize_conclusion(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> Dict[str, Any]: + """Synthesize final conclusion from best paths.""" + prompt = f""" + Synthesize conclusion from thought paths: + Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level + 3. Reasoning trace + 4. Supporting evidence + 5. Alternative perspectives + 6. Meta-insights + + Format as: + [Conclusion] + Answer: ... + Confidence: ... + Trace: ... + Evidence: ... + Alternatives: ... + + [Meta] + Insights: ... + Patterns: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_conclusion(response["answer"]) + + def _parse_root_node(self, response: str, query: str) -> TreeNode: + """Parse root node from response.""" + root = TreeNode( + id="root", + type=NodeType.ROOT, + content=query, + confidence=1.0, + children=[], + parent=None, + metadata={}, + depth=0 + ) + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Decomposition:'): + root.metadata["decomposition"] = line[14:].strip() + elif line.startswith('Aspects:'): + root.metadata["aspects"] = [a.strip() for a in line[8:].split(',')] + elif line.startswith('Criteria:'): + root.metadata["criteria"] = [c.strip() for c in line[9:].split(',')] + elif line.startswith('Metrics:'): + root.metadata["metrics"] = [m.strip() for m in line[8:].split(',')] + + return root + + def _parse_child_nodes(self, response: str, parent: TreeNode) -> List[TreeNode]: + """Parse child nodes from response.""" + children = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[C'): + if current: + children.append(current) + current = None + elif line.startswith('Type:'): + type_str = line[5:].strip() + try: + node_type = NodeType(type_str.lower()) + current = TreeNode( + id=f"{parent.id}_{len(children)}", + type=node_type, + content="", + confidence=0.0, + children=[], + parent=parent, + metadata={}, + depth=parent.depth + 1 + ) + except ValueError: + logging.warning(f"Invalid node type: {type_str}") + elif current: + if line.startswith('Content:'): + current.content = line[8:].strip() + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + current.confidence = 0.5 + elif line.startswith('Rationale:'): + current.metadata["rationale"] = line[10:].strip() + elif line.startswith('Potential:'): + current.metadata["potential"] = line[10:].strip() + + if current: + children.append(current) + + return children + + def _apply_evaluations(self, nodes: List[TreeNode], response: str) -> List[TreeNode]: + """Apply evaluation scores to nodes.""" + current_node_idx = 0 + current_scores = {} + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[N'): + if current_scores and current_node_idx < len(nodes): + nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0) + nodes[current_node_idx].metadata.update(current_scores) + current_node_idx += 1 + current_scores = {} + elif ':' in line: + key, value = line.split(':') + try: + current_scores[key.strip()] = float(value.strip()) + except: + pass + + if current_scores and current_node_idx < len(nodes): + nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0) + nodes[current_node_idx].metadata.update(current_scores) + + return nodes + + async def _evaluate_paths(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> List[List[TreeNode]]: + """Evaluate complete reasoning paths.""" + prompt = f""" + Evaluate complete reasoning paths: + Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])} + Context: {json.dumps(context)} + + For each path evaluate: + 1. Coherence of progression + 2. Evidence support + 3. Conclusion strength + 4. Novel insights + + Format as: + [P1] + Coherence: 0-1 + Evidence: 0-1 + Conclusion: 0-1 + Insights: 0-1 + Overall: 0-1 + """ + + response = await context["groq_api"].predict(prompt) + scores = self._parse_path_scores(response["answer"]) + + # Apply scores to paths + for i, path in enumerate(paths): + if i < len(scores): + for node in path: + node.evaluation_score *= scores[i] + + return paths + + def _parse_path_scores(self, response: str) -> List[float]: + """Parse path evaluation scores.""" + scores = [] + current_score = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current_score is not None: + scores.append(current_score) + current_score = None + elif line.startswith('Overall:'): + try: + current_score = float(line[8:].strip()) + except: + current_score = 0.5 + + if current_score is not None: + scores.append(current_score) + + return scores + + def _parse_conclusion(self, response: str) -> Dict[str, Any]: + """Parse final conclusion.""" + conclusion = { + "answer": "", + "confidence": 0.0, + "trace": [], + "evidence": [], + "alternatives": [], + "meta_insights": [] + } + + section = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Conclusion]'): + section = "conclusion" + elif line.startswith('[Meta]'): + section = "meta" + elif section == "conclusion": + if line.startswith('Answer:'): + conclusion["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + conclusion["confidence"] = float(line[11:].strip()) + except: + conclusion["confidence"] = 0.5 + elif line.startswith('Trace:'): + conclusion["trace"] = [t.strip() for t in line[6:].split(',')] + elif line.startswith('Evidence:'): + conclusion["evidence"] = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')] + elif section == "meta": + if line.startswith('Insights:'): + conclusion["meta_insights"].extend([i.strip() for i in line[9:].split(',')]) + + return conclusion + + def _node_to_dict(self, node: TreeNode) -> Dict[str, Any]: + """Convert node to dictionary for serialization.""" + return { + "id": node.id, + "type": node.type.value, + "content": node.content, + "confidence": node.confidence, + "evaluation_score": node.evaluation_score, + "metadata": node.metadata, + "depth": node.depth + } + + def _tree_to_dict(self, root: TreeNode) -> Dict[str, Any]: + """Convert entire tree to dictionary.""" + def convert_node(node: TreeNode) -> Dict[str, Any]: + node_dict = self._node_to_dict(node) + node_dict["children"] = [convert_node(c) for c in node.children] + return node_dict + + return convert_node(root) + + def _path_to_dict(self, path: List[TreeNode]) -> List[Dict[str, Any]]: + """Convert path to dictionary.""" + return [self._node_to_dict(n) for n in path] + + def _update_history(self, root: TreeNode): + """Update node history.""" + def add_to_history(node: TreeNode): + self.node_history[node.id] = node + for child in node.children: + add_to_history(child) + + add_to_history(root) + + def _update_patterns(self, paths: List[List[TreeNode]]): + """Update path patterns.""" + for path in paths: + pattern = "->".join(n.type.value for n in path) + self.path_patterns[pattern] += path[-1].evaluation_score + + def get_node_history(self) -> Dict[str, Dict[str, Any]]: + """Get history of all nodes.""" + return {k: self._node_to_dict(v) for k, v in self.node_history.items()} + + def get_successful_patterns(self) -> Dict[str, float]: + """Get successful reasoning patterns.""" + return dict(sorted(self.path_patterns.items(), key=lambda x: x[1], reverse=True)) + + def clear_history(self): + """Clear node history and patterns.""" + self.node_history.clear() + self.path_patterns.clear() diff --git a/reasoning/unified_engine.py b/reasoning/unified_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..3d751ecf7595b588dfb456ea8537a968984e9f4b --- /dev/null +++ b/reasoning/unified_engine.py @@ -0,0 +1,707 @@ +"""Unified reasoning engine that combines multiple reasoning strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict +import numpy as np + +from .base import ReasoningStrategy +from .groq_strategy import GroqStrategy +from .chain_of_thought import ChainOfThoughtStrategy +from .tree_of_thoughts import TreeOfThoughtsStrategy +from .meta_learning import MetaLearningStrategy +from .recursive import RecursiveReasoning +from .analogical import AnalogicalReasoning +from .local_llm import LocalLLMStrategy +from .agentic import ( + TaskDecompositionStrategy, + ResourceManagementStrategy, + ContextualPlanningStrategy, + AdaptiveExecutionStrategy, + FeedbackIntegrationStrategy +) +# Import additional strategies +from .bayesian import BayesianStrategy +from .market_analysis import MarketAnalysisStrategy +from .monetization import MonetizationStrategy +from .multimodal import MultimodalStrategy +from .neurosymbolic import NeurosymbolicStrategy +from .portfolio_optimization import PortfolioOptimizationStrategy +from .specialized import SpecializedStrategy +from .venture_strategies import VentureStrategy +from .venture_types import VentureTypeStrategy + +class StrategyType(str, Enum): + """Types of reasoning strategies.""" + GROQ = "groq" + CHAIN_OF_THOUGHT = "chain_of_thought" + TREE_OF_THOUGHTS = "tree_of_thoughts" + META_LEARNING = "meta_learning" + RECURSIVE = "recursive" + ANALOGICAL = "analogical" + LOCAL_LLM = "local_llm" + TASK_DECOMPOSITION = "task_decomposition" + RESOURCE_MANAGEMENT = "resource_management" + CONTEXTUAL_PLANNING = "contextual_planning" + ADAPTIVE_EXECUTION = "adaptive_execution" + FEEDBACK_INTEGRATION = "feedback_integration" + BAYESIAN = "bayesian" + MARKET_ANALYSIS = "market_analysis" + MONETIZATION = "monetization" + MULTIMODAL = "multimodal" + NEUROSYMBOLIC = "neurosymbolic" + PORTFOLIO_OPTIMIZATION = "portfolio_optimization" + SPECIALIZED = "specialized" + VENTURE = "venture" + VENTURE_TYPE = "venture_type" + +@dataclass +class StrategyResult: + """Result from a reasoning strategy.""" + strategy_type: StrategyType + success: bool + answer: Optional[str] + confidence: float + reasoning_trace: List[Dict[str, Any]] + metadata: Dict[str, Any] + performance_metrics: Dict[str, Any] + timestamp: datetime = field(default_factory=datetime.now) + +@dataclass +class UnifiedResult: + """Combined result from multiple strategies.""" + success: bool + answer: str + confidence: float + strategy_results: Dict[StrategyType, StrategyResult] + synthesis_method: str + meta_insights: List[str] + performance_metrics: Dict[str, Any] + timestamp: datetime = field(default_factory=datetime.now) + +class UnifiedReasoningEngine: + """ + Advanced unified reasoning engine that: + 1. Combines multiple reasoning strategies + 2. Dynamically selects and weights strategies + 3. Synthesizes results from different approaches + 4. Learns from experience + 5. Adapts to different types of tasks + """ + + def __init__(self, + min_confidence: float = 0.7, + strategy_weights: Optional[Dict[StrategyType, float]] = None, + parallel_threshold: int = 3, + learning_rate: float = 0.1): + self.min_confidence = min_confidence + self.parallel_threshold = parallel_threshold + self.learning_rate = learning_rate + + # Initialize strategies + self.strategies: Dict[StrategyType, ReasoningStrategy] = { + # Primary strategy (Groq) + StrategyType.GROQ: GroqStrategy(), + + # Core strategies + StrategyType.CHAIN_OF_THOUGHT: ChainOfThoughtStrategy(), + StrategyType.TREE_OF_THOUGHTS: TreeOfThoughtsStrategy(), + StrategyType.META_LEARNING: MetaLearningStrategy(), + StrategyType.RECURSIVE: RecursiveReasoning(), + StrategyType.ANALOGICAL: AnalogicalReasoning(), + StrategyType.LOCAL_LLM: LocalLLMStrategy(), + + # Agentic strategies + StrategyType.TASK_DECOMPOSITION: TaskDecompositionStrategy(), + StrategyType.RESOURCE_MANAGEMENT: ResourceManagementStrategy(), + StrategyType.CONTEXTUAL_PLANNING: ContextualPlanningStrategy(), + StrategyType.ADAPTIVE_EXECUTION: AdaptiveExecutionStrategy(), + StrategyType.FEEDBACK_INTEGRATION: FeedbackIntegrationStrategy(), + + # Additional specialized strategies + StrategyType.BAYESIAN: BayesianStrategy(), + StrategyType.MARKET_ANALYSIS: MarketAnalysisStrategy(), + StrategyType.MONETIZATION: MonetizationStrategy(), + StrategyType.MULTIMODAL: MultimodalStrategy(), + StrategyType.NEUROSYMBOLIC: NeurosymbolicStrategy(), + StrategyType.PORTFOLIO_OPTIMIZATION: PortfolioOptimizationStrategy(), + StrategyType.SPECIALIZED: SpecializedStrategy(), + StrategyType.VENTURE: VentureStrategy(), + StrategyType.VENTURE_TYPE: VentureTypeStrategy() + } + + # Strategy weights with Groq as primary + self.strategy_weights = strategy_weights or { + # Primary strategy (highest weight) + StrategyType.GROQ: 2.5, + + # Core strategies (high weights) + StrategyType.CHAIN_OF_THOUGHT: 1.5, + StrategyType.TREE_OF_THOUGHTS: 1.5, + StrategyType.META_LEARNING: 1.5, + + # Agentic strategies (medium-high weights) + StrategyType.TASK_DECOMPOSITION: 1.3, + StrategyType.RESOURCE_MANAGEMENT: 1.3, + StrategyType.CONTEXTUAL_PLANNING: 1.3, + StrategyType.ADAPTIVE_EXECUTION: 1.3, + StrategyType.FEEDBACK_INTEGRATION: 1.3, + + # Domain-specific strategies (context-dependent weights) + StrategyType.BAYESIAN: 1.2, + StrategyType.MARKET_ANALYSIS: 1.2, + StrategyType.PORTFOLIO_OPTIMIZATION: 1.2, + StrategyType.VENTURE: 1.2, + + # Other specialized strategies (base weights) + StrategyType.MONETIZATION: 1.0, + StrategyType.MULTIMODAL: 1.0, + StrategyType.NEUROSYMBOLIC: 1.0, + StrategyType.SPECIALIZED: 1.0, + StrategyType.VENTURE_TYPE: 1.0, + StrategyType.RECURSIVE: 1.0, + StrategyType.ANALOGICAL: 1.0, + StrategyType.LOCAL_LLM: 1.0 # Reduced weight since using Groq + } + + # Performance tracking + self.strategy_performance: Dict[StrategyType, List[float]] = defaultdict(list) + self.task_type_performance: Dict[str, Dict[StrategyType, float]] = defaultdict(lambda: defaultdict(float)) + self.synthesis_performance: Dict[str, List[float]] = defaultdict(list) + + async def reason(self, query: str, context: Dict[str, Any]) -> UnifiedResult: + """Main reasoning method combining multiple strategies.""" + try: + # Analyze task + task_analysis = await self._analyze_task(query, context) + + # Select strategies + selected_strategies = await self._select_strategies(task_analysis, context) + + # Execute strategies + strategy_results = await self._execute_strategies( + selected_strategies, query, context) + + # Synthesize results + unified_result = await self._synthesize_results( + strategy_results, task_analysis, context) + + # Learn from experience + self._update_performance(unified_result) + + return unified_result + + except Exception as e: + logging.error(f"Error in unified reasoning: {str(e)}") + return UnifiedResult( + success=False, + answer=f"Error: {str(e)}", + confidence=0.0, + strategy_results={}, + synthesis_method="failed", + meta_insights=[f"Error occurred: {str(e)}"], + performance_metrics={} + ) + + async def reason_stream( + self, + query: str, + context: Dict[str, Any] = None, + strategy_type: Optional[StrategyType] = None, + chunk_handler: Optional[callable] = None + ) -> AsyncGenerator[str, None]: + """ + Stream reasoning results from the selected strategy. + + Args: + query: Query to reason about + context: Additional context for reasoning + strategy_type: Specific strategy to use (optional) + chunk_handler: Optional callback for handling chunks + """ + context = context or {} + + # Default to Groq strategy for streaming + if not strategy_type: + strategy_type = StrategyType.GROQ + + strategy = self.strategies.get(strategy_type) + if not strategy: + yield f"Error: Strategy {strategy_type} not found" + return + + if not hasattr(strategy, 'reason_stream'): + yield f"Error: Strategy {strategy_type} does not support streaming" + return + + try: + async for chunk in strategy.reason_stream( + query=query, + context=context, + chunk_handler=chunk_handler + ): + yield chunk + except Exception as e: + logging.error(f"Streaming error: {str(e)}") + yield f"Error: {str(e)}" + + async def _analyze_task(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze the task to determine optimal strategy selection.""" + prompt = f""" + Analyze reasoning task: + Query: {query} + Context: {json.dumps(context)} + + Determine: + 1. Task type and complexity + 2. Required reasoning capabilities + 3. Resource requirements + 4. Success criteria + 5. Risk factors + + Format as: + [Analysis] + Type: ... + Complexity: ... + Capabilities: ... + Resources: ... + Criteria: ... + Risks: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_task_analysis(response["answer"]) + + async def _select_strategies(self, task_analysis: Dict[str, Any], context: Dict[str, Any]) -> List[StrategyType]: + """Select appropriate strategies based on task analysis.""" + # Calculate strategy scores + scores: Dict[StrategyType, float] = {} + for strategy_type in StrategyType: + base_score = self.strategy_weights[strategy_type] + + # Task type performance + task_type = task_analysis["type"] + type_score = self.task_type_performance[task_type][strategy_type] + + # Recent performance + recent_performance = ( + sum(self.strategy_performance[strategy_type][-5:]) / 5 + if self.strategy_performance[strategy_type] else 0.5 + ) + + # Resource match + resource_match = self._calculate_resource_match( + strategy_type, task_analysis["resources"]) + + # Capability match + capability_match = self._calculate_capability_match( + strategy_type, task_analysis["capabilities"]) + + # Combined score + scores[strategy_type] = ( + 0.3 * base_score + + 0.2 * type_score + + 0.2 * recent_performance + + 0.15 * resource_match + + 0.15 * capability_match + ) + + # Select top strategies + selected = sorted( + StrategyType, + key=lambda x: scores[x], + reverse=True + )[:self.parallel_threshold] + + return selected + + async def _execute_strategies(self, + strategies: List[StrategyType], + query: str, + context: Dict[str, Any]) -> Dict[StrategyType, StrategyResult]: + """Execute selected strategies in parallel.""" + async def execute_strategy(strategy_type: StrategyType) -> StrategyResult: + strategy = self.strategies[strategy_type] + start_time = datetime.now() + + try: + result = await strategy.reason(query, context) + + return StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics={ + "execution_time": (datetime.now() - start_time).total_seconds(), + **result.get("performance_metrics", {}) + } + ) + except Exception as e: + logging.error(f"Error in strategy {strategy_type}: {str(e)}") + return StrategyResult( + strategy_type=strategy_type, + success=False, + answer=None, + confidence=0.0, + reasoning_trace=[{"error": str(e)}], + metadata={}, + performance_metrics={"execution_time": (datetime.now() - start_time).total_seconds()} + ) + + # Execute strategies in parallel + tasks = [execute_strategy(strategy) for strategy in strategies] + results = await asyncio.gather(*tasks) + + return {result.strategy_type: result for result in results} + + async def _synthesize_results(self, + strategy_results: Dict[StrategyType, StrategyResult], + task_analysis: Dict[str, Any], + context: Dict[str, Any]) -> UnifiedResult: + """Synthesize results from multiple strategies with specialized combination methods.""" + if not strategy_results: + return UnifiedResult( + success=False, + answer="No strategy results available", + confidence=0.0, + strategy_results={}, + synthesis_method="none", + meta_insights=[], + performance_metrics={} + ) + + # Group results by strategy category + core_results = {k: v for k, v in strategy_results.items() + if k in {StrategyType.CHAIN_OF_THOUGHT, StrategyType.TREE_OF_THOUGHTS, + StrategyType.META_LEARNING, StrategyType.LOCAL_LLM}} + + agentic_results = {k: v for k, v in strategy_results.items() + if k in {StrategyType.TASK_DECOMPOSITION, StrategyType.RESOURCE_MANAGEMENT, + StrategyType.CONTEXTUAL_PLANNING, StrategyType.ADAPTIVE_EXECUTION, + StrategyType.FEEDBACK_INTEGRATION}} + + market_results = {k: v for k, v in strategy_results.items() + if k in {StrategyType.MARKET_ANALYSIS, StrategyType.PORTFOLIO_OPTIMIZATION, + StrategyType.VENTURE, StrategyType.MONETIZATION}} + + analytical_results = {k: v for k, v in strategy_results.items() + if k in {StrategyType.BAYESIAN, StrategyType.NEUROSYMBOLIC, + StrategyType.SPECIALIZED, StrategyType.MULTIMODAL}} + + # Determine synthesis method based on task type and available results + task_type = task_analysis.get('task_type', 'general') + synthesis_method = self._determine_synthesis_method(task_type, strategy_results.keys()) + + # Apply specialized synthesis based on method + if synthesis_method == "weighted_voting": + final_result = await self._weighted_voting_synthesis(strategy_results) + elif synthesis_method == "market_focused": + final_result = await self._market_focused_synthesis(market_results, core_results) + elif synthesis_method == "analytical_consensus": + final_result = await self._analytical_consensus_synthesis(analytical_results, core_results) + elif synthesis_method == "agentic_orchestration": + final_result = await self._agentic_orchestration_synthesis(agentic_results, strategy_results) + else: + final_result = await self._ensemble_synthesis(strategy_results) + + # Generate meta-insights about the synthesis process + meta_insights = self._generate_meta_insights(strategy_results, synthesis_method) + + # Calculate aggregate performance metrics + performance_metrics = self._calculate_synthesis_metrics(strategy_results, final_result) + + return UnifiedResult( + success=final_result['success'], + answer=final_result['answer'], + confidence=final_result['confidence'], + strategy_results=strategy_results, + synthesis_method=synthesis_method, + meta_insights=meta_insights, + performance_metrics=performance_metrics + ) + + def _determine_synthesis_method(self, task_type: str, available_strategies: Set[StrategyType]) -> str: + """Determine the best synthesis method based on task type and available strategies.""" + market_strategies = {StrategyType.MARKET_ANALYSIS, StrategyType.PORTFOLIO_OPTIMIZATION, + StrategyType.VENTURE, StrategyType.MONETIZATION} + analytical_strategies = {StrategyType.BAYESIAN, StrategyType.NEUROSYMBOLIC} + agentic_strategies = {StrategyType.TASK_DECOMPOSITION, StrategyType.RESOURCE_MANAGEMENT, + StrategyType.CONTEXTUAL_PLANNING} + + # Calculate strategy type coverage + market_coverage = len(market_strategies.intersection(available_strategies)) + analytical_coverage = len(analytical_strategies.intersection(available_strategies)) + agentic_coverage = len(agentic_strategies.intersection(available_strategies)) + + if task_type in ['market_analysis', 'investment'] and market_coverage >= 2: + return "market_focused" + elif task_type in ['analysis', 'prediction'] and analytical_coverage >= 2: + return "analytical_consensus" + elif task_type in ['planning', 'execution'] and agentic_coverage >= 2: + return "agentic_orchestration" + else: + return "weighted_voting" + + async def _weighted_voting_synthesis(self, strategy_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]: + """Combine results using weighted voting based on strategy confidence and historical performance.""" + weighted_answers = defaultdict(float) + total_weight = 0 + + for strategy_type, result in strategy_results.items(): + # Calculate weight based on strategy confidence and historical performance + historical_performance = np.mean(self.strategy_performance[strategy_type]) if self.strategy_performance[strategy_type] else 1.0 + weight = self.strategy_weights[strategy_type] * result.confidence * historical_performance + + weighted_answers[result.answer] += weight + total_weight += weight + + if not total_weight: + return {'success': False, 'answer': '', 'confidence': 0.0} + + # Select answer with highest weighted votes + best_answer = max(weighted_answers.items(), key=lambda x: x[1]) + confidence = best_answer[1] / total_weight + + return { + 'success': confidence >= self.min_confidence, + 'answer': best_answer[0], + 'confidence': confidence + } + + async def _market_focused_synthesis(self, market_results: Dict[StrategyType, StrategyResult], + core_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]: + """Synthesize results with emphasis on market-related strategies.""" + market_consensus = await self._weighted_voting_synthesis(market_results) + core_consensus = await self._weighted_voting_synthesis(core_results) + + # Combine market and core insights with higher weight for market results + if market_consensus['confidence'] >= self.min_confidence: + return { + 'success': True, + 'answer': f"{market_consensus['answer']} (Supported by core analysis: {core_consensus['answer']})", + 'confidence': 0.7 * market_consensus['confidence'] + 0.3 * core_consensus['confidence'] + } + else: + return core_consensus + + async def _analytical_consensus_synthesis(self, analytical_results: Dict[StrategyType, StrategyResult], + core_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]: + """Synthesize results with emphasis on analytical and probabilistic reasoning.""" + analytical_consensus = await self._weighted_voting_synthesis(analytical_results) + core_consensus = await self._weighted_voting_synthesis(core_results) + + # Combine analytical and core insights with uncertainty quantification + if analytical_consensus['confidence'] >= self.min_confidence: + return { + 'success': True, + 'answer': f"{analytical_consensus['answer']} (Confidence interval: {analytical_consensus['confidence']:.2f})", + 'confidence': 0.6 * analytical_consensus['confidence'] + 0.4 * core_consensus['confidence'] + } + else: + return core_consensus + + async def _agentic_orchestration_synthesis(self, agentic_results: Dict[StrategyType, StrategyResult], + all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]: + """Synthesize results with emphasis on task decomposition and execution planning.""" + # Extract task decomposition and planning insights + task_structure = self._extract_task_structure(agentic_results) + execution_plan = self._create_execution_plan(task_structure, all_results) + + # Combine results according to the execution plan + synthesized_result = self._execute_synthesis_plan(execution_plan, all_results) + + return { + 'success': synthesized_result['confidence'] >= self.min_confidence, + 'answer': synthesized_result['answer'], + 'confidence': synthesized_result['confidence'] + } + + def _generate_meta_insights(self, strategy_results: Dict[StrategyType, StrategyResult], + synthesis_method: str) -> List[str]: + """Generate meta-insights about the synthesis process and strategy performance.""" + insights = [] + + # Analyze strategy agreement + agreement_rate = self._calculate_strategy_agreement(strategy_results) + insights.append(f"Strategy agreement rate: {agreement_rate:.2f}") + + # Identify strongest and weakest strategies + strategy_performances = [(st, res.confidence) for st, res in strategy_results.items()] + best_strategy = max(strategy_performances, key=lambda x: x[1]) + worst_strategy = min(strategy_performances, key=lambda x: x[1]) + + insights.append(f"Most confident strategy: {best_strategy[0]} ({best_strategy[1]:.2f})") + insights.append(f"Synthesis method used: {synthesis_method}") + + return insights + + def _calculate_synthesis_metrics(self, strategy_results: Dict[StrategyType, StrategyResult], + final_result: Dict[str, Any]) -> Dict[str, Any]: + """Calculate comprehensive metrics about the synthesis process.""" + return { + 'strategy_count': len(strategy_results), + 'average_confidence': np.mean([r.confidence for r in strategy_results.values()]), + 'confidence_std': np.std([r.confidence for r in strategy_results.values()]), + 'final_confidence': final_result['confidence'], + 'strategy_agreement': self._calculate_strategy_agreement(strategy_results) + } + + def _update_performance(self, result: UnifiedResult): + """Update performance metrics and strategy weights.""" + # Update strategy performance + for strategy_type, strategy_result in result.strategy_results.items(): + self.strategy_performance[strategy_type].append(strategy_result.confidence) + + # Update weights using exponential moving average + current_weight = self.strategy_weights[strategy_type] + performance = strategy_result.confidence + self.strategy_weights[strategy_type] = ( + (1 - self.learning_rate) * current_weight + + self.learning_rate * performance + ) + + # Update synthesis performance + self.synthesis_performance[result.synthesis_method].append(result.confidence) + + def _calculate_resource_match(self, strategy_type: StrategyType, required_resources: Dict[str, Any]) -> float: + """Calculate how well a strategy matches required resources.""" + # Implementation-specific resource matching logic + return 0.8 # Placeholder + + def _calculate_capability_match(self, strategy_type: StrategyType, required_capabilities: List[str]) -> float: + """Calculate how well a strategy matches required capabilities.""" + # Implementation-specific capability matching logic + return 0.8 # Placeholder + + def _parse_task_analysis(self, response: str) -> Dict[str, Any]: + """Parse task analysis from response.""" + analysis = { + "type": "", + "complexity": 0.0, + "capabilities": [], + "resources": {}, + "criteria": [], + "risks": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Type:'): + analysis["type"] = line[5:].strip() + elif line.startswith('Complexity:'): + try: + analysis["complexity"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Capabilities:'): + analysis["capabilities"] = [c.strip() for c in line[13:].split(',')] + elif line.startswith('Resources:'): + try: + analysis["resources"] = json.loads(line[10:].strip()) + except: + analysis["resources"] = {"raw": line[10:].strip()} + elif line.startswith('Criteria:'): + analysis["criteria"] = [c.strip() for c in line[9:].split(',')] + elif line.startswith('Risks:'): + analysis["risks"] = [r.strip() for r in line[7:].split(',')] + + return analysis + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis result from response.""" + synthesis = { + "method": "", + "answer": "", + "confidence": 0.0, + "insights": [], + "performance": {} + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Method:'): + synthesis["method"] = line[7:].strip() + elif line.startswith('Answer:'): + synthesis["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Insights:'): + synthesis["insights"] = [i.strip() for i in line[9:].split(',')] + elif line.startswith('Performance:'): + try: + synthesis["performance"] = json.loads(line[12:].strip()) + except: + synthesis["performance"] = {"raw": line[12:].strip()} + + return synthesis + + def _strategy_result_to_dict(self, result: StrategyResult) -> Dict[str, Any]: + """Convert strategy result to dictionary for serialization.""" + return { + "strategy_type": result.strategy_type.value, + "success": result.success, + "answer": result.answer, + "confidence": result.confidence, + "reasoning_trace": result.reasoning_trace, + "metadata": result.metadata, + "performance_metrics": result.performance_metrics, + "timestamp": result.timestamp.isoformat() + } + + def get_performance_metrics(self) -> Dict[str, Any]: + """Get comprehensive performance metrics.""" + return { + "strategy_weights": dict(self.strategy_weights), + "average_performance": { + strategy_type.value: sum(scores) / len(scores) if scores else 0 + for strategy_type, scores in self.strategy_performance.items() + }, + "synthesis_success": { + method: sum(scores) / len(scores) if scores else 0 + for method, scores in self.synthesis_performance.items() + }, + "task_type_performance": { + task_type: dict(strategy_scores) + for task_type, strategy_scores in self.task_type_performance.items() + } + } + + def clear_performance_history(self): + """Clear performance history and reset weights.""" + self.strategy_performance.clear() + self.task_type_performance.clear() + self.synthesis_performance.clear() + self.strategy_weights = { + strategy_type: 1.0 for strategy_type in StrategyType + } + + def _extract_task_structure(self, agentic_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]: + """Extract task structure from agentic strategy results.""" + # Implementation-specific task structure extraction logic + return {} + + def _create_execution_plan(self, task_structure: Dict[str, Any], all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]: + """Create execution plan based on task structure and strategy results.""" + # Implementation-specific execution plan creation logic + return {} + + def _execute_synthesis_plan(self, execution_plan: Dict[str, Any], all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]: + """Execute synthesis plan and combine results.""" + # Implementation-specific synthesis plan execution logic + return {} + + def _calculate_strategy_agreement(self, strategy_results: Dict[StrategyType, StrategyResult]) -> float: + """Calculate agreement rate among strategies.""" + # Implementation-specific strategy agreement calculation logic + return 0.0 diff --git a/reasoning/venture_strategies.py b/reasoning/venture_strategies.py new file mode 100644 index 0000000000000000000000000000000000000000..ac444bfc7f669f43d33de1ad8d660c2d6c0890aa --- /dev/null +++ b/reasoning/venture_strategies.py @@ -0,0 +1,701 @@ +"""Specialized strategies for autonomous business and revenue generation.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +class VentureType(Enum): + """Types of business ventures.""" + AI_STARTUP = "ai_startup" + SAAS = "saas" + API_SERVICE = "api_service" + DATA_ANALYTICS = "data_analytics" + AUTOMATION_SERVICE = "automation_service" + CONSULTING = "consulting" + DIGITAL_PRODUCTS = "digital_products" + MARKETPLACE = "marketplace" + +class RevenueStream(Enum): + """Types of revenue streams.""" + SUBSCRIPTION = "subscription" + USAGE_BASED = "usage_based" + LICENSING = "licensing" + CONSULTING = "consulting" + PRODUCT_SALES = "product_sales" + COMMISSION = "commission" + ADVERTISING = "advertising" + PARTNERSHIP = "partnership" + +@dataclass +class VentureMetrics: + """Key business metrics.""" + revenue: float + profit_margin: float + customer_acquisition_cost: float + lifetime_value: float + churn_rate: float + growth_rate: float + burn_rate: float + runway_months: float + roi: float + +@dataclass +class MarketOpportunity: + """Market opportunity analysis.""" + market_size: float + growth_potential: float + competition_level: float + entry_barriers: float + regulatory_risks: float + technology_risks: float + monetization_potential: float + +class AIStartupStrategy(ReasoningStrategy): + """ + Advanced AI startup strategy that: + 1. Identifies profitable AI applications + 2. Analyzes market opportunities + 3. Develops MVP strategies + 4. Plans scaling approaches + 5. Optimizes revenue streams + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI startup strategy.""" + try: + # Market analysis + market = await self._analyze_market(query, context) + + # Technology assessment + tech = await self._assess_technology(market, context) + + # Business model + model = await self._develop_business_model(tech, context) + + # Growth strategy + strategy = await self._create_growth_strategy(model, context) + + # Financial projections + projections = await self._project_financials(strategy, context) + + return { + "success": projections["annual_profit"] >= 1_000_000, + "market_analysis": market, + "tech_assessment": tech, + "business_model": model, + "growth_strategy": strategy, + "financials": projections, + "confidence": self._calculate_confidence(projections) + } + except Exception as e: + logging.error(f"Error in AI startup strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class SaaSVentureStrategy(ReasoningStrategy): + """ + Advanced SaaS venture strategy that: + 1. Identifies scalable SaaS opportunities + 2. Develops pricing strategies + 3. Plans customer acquisition + 4. Optimizes retention + 5. Maximizes recurring revenue + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate SaaS venture strategy.""" + try: + # Opportunity analysis + opportunity = await self._analyze_opportunity(query, context) + + # Product strategy + product = await self._develop_product_strategy(opportunity, context) + + # Pricing model + pricing = await self._create_pricing_model(product, context) + + # Growth plan + growth = await self._plan_growth(pricing, context) + + # Revenue projections + projections = await self._project_revenue(growth, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "product": product, + "pricing": pricing, + "growth": growth, + "projections": projections + } + except Exception as e: + logging.error(f"Error in SaaS venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AutomationVentureStrategy(ReasoningStrategy): + """ + Advanced automation venture strategy that: + 1. Identifies automation opportunities + 2. Analyzes cost-saving potential + 3. Develops automation solutions + 4. Plans implementation + 5. Maximizes ROI + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate automation venture strategy.""" + try: + # Opportunity identification + opportunities = await self._identify_opportunities(query, context) + + # Solution development + solutions = await self._develop_solutions(opportunities, context) + + # Implementation strategy + implementation = await self._create_implementation_strategy(solutions, context) + + # ROI analysis + roi = await self._analyze_roi(implementation, context) + + # Scale strategy + scale = await self._create_scale_strategy(roi, context) + + return { + "success": roi["annual_profit"] >= 1_000_000, + "opportunities": opportunities, + "solutions": solutions, + "implementation": implementation, + "roi": roi, + "scale": scale + } + except Exception as e: + logging.error(f"Error in automation venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class DataVentureStrategy(ReasoningStrategy): + """ + Advanced data venture strategy that: + 1. Identifies valuable data opportunities + 2. Develops data products + 3. Creates monetization strategies + 4. Ensures compliance + 5. Maximizes data value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate data venture strategy.""" + try: + # Data opportunity analysis + opportunity = await self._analyze_data_opportunity(query, context) + + # Product development + product = await self._develop_data_product(opportunity, context) + + # Monetization strategy + monetization = await self._create_monetization_strategy(product, context) + + # Compliance plan + compliance = await self._ensure_compliance(monetization, context) + + # Scale plan + scale = await self._plan_scaling(compliance, context) + + return { + "success": monetization["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "product": product, + "monetization": monetization, + "compliance": compliance, + "scale": scale + } + except Exception as e: + logging.error(f"Error in data venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class APIVentureStrategy(ReasoningStrategy): + """ + Advanced API venture strategy that: + 1. Identifies API opportunities + 2. Develops API products + 3. Creates pricing models + 4. Plans scaling + 5. Maximizes API value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate API venture strategy.""" + try: + # API opportunity analysis + opportunity = await self._analyze_api_opportunity(query, context) + + # Product development + product = await self._develop_api_product(opportunity, context) + + # Pricing strategy + pricing = await self._create_api_pricing(product, context) + + # Scale strategy + scale = await self._plan_api_scaling(pricing, context) + + # Revenue projections + projections = await self._project_api_revenue(scale, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "product": product, + "pricing": pricing, + "scale": scale, + "projections": projections + } + except Exception as e: + logging.error(f"Error in API venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class MarketplaceVentureStrategy(ReasoningStrategy): + """ + Advanced marketplace venture strategy that: + 1. Identifies marketplace opportunities + 2. Develops platform strategy + 3. Plans liquidity generation + 4. Optimizes matching + 5. Maximizes transaction value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate marketplace venture strategy.""" + try: + # Opportunity analysis + opportunity = await self._analyze_marketplace_opportunity(query, context) + + # Platform strategy + platform = await self._develop_platform_strategy(opportunity, context) + + # Liquidity strategy + liquidity = await self._create_liquidity_strategy(platform, context) + + # Growth strategy + growth = await self._plan_marketplace_growth(liquidity, context) + + # Revenue projections + projections = await self._project_marketplace_revenue(growth, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "platform": platform, + "liquidity": liquidity, + "growth": growth, + "projections": projections + } + except Exception as e: + logging.error(f"Error in marketplace venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class VenturePortfolioStrategy(ReasoningStrategy): + """ + Advanced venture portfolio strategy that: + 1. Optimizes venture mix + 2. Balances risk-reward + 3. Allocates resources + 4. Manages dependencies + 5. Maximizes portfolio value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate venture portfolio strategy.""" + try: + # Portfolio analysis + analysis = await self._analyze_portfolio(query, context) + + # Venture selection + selection = await self._select_ventures(analysis, context) + + # Resource allocation + allocation = await self._allocate_resources(selection, context) + + # Risk management + risk = await self._manage_risk(allocation, context) + + # Portfolio projections + projections = await self._project_portfolio(risk, context) + + return { + "success": projections["annual_profit"] >= 1_000_000, + "analysis": analysis, + "selection": selection, + "allocation": allocation, + "risk": risk, + "projections": projections + } + except Exception as e: + logging.error(f"Error in venture portfolio strategy: {str(e)}") + return {"success": False, "error": str(e)} + + async def _analyze_portfolio(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze potential venture portfolio.""" + prompt = f""" + Analyze venture portfolio opportunities: + Query: {query} + Context: {json.dumps(context)} + + Consider: + 1. Market opportunities + 2. Technology trends + 3. Resource requirements + 4. Risk factors + 5. Synergy potential + + Format as: + [Analysis] + Opportunities: ... + Trends: ... + Resources: ... + Risks: ... + Synergies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_portfolio_analysis(response["answer"]) + + def _parse_portfolio_analysis(self, response: str) -> Dict[str, Any]: + """Parse portfolio analysis from response.""" + analysis = { + "opportunities": [], + "trends": [], + "resources": {}, + "risks": [], + "synergies": [] + } + + current_section = None + for line in response.split('\n'): + line = line.strip() + if line.startswith('Opportunities:'): + current_section = "opportunities" + elif line.startswith('Trends:'): + current_section = "trends" + elif line.startswith('Resources:'): + current_section = "resources" + elif line.startswith('Risks:'): + current_section = "risks" + elif line.startswith('Synergies:'): + current_section = "synergies" + elif current_section and line: + if current_section == "resources": + try: + key, value = line.split(':') + analysis[current_section][key.strip()] = value.strip() + except: + pass + else: + analysis[current_section].append(line) + + return analysis + + def get_venture_metrics(self) -> Dict[str, Any]: + """Get comprehensive venture metrics.""" + return { + "portfolio_metrics": { + "total_ventures": len(self.ventures), + "profitable_ventures": sum(1 for v in self.ventures if v.metrics.profit_margin > 0), + "total_revenue": sum(v.metrics.revenue for v in self.ventures), + "average_margin": np.mean([v.metrics.profit_margin for v in self.ventures]), + "portfolio_roi": np.mean([v.metrics.roi for v in self.ventures]) + }, + "market_metrics": { + "total_market_size": sum(v.opportunity.market_size for v in self.ventures), + "average_growth": np.mean([v.opportunity.growth_potential for v in self.ventures]), + "risk_score": np.mean([v.opportunity.regulatory_risks + v.opportunity.technology_risks for v in self.ventures]) + }, + "performance_metrics": { + "customer_acquisition": np.mean([v.metrics.customer_acquisition_cost for v in self.ventures]), + "lifetime_value": np.mean([v.metrics.lifetime_value for v in self.ventures]), + "churn_rate": np.mean([v.metrics.churn_rate for v in self.ventures]), + "burn_rate": sum(v.metrics.burn_rate for v in self.ventures) + } + } + +class VentureStrategy(ReasoningStrategy): + """ + Advanced venture strategy that combines multiple specialized strategies + to generate comprehensive business plans and recommendations. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize venture strategy with component strategies.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize component strategies with shared config + strategy_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights + } + + self.strategies = { + VentureType.AI_STARTUP: AIStartupStrategy(strategy_config), + VentureType.SAAS: SaaSVentureStrategy(strategy_config), + VentureType.AUTOMATION_SERVICE: AutomationVentureStrategy(strategy_config), + VentureType.DATA_ANALYTICS: DataVentureStrategy(strategy_config), + VentureType.API_SERVICE: APIVentureStrategy(strategy_config), + VentureType.MARKETPLACE: MarketplaceVentureStrategy(strategy_config) + } + + # Portfolio strategy for multi-venture optimization + self.portfolio_strategy = VenturePortfolioStrategy(strategy_config) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Generate venture strategy based on query and context. + + Args: + query: The venture strategy query + context: Additional context and parameters + + Returns: + Dict containing venture strategy and confidence scores + """ + try: + # Determine venture type from query/context + venture_type = self._determine_venture_type(query, context) + + # Get strategy for venture type + strategy = self.strategies.get(venture_type) + if not strategy: + raise ValueError(f"Unsupported venture type: {venture_type}") + + # Generate strategy + strategy_result = await strategy.reason(query, context) + + # Get portfolio analysis + portfolio_result = await self.portfolio_strategy.reason(query, context) + + # Combine results + combined_result = self._combine_results( + strategy_result, + portfolio_result, + venture_type + ) + + return { + 'answer': self._format_strategy(combined_result), + 'confidence': combined_result.get('confidence', 0.0), + 'venture_type': venture_type.value, + 'strategy': strategy_result, + 'portfolio_analysis': portfolio_result + } + + except Exception as e: + logging.error(f"Venture strategy generation failed: {str(e)}") + return { + 'error': f"Venture strategy generation failed: {str(e)}", + 'confidence': 0.0 + } + + def _determine_venture_type(self, query: str, context: Dict[str, Any]) -> VentureType: + """Determine venture type from query and context.""" + # Use context if available + if 'venture_type' in context: + return VentureType(context['venture_type']) + + # Simple keyword matching + query_lower = query.lower() + if any(term in query_lower for term in ['ai', 'ml', 'model', 'neural']): + return VentureType.AI_STARTUP + elif any(term in query_lower for term in ['saas', 'software', 'cloud']): + return VentureType.SAAS + elif any(term in query_lower for term in ['automate', 'automation', 'workflow']): + return VentureType.AUTOMATION_SERVICE + elif any(term in query_lower for term in ['data', 'analytics', 'insights']): + return VentureType.DATA_ANALYTICS + elif any(term in query_lower for term in ['api', 'service', 'endpoint']): + return VentureType.API_SERVICE + elif any(term in query_lower for term in ['marketplace', 'platform', 'network']): + return VentureType.MARKETPLACE + + # Default to AI startup if unclear + return VentureType.AI_STARTUP + + def _combine_results( + self, + strategy_result: Dict[str, Any], + portfolio_result: Dict[str, Any], + venture_type: VentureType + ) -> Dict[str, Any]: + """Combine strategy and portfolio results.""" + return { + 'venture_type': venture_type.value, + 'strategy': strategy_result.get('strategy', {}), + 'metrics': strategy_result.get('metrics', {}), + 'portfolio_fit': portfolio_result.get('portfolio_fit', {}), + 'recommendations': strategy_result.get('recommendations', []), + 'confidence': min( + strategy_result.get('confidence', 0.0), + portfolio_result.get('confidence', 0.0) + ) + } + + def _format_strategy(self, result: Dict[str, Any]) -> str: + """Format venture strategy into readable text.""" + sections = [] + + # Venture type + sections.append(f"Venture Type: {result['venture_type'].replace('_', ' ').title()}") + + # Strategy overview + if 'strategy' in result: + strategy = result['strategy'] + sections.append("\nStrategy Overview:") + for key, value in strategy.items(): + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Key metrics + if 'metrics' in result: + metrics = result['metrics'] + sections.append("\nKey Metrics:") + for key, value in metrics.items(): + if isinstance(value, (int, float)): + sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}") + else: + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Portfolio fit + if 'portfolio_fit' in result: + fit = result['portfolio_fit'] + sections.append("\nPortfolio Analysis:") + for key, value in fit.items(): + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Recommendations + if 'recommendations' in result: + recs = result['recommendations'] + sections.append("\nKey Recommendations:") + for rec in recs: + sections.append(f"- {rec}") + + return "\n".join(sections) diff --git a/reasoning/venture_types.py b/reasoning/venture_types.py new file mode 100644 index 0000000000000000000000000000000000000000..05b4422f761b01d7a36b9f4413798d2a194ce844 --- /dev/null +++ b/reasoning/venture_types.py @@ -0,0 +1,332 @@ +"""Additional venture types for business optimization.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +class AIInfrastructureStrategy(ReasoningStrategy): + """ + AI infrastructure venture strategy that: + 1. Identifies infrastructure needs + 2. Develops cloud solutions + 3. Optimizes compute resources + 4. Manages scalability + 5. Ensures reliability + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI infrastructure strategy.""" + try: + # Market analysis + market = await self._analyze_market(query, context) + + # Infrastructure design + design = await self._design_infrastructure(market, context) + + # Optimization strategy + optimization = await self._create_optimization_strategy(design, context) + + # Scaling plan + scaling = await self._plan_scaling(optimization, context) + + # Revenue projections + projections = await self._project_revenue(scaling, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "market": market, + "design": design, + "optimization": optimization, + "scaling": scaling, + "projections": projections + } + except Exception as e: + logging.error(f"Error in AI infrastructure strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AIConsultingStrategy(ReasoningStrategy): + """ + AI consulting venture strategy that: + 1. Identifies consulting opportunities + 2. Develops service offerings + 3. Creates delivery frameworks + 4. Manages client relationships + 5. Scales operations + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI consulting strategy.""" + try: + # Market analysis + market = await self._analyze_consulting_market(query, context) + + # Service design + services = await self._design_services(market, context) + + # Delivery framework + framework = await self._create_delivery_framework(services, context) + + # Growth strategy + growth = await self._plan_growth(framework, context) + + # Revenue projections + projections = await self._project_consulting_revenue(growth, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "market": market, + "services": services, + "framework": framework, + "growth": growth, + "projections": projections + } + except Exception as e: + logging.error(f"Error in AI consulting strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AIProductStrategy(ReasoningStrategy): + """ + AI product venture strategy that: + 1. Identifies product opportunities + 2. Develops product roadmap + 3. Creates go-to-market strategy + 4. Manages product lifecycle + 5. Scales distribution + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI product strategy.""" + try: + # Market analysis + market = await self._analyze_product_market(query, context) + + # Product development + product = await self._develop_product_strategy(market, context) + + # Go-to-market + gtm = await self._create_gtm_strategy(product, context) + + # Scale strategy + scale = await self._plan_product_scaling(gtm, context) + + # Revenue projections + projections = await self._project_product_revenue(scale, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "market": market, + "product": product, + "gtm": gtm, + "scale": scale, + "projections": projections + } + except Exception as e: + logging.error(f"Error in AI product strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class FinTechStrategy(ReasoningStrategy): + """ + FinTech venture strategy that: + 1. Identifies fintech opportunities + 2. Develops financial products + 3. Ensures compliance + 4. Manages risk + 5. Scales operations + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate FinTech strategy.""" + try: + # Market analysis + market = await self._analyze_fintech_market(query, context) + + # Product development + product = await self._develop_fintech_product(market, context) + + # Compliance strategy + compliance = await self._ensure_compliance(product, context) + + # Risk management + risk = await self._manage_risk(compliance, context) + + # Scale strategy + scale = await self._plan_fintech_scaling(risk, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "product": product, + "compliance": compliance, + "risk": risk, + "scale": scale + } + except Exception as e: + logging.error(f"Error in FinTech strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class HealthTechStrategy(ReasoningStrategy): + """ + HealthTech venture strategy that: + 1. Identifies healthcare opportunities + 2. Develops health solutions + 3. Ensures compliance + 4. Manages patient data + 5. Scales operations + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate HealthTech strategy.""" + try: + # Market analysis + market = await self._analyze_healthtech_market(query, context) + + # Solution development + solution = await self._develop_health_solution(market, context) + + # Compliance strategy + compliance = await self._ensure_health_compliance(solution, context) + + # Data strategy + data = await self._manage_health_data(compliance, context) + + # Scale strategy + scale = await self._plan_healthtech_scaling(data, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "solution": solution, + "compliance": compliance, + "data": data, + "scale": scale + } + except Exception as e: + logging.error(f"Error in HealthTech strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class EdTechStrategy(ReasoningStrategy): + """ + EdTech venture strategy that: + 1. Identifies education opportunities + 2. Develops learning solutions + 3. Creates content strategy + 4. Manages user engagement + 5. Scales platform + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate EdTech strategy.""" + try: + # Market analysis + market = await self._analyze_edtech_market(query, context) + + # Solution development + solution = await self._develop_learning_solution(market, context) + + # Content strategy + content = await self._create_content_strategy(solution, context) + + # Engagement strategy + engagement = await self._manage_engagement(content, context) + + # Scale strategy + scale = await self._plan_edtech_scaling(engagement, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "solution": solution, + "content": content, + "engagement": engagement, + "scale": scale + } + except Exception as e: + logging.error(f"Error in EdTech strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class BlockchainStrategy(ReasoningStrategy): + """ + Blockchain venture strategy that: + 1. Identifies blockchain opportunities + 2. Develops blockchain solutions + 3. Ensures security + 4. Manages tokenomics + 5. Scales network + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate blockchain strategy.""" + try: + # Market analysis + market = await self._analyze_blockchain_market(query, context) + + # Solution development + solution = await self._develop_blockchain_solution(market, context) + + # Security strategy + security = await self._ensure_blockchain_security(solution, context) + + # Tokenomics + tokenomics = await self._design_tokenomics(security, context) + + # Scale strategy + scale = await self._plan_blockchain_scaling(tokenomics, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "solution": solution, + "security": security, + "tokenomics": tokenomics, + "scale": scale + } + except Exception as e: + logging.error(f"Error in blockchain strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AIMarketplaceStrategy(ReasoningStrategy): + """ + AI marketplace venture strategy that: + 1. Creates AI model marketplace + 2. Manages model deployment + 3. Handles transactions + 4. Ensures quality + 5. Scales platform + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI marketplace strategy.""" + try: + # Market analysis + market = await self._analyze_ai_marketplace(query, context) + + # Platform development + platform = await self._develop_marketplace_platform(market, context) + + # Quality strategy + quality = await self._ensure_model_quality(platform, context) + + # Transaction system + transactions = await self._design_transaction_system(quality, context) + + # Scale strategy + scale = await self._plan_marketplace_scaling(transactions, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "platform": platform, + "quality": quality, + "transactions": transactions, + "scale": scale + } + except Exception as e: + logging.error(f"Error in AI marketplace strategy: {str(e)}") + return {"success": False, "error": str(e)} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..95f85a093b6832db517b730257b50e016011a2f8 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,35 @@ +# Core dependencies +fastapi>=0.68.0 +uvicorn>=0.15.0 +gradio==4.44.1 +pydantic>=2.0.0 +python-dotenv>=0.19.0 + +# API and networking +httpx>=0.24.0 +requests>=2.31.0 +aiohttp>=3.8.0 +urllib3>=2.0.7 +websockets>=10.0 + +# ML and data processing +numpy>=1.24.0 +pandas>=2.1.0 +scikit-learn>=1.3.2 +plotly>=5.18.0 + +# Model integration +huggingface-hub>=0.19.4 +groq>=0.4.1 + +# Utilities +typing-extensions>=4.0.0 +asyncio>=3.4.3 +tqdm>=4.66.0 +joblib==1.3.2 + +# Development +pytest>=7.0.0 +black>=22.0.0 +isort>=5.0.0 +mypy>=1.0.0 diff --git a/space.yml b/space.yml new file mode 100644 index 0000000000000000000000000000000000000000..111befbe61ade13de94097efffb1b645d88b9469 --- /dev/null +++ b/space.yml @@ -0,0 +1,124 @@ +title: Advanced Agentic System +emoji: 🤖 +colorFrom: indigo +colorTo: purple +sdk: gradio +sdk_version: latest +app_file: startup.sh +pinned: true +license: apache-2.0 +duplicated_from: nananie143/agentic-system +python_version: "3.10" +cuda: "11.8" +hardware: t4-medium + +# System requirements +compute: + instance: t4-medium + storage: large + +# Environment setup +env: + - MODEL_BACKEND=groq + - GROQ_API_KEY + - HUGGINGFACE_TOKEN + - ENABLE_LOCAL_FALLBACK=true + - CACHE_MODELS=false + - GRADIO_SERVER_PORT=7860 + - GRADIO_SERVER_NAME=0.0.0.0 + - MAX_PARALLEL_REQUESTS=10 + - REQUEST_TIMEOUT=30 + - BATCH_SIZE=4 + - GRADIO_ANALYTICS_ENABLED=false + - PYTHONUNBUFFERED=1 + - SPACE_CACHE_DIR=/data/models + - TORCH_CUDA_ARCH_LIST="7.5" + - CUDA_VISIBLE_DEVICES=0 + +# Model configurations +models: + - rrbale/pruned-qwen-moe/model-Q6_K.gguf + - YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF/model.gguf + - Nidum-Llama-3.2-3B-Uncensored-GGUF/model-Q6_K.gguf + - deepseek-ai/JanusFlow-1.3B/model.gguf + - prithivMLmods/QwQ-4B-Instruct/model.gguf + - gpt-omni/mini-omni2/mini-omni2.gguf + +# Dependencies +dependencies: + python: + - "gradio==4.44.1" + - "groq>=0.4.1" + - "huggingface-hub>=0.19.4" + - "fastapi>=0.68.0" + - "uvicorn>=0.15.0" + - "pydantic>=2.0.0" + - "python-dotenv>=0.19.0" + - "aiohttp>=3.8.0" + - "asyncio>=3.4.3" + - "numpy>=1.24.0" + - "pandas>=2.1.0" + - "scikit-learn>=1.3.2" + - "plotly>=5.18.0" + system: + - git-lfs + - cmake + - build-essential + - cuda-toolkit-11-8 + - nvidia-cuda-toolkit + - libcudnn8 + +# Inference settings +inference: + model_backend: groq + models: + - name: mixtral-8x7b-32768 + provider: groq + max_tokens: 32768 + - name: llama2-70b-4096 + provider: groq + max_tokens: 4096 + fallback: + enabled: true + provider: huggingface + model: mistral-7b-instruct-v0.2 + +# Resource limits +resources: + memory: 16 + cpu: 4 + gpu: 1 + gpu_memory: 16 + disk: 50 + +# Monitoring +monitoring: + enable_logging: true + log_level: INFO + metrics_enabled: true + +# Build configuration +build: + system_packages: + - cmake + - build-essential + - cuda-toolkit-11-8 + - nvidia-cuda-toolkit + - libcudnn8 + python_packages: + - --upgrade pip + - -r requirements.txt + - torch --index-url https://download.pytorch.org/whl/cu118 + - llama-cpp-python --no-cache-dir + +# Runtime configuration +runtime: + build: + cuda: "11.8" + python: "3.10" + env: + - PYTHONUNBUFFERED=1 + - GRADIO_SERVER_NAME=0.0.0.0 + - TORCH_CUDA_ARCH_LIST="7.5" + - CUDA_VISIBLE_DEVICES=0 + - GRADIO_ANALYTICS_ENABLED=false diff --git a/space/__init__.py b/space/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..32178de58cf5044c09321e5560d64e93af747534 --- /dev/null +++ b/space/__init__.py @@ -0,0 +1,70 @@ +""" +Advanced Reasoning Engine for Multi-Model System +--------------------------------------------- +A highly sophisticated reasoning system combining multiple reasoning strategies. + +Core Reasoning: +1. Chain of Thought (CoT) +2. Tree of Thoughts (ToT) +3. Recursive Reasoning +4. Analogical Reasoning +5. Meta-Learning +6. Local LLM + +Advanced Reasoning: +7. Neurosymbolic Reasoning +8. Bayesian Reasoning +9. Quantum Reasoning +10. Emergent Reasoning +11. Multimodal Reasoning +12. Specialized Reasoning + +Learning & Adaptation: +13. Market Analysis +14. Portfolio Optimization +15. Venture Strategies +16. Monetization Strategies +""" + +from .base import ReasoningStrategy +from .multimodal import MultiModalReasoning +from .bayesian import BayesianReasoning +from .quantum import QuantumReasoning +from .neurosymbolic import NeurosymbolicReasoning +from .emergent import EmergentReasoning +from .meta_learning import MetaLearningStrategy +from .chain_of_thought import ChainOfThoughtStrategy +from .tree_of_thoughts import TreeOfThoughtsStrategy +from .recursive import RecursiveReasoning +from .analogical import AnalogicalReasoning +from .specialized import SpecializedReasoning +from .local_llm import LocalLLMStrategy +from .market_analysis import MarketAnalysisStrategy +from .portfolio_optimization import PortfolioOptimizationStrategy +from .venture_strategies import VentureStrategy +from .monetization import MonetizationStrategy +from .unified_engine import UnifiedReasoningEngine, StrategyType, StrategyResult, UnifiedResult + +__all__ = [ + 'ReasoningStrategy', + 'MultiModalReasoning', + 'BayesianReasoning', + 'QuantumReasoning', + 'NeurosymbolicReasoning', + 'EmergentReasoning', + 'MetaLearningStrategy', + 'ChainOfThoughtStrategy', + 'TreeOfThoughtsStrategy', + 'RecursiveReasoning', + 'AnalogicalReasoning', + 'SpecializedReasoning', + 'LocalLLMStrategy', + 'MarketAnalysisStrategy', + 'PortfolioOptimizationStrategy', + 'VentureStrategy', + 'MonetizationStrategy', + 'UnifiedReasoningEngine', + 'StrategyType', + 'StrategyResult', + 'UnifiedResult' +] diff --git a/space/agentic.py b/space/agentic.py new file mode 100644 index 0000000000000000000000000000000000000000..b5f2da73606766f7115a031a1d08eeb949800bb9 --- /dev/null +++ b/space/agentic.py @@ -0,0 +1,345 @@ +"""Specialized reasoning strategies for Agentic Workflow.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy + +class TaskType(Enum): + """Types of tasks in agentic workflow.""" + CODE_GENERATION = "code_generation" + CODE_MODIFICATION = "code_modification" + CODE_REVIEW = "code_review" + DEBUGGING = "debugging" + ARCHITECTURE = "architecture" + OPTIMIZATION = "optimization" + DOCUMENTATION = "documentation" + TESTING = "testing" + +class ResourceType(Enum): + """Types of resources in agentic workflow.""" + CODE_CONTEXT = "code_context" + SYSTEM_CONTEXT = "system_context" + USER_CONTEXT = "user_context" + TOOLS = "tools" + APIS = "apis" + DOCUMENTATION = "documentation" + DEPENDENCIES = "dependencies" + HISTORY = "history" + +@dataclass +class TaskComponent: + """Component of a decomposed task.""" + id: str + type: TaskType + description: str + dependencies: List[str] + resources: Dict[ResourceType, Any] + constraints: List[str] + priority: float + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class ResourceAllocation: + """Resource allocation for a task.""" + resource_type: ResourceType + quantity: Union[int, float] + priority: float + constraints: List[str] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class ExecutionStep: + """Step in task execution.""" + id: str + task_id: str + action: str + resources: Dict[ResourceType, Any] + status: str + result: Optional[Dict[str, Any]] + feedback: List[str] + timestamp: datetime = field(default_factory=datetime.now) + +class TaskDecompositionStrategy(ReasoningStrategy): + """ + Advanced task decomposition strategy that: + 1. Analyzes task complexity and dependencies + 2. Breaks down tasks into manageable components + 3. Identifies resource requirements + 4. Establishes execution order + 5. Manages constraints and priorities + """ + + def __init__(self, max_components: int = 10): + self.max_components = max_components + self.components: Dict[str, TaskComponent] = {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Decompose task into components.""" + try: + # Analyze task + task_analysis = await self._analyze_task(query, context) + + # Generate components + components = await self._generate_components(task_analysis, context) + + # Establish dependencies + dependency_graph = await self._establish_dependencies(components, context) + + # Determine execution order + execution_order = await self._determine_execution_order( + components, dependency_graph, context) + + return { + "success": True, + "components": [self._component_to_dict(c) for c in components], + "dependency_graph": dependency_graph, + "execution_order": execution_order, + "metadata": { + "total_components": len(components), + "complexity_score": task_analysis.get("complexity_score", 0.0), + "resource_requirements": task_analysis.get("resource_requirements", {}) + } + } + except Exception as e: + logging.error(f"Error in task decomposition: {str(e)}") + return {"success": False, "error": str(e)} + +class ResourceManagementStrategy(ReasoningStrategy): + """ + Advanced resource management strategy that: + 1. Tracks available resources + 2. Allocates resources to tasks + 3. Handles resource constraints + 4. Optimizes resource utilization + 5. Manages resource dependencies + """ + + def __init__(self): + self.allocations: Dict[str, ResourceAllocation] = {} + self.utilization_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Manage resource allocation.""" + try: + # Analyze resource requirements + requirements = await self._analyze_requirements(query, context) + + # Check resource availability + availability = await self._check_availability(requirements, context) + + # Generate allocation plan + allocation_plan = await self._generate_allocation_plan( + requirements, availability, context) + + # Optimize allocations + optimized_plan = await self._optimize_allocations(allocation_plan, context) + + return { + "success": True, + "allocation_plan": optimized_plan, + "resource_metrics": { + "utilization": self._calculate_utilization(), + "efficiency": self._calculate_efficiency(), + "constraints_satisfied": self._check_constraints(optimized_plan) + } + } + except Exception as e: + logging.error(f"Error in resource management: {str(e)}") + return {"success": False, "error": str(e)} + +class ContextualPlanningStrategy(ReasoningStrategy): + """ + Advanced contextual planning strategy that: + 1. Analyzes multiple context types + 2. Generates context-aware plans + 3. Handles context changes + 4. Maintains context consistency + 5. Optimizes for context constraints + """ + + def __init__(self): + self.context_history: List[Dict[str, Any]] = [] + self.plan_adaptations: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate context-aware plan.""" + try: + # Analyze contexts + context_analysis = await self._analyze_contexts(query, context) + + # Generate base plan + base_plan = await self._generate_base_plan(context_analysis, context) + + # Adapt to contexts + adapted_plan = await self._adapt_to_contexts(base_plan, context_analysis) + + # Validate plan + validation = await self._validate_plan(adapted_plan, context) + + return { + "success": True, + "plan": adapted_plan, + "context_impact": context_analysis.get("impact_assessment", {}), + "adaptations": self.plan_adaptations, + "validation_results": validation + } + except Exception as e: + logging.error(f"Error in contextual planning: {str(e)}") + return {"success": False, "error": str(e)} + +class AdaptiveExecutionStrategy(ReasoningStrategy): + """ + Advanced adaptive execution strategy that: + 1. Monitors execution progress + 2. Adapts to changes and feedback + 3. Handles errors and exceptions + 4. Optimizes execution flow + 5. Maintains execution state + """ + + def __init__(self): + self.execution_steps: List[ExecutionStep] = [] + self.adaptation_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Execute task adaptively.""" + try: + # Initialize execution + execution_state = await self._initialize_execution(query, context) + + # Monitor and adapt + while not self._is_execution_complete(execution_state): + # Execute step + step_result = await self._execute_step(execution_state, context) + + # Process feedback + feedback = await self._process_feedback(step_result, context) + + # Adapt execution + execution_state = await self._adapt_execution( + execution_state, feedback, context) + + # Record step + self._record_step(step_result, feedback) + + return { + "success": True, + "execution_trace": [self._step_to_dict(s) for s in self.execution_steps], + "adaptations": self.adaptation_history, + "final_state": execution_state + } + except Exception as e: + logging.error(f"Error in adaptive execution: {str(e)}") + return {"success": False, "error": str(e)} + +class FeedbackIntegrationStrategy(ReasoningStrategy): + """ + Advanced feedback integration strategy that: + 1. Collects multiple types of feedback + 2. Analyzes feedback patterns + 3. Generates improvement suggestions + 4. Tracks feedback implementation + 5. Measures feedback impact + """ + + def __init__(self): + self.feedback_history: List[Dict[str, Any]] = [] + self.improvement_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Integrate and apply feedback.""" + try: + # Collect feedback + feedback = await self._collect_feedback(query, context) + + # Analyze patterns + patterns = await self._analyze_patterns(feedback, context) + + # Generate improvements + improvements = await self._generate_improvements(patterns, context) + + # Implement changes + implementation = await self._implement_improvements(improvements, context) + + # Measure impact + impact = await self._measure_impact(implementation, context) + + return { + "success": True, + "feedback_analysis": patterns, + "improvements": improvements, + "implementation_status": implementation, + "impact_metrics": impact + } + except Exception as e: + logging.error(f"Error in feedback integration: {str(e)}") + return {"success": False, "error": str(e)} + + async def _collect_feedback(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Collect feedback from multiple sources.""" + prompt = f""" + Collect feedback from: + Query: {query} + Context: {json.dumps(context)} + + Consider: + 1. User feedback + 2. System metrics + 3. Code analysis + 4. Performance data + 5. Error patterns + + Format as: + [Feedback] + Source: ... + Type: ... + Content: ... + Priority: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_feedback(response["answer"]) + + def _parse_feedback(self, response: str) -> List[Dict[str, Any]]: + """Parse feedback from response.""" + feedback_items = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Feedback]'): + if current: + feedback_items.append(current) + current = { + "source": "", + "type": "", + "content": "", + "priority": 0.0 + } + elif current: + if line.startswith('Source:'): + current["source"] = line[7:].strip() + elif line.startswith('Type:'): + current["type"] = line[5:].strip() + elif line.startswith('Content:'): + current["content"] = line[8:].strip() + elif line.startswith('Priority:'): + try: + current["priority"] = float(line[9:].strip()) + except: + pass + + if current: + feedback_items.append(current) + + return feedback_items diff --git a/space/analogical.py b/space/analogical.py new file mode 100644 index 0000000000000000000000000000000000000000..378f9fd0ff25cc30d4f8a3aa129e3a3ab2493d66 --- /dev/null +++ b/space/analogical.py @@ -0,0 +1,611 @@ +"""Analogical reasoning implementation with advanced pattern matching and transfer learning.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Tuple, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +class AnalogicalLevel(Enum): + """Levels of analogical similarity.""" + SURFACE = "surface" + STRUCTURAL = "structural" + SEMANTIC = "semantic" + FUNCTIONAL = "functional" + CAUSAL = "causal" + ABSTRACT = "abstract" + +class MappingType(Enum): + """Types of analogical mappings.""" + DIRECT = "direct" + TRANSFORMED = "transformed" + COMPOSITE = "composite" + ABSTRACT = "abstract" + METAPHORICAL = "metaphorical" + HYBRID = "hybrid" + +@dataclass +class AnalogicalPattern: + """Represents a pattern for analogical matching.""" + id: str + level: AnalogicalLevel + features: Dict[str, Any] + relations: List[Tuple[str, str, str]] # (entity1, relation, entity2) + constraints: List[str] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class AnalogicalMapping: + """Represents a mapping between source and target domains.""" + id: str + type: MappingType + source_elements: Dict[str, Any] + target_elements: Dict[str, Any] + correspondences: List[Tuple[str, str, float]] # (source, target, strength) + transformations: List[Dict[str, Any]] + confidence: float + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class AnalogicalSolution: + """Represents a solution derived through analogical reasoning.""" + id: str + source_analogy: str + mapping: AnalogicalMapping + adaptation: Dict[str, Any] + inference: Dict[str, Any] + confidence: float + validation: Dict[str, Any] + metadata: Dict[str, Any] = field(default_factory=dict) + +class AnalogicalReasoning(ReasoningStrategy): + """ + Advanced Analogical Reasoning implementation with: + - Multi-level pattern matching + - Sophisticated similarity metrics + - Transfer learning capabilities + - Dynamic adaptation mechanisms + - Quality assessment + - Learning from experience + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize analogical reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Analogical reasoning specific parameters + self.min_similarity = self.config.get('min_similarity', 0.6) + self.max_candidates = self.config.get('max_candidates', 5) + self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7) + + # Knowledge base + self.patterns: Dict[str, AnalogicalPattern] = {} + self.mappings: Dict[str, AnalogicalMapping] = {} + self.solutions: Dict[str, AnalogicalSolution] = {} + + # Learning components + self.pattern_weights: Dict[str, float] = defaultdict(float) + self.success_history: List[Dict[str, Any]] = [] + self.adaptation_history: List[Dict[str, Any]] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing analogical reasoning.""" + try: + # Extract patterns from query + patterns = await self._extract_patterns(query, context) + + # Find analogical matches + matches = await self._find_matches(patterns, context) + + # Create and evaluate mappings + mappings = await self._create_mappings(matches, context) + + # Generate and adapt solutions + solutions = await self._generate_solutions(mappings, context) + + # Select best solution + best_solution = await self._select_best_solution(solutions, context) + + # Learn from experience + self._update_knowledge(patterns, mappings, best_solution) + + return { + "success": True, + "answer": best_solution.inference["conclusion"], + "confidence": best_solution.confidence, + "analogy": { + "source": best_solution.source_analogy, + "mapping": self._mapping_to_dict(best_solution.mapping), + "adaptation": best_solution.adaptation + }, + "reasoning_trace": best_solution.metadata.get("reasoning_trace", []), + "meta_insights": best_solution.metadata.get("meta_insights", []) + } + except Exception as e: + logging.error(f"Error in analogical reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _extract_patterns(self, query: str, context: Dict[str, Any]) -> List[AnalogicalPattern]: + """Extract patterns from query for analogical matching.""" + prompt = f""" + Extract analogical patterns from query: + Query: {query} + Context: {json.dumps(context)} + + For each pattern level: + 1. Surface features + 2. Structural relations + 3. Semantic concepts + 4. Functional roles + 5. Causal relationships + 6. Abstract principles + + Format as: + [P1] + Level: ... + Features: ... + Relations: ... + Constraints: ... + + [P2] + ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_patterns(response["answer"]) + + async def _find_matches(self, patterns: List[AnalogicalPattern], context: Dict[str, Any]) -> List[Dict[str, Any]]: + """Find matching patterns in knowledge base.""" + prompt = f""" + Find analogical matches: + Patterns: {json.dumps([self._pattern_to_dict(p) for p in patterns])} + Context: {json.dumps(context)} + + For each match provide: + 1. Source domain + 2. Similarity assessment + 3. Key correspondences + 4. Transfer potential + + Format as: + [M1] + Source: ... + Similarity: ... + Correspondences: ... + Transfer: ... + + [M2] + ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_matches(response["answer"]) + + async def _create_mappings(self, matches: List[Dict[str, Any]], context: Dict[str, Any]) -> List[AnalogicalMapping]: + """Create mappings between source and target domains.""" + prompt = f""" + Create analogical mappings: + Matches: {json.dumps(matches)} + Context: {json.dumps(context)} + + For each mapping specify: + 1. [Type]: {" | ".join([t.value for t in MappingType])} + 2. [Elements]: Source and target elements + 3. [Correspondences]: Element mappings + 4. [Transformations]: Required adaptations + 5. [Confidence]: Mapping strength + + Format as: + [Map1] + Type: ... + Elements: ... + Correspondences: ... + Transformations: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_mappings(response["answer"]) + + async def _generate_solutions(self, mappings: List[AnalogicalMapping], context: Dict[str, Any]) -> List[AnalogicalSolution]: + """Generate solutions through analogical transfer.""" + prompt = f""" + Generate analogical solutions: + Mappings: {json.dumps([self._mapping_to_dict(m) for m in mappings])} + Context: {json.dumps(context)} + + For each solution provide: + 1. Analogical inference + 2. Required adaptations + 3. Validation criteria + 4. Confidence assessment + 5. Reasoning trace + + Format as: + [S1] + Inference: ... + Adaptation: ... + Validation: ... + Confidence: ... + Trace: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_solutions(response["answer"], mappings) + + async def _select_best_solution(self, solutions: List[AnalogicalSolution], context: Dict[str, Any]) -> AnalogicalSolution: + """Select the best solution based on multiple criteria.""" + prompt = f""" + Evaluate and select best solution: + Solutions: {json.dumps([self._solution_to_dict(s) for s in solutions])} + Context: {json.dumps(context)} + + Evaluate based on: + 1. Inference quality + 2. Adaptation feasibility + 3. Validation strength + 4. Overall confidence + + Format as: + [Evaluation] + Rankings: ... + Rationale: ... + Selection: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + selection = self._parse_selection(response["answer"]) + + # Find selected solution + selected = max(solutions, key=lambda s: s.confidence) + for solution in solutions: + if solution.id == selection.get("selected_id"): + selected = solution + break + + return selected + + def _update_knowledge(self, patterns: List[AnalogicalPattern], mappings: List[AnalogicalMapping], solution: AnalogicalSolution): + """Update knowledge base with new patterns and successful mappings.""" + # Update patterns + for pattern in patterns: + if pattern.id not in self.patterns: + self.patterns[pattern.id] = pattern + self.pattern_weights[pattern.id] += self.learning_rate * solution.confidence + + # Update mappings + if solution.mapping.id not in self.mappings: + self.mappings[solution.mapping.id] = solution.mapping + + # Record solution + self.solutions[solution.id] = solution + + # Update history + self.success_history.append({ + "timestamp": datetime.now().isoformat(), + "solution_id": solution.id, + "confidence": solution.confidence, + "patterns": [p.id for p in patterns], + "mapping_type": solution.mapping.type.value + }) + + # Update adaptation history + self.adaptation_history.append({ + "timestamp": datetime.now().isoformat(), + "solution_id": solution.id, + "adaptations": solution.adaptation, + "success": solution.confidence >= self.adaptation_threshold + }) + + def _parse_patterns(self, response: str) -> List[AnalogicalPattern]: + """Parse patterns from response.""" + patterns = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current: + patterns.append(current) + current = None + elif line.startswith('Level:'): + level_str = line[6:].strip().lower() + try: + level = AnalogicalLevel(level_str) + current = AnalogicalPattern( + id=f"pattern_{len(patterns)}", + level=level, + features={}, + relations=[], + constraints=[], + metadata={} + ) + except ValueError: + logging.warning(f"Invalid analogical level: {level_str}") + elif current: + if line.startswith('Features:'): + try: + current.features = json.loads(line[9:].strip()) + except: + current.features = {"raw": line[9:].strip()} + elif line.startswith('Relations:'): + relations = [r.strip() for r in line[10:].split(',')] + current.relations = [(r.split()[0], r.split()[1], r.split()[2]) + for r in relations if len(r.split()) >= 3] + elif line.startswith('Constraints:'): + current.constraints = [c.strip() for c in line[12:].split(',')] + + if current: + patterns.append(current) + + return patterns + + def _parse_matches(self, response: str) -> List[Dict[str, Any]]: + """Parse matches from response.""" + matches = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[M'): + if current: + matches.append(current) + current = { + "source": "", + "similarity": 0.0, + "correspondences": [], + "transfer": [] + } + elif current: + if line.startswith('Source:'): + current["source"] = line[7:].strip() + elif line.startswith('Similarity:'): + try: + current["similarity"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Correspondences:'): + current["correspondences"] = [c.strip() for c in line[16:].split(',')] + elif line.startswith('Transfer:'): + current["transfer"] = [t.strip() for t in line[9:].split(',')] + + if current: + matches.append(current) + + return matches + + def _parse_mappings(self, response: str) -> List[AnalogicalMapping]: + """Parse mappings from response.""" + mappings = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Map'): + if current: + mappings.append(current) + current = None + elif line.startswith('Type:'): + type_str = line[5:].strip().lower() + try: + mapping_type = MappingType(type_str) + current = AnalogicalMapping( + id=f"mapping_{len(mappings)}", + type=mapping_type, + source_elements={}, + target_elements={}, + correspondences=[], + transformations=[], + confidence=0.0, + metadata={} + ) + except ValueError: + logging.warning(f"Invalid mapping type: {type_str}") + elif current: + if line.startswith('Elements:'): + try: + elements = json.loads(line[9:].strip()) + current.source_elements = elements.get("source", {}) + current.target_elements = elements.get("target", {}) + except: + pass + elif line.startswith('Correspondences:'): + pairs = [c.strip() for c in line[16:].split(',')] + for pair in pairs: + parts = pair.split(':') + if len(parts) >= 2: + source = parts[0].strip() + target = parts[1].strip() + strength = float(parts[2]) if len(parts) > 2 else 1.0 + current.correspondences.append((source, target, strength)) + elif line.startswith('Transformations:'): + try: + current.transformations = json.loads(line[16:].strip()) + except: + current.transformations = [{"raw": line[16:].strip()}] + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + pass + + if current: + mappings.append(current) + + return mappings + + def _parse_solutions(self, response: str, mappings: List[AnalogicalMapping]) -> List[AnalogicalSolution]: + """Parse solutions from response.""" + solutions = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current: + solutions.append(current) + current = None + mapping_idx = len(solutions) + if mapping_idx < len(mappings): + current = AnalogicalSolution( + id=f"solution_{len(solutions)}", + source_analogy="", + mapping=mappings[mapping_idx], + adaptation={}, + inference={}, + confidence=0.0, + validation={}, + metadata={} + ) + elif current: + if line.startswith('Inference:'): + try: + current.inference = json.loads(line[10:].strip()) + except: + current.inference = {"conclusion": line[10:].strip()} + elif line.startswith('Adaptation:'): + try: + current.adaptation = json.loads(line[11:].strip()) + except: + current.adaptation = {"steps": [line[11:].strip()]} + elif line.startswith('Validation:'): + try: + current.validation = json.loads(line[11:].strip()) + except: + current.validation = {"criteria": [line[11:].strip()]} + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + pass + elif line.startswith('Trace:'): + current.metadata["reasoning_trace"] = [t.strip() for t in line[6:].split(',')] + + if current: + solutions.append(current) + + return solutions + + def _parse_selection(self, response: str) -> Dict[str, Any]: + """Parse solution selection from response.""" + selection = { + "selected_id": None, + "confidence": 0.0, + "rationale": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Selection:'): + selection["selected_id"] = line[10:].strip() + elif line.startswith('Confidence:'): + try: + selection["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Rationale:'): + selection["rationale"] = [r.strip() for r in line[10:].split(',')] + + return selection + + def _pattern_to_dict(self, pattern: AnalogicalPattern) -> Dict[str, Any]: + """Convert pattern to dictionary for serialization.""" + return { + "id": pattern.id, + "level": pattern.level.value, + "features": pattern.features, + "relations": pattern.relations, + "constraints": pattern.constraints, + "metadata": pattern.metadata + } + + def _mapping_to_dict(self, mapping: AnalogicalMapping) -> Dict[str, Any]: + """Convert mapping to dictionary for serialization.""" + return { + "id": mapping.id, + "type": mapping.type.value, + "source_elements": mapping.source_elements, + "target_elements": mapping.target_elements, + "correspondences": mapping.correspondences, + "transformations": mapping.transformations, + "confidence": mapping.confidence, + "metadata": mapping.metadata + } + + def _solution_to_dict(self, solution: AnalogicalSolution) -> Dict[str, Any]: + """Convert solution to dictionary for serialization.""" + return { + "id": solution.id, + "source_analogy": solution.source_analogy, + "mapping": self._mapping_to_dict(solution.mapping), + "adaptation": solution.adaptation, + "inference": solution.inference, + "confidence": solution.confidence, + "validation": solution.validation, + "metadata": solution.metadata + } + + def get_pattern_statistics(self) -> Dict[str, Any]: + """Get statistics about pattern usage and effectiveness.""" + return { + "total_patterns": len(self.patterns), + "level_distribution": defaultdict(int, {p.level.value: 1 for p in self.patterns.values()}), + "average_constraints": sum(len(p.constraints) for p in self.patterns.values()) / len(self.patterns) if self.patterns else 0, + "pattern_weights": dict(self.pattern_weights) + } + + def get_mapping_statistics(self) -> Dict[str, Any]: + """Get statistics about mapping effectiveness.""" + return { + "total_mappings": len(self.mappings), + "type_distribution": defaultdict(int, {m.type.value: 1 for m in self.mappings.values()}), + "average_confidence": sum(m.confidence for m in self.mappings.values()) / len(self.mappings) if self.mappings else 0, + "transformation_counts": defaultdict(int, {m.id: len(m.transformations) for m in self.mappings.values()}) + } + + def get_solution_statistics(self) -> Dict[str, Any]: + """Get statistics about solution quality.""" + return { + "total_solutions": len(self.solutions), + "average_confidence": sum(s.confidence for s in self.solutions.values()) / len(self.solutions) if self.solutions else 0, + "adaptation_success_rate": sum(1 for h in self.adaptation_history if h["success"]) / len(self.adaptation_history) if self.adaptation_history else 0 + } + + def clear_knowledge_base(self): + """Clear the knowledge base.""" + self.patterns.clear() + self.mappings.clear() + self.solutions.clear() + self.pattern_weights.clear() + self.success_history.clear() + self.adaptation_history.clear() diff --git a/space/base.py b/space/base.py new file mode 100644 index 0000000000000000000000000000000000000000..7989a7598eaa46d76c51d1cfcf26c2187b346f56 --- /dev/null +++ b/space/base.py @@ -0,0 +1,17 @@ +"""Base class for all reasoning strategies.""" + +from typing import Dict, Any + +class ReasoningStrategy: + """Base class for reasoning strategies.""" + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Apply reasoning strategy to query with context. + + Args: + query: The query to reason about + context: Additional context for reasoning + + Returns: + Dictionary containing reasoning results + """ + raise NotImplementedError diff --git a/space/bayesian.py b/space/bayesian.py new file mode 100644 index 0000000000000000000000000000000000000000..4c403de1b63abe448bcfaf1785a631d986145b18 --- /dev/null +++ b/space/bayesian.py @@ -0,0 +1,325 @@ +"""Advanced Bayesian reasoning for probabilistic analysis.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class BayesianHypothesis: + """Bayesian hypothesis with probabilities.""" + name: str + prior: float + likelihood: float + posterior: float = 0.0 + evidence: List[Dict[str, Any]] = field(default_factory=list) + +class BayesianReasoning(ReasoningStrategy): + """ + Advanced Bayesian reasoning that: + 1. Generates hypotheses + 2. Calculates prior probabilities + 3. Updates with evidence + 4. Computes posteriors + 5. Provides probabilistic analysis + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize Bayesian reasoning.""" + super().__init__() + self.config = config or {} + + # Configure Bayesian parameters + self.prior_weight = self.config.get('prior_weight', 0.3) + self.evidence_threshold = self.config.get('evidence_threshold', 0.1) + self.min_likelihood = self.config.get('min_likelihood', 0.01) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply Bayesian reasoning to analyze probabilities and update beliefs. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Generate hypotheses + hypotheses = await self._generate_hypotheses(query, context) + + # Calculate priors + priors = await self._calculate_priors(hypotheses, context) + + # Update with evidence + posteriors = await self._update_with_evidence( + hypotheses, + priors, + context + ) + + # Generate analysis + analysis = await self._generate_analysis(posteriors, context) + + return { + 'answer': self._format_analysis(analysis), + 'confidence': self._calculate_confidence(posteriors), + 'hypotheses': hypotheses, + 'priors': priors, + 'posteriors': posteriors, + 'analysis': analysis + } + + except Exception as e: + logging.error(f"Bayesian reasoning failed: {str(e)}") + return { + 'error': f"Bayesian reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _generate_hypotheses( + self, + query: str, + context: Dict[str, Any] + ) -> List[Dict[str, Any]]: + """Generate plausible hypotheses.""" + hypotheses = [] + + # Extract key terms for hypothesis generation + terms = set(query.lower().split()) + + # Generate hypotheses based on context and terms + if 'options' in context: + # Use provided options as hypotheses + for option in context['options']: + hypotheses.append({ + 'name': option, + 'description': f"Hypothesis based on option: {option}", + 'factors': self._extract_factors(option, terms) + }) + else: + # Generate default hypotheses + hypotheses.extend([ + { + 'name': 'primary', + 'description': "Primary hypothesis based on direct interpretation", + 'factors': self._extract_factors(query, terms) + }, + { + 'name': 'alternative', + 'description': "Alternative hypothesis considering other factors", + 'factors': self._generate_alternative_factors(terms) + } + ]) + + return hypotheses + + async def _calculate_priors( + self, + hypotheses: List[Dict[str, Any]], + context: Dict[str, Any] + ) -> Dict[str, float]: + """Calculate prior probabilities.""" + priors = {} + + # Get historical data if available + history = context.get('history', {}) + total_cases = sum(history.values()) if history else len(hypotheses) + + for hypothesis in hypotheses: + name = hypothesis['name'] + + # Calculate prior from history or use uniform prior + if name in history: + priors[name] = history[name] / total_cases + else: + priors[name] = 1.0 / len(hypotheses) + + # Adjust prior based on factors + factor_weight = len(hypothesis['factors']) / 10 # Normalize factor count + priors[name] = ( + priors[name] * (1 - self.prior_weight) + + factor_weight * self.prior_weight + ) + + # Normalize priors + total_prior = sum(priors.values()) + if total_prior > 0: + priors = { + name: prob / total_prior + for name, prob in priors.items() + } + + return priors + + async def _update_with_evidence( + self, + hypotheses: List[Dict[str, Any]], + priors: Dict[str, float], + context: Dict[str, Any] + ) -> Dict[str, float]: + """Update probabilities with evidence.""" + posteriors = priors.copy() + + # Get evidence from context + evidence = context.get('evidence', []) + if not evidence: + return posteriors + + for e in evidence: + # Calculate likelihood for each hypothesis + likelihoods = {} + for hypothesis in hypotheses: + name = hypothesis['name'] + likelihood = self._calculate_likelihood(hypothesis, e) + likelihoods[name] = max(likelihood, self.min_likelihood) + + # Update posteriors using Bayes' rule + total_probability = sum( + likelihoods[name] * posteriors[name] + for name in posteriors + ) + + if total_probability > 0: + posteriors = { + name: (likelihoods[name] * posteriors[name]) / total_probability + for name in posteriors + } + + return posteriors + + def _calculate_likelihood( + self, + hypothesis: Dict[str, Any], + evidence: Dict[str, Any] + ) -> float: + """Calculate likelihood of evidence given hypothesis.""" + # Extract evidence factors + evidence_factors = set( + str(v).lower() + for v in evidence.values() + if isinstance(v, (str, int, float)) + ) + + # Compare with hypothesis factors + common_factors = evidence_factors.intersection(hypothesis['factors']) + + if not evidence_factors: + return 0.5 # Neutral likelihood if no factors + + return len(common_factors) / len(evidence_factors) + + async def _generate_analysis( + self, + posteriors: Dict[str, float], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate probabilistic analysis.""" + # Sort hypotheses by posterior probability + ranked_hypotheses = sorted( + posteriors.items(), + key=lambda x: x[1], + reverse=True + ) + + # Calculate statistics + mean = np.mean(list(posteriors.values())) + std = np.std(list(posteriors.values())) + entropy = -sum( + p * np.log2(p) if p > 0 else 0 + for p in posteriors.values() + ) + + return { + 'top_hypothesis': ranked_hypotheses[0][0], + 'probability': ranked_hypotheses[0][1], + 'alternatives': [ + {'name': name, 'probability': prob} + for name, prob in ranked_hypotheses[1:] + ], + 'statistics': { + 'mean': mean, + 'std': std, + 'entropy': entropy + } + } + + def _format_analysis(self, analysis: Dict[str, Any]) -> str: + """Format analysis into readable text.""" + sections = [] + + # Top hypothesis + sections.append( + f"Most likely hypothesis: {analysis['top_hypothesis']} " + f"(probability: {analysis['probability']:.2%})" + ) + + # Alternative hypotheses + if analysis['alternatives']: + sections.append("\nAlternative hypotheses:") + for alt in analysis['alternatives']: + sections.append( + f"- {alt['name']}: {alt['probability']:.2%}" + ) + + # Statistics + stats = analysis['statistics'] + sections.append("\nDistribution statistics:") + sections.append(f"- Mean probability: {stats['mean']:.2%}") + sections.append(f"- Standard deviation: {stats['std']:.2%}") + sections.append(f"- Entropy: {stats['entropy']:.2f} bits") + + return "\n".join(sections) + + def _calculate_confidence(self, posteriors: Dict[str, float]) -> float: + """Calculate overall confidence score.""" + if not posteriors: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Adjust based on probability distribution + probs = list(posteriors.values()) + + # Strong leading hypothesis increases confidence + max_prob = max(probs) + if max_prob > 0.8: + confidence += 0.3 + elif max_prob > 0.6: + confidence += 0.2 + elif max_prob > 0.4: + confidence += 0.1 + + # Low entropy (clear distinction) increases confidence + entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs) + max_entropy = -np.log2(1/len(probs)) # Maximum possible entropy + + if entropy < 0.3 * max_entropy: + confidence += 0.2 + elif entropy < 0.6 * max_entropy: + confidence += 0.1 + + return min(confidence, 1.0) + + def _extract_factors(self, text: str, terms: Set[str]) -> Set[str]: + """Extract relevant factors from text.""" + return set(word.lower() for word in text.split() if word.lower() in terms) + + def _generate_alternative_factors(self, terms: Set[str]) -> Set[str]: + """Generate factors for alternative hypothesis.""" + # Simple approach: use terms not in primary hypothesis + return set( + word for word in terms + if not any( + similar in word or word in similar + for similar in terms + ) + ) diff --git a/space/chain_of_thought.py b/space/chain_of_thought.py new file mode 100644 index 0000000000000000000000000000000000000000..4c735b45160e2209418e717fabbdcbca89ff0074 --- /dev/null +++ b/space/chain_of_thought.py @@ -0,0 +1,415 @@ +"""Chain of Thought reasoning implementation with advanced features.""" + +import logging +from typing import Dict, Any, List, Optional, Tuple +import json +from dataclasses import dataclass +from enum import Enum + +from .base import ReasoningStrategy + +class ThoughtType(Enum): + """Types of thoughts in the chain.""" + OBSERVATION = "observation" + ANALYSIS = "analysis" + HYPOTHESIS = "hypothesis" + VERIFICATION = "verification" + CONCLUSION = "conclusion" + REFLECTION = "reflection" + REFINEMENT = "refinement" + +@dataclass +class Thought: + """Represents a single thought in the chain.""" + type: ThoughtType + content: str + confidence: float + evidence: List[str] + alternatives: List[str] + next_steps: List[str] + metadata: Dict[str, Any] + +class ChainOfThoughtStrategy(ReasoningStrategy): + """ + Advanced Chain of Thought reasoning implementation with: + - Hierarchical thought chains + - Confidence scoring + - Alternative path exploration + - Self-reflection and refinement + - Evidence tracking + - Meta-learning capabilities + """ + + def __init__(self, + min_confidence: float = 0.7, + parallel_threshold: int = 3, + learning_rate: float = 0.1, + strategy_weights: Optional[Dict[str, float]] = None): + self.min_confidence = min_confidence + self.parallel_threshold = parallel_threshold + self.learning_rate = learning_rate + self.strategy_weights = strategy_weights or { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + } + self.thought_history: List[Thought] = [] + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing chain of thought.""" + try: + # Initialize reasoning chain + chain = await self._initialize_chain(query, context) + + # Generate initial thoughts + thoughts = await self._generate_thoughts(query, context) + + # Build thought chain + chain = await self._build_chain(thoughts, context) + + # Reflect and refine + if self.enable_reflection: + chain = await self._reflect_and_refine(chain, context) + + # Extract conclusion + conclusion = await self._extract_conclusion(chain, context) + + # Update thought history + self.thought_history.extend(chain) + + return { + "success": True, + "answer": conclusion["answer"], + "confidence": conclusion["confidence"], + "reasoning_chain": [self._thought_to_dict(t) for t in chain], + "alternatives": conclusion["alternatives"], + "evidence": conclusion["evidence"], + "meta_insights": conclusion["meta_insights"] + } + except Exception as e: + logging.error(f"Error in chain of thought reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _initialize_chain(self, query: str, context: Dict[str, Any]) -> List[Thought]: + """Initialize the thought chain with observations.""" + prompt = f""" + Initialize chain of thought for query: + Query: {query} + Context: {json.dumps(context)} + + Provide initial observations: + 1. Key elements in query + 2. Relevant context factors + 3. Initial hypotheses + 4. Potential approaches + + Format as: + [O1] Element: ... | Relevance: ... | Confidence: ... + [O2] Context: ... | Impact: ... | Confidence: ... + [O3] Hypothesis: ... | Support: ... | Confidence: ... + [O4] Approach: ... | Rationale: ... | Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_observations(response["answer"]) + + async def _generate_thoughts(self, query: str, context: Dict[str, Any]) -> List[Thought]: + """Generate candidate thoughts for the chain.""" + prompt = f""" + Generate thoughts for query analysis: + Query: {query} + Context: {json.dumps(context)} + + For each thought provide: + 1. [Type]: {" | ".join([t.value for t in ThoughtType])} + 2. [Content]: Main thought + 3. [Evidence]: Supporting evidence + 4. [Alternatives]: Alternative perspectives + 5. [Next]: Potential next steps + 6. [Confidence]: 0-1 score + + Format as: + [T1] + Type: ... + Content: ... + Evidence: ... + Alternatives: ... + Next: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_thoughts(response["answer"]) + + async def _build_chain(self, thoughts: List[Thought], context: Dict[str, Any]) -> List[Thought]: + """Build coherent chain from candidate thoughts.""" + prompt = f""" + Build coherent thought chain: + Thoughts: {json.dumps([self._thought_to_dict(t) for t in thoughts])} + Context: {json.dumps(context)} + + For each step specify: + 1. Selected thought + 2. Reasoning for selection + 3. Connection to previous + 4. Expected impact + + Format as: + [S1] + Thought: ... + Reason: ... + Connection: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_chain(response["answer"], thoughts) + + async def _reflect_and_refine(self, chain: List[Thought], context: Dict[str, Any]) -> List[Thought]: + """Reflect on and refine the thought chain.""" + prompt = f""" + Reflect on thought chain: + Chain: {json.dumps([self._thought_to_dict(t) for t in chain])} + Context: {json.dumps(context)} + + Analyze for: + 1. Logical gaps + 2. Weak assumptions + 3. Missing evidence + 4. Alternative perspectives + + Suggest refinements: + 1. Additional thoughts + 2. Modified reasoning + 3. New connections + 4. Evidence needs + + Format as: + [Analysis] + Gaps: ... + Assumptions: ... + Missing: ... + Alternatives: ... + + [Refinements] + Thoughts: ... + Reasoning: ... + Connections: ... + Evidence: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._apply_refinements(chain, response["answer"]) + + async def _extract_conclusion(self, chain: List[Thought], context: Dict[str, Any]) -> Dict[str, Any]: + """Extract final conclusion from thought chain.""" + prompt = f""" + Extract conclusion from thought chain: + Chain: {json.dumps([self._thought_to_dict(t) for t in chain])} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level + 3. Supporting evidence + 4. Alternative conclusions + 5. Meta-insights gained + 6. Future considerations + + Format as: + [Conclusion] + Answer: ... + Confidence: ... + Evidence: ... + Alternatives: ... + + [Meta] + Insights: ... + Future: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_conclusion(response["answer"]) + + def _parse_observations(self, response: str) -> List[Thought]: + """Parse initial observations into thoughts.""" + observations = [] + lines = response.split('\n') + + for line in lines: + if line.startswith('[O'): + parts = line.split('|') + if len(parts) >= 3: + main_part = parts[0].split(']')[1].strip() + key, content = main_part.split(':', 1) + + evidence = [p.strip() for p in parts[1].split(':')[1].strip().split(',')] + + try: + confidence = float(parts[2].split(':')[1].strip()) + except: + confidence = 0.5 + + observations.append(Thought( + type=ThoughtType.OBSERVATION, + content=content.strip(), + confidence=confidence, + evidence=evidence, + alternatives=[], + next_steps=[], + metadata={"key": key} + )) + + return observations + + def _parse_thoughts(self, response: str) -> List[Thought]: + """Parse generated thoughts.""" + thoughts = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[T'): + if current: + thoughts.append(current) + current = None + elif line.startswith('Type:'): + type_str = line[5:].strip() + try: + thought_type = ThoughtType(type_str.lower()) + current = Thought( + type=thought_type, + content="", + confidence=0.0, + evidence=[], + alternatives=[], + next_steps=[], + metadata={} + ) + except ValueError: + logging.warning(f"Invalid thought type: {type_str}") + elif current: + if line.startswith('Content:'): + current.content = line[8:].strip() + elif line.startswith('Evidence:'): + current.evidence = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + current.alternatives = [a.strip() for a in line[13:].split(',')] + elif line.startswith('Next:'): + current.next_steps = [n.strip() for n in line[5:].split(',')] + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + current.confidence = 0.5 + + if current: + thoughts.append(current) + + return thoughts + + def _parse_chain(self, response: str, thoughts: List[Thought]) -> List[Thought]: + """Parse and order thoughts into a chain.""" + chain = [] + thought_map = {self._thought_to_dict(t)["content"]: t for t in thoughts} + + for line in response.split('\n'): + if line.startswith('Thought:'): + content = line[8:].strip() + if content in thought_map: + chain.append(thought_map[content]) + + return chain + + def _apply_refinements(self, chain: List[Thought], response: str) -> List[Thought]: + """Apply refinements to thought chain.""" + refined_chain = chain.copy() + + # Parse refinements + sections = response.split('[') + for section in sections: + if section.startswith('Refinements]'): + lines = section.split('\n')[1:] + for line in lines: + if line.startswith('Thoughts:'): + new_thoughts = self._parse_refinement_thoughts(line[9:]) + refined_chain.extend(new_thoughts) + + return refined_chain + + def _parse_refinement_thoughts(self, refinements: str) -> List[Thought]: + """Parse refinement thoughts.""" + thoughts = [] + for refinement in refinements.split(';'): + if refinement.strip(): + thoughts.append(Thought( + type=ThoughtType.REFINEMENT, + content=refinement.strip(), + confidence=0.8, # Refinements typically have high confidence + evidence=[], + alternatives=[], + next_steps=[], + metadata={"refined": True} + )) + return thoughts + + def _parse_conclusion(self, response: str) -> Dict[str, Any]: + """Parse final conclusion.""" + conclusion = { + "answer": "", + "confidence": 0.0, + "evidence": [], + "alternatives": [], + "meta_insights": [], + "future_considerations": [] + } + + sections = response.split('[') + for section in sections: + if section.startswith('Conclusion]'): + lines = section.split('\n')[1:] + for line in lines: + if line.startswith('Answer:'): + conclusion["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + conclusion["confidence"] = float(line[11:].strip()) + except: + conclusion["confidence"] = 0.5 + elif line.startswith('Evidence:'): + conclusion["evidence"] = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')] + elif section.startswith('Meta]'): + lines = section.split('\n')[1:] + for line in lines: + if line.startswith('Insights:'): + conclusion["meta_insights"] = [i.strip() for i in line[9:].split(',')] + elif line.startswith('Future:'): + conclusion["future_considerations"] = [f.strip() for f in line[7:].split(',')] + + return conclusion + + def _thought_to_dict(self, thought: Thought) -> Dict[str, Any]: + """Convert thought to dictionary for serialization.""" + return { + "type": thought.type.value, + "content": thought.content, + "confidence": thought.confidence, + "evidence": thought.evidence, + "alternatives": thought.alternatives, + "next_steps": thought.next_steps, + "metadata": thought.metadata + } + + def get_thought_history(self) -> List[Dict[str, Any]]: + """Get the history of all thoughts processed.""" + return [self._thought_to_dict(t) for t in self.thought_history] + + def clear_history(self) -> None: + """Clear thought history.""" + self.thought_history = [] diff --git a/space/config.py b/space/config.py new file mode 100644 index 0000000000000000000000000000000000000000..abc1d8ee79ac5c342c658189ac640693a1d1364d --- /dev/null +++ b/space/config.py @@ -0,0 +1,321 @@ +""" +System Configuration +------------------ +Central configuration for the Agentic System including: +1. Local Model Settings +2. Team Settings +3. System Parameters +4. Resource Limits +5. Free API Configurations +""" + +import os +from typing import Dict, Any +from pathlib import Path +from dotenv import load_dotenv + +# Load environment variables +load_dotenv() + +class SystemConfig: + """System-wide configuration.""" + + # Base Paths + BASE_DIR = Path(__file__).parent.absolute() + CACHE_DIR = BASE_DIR / "cache" + LOG_DIR = BASE_DIR / "logs" + DATA_DIR = BASE_DIR / "data" + MODEL_DIR = BASE_DIR / "models" + + # System Parameters + DEBUG_MODE = os.getenv("DEBUG_MODE", "False").lower() == "true" + LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO") + MAX_WORKERS = int(os.getenv("MAX_WORKERS", "4")) + ASYNC_TIMEOUT = int(os.getenv("ASYNC_TIMEOUT", "30")) + + # Local Model Configurations + MODEL_CONFIG = { + "quick_coder": { + "name": "tugstugi/Qwen2.5-Coder-0.5B-QwQ-draft", + "type": "transformers", + "description": "Fast code completion and simple tasks", + "temperature": 0.2, + "max_tokens": 1000, + "timeout": 30 + }, + "deep_coder": { + "name": "YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF", + "type": "gguf", + "description": "Complex code generation and refactoring", + "temperature": 0.3, + "max_tokens": 2000, + "timeout": 45 + }, + "text_gen": { + "name": "Orenguteng/Llama-3-8B-Lexi-Uncensored", + "type": "transformers", + "description": "General text generation and reasoning", + "temperature": 0.7, + "max_tokens": 1500, + "timeout": 40 + }, + "workflow": { + "name": "deepseek-ai/JanusFlow-1.3B", + "type": "transformers", + "description": "Task planning and workflow management", + "temperature": 0.5, + "max_tokens": 1000, + "timeout": 30 + } + } + + # Team Configurations + TEAM_CONFIG = { + "coders": { + "min_agents": 3, + "max_agents": 7, + "capabilities": [ + "full_stack_development", + "cloud_architecture", + "ai_ml", + "blockchain", + "mobile_development" + ], + "resource_limits": { + "cpu_percent": 80, + "memory_mb": 4096, + "gpu_memory_mb": 2048 + } + }, + "business": { + "min_agents": 2, + "max_agents": 5, + "capabilities": [ + "market_analysis", + "business_strategy", + "digital_transformation", + "startup_innovation", + "product_management" + ], + "resource_limits": { + "cpu_percent": 60, + "memory_mb": 2048, + "api_calls_per_minute": 100 + } + }, + "research": { + "min_agents": 2, + "max_agents": 6, + "capabilities": [ + "deep_research", + "data_analysis", + "trend_forecasting", + "competitive_analysis", + "technology_assessment" + ], + "resource_limits": { + "cpu_percent": 70, + "memory_mb": 3072, + "api_calls_per_minute": 150 + } + }, + "traders": { + "min_agents": 2, + "max_agents": 5, + "capabilities": [ + "crypto_trading", + "sports_betting", + "risk_management", + "market_timing", + "portfolio_optimization" + ], + "resource_limits": { + "cpu_percent": 60, + "memory_mb": 2048, + "api_calls_per_minute": 200 + } + } + } + + # Resource Management + RESOURCE_LIMITS = { + "total_cpu_percent": 90, + "total_memory_mb": 8192, + "total_gpu_memory_mb": 4096, + "max_api_calls_per_minute": 500, + "max_concurrent_tasks": 20 + } + + # Collaboration Settings + COLLABORATION_CONFIG = { + "min_confidence_threshold": 0.6, + "max_team_size": 10, + "max_concurrent_objectives": 5, + "objective_timeout_minutes": 60, + "team_sync_interval_seconds": 30 + } + + # Error Recovery + ERROR_RECOVERY = { + "max_retries": 3, + "retry_delay_seconds": 5, + "error_threshold": 0.2, + "recovery_timeout": 300 + } + + # Monitoring + MONITORING = { + "metrics_interval_seconds": 60, + "health_check_interval": 30, + "performance_log_retention_days": 7, + "alert_threshold": { + "cpu": 85, + "memory": 90, + "error_rate": 0.1 + } + } + + # Free API Configurations (No API Keys Required) + API_CONFIG = { + "search": { + "duckduckgo": { + "base_url": "https://api.duckduckgo.com", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + }, + "wikipedia": { + "base_url": "https://en.wikipedia.org/w/api.php", + "rate_limit": 200, + "requires_auth": False, + "method": "GET" + }, + "arxiv": { + "base_url": "http://export.arxiv.org/api/query", + "rate_limit": 60, + "requires_auth": False, + "method": "GET" + }, + "crossref": { + "base_url": "https://api.crossref.org/works", + "rate_limit": 50, + "requires_auth": False, + "method": "GET" + }, + "unpaywall": { + "base_url": "https://api.unpaywall.org/v2", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + } + }, + "crypto": { + "coincap": { + "base_url": "https://api.coincap.io/v2", + "rate_limit": 200, + "requires_auth": False, + "method": "GET", + "endpoints": { + "assets": "/assets", + "rates": "/rates", + "markets": "/markets" + } + }, + "blockchair": { + "base_url": "https://api.blockchair.com", + "rate_limit": 30, + "requires_auth": False, + "method": "GET" + } + }, + "news": { + "wikinews": { + "base_url": "https://en.wikinews.org/w/api.php", + "rate_limit": 200, + "requires_auth": False, + "method": "GET" + }, + "reddit": { + "base_url": "https://www.reddit.com/r/news/.json", + "rate_limit": 60, + "requires_auth": False, + "method": "GET" + }, + "hackernews": { + "base_url": "https://hacker-news.firebaseio.com/v0", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + } + }, + "market_data": { + "yahoo_finance": { + "base_url": "https://query1.finance.yahoo.com/v8/finance", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + }, + "marketstack_free": { + "base_url": "https://api.marketstack.com/v1", + "rate_limit": 100, + "requires_auth": False, + "method": "GET" + } + }, + "sports": { + "football_data": { + "base_url": "https://www.football-data.org/v4", + "rate_limit": 10, + "requires_auth": False, + "method": "GET", + "free_endpoints": [ + "/competitions", + "/matches" + ] + }, + "nhl": { + "base_url": "https://statsapi.web.nhl.com/api/v1", + "rate_limit": 50, + "requires_auth": False, + "method": "GET" + }, + "mlb": { + "base_url": "https://statsapi.mlb.com/api/v1", + "rate_limit": 50, + "requires_auth": False, + "method": "GET" + } + }, + "web_scraping": { + "web_archive": { + "base_url": "https://archive.org/wayback/available", + "rate_limit": 40, + "requires_auth": False, + "method": "GET" + }, + "metahtml": { + "base_url": "https://html.spec.whatwg.org/multipage", + "rate_limit": 30, + "requires_auth": False, + "method": "GET" + } + } + } + + @classmethod + def get_team_config(cls, team_name: str) -> Dict[str, Any]: + """Get configuration for a specific team.""" + return cls.TEAM_CONFIG.get(team_name, {}) + + @classmethod + def get_model_config(cls, model_type: str) -> Dict[str, Any]: + """Get configuration for a specific model type.""" + return cls.MODEL_CONFIG.get(model_type, {}) + + @classmethod + def get_api_config(cls, api_name: str) -> Dict[str, Any]: + """Get configuration for a specific API.""" + for category in cls.API_CONFIG.values(): + if api_name in category: + return category[api_name] + return {} diff --git a/space/coordination.py b/space/coordination.py new file mode 100644 index 0000000000000000000000000000000000000000..062c093b1c1e9f7fe1fd32e54e2791d347e85f93 --- /dev/null +++ b/space/coordination.py @@ -0,0 +1,525 @@ +"""Advanced strategy coordination patterns for the unified reasoning engine.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy +from .unified_engine import StrategyType, StrategyResult, UnifiedResult + +class CoordinationPattern(Enum): + """Types of strategy coordination patterns.""" + PIPELINE = "pipeline" + PARALLEL = "parallel" + HIERARCHICAL = "hierarchical" + FEEDBACK = "feedback" + ADAPTIVE = "adaptive" + ENSEMBLE = "ensemble" + +class CoordinationPhase(Enum): + """Phases in strategy coordination.""" + INITIALIZATION = "initialization" + EXECUTION = "execution" + SYNCHRONIZATION = "synchronization" + ADAPTATION = "adaptation" + COMPLETION = "completion" + +@dataclass +class CoordinationState: + """State of strategy coordination.""" + pattern: CoordinationPattern + active_strategies: Dict[StrategyType, bool] + phase: CoordinationPhase + shared_context: Dict[str, Any] + synchronization_points: List[str] + adaptation_history: List[Dict[str, Any]] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class StrategyInteraction: + """Interaction between strategies.""" + source: StrategyType + target: StrategyType + interaction_type: str + data: Dict[str, Any] + timestamp: datetime = field(default_factory=datetime.now) + +class StrategyCoordinator: + """ + Advanced strategy coordinator that: + 1. Manages strategy interactions + 2. Implements coordination patterns + 3. Handles state synchronization + 4. Adapts coordination dynamically + 5. Optimizes strategy combinations + """ + + def __init__(self, + strategies: Dict[StrategyType, ReasoningStrategy], + learning_rate: float = 0.1): + self.strategies = strategies + self.learning_rate = learning_rate + + # Coordination state + self.states: Dict[str, CoordinationState] = {} + self.interactions: List[StrategyInteraction] = [] + + # Pattern performance + self.pattern_performance: Dict[CoordinationPattern, List[float]] = defaultdict(list) + self.pattern_weights: Dict[CoordinationPattern, float] = { + pattern: 1.0 for pattern in CoordinationPattern + } + + async def coordinate(self, + query: str, + context: Dict[str, Any], + pattern: Optional[CoordinationPattern] = None) -> Dict[str, Any]: + """Coordinate strategy execution using specified pattern.""" + try: + # Select pattern if not specified + if not pattern: + pattern = await self._select_pattern(query, context) + + # Initialize coordination + state = await self._initialize_coordination(pattern, context) + + # Execute coordination pattern + if pattern == CoordinationPattern.PIPELINE: + result = await self._coordinate_pipeline(query, context, state) + elif pattern == CoordinationPattern.PARALLEL: + result = await self._coordinate_parallel(query, context, state) + elif pattern == CoordinationPattern.HIERARCHICAL: + result = await self._coordinate_hierarchical(query, context, state) + elif pattern == CoordinationPattern.FEEDBACK: + result = await self._coordinate_feedback(query, context, state) + elif pattern == CoordinationPattern.ADAPTIVE: + result = await self._coordinate_adaptive(query, context, state) + elif pattern == CoordinationPattern.ENSEMBLE: + result = await self._coordinate_ensemble(query, context, state) + else: + raise ValueError(f"Unsupported coordination pattern: {pattern}") + + # Update performance metrics + self._update_pattern_performance(pattern, result) + + return result + + except Exception as e: + logging.error(f"Error in strategy coordination: {str(e)}") + return { + "success": False, + "error": str(e), + "pattern": pattern.value if pattern else None + } + + async def _select_pattern(self, query: str, context: Dict[str, Any]) -> CoordinationPattern: + """Select appropriate coordination pattern.""" + prompt = f""" + Select coordination pattern: + Query: {query} + Context: {json.dumps(context)} + + Consider: + 1. Task complexity and type + 2. Strategy dependencies + 3. Resource constraints + 4. Performance history + 5. Adaptation needs + + Format as: + [Selection] + Pattern: ... + Rationale: ... + Confidence: ... + """ + + response = await context["groq_api"].predict(prompt) + selection = self._parse_pattern_selection(response["answer"]) + + # Weight by performance history + weighted_patterns = { + pattern: self.pattern_weights[pattern] * selection.get(pattern.value, 0.0) + for pattern in CoordinationPattern + } + + return max(weighted_patterns.items(), key=lambda x: x[1])[0] + + async def _coordinate_pipeline(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies in pipeline pattern.""" + results = [] + current_context = context.copy() + + # Determine optimal order + strategy_order = await self._determine_pipeline_order(query, context) + + for strategy_type in strategy_order: + try: + # Execute strategy + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, current_context) + + # Update context with result + current_context.update({ + "previous_result": result, + "pipeline_position": len(results) + }) + + results.append(StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + )) + + # Record interaction + self._record_interaction( + source=strategy_type, + target=strategy_order[len(results)] if len(results) < len(strategy_order) else None, + interaction_type="pipeline_transfer", + data={"result": result} + ) + + except Exception as e: + logging.error(f"Error in pipeline strategy {strategy_type}: {str(e)}") + + return { + "success": any(r.success for r in results), + "results": results, + "pattern": CoordinationPattern.PIPELINE.value, + "metrics": { + "total_steps": len(results), + "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0 + } + } + + async def _coordinate_parallel(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies in parallel pattern.""" + async def execute_strategy(strategy_type: StrategyType) -> StrategyResult: + try: + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, context) + + return StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + except Exception as e: + logging.error(f"Error in parallel strategy {strategy_type}: {str(e)}") + return StrategyResult( + strategy_type=strategy_type, + success=False, + answer=None, + confidence=0.0, + reasoning_trace=[{"error": str(e)}], + metadata={}, + performance_metrics={} + ) + + # Execute strategies in parallel + tasks = [execute_strategy(strategy_type) + for strategy_type in state.active_strategies + if state.active_strategies[strategy_type]] + + results = await asyncio.gather(*tasks) + + # Synthesize results + synthesis = await self._synthesize_parallel_results(results, context) + + return { + "success": synthesis.get("success", False), + "results": results, + "synthesis": synthesis, + "pattern": CoordinationPattern.PARALLEL.value, + "metrics": { + "total_strategies": len(results), + "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0 + } + } + + async def _coordinate_hierarchical(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies in hierarchical pattern.""" + # Build strategy hierarchy + hierarchy = await self._build_strategy_hierarchy(query, context) + results = {} + + async def execute_level(level_strategies: List[StrategyType], + level_context: Dict[str, Any]) -> List[StrategyResult]: + tasks = [] + for strategy_type in level_strategies: + if strategy_type in state.active_strategies and state.active_strategies[strategy_type]: + strategy = self.strategies[strategy_type] + tasks.append(strategy.reason(query, level_context)) + + level_results = await asyncio.gather(*tasks) + return [ + StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + for strategy_type, result in zip(level_strategies, level_results) + ] + + # Execute hierarchy levels + current_context = context.copy() + for level, level_strategies in enumerate(hierarchy): + results[level] = await execute_level(level_strategies, current_context) + + # Update context for next level + current_context.update({ + "previous_level_results": results[level], + "hierarchy_level": level + }) + + return { + "success": any(any(r.success for r in level_results) + for level_results in results.values()), + "results": results, + "hierarchy": hierarchy, + "pattern": CoordinationPattern.HIERARCHICAL.value, + "metrics": { + "total_levels": len(hierarchy), + "level_success_rates": { + level: sum(1 for r in results[level] if r.success) / len(results[level]) + for level in results if results[level] + } + } + } + + async def _coordinate_feedback(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies with feedback loops.""" + results = [] + feedback_history = [] + current_context = context.copy() + + max_iterations = 5 # Prevent infinite loops + iteration = 0 + + while iteration < max_iterations: + iteration += 1 + + # Execute strategies + iteration_results = [] + for strategy_type in state.active_strategies: + if state.active_strategies[strategy_type]: + try: + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, current_context) + + strategy_result = StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + + iteration_results.append(strategy_result) + + except Exception as e: + logging.error(f"Error in feedback strategy {strategy_type}: {str(e)}") + + results.append(iteration_results) + + # Generate feedback + feedback = await self._generate_feedback(iteration_results, current_context) + feedback_history.append(feedback) + + # Check termination condition + if self._should_terminate_feedback(feedback, iteration_results): + break + + # Update context with feedback + current_context.update({ + "previous_results": iteration_results, + "feedback": feedback, + "iteration": iteration + }) + + return { + "success": any(any(r.success for r in iteration_results) + for iteration_results in results), + "results": results, + "feedback_history": feedback_history, + "pattern": CoordinationPattern.FEEDBACK.value, + "metrics": { + "total_iterations": iteration, + "feedback_impact": self._calculate_feedback_impact(results, feedback_history) + } + } + + async def _coordinate_adaptive(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies with adaptive selection.""" + results = [] + adaptations = [] + current_context = context.copy() + + while len(results) < len(state.active_strategies): + # Select next strategy + next_strategy = await self._select_next_strategy( + results, state.active_strategies, current_context) + + if not next_strategy: + break + + try: + # Execute strategy + strategy = self.strategies[next_strategy] + result = await strategy.reason(query, current_context) + + strategy_result = StrategyResult( + strategy_type=next_strategy, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + + results.append(strategy_result) + + # Adapt strategy selection + adaptation = await self._adapt_strategy_selection( + strategy_result, current_context) + adaptations.append(adaptation) + + # Update context + current_context.update({ + "previous_results": results, + "adaptations": adaptations, + "current_strategy": next_strategy + }) + + except Exception as e: + logging.error(f"Error in adaptive strategy {next_strategy}: {str(e)}") + + return { + "success": any(r.success for r in results), + "results": results, + "adaptations": adaptations, + "pattern": CoordinationPattern.ADAPTIVE.value, + "metrics": { + "total_strategies": len(results), + "adaptation_impact": self._calculate_adaptation_impact(results, adaptations) + } + } + + async def _coordinate_ensemble(self, + query: str, + context: Dict[str, Any], + state: CoordinationState) -> Dict[str, Any]: + """Coordinate strategies as an ensemble.""" + # Execute all strategies + results = [] + for strategy_type in state.active_strategies: + if state.active_strategies[strategy_type]: + try: + strategy = self.strategies[strategy_type] + result = await strategy.reason(query, context) + + strategy_result = StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics=result.get("performance_metrics", {}) + ) + + results.append(strategy_result) + + except Exception as e: + logging.error(f"Error in ensemble strategy {strategy_type}: {str(e)}") + + # Combine results using ensemble methods + ensemble_result = await self._combine_ensemble_results(results, context) + + return { + "success": ensemble_result.get("success", False), + "results": results, + "ensemble_result": ensemble_result, + "pattern": CoordinationPattern.ENSEMBLE.value, + "metrics": { + "total_members": len(results), + "ensemble_confidence": ensemble_result.get("confidence", 0.0) + } + } + + def _record_interaction(self, + source: StrategyType, + target: Optional[StrategyType], + interaction_type: str, + data: Dict[str, Any]): + """Record strategy interaction.""" + self.interactions.append(StrategyInteraction( + source=source, + target=target, + interaction_type=interaction_type, + data=data + )) + + def _update_pattern_performance(self, pattern: CoordinationPattern, result: Dict[str, Any]): + """Update pattern performance metrics.""" + success_rate = result["metrics"].get("success_rate", 0.0) + self.pattern_performance[pattern].append(success_rate) + + # Update weights using exponential moving average + current_weight = self.pattern_weights[pattern] + self.pattern_weights[pattern] = ( + (1 - self.learning_rate) * current_weight + + self.learning_rate * success_rate + ) + + def get_performance_metrics(self) -> Dict[str, Any]: + """Get comprehensive performance metrics.""" + return { + "pattern_weights": dict(self.pattern_weights), + "average_performance": { + pattern.value: sum(scores) / len(scores) if scores else 0 + for pattern, scores in self.pattern_performance.items() + }, + "interaction_counts": defaultdict(int, { + interaction.interaction_type: 1 + for interaction in self.interactions + }), + "active_patterns": [ + pattern.value for pattern, weight in self.pattern_weights.items() + if weight > 0.5 + ] + } diff --git a/space/emergent.py b/space/emergent.py new file mode 100644 index 0000000000000000000000000000000000000000..c41c0c4b05b6e1f9c66d87da4fe03fe263f50355 --- /dev/null +++ b/space/emergent.py @@ -0,0 +1,133 @@ +""" +Emergent Reasoning Module +------------------------ +Implements emergent reasoning capabilities that arise from the interaction +of multiple reasoning strategies and patterns. +""" + +from typing import Dict, Any, List, Optional +from .base import ReasoningStrategy +from .meta_learning import MetaLearningStrategy +from .chain_of_thought import ChainOfThoughtStrategy +from .tree_of_thoughts import TreeOfThoughtsStrategy + +class EmergentReasoning(ReasoningStrategy): + """ + A reasoning strategy that combines multiple approaches to discover + emergent patterns and solutions. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize emergent reasoning with component strategies.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize component strategies with shared config + strategy_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights + } + + self.meta_learner = MetaLearningStrategy(strategy_config) + self.chain_of_thought = ChainOfThoughtStrategy(strategy_config) + self.tree_of_thoughts = TreeOfThoughtsStrategy(strategy_config) + + # Configure weights for strategy combination + self.weights = self.config.get('combination_weights', { + 'meta': 0.4, + 'chain': 0.3, + 'tree': 0.3 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply emergent reasoning by combining multiple strategies and + identifying patterns that emerge from their interaction. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Get results from each strategy + meta_result = await self.meta_learner.reason(query, context) + chain_result = await self.chain_of_thought.reason(query, context) + tree_result = await self.tree_of_thoughts.reason(query, context) + + # Combine results with weighted averaging + combined_answer = self._combine_results([ + (meta_result.get('answer', ''), self.weights['meta']), + (chain_result.get('answer', ''), self.weights['chain']), + (tree_result.get('answer', ''), self.weights['tree']) + ]) + + # Calculate overall confidence + confidence = ( + meta_result.get('confidence', 0) * self.weights['meta'] + + chain_result.get('confidence', 0) * self.weights['chain'] + + tree_result.get('confidence', 0) * self.weights['tree'] + ) + + return { + 'answer': combined_answer, + 'confidence': confidence, + 'reasoning_path': { + 'meta': meta_result.get('reasoning_path'), + 'chain': chain_result.get('reasoning_path'), + 'tree': tree_result.get('reasoning_path') + }, + 'emergent_patterns': self._identify_patterns([ + meta_result, chain_result, tree_result + ]) + } + + except Exception as e: + return { + 'error': f"Emergent reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + def _combine_results(self, weighted_results: List[tuple[str, float]]) -> str: + """Combine multiple reasoning results with weights.""" + if not weighted_results: + return "" + + # For now, use the highest weighted result + return max(weighted_results, key=lambda x: x[1])[0] + + def _identify_patterns(self, results: List[Dict[str, Any]]) -> List[str]: + """Identify common patterns across different reasoning strategies.""" + patterns = [] + + # Extract common themes or conclusions + answers = [r.get('answer', '') for r in results if r.get('answer')] + if len(set(answers)) == 1: + patterns.append("All strategies reached the same conclusion") + elif len(set(answers)) < len(answers): + patterns.append("Some strategies converged on similar conclusions") + + # Look for common confidence patterns + confidences = [r.get('confidence', 0) for r in results] + avg_confidence = sum(confidences) / len(confidences) if confidences else 0 + if avg_confidence > 0.8: + patterns.append("High confidence across all strategies") + elif avg_confidence < 0.3: + patterns.append("Low confidence across strategies") + + return patterns diff --git a/space/learning.py b/space/learning.py new file mode 100644 index 0000000000000000000000000000000000000000..e20db5e08ed2a80706f75bf5413cc3d3beff61dd --- /dev/null +++ b/space/learning.py @@ -0,0 +1,394 @@ +"""Enhanced learning mechanisms for reasoning strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +@dataclass +class LearningEvent: + """Event for strategy learning.""" + strategy_type: str + event_type: str + data: Dict[str, Any] + outcome: Optional[float] + timestamp: datetime = field(default_factory=datetime.now) + +class LearningMode(Enum): + """Types of learning modes.""" + SUPERVISED = "supervised" + REINFORCEMENT = "reinforcement" + ACTIVE = "active" + TRANSFER = "transfer" + META = "meta" + ENSEMBLE = "ensemble" + +@dataclass +class LearningState: + """State for learning process.""" + mode: LearningMode + parameters: Dict[str, Any] + history: List[LearningEvent] + metrics: Dict[str, float] + metadata: Dict[str, Any] = field(default_factory=dict) + +class EnhancedLearningManager: + """ + Advanced learning manager that: + 1. Implements multiple learning modes + 2. Tracks learning progress + 3. Adapts learning parameters + 4. Optimizes strategy performance + 5. Transfers knowledge between strategies + """ + + def __init__(self, + learning_rate: float = 0.1, + exploration_rate: float = 0.2, + memory_size: int = 1000): + self.learning_rate = learning_rate + self.exploration_rate = exploration_rate + self.memory_size = memory_size + + # Learning states + self.states: Dict[str, LearningState] = {} + + # Performance tracking + self.performance_history: List[Dict[str, Any]] = [] + self.strategy_metrics: Dict[str, List[float]] = defaultdict(list) + + # Knowledge transfer + self.knowledge_base: Dict[str, Any] = {} + self.transfer_history: List[Dict[str, Any]] = [] + + async def learn(self, + strategy_type: str, + event: LearningEvent, + context: Dict[str, Any]) -> Dict[str, Any]: + """Learn from strategy execution event.""" + try: + # Initialize or get learning state + state = self._get_learning_state(strategy_type) + + # Select learning mode + mode = await self._select_learning_mode(event, state, context) + + # Execute learning + if mode == LearningMode.SUPERVISED: + result = await self._supervised_learning(event, state, context) + elif mode == LearningMode.REINFORCEMENT: + result = await self._reinforcement_learning(event, state, context) + elif mode == LearningMode.ACTIVE: + result = await self._active_learning(event, state, context) + elif mode == LearningMode.TRANSFER: + result = await self._transfer_learning(event, state, context) + elif mode == LearningMode.META: + result = await self._meta_learning(event, state, context) + elif mode == LearningMode.ENSEMBLE: + result = await self._ensemble_learning(event, state, context) + else: + raise ValueError(f"Unsupported learning mode: {mode}") + + # Update state + self._update_learning_state(state, result) + + # Record performance + self._record_performance(strategy_type, result) + + return result + + except Exception as e: + logging.error(f"Error in learning: {str(e)}") + return { + "success": False, + "error": str(e), + "mode": mode.value if 'mode' in locals() else None + } + + async def _supervised_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement supervised learning.""" + # Extract features and labels + features = await self._extract_features(event.data, context) + labels = event.outcome if event.outcome is not None else 0.0 + + # Train model + model_update = await self._update_model(features, labels, state, context) + + # Validate performance + validation = await self._validate_model(model_update, state, context) + + return { + "success": True, + "mode": LearningMode.SUPERVISED.value, + "model_update": model_update, + "validation": validation, + "metrics": { + "accuracy": validation.get("accuracy", 0.0), + "loss": validation.get("loss", 0.0) + } + } + + async def _reinforcement_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement reinforcement learning.""" + # Extract state and action + current_state = await self._extract_state(event.data, context) + action = event.data.get("action") + reward = event.outcome if event.outcome is not None else 0.0 + + # Update policy + policy_update = await self._update_policy( + current_state, action, reward, state, context) + + # Optimize value function + value_update = await self._update_value_function( + current_state, reward, state, context) + + return { + "success": True, + "mode": LearningMode.REINFORCEMENT.value, + "policy_update": policy_update, + "value_update": value_update, + "metrics": { + "reward": reward, + "value_error": value_update.get("error", 0.0) + } + } + + async def _active_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement active learning.""" + # Query selection + query = await self._select_query(event.data, state, context) + + # Get feedback + feedback = await self._get_feedback(query, context) + + # Update model + model_update = await self._update_model_active( + query, feedback, state, context) + + return { + "success": True, + "mode": LearningMode.ACTIVE.value, + "query": query, + "feedback": feedback, + "model_update": model_update, + "metrics": { + "uncertainty": query.get("uncertainty", 0.0), + "feedback_quality": feedback.get("quality", 0.0) + } + } + + async def _transfer_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement transfer learning.""" + # Source task selection + source_task = await self._select_source_task(event.data, state, context) + + # Knowledge extraction + knowledge = await self._extract_knowledge(source_task, context) + + # Transfer adaptation + adaptation = await self._adapt_knowledge( + knowledge, event.data, state, context) + + # Apply transfer + transfer = await self._apply_transfer(adaptation, state, context) + + return { + "success": True, + "mode": LearningMode.TRANSFER.value, + "source_task": source_task, + "knowledge": knowledge, + "adaptation": adaptation, + "transfer": transfer, + "metrics": { + "transfer_efficiency": transfer.get("efficiency", 0.0), + "adaptation_quality": adaptation.get("quality", 0.0) + } + } + + async def _meta_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement meta-learning.""" + # Task characterization + task_char = await self._characterize_task(event.data, context) + + # Strategy selection + strategy = await self._select_strategy(task_char, state, context) + + # Parameter optimization + optimization = await self._optimize_parameters( + strategy, task_char, state, context) + + # Apply meta-learning + meta_update = await self._apply_meta_learning( + optimization, state, context) + + return { + "success": True, + "mode": LearningMode.META.value, + "task_characterization": task_char, + "strategy": strategy, + "optimization": optimization, + "meta_update": meta_update, + "metrics": { + "strategy_fit": strategy.get("fit_score", 0.0), + "optimization_improvement": optimization.get("improvement", 0.0) + } + } + + async def _ensemble_learning(self, + event: LearningEvent, + state: LearningState, + context: Dict[str, Any]) -> Dict[str, Any]: + """Implement ensemble learning.""" + # Member selection + members = await self._select_members(event.data, state, context) + + # Weight optimization + weights = await self._optimize_weights(members, state, context) + + # Combine predictions + combination = await self._combine_predictions( + members, weights, event.data, context) + + return { + "success": True, + "mode": LearningMode.ENSEMBLE.value, + "members": members, + "weights": weights, + "combination": combination, + "metrics": { + "ensemble_diversity": weights.get("diversity", 0.0), + "combination_strength": combination.get("strength", 0.0) + } + } + + def _get_learning_state(self, strategy_type: str) -> LearningState: + """Get or initialize learning state for strategy.""" + if strategy_type not in self.states: + self.states[strategy_type] = LearningState( + mode=LearningMode.SUPERVISED, + parameters={ + "learning_rate": self.learning_rate, + "exploration_rate": self.exploration_rate + }, + history=[], + metrics={} + ) + return self.states[strategy_type] + + def _update_learning_state(self, state: LearningState, result: Dict[str, Any]): + """Update learning state with result.""" + # Update history + state.history.append(LearningEvent( + strategy_type=result.get("strategy_type", "unknown"), + event_type="learning_update", + data=result, + outcome=result.get("metrics", {}).get("accuracy", 0.0), + timestamp=datetime.now() + )) + + # Update metrics + for metric, value in result.get("metrics", {}).items(): + if metric in state.metrics: + state.metrics[metric] = ( + 0.9 * state.metrics[metric] + 0.1 * value # Exponential moving average + ) + else: + state.metrics[metric] = value + + # Adapt parameters + self._adapt_parameters(state, result) + + def _record_performance(self, strategy_type: str, result: Dict[str, Any]): + """Record learning performance.""" + self.performance_history.append({ + "timestamp": datetime.now().isoformat(), + "strategy_type": strategy_type, + "mode": result.get("mode"), + "metrics": result.get("metrics", {}), + "success": result.get("success", False) + }) + + # Update strategy metrics + for metric, value in result.get("metrics", {}).items(): + self.strategy_metrics[f"{strategy_type}_{metric}"].append(value) + + # Maintain memory size + if len(self.performance_history) > self.memory_size: + self.performance_history = self.performance_history[-self.memory_size:] + + def _adapt_parameters(self, state: LearningState, result: Dict[str, Any]): + """Adapt learning parameters based on performance.""" + # Adapt learning rate + if "accuracy" in result.get("metrics", {}): + accuracy = result["metrics"]["accuracy"] + if accuracy > 0.8: + state.parameters["learning_rate"] *= 0.95 # Decrease if performing well + elif accuracy < 0.6: + state.parameters["learning_rate"] *= 1.05 # Increase if performing poorly + + # Adapt exploration rate + if "reward" in result.get("metrics", {}): + reward = result["metrics"]["reward"] + if reward > 0: + state.parameters["exploration_rate"] *= 0.95 # Decrease if getting rewards + else: + state.parameters["exploration_rate"] *= 1.05 # Increase if not getting rewards + + # Clip parameters to reasonable ranges + state.parameters["learning_rate"] = np.clip( + state.parameters["learning_rate"], 0.001, 0.5) + state.parameters["exploration_rate"] = np.clip( + state.parameters["exploration_rate"], 0.01, 0.5) + + def get_performance_metrics(self) -> Dict[str, Any]: + """Get comprehensive performance metrics.""" + return { + "learning_states": { + strategy_type: { + "mode": state.mode.value, + "parameters": state.parameters, + "metrics": state.metrics + } + for strategy_type, state in self.states.items() + }, + "strategy_performance": { + metric: { + "mean": np.mean(values) if values else 0.0, + "std": np.std(values) if values else 0.0, + "min": min(values) if values else 0.0, + "max": max(values) if values else 0.0 + } + for metric, values in self.strategy_metrics.items() + }, + "transfer_metrics": { + "total_transfers": len(self.transfer_history), + "success_rate": sum(1 for t in self.transfer_history if t.get("success", False)) / len(self.transfer_history) if self.transfer_history else 0 + } + } + + def clear_history(self): + """Clear learning history and reset states.""" + self.states.clear() + self.performance_history.clear() + self.strategy_metrics.clear() + self.transfer_history.clear() diff --git a/space/local_llm.py b/space/local_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..9718e68603d3cbde8177d77e58f9785d611d5cc1 --- /dev/null +++ b/space/local_llm.py @@ -0,0 +1,117 @@ +"""Local LLM integration for the reasoning system.""" + +import os +from typing import Dict, Any, Optional +from datetime import datetime +import logging +from llama_cpp import Llama +import huggingface_hub +from .base import ReasoningStrategy +from .model_manager import ModelManager, ModelType + +class LocalLLMStrategy(ReasoningStrategy): + """Implements reasoning using local LLM.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the local LLM strategy.""" + super().__init__() + self.config = config or {} + + # Initialize model manager + self.model_manager = ModelManager(self.config.get('model_dir', "models")) + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + self.logger = logging.getLogger(__name__) + + async def initialize(self): + """Initialize all models.""" + await self.model_manager.initialize_all_models() + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate reasoning response using appropriate local LLM.""" + try: + # Determine best model for the task + task_type = context.get('task_type', 'general') + model_key = self.model_manager.get_best_model_for_task(task_type) + + # Get or initialize the model + model = await self.model_manager.get_model(model_key) + if not model: + raise Exception(f"Failed to initialize {model_key} model") + + # Format prompt with context + prompt = self._format_prompt(query, context) + + # Generate response + response = model( + prompt, + max_tokens=1024 if model.n_ctx >= 4096 else 512, + temperature=0.7, + top_p=0.95, + repeat_penalty=1.1, + echo=False + ) + + # Extract and structure the response + result = self._parse_response(response['choices'][0]['text']) + + return { + 'success': True, + 'answer': result['answer'], + 'reasoning': result['reasoning'], + 'confidence': result['confidence'], + 'timestamp': datetime.now(), + 'metadata': { + 'model': model_key, + 'strategy': 'local_llm', + 'context_length': len(prompt), + 'response_length': len(response['choices'][0]['text']) + } + } + + except Exception as e: + self.logger.error(f"Error in reasoning: {e}") + return { + 'success': False, + 'error': str(e), + 'timestamp': datetime.now() + } + + def _format_prompt(self, query: str, context: Dict[str, Any]) -> str: + """Format the prompt with query and context.""" + # Include relevant context + context_str = "\n".join([ + f"{k}: {v}" for k, v in context.items() + if k in ['objective', 'constraints', 'background'] + ]) + + return f"""Let's solve this problem step by step. + +Context: +{context_str} + +Question: {query} + +Let me break this down: +1.""" + + def _parse_response(self, text: str) -> Dict[str, Any]: + """Parse the response into structured output.""" + # Simple parsing for now + lines = text.strip().split('\n') + + return { + 'answer': lines[-1] if lines else '', + 'reasoning': '\n'.join(lines[:-1]) if len(lines) > 1 else '', + 'confidence': 0.8 # Default confidence + } diff --git a/space/market_analysis.py b/space/market_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..01a7ba5d0471e9b5c9fa9a5c731f53cef3902830 --- /dev/null +++ b/space/market_analysis.py @@ -0,0 +1,450 @@ +"""Advanced market analysis tools for venture strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class MarketSegment: + """Market segment analysis.""" + size: float + growth_rate: float + cagr: float + competition: List[Dict[str, Any]] + barriers: List[str] + opportunities: List[str] + risks: List[str] + +@dataclass +class CompetitorAnalysis: + """Competitor analysis.""" + name: str + market_share: float + strengths: List[str] + weaknesses: List[str] + strategy: str + revenue: Optional[float] + valuation: Optional[float] + +@dataclass +class MarketTrend: + """Market trend analysis.""" + name: str + impact: float + timeline: str + adoption_rate: float + market_potential: float + risk_level: float + +class MarketAnalyzer: + """ + Advanced market analysis toolkit that: + 1. Analyzes market segments + 2. Tracks competitors + 3. Identifies trends + 4. Predicts opportunities + 5. Assesses risks + """ + + def __init__(self): + self.segments: Dict[str, MarketSegment] = {} + self.competitors: Dict[str, CompetitorAnalysis] = {} + self.trends: List[MarketTrend] = [] + + async def analyze_market(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Perform comprehensive market analysis.""" + try: + # Segment analysis + segment_analysis = await self._analyze_segment(segment, context) + + # Competitor analysis + competitor_analysis = await self._analyze_competitors(segment, context) + + # Trend analysis + trend_analysis = await self._analyze_trends(segment, context) + + # Opportunity analysis + opportunity_analysis = await self._analyze_opportunities( + segment_analysis, competitor_analysis, trend_analysis, context) + + # Risk analysis + risk_analysis = await self._analyze_risks( + segment_analysis, competitor_analysis, trend_analysis, context) + + return { + "success": True, + "segment_analysis": segment_analysis, + "competitor_analysis": competitor_analysis, + "trend_analysis": trend_analysis, + "opportunity_analysis": opportunity_analysis, + "risk_analysis": risk_analysis, + "metrics": { + "market_score": self._calculate_market_score(segment_analysis), + "opportunity_score": self._calculate_opportunity_score(opportunity_analysis), + "risk_score": self._calculate_risk_score(risk_analysis) + } + } + except Exception as e: + logging.error(f"Error in market analysis: {str(e)}") + return {"success": False, "error": str(e)} + + async def _analyze_segment(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market segment.""" + prompt = f""" + Analyze market segment: + Segment: {segment} + Context: {json.dumps(context)} + + Analyze: + 1. Market size and growth + 2. Customer segments + 3. Value chain + 4. Entry barriers + 5. Competitive dynamics + + Format as: + [Analysis] + Size: ... + Growth: ... + Segments: ... + Value_Chain: ... + Barriers: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_segment_analysis(response["answer"]) + + async def _analyze_competitors(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze competitors in segment.""" + prompt = f""" + Analyze competitors: + Segment: {segment} + Context: {json.dumps(context)} + + For each competitor analyze: + 1. Market share + 2. Business model + 3. Strengths/weaknesses + 4. Strategy + 5. Performance metrics + + Format as: + [Competitor1] + Share: ... + Model: ... + Strengths: ... + Weaknesses: ... + Strategy: ... + Metrics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_competitor_analysis(response["answer"]) + + async def _analyze_trends(self, + segment: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market trends.""" + prompt = f""" + Analyze market trends: + Segment: {segment} + Context: {json.dumps(context)} + + Analyze trends in: + 1. Technology + 2. Customer behavior + 3. Business models + 4. Regulation + 5. Market dynamics + + Format as: + [Trend1] + Type: ... + Impact: ... + Timeline: ... + Adoption: ... + Potential: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_trend_analysis(response["answer"]) + + async def _analyze_opportunities(self, + segment_analysis: Dict[str, Any], + competitor_analysis: Dict[str, Any], + trend_analysis: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market opportunities.""" + prompt = f""" + Analyze market opportunities: + Segment: {json.dumps(segment_analysis)} + Competitors: {json.dumps(competitor_analysis)} + Trends: {json.dumps(trend_analysis)} + Context: {json.dumps(context)} + + Identify opportunities in: + 1. Unmet needs + 2. Market gaps + 3. Innovation potential + 4. Scaling potential + 5. Value creation + + Format as: + [Opportunity1] + Type: ... + Description: ... + Potential: ... + Requirements: ... + Timeline: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_opportunity_analysis(response["answer"]) + + async def _analyze_risks(self, + segment_analysis: Dict[str, Any], + competitor_analysis: Dict[str, Any], + trend_analysis: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze market risks.""" + prompt = f""" + Analyze market risks: + Segment: {json.dumps(segment_analysis)} + Competitors: {json.dumps(competitor_analysis)} + Trends: {json.dumps(trend_analysis)} + Context: {json.dumps(context)} + + Analyze risks in: + 1. Market dynamics + 2. Competition + 3. Technology + 4. Regulation + 5. Execution + + Format as: + [Risk1] + Type: ... + Description: ... + Impact: ... + Probability: ... + Mitigation: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_risk_analysis(response["answer"]) + + def _calculate_market_score(self, analysis: Dict[str, Any]) -> float: + """Calculate market attractiveness score.""" + weights = { + "size": 0.3, + "growth": 0.3, + "competition": 0.2, + "barriers": 0.1, + "dynamics": 0.1 + } + + scores = { + "size": min(analysis.get("size", 0) / 1e9, 1.0), # Normalize to 1B + "growth": min(analysis.get("growth", 0) / 30, 1.0), # Normalize to 30% + "competition": 1.0 - min(len(analysis.get("competitors", [])) / 10, 1.0), + "barriers": 1.0 - min(len(analysis.get("barriers", [])) / 5, 1.0), + "dynamics": analysis.get("dynamics_score", 0.5) + } + + return sum(weights[k] * scores[k] for k in weights) + + def _calculate_opportunity_score(self, analysis: Dict[str, Any]) -> float: + """Calculate opportunity attractiveness score.""" + weights = { + "market_potential": 0.3, + "innovation_potential": 0.2, + "execution_feasibility": 0.2, + "competitive_advantage": 0.2, + "timing": 0.1 + } + + scores = { + "market_potential": analysis.get("market_potential", 0.5), + "innovation_potential": analysis.get("innovation_potential", 0.5), + "execution_feasibility": analysis.get("execution_feasibility", 0.5), + "competitive_advantage": analysis.get("competitive_advantage", 0.5), + "timing": analysis.get("timing_score", 0.5) + } + + return sum(weights[k] * scores[k] for k in weights) + + def _calculate_risk_score(self, analysis: Dict[str, Any]) -> float: + """Calculate risk level score.""" + weights = { + "market_risk": 0.2, + "competition_risk": 0.2, + "technology_risk": 0.2, + "regulatory_risk": 0.2, + "execution_risk": 0.2 + } + + scores = { + "market_risk": analysis.get("market_risk", 0.5), + "competition_risk": analysis.get("competition_risk", 0.5), + "technology_risk": analysis.get("technology_risk", 0.5), + "regulatory_risk": analysis.get("regulatory_risk", 0.5), + "execution_risk": analysis.get("execution_risk", 0.5) + } + + return sum(weights[k] * scores[k] for k in weights) + + def get_market_insights(self) -> Dict[str, Any]: + """Get comprehensive market insights.""" + return { + "segment_insights": { + segment: { + "size": s.size, + "growth_rate": s.growth_rate, + "cagr": s.cagr, + "opportunity_score": self._calculate_market_score({ + "size": s.size, + "growth": s.growth_rate, + "competitors": s.competition, + "barriers": s.barriers + }) + } + for segment, s in self.segments.items() + }, + "competitor_insights": { + competitor: { + "market_share": c.market_share, + "strength_score": len(c.strengths) / (len(c.strengths) + len(c.weaknesses)), + "revenue": c.revenue, + "valuation": c.valuation + } + for competitor, c in self.competitors.items() + }, + "trend_insights": [ + { + "name": t.name, + "impact": t.impact, + "potential": t.market_potential, + "risk": t.risk_level + } + for t in self.trends + ] + } + +class MarketAnalysisStrategy(ReasoningStrategy): + """ + Advanced market analysis strategy that combines multiple analytical tools + to provide comprehensive market insights. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize market analysis strategy.""" + super().__init__() + self.config = config or {} + self.analyzer = MarketAnalyzer() + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Perform market analysis based on query and context. + + Args: + query: The market analysis query + context: Additional context and parameters + + Returns: + Dict containing market analysis results and confidence scores + """ + try: + # Extract market segment from query/context + segment = self._extract_segment(query, context) + + # Perform market analysis + analysis = await self._analyze_market(segment, context) + + # Get insights + insights = self.analyzer.get_market_insights() + + # Calculate confidence based on data quality and completeness + confidence = self._calculate_confidence(analysis, insights) + + return { + 'answer': self._format_insights(insights), + 'confidence': confidence, + 'analysis': analysis, + 'insights': insights, + 'segment': segment + } + + except Exception as e: + logging.error(f"Market analysis failed: {str(e)}") + return { + 'error': f"Market analysis failed: {str(e)}", + 'confidence': 0.0 + } + + def _extract_segment(self, query: str, context: Dict[str, Any]) -> str: + """Extract market segment from query and context.""" + # Use context if available + if 'segment' in context: + return context['segment'] + + # Default to general market + return 'general' + + async def _analyze_market(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Perform comprehensive market analysis.""" + return await self.analyzer.analyze_market(segment, context) + + def _calculate_confidence(self, analysis: Dict[str, Any], insights: Dict[str, Any]) -> float: + """Calculate confidence score based on analysis quality.""" + # Base confidence + confidence = 0.5 + + # Adjust based on data completeness + if analysis.get('segment_analysis'): + confidence += 0.1 + if analysis.get('competitor_analysis'): + confidence += 0.1 + if analysis.get('trend_analysis'): + confidence += 0.1 + + # Adjust based on insight quality + if insights.get('opportunities'): + confidence += 0.1 + if insights.get('risks'): + confidence += 0.1 + + return min(confidence, 1.0) + + def _format_insights(self, insights: Dict[str, Any]) -> str: + """Format market insights into readable text.""" + sections = [] + + if 'market_overview' in insights: + sections.append(f"Market Overview: {insights['market_overview']}") + + if 'opportunities' in insights: + opps = insights['opportunities'] + sections.append("Key Opportunities:\n- " + "\n- ".join(opps)) + + if 'risks' in insights: + risks = insights['risks'] + sections.append("Key Risks:\n- " + "\n- ".join(risks)) + + if 'recommendations' in insights: + recs = insights['recommendations'] + sections.append("Recommendations:\n- " + "\n- ".join(recs)) + + return "\n\n".join(sections) diff --git a/space/meta_learning.py b/space/meta_learning.py new file mode 100644 index 0000000000000000000000000000000000000000..f58e6fd7302c77a255e99a36fa465c61ddfda849 --- /dev/null +++ b/space/meta_learning.py @@ -0,0 +1,436 @@ +""" +Meta-Learning System +------------------ +Implements meta-learning capabilities for improved learning and adaptation. +""" + +from typing import Dict, Any, List, Optional, Tuple +import numpy as np +from dataclasses import dataclass, field +import logging +from datetime import datetime +from enum import Enum +import json +from quantum_learning import QuantumLearningSystem, Pattern, PatternType + +class LearningStrategy(Enum): + GRADIENT_BASED = "gradient_based" + MEMORY_BASED = "memory_based" + EVOLUTIONARY = "evolutionary" + REINFORCEMENT = "reinforcement" + QUANTUM = "quantum" + +@dataclass +class MetaParameters: + """Meta-parameters for learning strategies""" + learning_rate: float = 0.01 + memory_size: int = 1000 + evolution_rate: float = 0.1 + exploration_rate: float = 0.2 + quantum_interference: float = 0.5 + adaptation_threshold: float = 0.7 + +@dataclass +class LearningMetrics: + """Metrics for learning performance""" + accuracy: float + convergence_rate: float + adaptation_speed: float + resource_usage: float + timestamp: str = field(default_factory=lambda: datetime.now().isoformat()) + +class MetaLearningSystem: + """Meta-learning system for optimizing learning strategies""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + self.logger = logging.getLogger(__name__) + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize quantum system with shared config + quantum_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights, + 'num_qubits': self.config.get('num_qubits', 8), + 'entanglement_strength': self.config.get('entanglement_strength', 0.5), + 'interference_threshold': self.config.get('interference_threshold', 0.3), + 'tunneling_rate': self.config.get('tunneling_rate', 0.1), + 'annealing_schedule': self.config.get('annealing_schedule', { + 'initial_temp': 1.0, + 'final_temp': 0.01, + 'steps': 100, + 'cooling_rate': 0.95 + }) + } + self.quantum_system = QuantumLearningSystem(quantum_config) + self.strategies = {} + self.performance_history = [] + self.meta_parameters = MetaParameters() + + async def optimize_learning( + self, + observation: Dict[str, Any], + current_strategy: LearningStrategy + ) -> Tuple[Dict[str, Any], LearningMetrics]: + """Optimize learning strategy based on observation""" + try: + # Process with quantum system + quantum_result = await self.quantum_system.process_observation(observation) + + # Evaluate current strategy + current_metrics = self._evaluate_strategy( + current_strategy, + observation, + quantum_result + ) + + # Update performance history + self._update_performance_history(current_metrics) + + # Adapt meta-parameters + self._adapt_meta_parameters(current_metrics) + + # Select optimal strategy + optimal_strategy = self._select_optimal_strategy( + observation, + current_metrics + ) + + # Apply selected strategy + result = await self._apply_strategy( + optimal_strategy, + observation, + quantum_result + ) + + return result, current_metrics + + except Exception as e: + self.logger.error(f"Failed to optimize learning: {str(e)}") + raise + + def _evaluate_strategy( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> LearningMetrics: + """Evaluate performance of current learning strategy""" + # Calculate accuracy + accuracy = self._calculate_accuracy( + strategy, + observation, + quantum_result + ) + + # Calculate convergence rate + convergence_rate = self._calculate_convergence_rate( + strategy, + self.performance_history + ) + + # Calculate adaptation speed + adaptation_speed = self._calculate_adaptation_speed( + strategy, + observation + ) + + # Calculate resource usage + resource_usage = self._calculate_resource_usage(strategy) + + return LearningMetrics( + accuracy=accuracy, + convergence_rate=convergence_rate, + adaptation_speed=adaptation_speed, + resource_usage=resource_usage + ) + + def _update_performance_history( + self, + metrics: LearningMetrics + ) -> None: + """Update performance history with new metrics""" + self.performance_history.append(metrics) + + # Maintain history size + if len(self.performance_history) > self.meta_parameters.memory_size: + self.performance_history.pop(0) + + def _adapt_meta_parameters( + self, + metrics: LearningMetrics + ) -> None: + """Adapt meta-parameters based on performance metrics""" + # Adjust learning rate + if metrics.convergence_rate < self.meta_parameters.adaptation_threshold: + self.meta_parameters.learning_rate *= 0.9 + else: + self.meta_parameters.learning_rate *= 1.1 + + # Adjust memory size + if metrics.resource_usage > 0.8: + self.meta_parameters.memory_size = int( + self.meta_parameters.memory_size * 0.9 + ) + elif metrics.resource_usage < 0.2: + self.meta_parameters.memory_size = int( + self.meta_parameters.memory_size * 1.1 + ) + + # Adjust evolution rate + if metrics.adaptation_speed < self.meta_parameters.adaptation_threshold: + self.meta_parameters.evolution_rate *= 1.1 + else: + self.meta_parameters.evolution_rate *= 0.9 + + # Adjust exploration rate + if metrics.accuracy < self.meta_parameters.adaptation_threshold: + self.meta_parameters.exploration_rate *= 1.1 + else: + self.meta_parameters.exploration_rate *= 0.9 + + # Adjust quantum interference + if metrics.accuracy > 0.8: + self.meta_parameters.quantum_interference *= 1.1 + else: + self.meta_parameters.quantum_interference *= 0.9 + + # Ensure parameters stay within reasonable bounds + self._normalize_parameters() + + def _normalize_parameters(self) -> None: + """Normalize meta-parameters to stay within bounds""" + self.meta_parameters.learning_rate = np.clip( + self.meta_parameters.learning_rate, + 0.001, + 0.1 + ) + self.meta_parameters.memory_size = np.clip( + self.meta_parameters.memory_size, + 100, + 10000 + ) + self.meta_parameters.evolution_rate = np.clip( + self.meta_parameters.evolution_rate, + 0.01, + 0.5 + ) + self.meta_parameters.exploration_rate = np.clip( + self.meta_parameters.exploration_rate, + 0.1, + 0.9 + ) + self.meta_parameters.quantum_interference = np.clip( + self.meta_parameters.quantum_interference, + 0.1, + 0.9 + ) + + def _select_optimal_strategy( + self, + observation: Dict[str, Any], + metrics: LearningMetrics + ) -> LearningStrategy: + """Select optimal learning strategy""" + strategies = list(LearningStrategy) + scores = [] + + for strategy in strategies: + # Calculate strategy score + score = self._calculate_strategy_score( + strategy, + observation, + metrics + ) + scores.append((strategy, score)) + + # Select strategy with highest score + optimal_strategy = max(scores, key=lambda x: x[1])[0] + + return optimal_strategy + + async def _apply_strategy( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply selected learning strategy""" + if strategy == LearningStrategy.GRADIENT_BASED: + return await self._apply_gradient_strategy( + observation, + quantum_result + ) + elif strategy == LearningStrategy.MEMORY_BASED: + return await self._apply_memory_strategy( + observation, + quantum_result + ) + elif strategy == LearningStrategy.EVOLUTIONARY: + return await self._apply_evolutionary_strategy( + observation, + quantum_result + ) + elif strategy == LearningStrategy.REINFORCEMENT: + return await self._apply_reinforcement_strategy( + observation, + quantum_result + ) + else: # QUANTUM + return quantum_result + + def _calculate_accuracy( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> float: + """Calculate accuracy of learning strategy""" + if "patterns" not in quantum_result: + return 0.0 + + patterns = quantum_result["patterns"] + if not patterns: + return 0.0 + + # Calculate pattern confidence + confidence_sum = sum(pattern.confidence for pattern in patterns) + return confidence_sum / len(patterns) + + def _calculate_convergence_rate( + self, + strategy: LearningStrategy, + history: List[LearningMetrics] + ) -> float: + """Calculate convergence rate of learning strategy""" + if not history: + return 0.0 + + # Calculate rate of improvement + accuracies = [metrics.accuracy for metrics in history[-10:]] + if len(accuracies) < 2: + return 0.0 + + differences = np.diff(accuracies) + return float(np.mean(differences > 0)) + + def _calculate_adaptation_speed( + self, + strategy: LearningStrategy, + observation: Dict[str, Any] + ) -> float: + """Calculate adaptation speed of learning strategy""" + if not self.performance_history: + return 0.0 + + # Calculate time to reach adaptation threshold + threshold = self.meta_parameters.adaptation_threshold + for i, metrics in enumerate(self.performance_history): + if metrics.accuracy >= threshold: + return 1.0 / (i + 1) + + return 0.0 + + def _calculate_resource_usage( + self, + strategy: LearningStrategy + ) -> float: + """Calculate resource usage of learning strategy""" + # Simulate resource usage based on strategy + base_usage = { + LearningStrategy.GRADIENT_BASED: 0.4, + LearningStrategy.MEMORY_BASED: 0.6, + LearningStrategy.EVOLUTIONARY: 0.7, + LearningStrategy.REINFORCEMENT: 0.5, + LearningStrategy.QUANTUM: 0.8 + } + + return base_usage[strategy] + + def _calculate_strategy_score( + self, + strategy: LearningStrategy, + observation: Dict[str, Any], + metrics: LearningMetrics + ) -> float: + """Calculate score for learning strategy""" + # Weight different factors + weights = { + "accuracy": 0.4, + "convergence": 0.2, + "adaptation": 0.2, + "resources": 0.2 + } + + score = ( + weights["accuracy"] * metrics.accuracy + + weights["convergence"] * metrics.convergence_rate + + weights["adaptation"] * metrics.adaptation_speed + + weights["resources"] * (1 - metrics.resource_usage) + ) + + # Add exploration bonus + if np.random.random() < self.meta_parameters.exploration_rate: + score += 0.1 + + return score + + async def _apply_gradient_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply gradient-based learning strategy""" + return { + "result": "gradient_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } + + async def _apply_memory_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply memory-based learning strategy""" + return { + "result": "memory_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } + + async def _apply_evolutionary_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply evolutionary learning strategy""" + return { + "result": "evolutionary_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } + + async def _apply_reinforcement_strategy( + self, + observation: Dict[str, Any], + quantum_result: Dict[str, Any] + ) -> Dict[str, Any]: + """Apply reinforcement learning strategy""" + return { + "result": "reinforcement_optimization", + "quantum_enhanced": quantum_result, + "meta_parameters": self.meta_parameters.__dict__ + } diff --git a/space/model_manager.py b/space/model_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..3bcf2f0459b05e757c366ec62b627e0d92eb2934 --- /dev/null +++ b/space/model_manager.py @@ -0,0 +1,145 @@ +"""Model manager for handling multiple LLMs in Hugging Face Spaces.""" + +import os +from typing import Dict, Any, Optional, List +import logging +from dataclasses import dataclass +from enum import Enum +import huggingface_hub +from llama_cpp import Llama + +class ModelType(Enum): + """Types of models and their specific tasks.""" + REASONING = "reasoning" + CODE = "code" + CHAT = "chat" + PLANNING = "planning" + ANALYSIS = "analysis" + +@dataclass +class ModelConfig: + """Configuration for a specific model.""" + repo_id: str + filename: str + model_type: ModelType + context_size: int = 4096 + gpu_layers: int = 35 + batch_size: int = 512 + threads: int = 8 + +class ModelManager: + """Manages multiple LLM models for different tasks in Spaces.""" + + def __init__(self): + # In Spaces, models are stored in the cache directory + self.model_dir = os.getenv('SPACE_CACHE_DIR', '/tmp/models') + self.models: Dict[str, Llama] = {} + self.logger = logging.getLogger(__name__) + + # Define model configurations + self.model_configs = { + "reasoning": ModelConfig( + repo_id="rrbale/pruned-qwen-moe", + filename="model-Q6_K.gguf", + model_type=ModelType.REASONING + ), + "code": ModelConfig( + repo_id="YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF", + filename="model.gguf", + model_type=ModelType.CODE + ), + "chat": ModelConfig( + repo_id="Nidum-Llama-3.2-3B-Uncensored-GGUF", + filename="model-Q6_K.gguf", + model_type=ModelType.CHAT + ), + "planning": ModelConfig( + repo_id="deepseek-ai/JanusFlow-1.3B", + filename="model.gguf", + model_type=ModelType.PLANNING + ), + "analysis": ModelConfig( + repo_id="prithivMLmods/QwQ-4B-Instruct", + filename="model.gguf", + model_type=ModelType.ANALYSIS, + context_size=8192, + gpu_layers=40 + ), + "general": ModelConfig( + repo_id="gpt-omni/mini-omni2", + filename="mini-omni2.gguf", + model_type=ModelType.CHAT + ) + } + + os.makedirs(self.model_dir, exist_ok=True) + + async def initialize_model(self, model_key: str) -> Optional[Llama]: + """Initialize a specific model in Spaces.""" + try: + config = self.model_configs[model_key] + cache_dir = os.path.join(self.model_dir, model_key) + os.makedirs(cache_dir, exist_ok=True) + + # Download model using HF Hub + self.logger.info(f"Downloading {model_key} model...") + model_path = huggingface_hub.hf_hub_download( + repo_id=config.repo_id, + filename=config.filename, + repo_type="model", + cache_dir=cache_dir, + local_dir_use_symlinks=False + ) + + # Configure for Spaces GPU environment + try: + model = Llama( + model_path=model_path, + n_ctx=config.context_size, + n_batch=config.batch_size, + n_threads=config.threads, + n_gpu_layers=config.gpu_layers, + main_gpu=0, + tensor_split=None # Let it use all available GPU memory + ) + self.logger.info(f"{model_key} model loaded with GPU acceleration!") + except Exception as e: + self.logger.warning(f"GPU loading failed for {model_key}: {e}, falling back to CPU...") + model = Llama( + model_path=model_path, + n_ctx=2048, + n_batch=256, + n_threads=4, + n_gpu_layers=0 + ) + self.logger.info(f"{model_key} model loaded in CPU-only mode") + + self.models[model_key] = model + return model + + except Exception as e: + self.logger.error(f"Error initializing {model_key} model: {e}") + return None + + async def get_model(self, model_key: str) -> Optional[Llama]: + """Get a model, initializing it if necessary.""" + if model_key not in self.models: + return await self.initialize_model(model_key) + return self.models[model_key] + + async def initialize_all_models(self): + """Initialize all configured models.""" + for model_key in self.model_configs.keys(): + await self.initialize_model(model_key) + + def get_best_model_for_task(self, task_type: str) -> str: + """Get the best model key for a specific task type.""" + task_model_mapping = { + "reasoning": "reasoning", + "code": "code", + "chat": "chat", + "planning": "planning", + "analysis": "analysis", + "general": "general" + } + return task_model_mapping.get(task_type, "general") diff --git a/space/monetization.py b/space/monetization.py new file mode 100644 index 0000000000000000000000000000000000000000..abde3dead8654dcf6ee90b4fa119235bba7eb414 --- /dev/null +++ b/space/monetization.py @@ -0,0 +1,447 @@ +"""Advanced monetization strategies for venture optimization.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class MonetizationModel: + """Monetization model configuration.""" + name: str + type: str + pricing_tiers: List[Dict[str, Any]] + features: List[str] + constraints: List[str] + metrics: Dict[str, float] + +@dataclass +class RevenueStream: + """Revenue stream configuration.""" + name: str + type: str + volume: float + unit_economics: Dict[str, float] + growth_rate: float + churn_rate: float + +class MonetizationOptimizer: + """ + Advanced monetization optimization that: + 1. Designs pricing models + 2. Optimizes revenue streams + 3. Maximizes customer value + 4. Reduces churn + 5. Increases lifetime value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize monetization optimizer.""" + self.config = config or {} + + # Configure optimization parameters + self.min_revenue = self.config.get('min_revenue', 1_000_000) + self.min_margin = self.config.get('min_margin', 0.3) + self.max_churn = self.config.get('max_churn', 0.1) + self.target_ltv = self.config.get('target_ltv', 1000) + + self.models: Dict[str, MonetizationModel] = {} + self.streams: Dict[str, RevenueStream] = {} + + async def optimize_monetization(self, + venture_type: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize monetization strategy.""" + try: + # Design models + models = await self._design_models(venture_type, context) + + # Optimize pricing + pricing = await self._optimize_pricing(models, context) + + # Revenue optimization + revenue = await self._optimize_revenue(pricing, context) + + # Value optimization + value = await self._optimize_value(revenue, context) + + # Performance projections + projections = await self._project_performance(value, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "models": models, + "pricing": pricing, + "revenue": revenue, + "value": value, + "projections": projections + } + except Exception as e: + logging.error(f"Error in monetization optimization: {str(e)}") + return {"success": False, "error": str(e)} + + async def _design_models(self, + venture_type: str, + context: Dict[str, Any]) -> Dict[str, Any]: + """Design monetization models.""" + prompt = f""" + Design monetization models: + Venture: {venture_type} + Context: {json.dumps(context)} + + Design models for: + 1. Subscription tiers + 2. Usage-based pricing + 3. Hybrid models + 4. Enterprise pricing + 5. Marketplace fees + + Format as: + [Model1] + Name: ... + Type: ... + Tiers: ... + Features: ... + Constraints: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_model_design(response["answer"]) + + async def _optimize_pricing(self, + models: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize pricing strategy.""" + prompt = f""" + Optimize pricing strategy: + Models: {json.dumps(models)} + Context: {json.dumps(context)} + + Optimize for: + 1. Market positioning + 2. Value perception + 3. Competitive dynamics + 4. Customer segments + 5. Growth potential + + Format as: + [Strategy1] + Model: ... + Positioning: ... + Value_Props: ... + Segments: ... + Growth: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_pricing_strategy(response["answer"]) + + async def _optimize_revenue(self, + pricing: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize revenue streams.""" + prompt = f""" + Optimize revenue streams: + Pricing: {json.dumps(pricing)} + Context: {json.dumps(context)} + + Optimize for: + 1. Revenue mix + 2. Growth drivers + 3. Retention factors + 4. Expansion potential + 5. Risk mitigation + + Format as: + [Stream1] + Type: ... + Drivers: ... + Retention: ... + Expansion: ... + Risks: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_revenue_optimization(response["answer"]) + + async def _optimize_value(self, + revenue: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize customer value.""" + prompt = f""" + Optimize customer value: + Revenue: {json.dumps(revenue)} + Context: {json.dumps(context)} + + Optimize for: + 1. Acquisition cost + 2. Lifetime value + 3. Churn reduction + 4. Upsell potential + 5. Network effects + + Format as: + [Value1] + Metric: ... + Strategy: ... + Potential: ... + Actions: ... + Timeline: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_value_optimization(response["answer"]) + + async def _project_performance(self, + value: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Project monetization performance.""" + prompt = f""" + Project performance: + Value: {json.dumps(value)} + Context: {json.dumps(context)} + + Project: + 1. Revenue growth + 2. Customer metrics + 3. Unit economics + 4. Profitability + 5. Scale effects + + Format as: + [Projections] + Revenue: ... + Metrics: ... + Economics: ... + Profit: ... + Scale: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_performance_projections(response["answer"]) + + def _calculate_revenue_potential(self, model: MonetizationModel) -> float: + """Calculate revenue potential for model.""" + base_potential = sum( + tier.get("price", 0) * tier.get("volume", 0) + for tier in model.pricing_tiers + ) + + growth_factor = 1.0 + (model.metrics.get("growth_rate", 0) / 100) + retention_factor = 1.0 - (model.metrics.get("churn_rate", 0) / 100) + + return base_potential * growth_factor * retention_factor + + def _calculate_customer_ltv(self, stream: RevenueStream) -> float: + """Calculate customer lifetime value.""" + monthly_revenue = stream.volume * stream.unit_economics.get("arpu", 0) + churn_rate = stream.churn_rate / 100 + discount_rate = 0.1 # 10% annual discount rate + + if churn_rate > 0: + ltv = monthly_revenue / churn_rate + else: + ltv = monthly_revenue * 12 # Assume 1 year if no churn + + return ltv / (1 + discount_rate) + + def get_monetization_metrics(self) -> Dict[str, Any]: + """Get comprehensive monetization metrics.""" + return { + "model_metrics": { + model.name: { + "revenue_potential": self._calculate_revenue_potential(model), + "tier_count": len(model.pricing_tiers), + "feature_count": len(model.features), + "constraint_count": len(model.constraints) + } + for model in self.models.values() + }, + "stream_metrics": { + stream.name: { + "monthly_revenue": stream.volume * stream.unit_economics.get("arpu", 0), + "ltv": self._calculate_customer_ltv(stream), + "growth_rate": stream.growth_rate, + "churn_rate": stream.churn_rate + } + for stream in self.streams.values() + }, + "aggregate_metrics": { + "total_revenue_potential": sum( + self._calculate_revenue_potential(model) + for model in self.models.values() + ), + "average_ltv": np.mean([ + self._calculate_customer_ltv(stream) + for stream in self.streams.values() + ]) if self.streams else 0, + "weighted_growth_rate": np.average( + [stream.growth_rate for stream in self.streams.values()], + weights=[stream.volume for stream in self.streams.values()] + ) if self.streams else 0 + } + } + +class MonetizationStrategy(ReasoningStrategy): + """ + Advanced monetization strategy that: + 1. Designs optimal pricing models + 2. Optimizes revenue streams + 3. Maximizes customer lifetime value + 4. Reduces churn + 5. Increases profitability + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize monetization strategy.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize optimizer with shared config + optimizer_config = { + 'min_revenue': self.config.get('min_revenue', 1_000_000), + 'min_margin': self.config.get('min_margin', 0.3), + 'max_churn': self.config.get('max_churn', 0.1), + 'target_ltv': self.config.get('target_ltv', 1000) + } + self.optimizer = MonetizationOptimizer(optimizer_config) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Generate monetization strategy based on query and context. + + Args: + query: The monetization query + context: Additional context and parameters + + Returns: + Dict containing monetization strategy and confidence scores + """ + try: + # Extract venture type + venture_type = self._extract_venture_type(query, context) + + # Optimize monetization + optimization_result = await self.optimizer.optimize_monetization( + venture_type=venture_type, + context=context + ) + + # Format results + formatted_result = self._format_strategy(optimization_result) + + return { + 'answer': formatted_result, + 'confidence': self._calculate_confidence(optimization_result), + 'optimization': optimization_result + } + + except Exception as e: + logging.error(f"Monetization strategy generation failed: {str(e)}") + return { + 'error': f"Monetization strategy generation failed: {str(e)}", + 'confidence': 0.0 + } + + def _extract_venture_type(self, query: str, context: Dict[str, Any]) -> str: + """Extract venture type from query and context.""" + # Use context if available + if 'venture_type' in context: + return context['venture_type'] + + # Simple keyword matching + query_lower = query.lower() + if any(term in query_lower for term in ['ai', 'ml', 'model']): + return 'ai_startup' + elif any(term in query_lower for term in ['saas', 'software']): + return 'saas' + elif any(term in query_lower for term in ['api', 'service']): + return 'api_service' + elif any(term in query_lower for term in ['data', 'analytics']): + return 'data_analytics' + + # Default to SaaS if unclear + return 'saas' + + def _calculate_confidence(self, result: Dict[str, Any]) -> float: + """Calculate confidence score based on optimization quality.""" + # Base confidence + confidence = 0.5 + + # Adjust based on optimization completeness + if result.get('models'): + confidence += 0.1 + if result.get('pricing'): + confidence += 0.1 + if result.get('revenue'): + confidence += 0.1 + if result.get('value'): + confidence += 0.1 + + # Adjust based on projected performance + performance = result.get('performance', {}) + if performance.get('roi', 0) > 2.0: + confidence += 0.1 + if performance.get('ltv', 0) > 1000: + confidence += 0.1 + + return min(confidence, 1.0) + + def _format_strategy(self, result: Dict[str, Any]) -> str: + """Format monetization strategy into readable text.""" + sections = [] + + # Monetization models + if 'models' in result: + models = result['models'] + sections.append("Monetization Models:") + for model in models: + sections.append(f"- {model['name']}: {model['type']}") + if 'pricing_tiers' in model: + sections.append(" Pricing Tiers:") + for tier in model['pricing_tiers']: + sections.append(f" * {tier['name']}: ${tier['price']}/mo") + + # Revenue optimization + if 'revenue' in result: + revenue = result['revenue'] + sections.append("\nRevenue Optimization:") + for stream, details in revenue.items(): + sections.append(f"- {stream.replace('_', ' ').title()}:") + sections.append(f" * Projected Revenue: ${details['projected_revenue']:,.2f}") + sections.append(f" * Growth Rate: {details['growth_rate']*100:.1f}%") + + # Customer value optimization + if 'value' in result: + value = result['value'] + sections.append("\nCustomer Value Optimization:") + sections.append(f"- Customer Acquisition Cost: ${value['cac']:,.2f}") + sections.append(f"- Lifetime Value: ${value['ltv']:,.2f}") + sections.append(f"- Churn Rate: {value['churn_rate']*100:.1f}%") + + # Performance projections + if 'performance' in result: + perf = result['performance'] + sections.append("\nPerformance Projections:") + sections.append(f"- ROI: {perf['roi']*100:.1f}%") + sections.append(f"- Payback Period: {perf['payback_months']:.1f} months") + sections.append(f"- Break-even Point: ${perf['breakeven']:,.2f}") + + return "\n".join(sections) diff --git a/space/multimodal.py b/space/multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..cc7d38b3c9a09f5f0fab5404158a17a0f1bf3d8d --- /dev/null +++ b/space/multimodal.py @@ -0,0 +1,305 @@ +"""Advanced multimodal reasoning combining different types of information.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class ModalityFeatures: + """Features extracted from different modalities.""" + text: List[Dict[str, Any]] + image: Optional[List[Dict[str, Any]]] = None + audio: Optional[List[Dict[str, Any]]] = None + video: Optional[List[Dict[str, Any]]] = None + structured: Optional[List[Dict[str, Any]]] = None + +class MultiModalReasoning(ReasoningStrategy): + """ + Advanced multimodal reasoning that: + 1. Processes different types of information + 2. Aligns cross-modal features + 3. Integrates multimodal context + 4. Generates coherent responses + 5. Handles uncertainty + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize multimodal reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Configure model repositories + self.models = self.config.get('models', { + 'img2img': { + 'repo_id': 'enhanceaiteam/Flux-Uncensored-V2', + 'filename': 'Flux-Uncensored-V2.safetensors' + }, + 'img2vid': { + 'repo_id': 'stabilityai/stable-video-diffusion-img2vid-xt', + 'filename': 'svd_xt.safetensors' + }, + 'any2any': { + 'repo_id': 'deepseek-ai/JanusFlow-1.3B', + 'filename': 'janusflow-1.3b.safetensors' + } + }) + + # Configure modality weights + self.weights = self.config.get('modality_weights', { + 'text': 0.4, + 'image': 0.3, + 'audio': 0.1, + 'video': 0.1, + 'structured': 0.1 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply multimodal reasoning to process and integrate different types of information. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Process across modalities + modalities = await self._process_modalities(query, context) + + # Align cross-modal information + alignment = await self._cross_modal_alignment(modalities, context) + + # Integrate aligned information + integration = await self._integrated_analysis(alignment, context) + + # Generate final response + response = await self._generate_response(integration, context) + + return { + 'answer': response.get('text', ''), + 'confidence': self._calculate_confidence(integration), + 'modalities': modalities, + 'alignment': alignment, + 'integration': integration + } + + except Exception as e: + logging.error(f"Multimodal reasoning failed: {str(e)}") + return { + 'error': f"Multimodal reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _process_modalities( + self, + query: str, + context: Dict[str, Any] + ) -> Dict[str, List[Dict[str, Any]]]: + """Process query across different modalities.""" + modalities = {} + + # Process text + if 'text' in context: + modalities['text'] = self._process_text(context['text']) + + # Process images + if 'images' in context: + modalities['image'] = self._process_images(context['images']) + + # Process audio + if 'audio' in context: + modalities['audio'] = self._process_audio(context['audio']) + + # Process video + if 'video' in context: + modalities['video'] = self._process_video(context['video']) + + # Process structured data + if 'structured' in context: + modalities['structured'] = self._process_structured(context['structured']) + + return modalities + + async def _cross_modal_alignment( + self, + modalities: Dict[str, List[Dict[str, Any]]], + context: Dict[str, Any] + ) -> List[Dict[str, Any]]: + """Align information across different modalities.""" + alignments = [] + + # Get all modality pairs + modality_pairs = [ + (m1, m2) for i, m1 in enumerate(modalities.keys()) + for m2 in list(modalities.keys())[i+1:] + ] + + # Align each pair + for mod1, mod2 in modality_pairs: + items1 = modalities[mod1] + items2 = modalities[mod2] + + # Calculate cross-modal similarities + for item1 in items1: + for item2 in items2: + similarity = self._calculate_similarity(item1, item2) + if similarity > 0.7: # Alignment threshold + alignments.append({ + 'modality1': mod1, + 'modality2': mod2, + 'item1': item1, + 'item2': item2, + 'similarity': similarity + }) + + return alignments + + def _calculate_similarity( + self, + item1: Dict[str, Any], + item2: Dict[str, Any] + ) -> float: + """Calculate similarity between two items from different modalities.""" + # Simple feature overlap for now + features1 = set(str(v) for v in item1.values()) + features2 = set(str(v) for v in item2.values()) + + if not features1 or not features2: + return 0.0 + + overlap = len(features1.intersection(features2)) + total = len(features1.union(features2)) + + return overlap / total if total > 0 else 0.0 + + async def _integrated_analysis( + self, + alignment: List[Dict[str, Any]], + context: Dict[str, Any] + ) -> List[Dict[str, Any]]: + """Perform integrated analysis of aligned information.""" + integrated = [] + + # Group alignments by similarity + similarity_groups = defaultdict(list) + for align in alignment: + similarity_groups[align['similarity']].append(align) + + # Process groups in order of similarity + for similarity, group in sorted( + similarity_groups.items(), + key=lambda x: x[0], + reverse=True + ): + # Combine aligned features + for align in group: + integrated.append({ + 'features': { + **align['item1'], + **align['item2'] + }, + 'modalities': [align['modality1'], align['modality2']], + 'confidence': align['similarity'] + }) + + return integrated + + async def _generate_response( + self, + integration: List[Dict[str, Any]], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate coherent response from integrated analysis.""" + if not integration: + return {'text': '', 'confidence': 0.0} + + # Combine all integrated features + all_features = {} + for item in integration: + all_features.update(item['features']) + + # Generate response text + response_text = [] + + # Add main findings + response_text.append("Main findings across modalities:") + for feature, value in all_features.items(): + response_text.append(f"- {feature}: {value}") + + # Add confidence + confidence = sum(item['confidence'] for item in integration) / len(integration) + response_text.append(f"\nOverall confidence: {confidence:.2f}") + + return { + 'text': "\n".join(response_text), + 'confidence': confidence + } + + def _calculate_confidence(self, integration: List[Dict[str, Any]]) -> float: + """Calculate overall confidence score.""" + if not integration: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Adjust based on number of modalities + unique_modalities = set() + for item in integration: + unique_modalities.update(item['modalities']) + + modality_bonus = len(unique_modalities) * 0.1 + confidence += min(modality_bonus, 0.3) + + # Adjust based on integration quality + avg_similarity = sum( + item['confidence'] for item in integration + ) / len(integration) + confidence += avg_similarity * 0.2 + + return min(confidence, 1.0) + + def _process_text(self, text: str) -> List[Dict[str, Any]]: + """Process text modality.""" + # Simple text processing for now + return [{'text': text}] + + def _process_images(self, images: List[str]) -> List[Dict[str, Any]]: + """Process image modality.""" + # Simple image processing for now + return [{'image': image} for image in images] + + def _process_audio(self, audio: List[str]) -> List[Dict[str, Any]]: + """Process audio modality.""" + # Simple audio processing for now + return [{'audio': audio_file} for audio_file in audio] + + def _process_video(self, video: List[str]) -> List[Dict[str, Any]]: + """Process video modality.""" + # Simple video processing for now + return [{'video': video_file} for video_file in video] + + def _process_structured(self, structured: Dict[str, Any]) -> List[Dict[str, Any]]: + """Process structured data modality.""" + # Simple structured data processing for now + return [{'structured': structured}] diff --git a/space/neurosymbolic.py b/space/neurosymbolic.py new file mode 100644 index 0000000000000000000000000000000000000000..3d9c3efe88946937dd574cd937fef24fe28b8ca6 --- /dev/null +++ b/space/neurosymbolic.py @@ -0,0 +1,316 @@ +"""Advanced neurosymbolic reasoning combining neural and symbolic approaches.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class NeuralFeature: + """Neural features extracted from data.""" + name: str + values: np.ndarray + importance: float + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class SymbolicRule: + """Symbolic rule with conditions and confidence.""" + name: str + conditions: List[str] + conclusion: str + confidence: float + metadata: Dict[str, Any] = field(default_factory=dict) + +class NeurosymbolicReasoning(ReasoningStrategy): + """ + Advanced neurosymbolic reasoning that: + 1. Extracts neural features + 2. Generates symbolic rules + 3. Combines approaches + 4. Handles uncertainty + 5. Provides interpretable results + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize neurosymbolic reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Neurosymbolic specific parameters + self.feature_threshold = self.config.get('feature_threshold', 0.1) + self.rule_confidence_threshold = self.config.get('rule_confidence', 0.7) + self.max_rules = self.config.get('max_rules', 10) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply neurosymbolic reasoning to combine neural and symbolic approaches. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Extract neural features + features = await self._extract_features(query, context) + + # Generate symbolic rules + rules = await self._generate_rules(features, context) + + # Combine approaches + combined = await self._combine_approaches(features, rules, context) + + # Generate analysis + analysis = await self._generate_analysis(combined, context) + + return { + 'answer': self._format_analysis(analysis), + 'confidence': self._calculate_confidence(combined), + 'features': features, + 'rules': rules, + 'combined': combined, + 'analysis': analysis + } + + except Exception as e: + logging.error(f"Neurosymbolic reasoning failed: {str(e)}") + return { + 'error': f"Neurosymbolic reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _extract_features( + self, + query: str, + context: Dict[str, Any] + ) -> List[NeuralFeature]: + """Extract neural features from input.""" + features = [] + + # Extract key terms + terms = query.lower().split() + + # Process each term + for term in terms: + # Simple feature extraction for now + values = np.random.randn(10) # Placeholder for real feature extraction + importance = np.abs(values).mean() + + if importance > self.feature_threshold: + features.append(NeuralFeature( + name=term, + values=values, + importance=importance, + metadata={'source': 'term_extraction'} + )) + + # Sort by importance + features.sort(key=lambda x: x.importance, reverse=True) + + return features + + async def _generate_rules( + self, + features: List[NeuralFeature], + context: Dict[str, Any] + ) -> List[SymbolicRule]: + """Generate symbolic rules from features.""" + rules = [] + + # Process feature combinations + for i, feature1 in enumerate(features): + for j, feature2 in enumerate(features[i+1:], i+1): + # Calculate correlation + correlation = np.corrcoef(feature1.values, feature2.values)[0, 1] + + if abs(correlation) > self.rule_confidence_threshold: + # Create rule based on correlation + if correlation > 0: + condition = f"{feature1.name} AND {feature2.name}" + conclusion = "positively_correlated" + else: + condition = f"{feature1.name} XOR {feature2.name}" + conclusion = "negatively_correlated" + + rules.append(SymbolicRule( + name=f"rule_{len(rules)}", + conditions=[condition], + conclusion=conclusion, + confidence=abs(correlation), + metadata={ + 'features': [feature1.name, feature2.name], + 'correlation': correlation + } + )) + + if len(rules) >= self.max_rules: + break + + if len(rules) >= self.max_rules: + break + + return rules + + async def _combine_approaches( + self, + features: List[NeuralFeature], + rules: List[SymbolicRule], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Combine neural and symbolic approaches.""" + combined = { + 'neural_weights': {}, + 'symbolic_weights': {}, + 'combined_scores': {} + } + + # Calculate neural weights + total_importance = sum(f.importance for f in features) + if total_importance > 0: + combined['neural_weights'] = { + f.name: f.importance / total_importance + for f in features + } + + # Calculate symbolic weights + total_confidence = sum(r.confidence for r in rules) + if total_confidence > 0: + combined['symbolic_weights'] = { + r.name: r.confidence / total_confidence + for r in rules + } + + # Combine scores + all_elements = set( + list(combined['neural_weights'].keys()) + + list(combined['symbolic_weights'].keys()) + ) + + for element in all_elements: + neural_score = combined['neural_weights'].get(element, 0) + symbolic_score = combined['symbolic_weights'].get(element, 0) + + # Simple weighted average + combined['combined_scores'][element] = ( + neural_score * 0.6 + # Favor neural slightly + symbolic_score * 0.4 + ) + + return combined + + async def _generate_analysis( + self, + combined: Dict[str, Any], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate neurosymbolic analysis.""" + # Sort elements by combined score + ranked_elements = sorted( + combined['combined_scores'].items(), + key=lambda x: x[1], + reverse=True + ) + + # Calculate statistics + scores = list(combined['combined_scores'].values()) + mean = np.mean(scores) if scores else 0 + std = np.std(scores) if scores else 0 + + # Calculate entropy + entropy = -sum( + s * np.log2(s) if s > 0 else 0 + for s in combined['combined_scores'].values() + ) + + return { + 'top_element': ranked_elements[0][0] if ranked_elements else '', + 'score': ranked_elements[0][1] if ranked_elements else 0, + 'alternatives': [ + {'name': name, 'score': score} + for name, score in ranked_elements[1:] + ], + 'statistics': { + 'mean': mean, + 'std': std, + 'entropy': entropy + } + } + + def _format_analysis(self, analysis: Dict[str, Any]) -> str: + """Format analysis into readable text.""" + sections = [] + + # Top element + if analysis['top_element']: + sections.append( + f"Most significant element: {analysis['top_element']} " + f"(score: {analysis['score']:.2%})" + ) + + # Alternative elements + if analysis['alternatives']: + sections.append("\nAlternative elements:") + for alt in analysis['alternatives']: + sections.append( + f"- {alt['name']}: {alt['score']:.2%}" + ) + + # Statistics + stats = analysis['statistics'] + sections.append("\nAnalysis statistics:") + sections.append(f"- Mean score: {stats['mean']:.2%}") + sections.append(f"- Standard deviation: {stats['std']:.2%}") + sections.append(f"- Information entropy: {stats['entropy']:.2f} bits") + + return "\n".join(sections) + + def _calculate_confidence(self, combined: Dict[str, Any]) -> float: + """Calculate overall confidence score.""" + if not combined['combined_scores']: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Get scores + scores = list(combined['combined_scores'].values()) + + # Strong leading score increases confidence + max_score = max(scores) + if max_score > 0.8: + confidence += 0.3 + elif max_score > 0.6: + confidence += 0.2 + elif max_score > 0.4: + confidence += 0.1 + + # Low entropy (clear distinction) increases confidence + entropy = -sum(s * np.log2(s) if s > 0 else 0 for s in scores) + max_entropy = -np.log2(1/len(scores)) # Maximum possible entropy + + if entropy < 0.3 * max_entropy: + confidence += 0.2 + elif entropy < 0.6 * max_entropy: + confidence += 0.1 + + return min(confidence, 1.0) diff --git a/space/orchestrator.py b/space/orchestrator.py new file mode 100644 index 0000000000000000000000000000000000000000..034ad0f25eba6b141ec2b23e413c250d00771846 --- /dev/null +++ b/space/orchestrator.py @@ -0,0 +1,628 @@ +""" +Agentic Orchestrator for Advanced AI System +----------------------------------------- +Manages and coordinates multiple agentic components: +1. Task Planning & Decomposition +2. Resource Management +3. Agent Communication +4. State Management +5. Error Recovery +6. Performance Monitoring +""" + +import logging +from typing import Dict, Any, List, Optional, Union, TypeVar, Generic +from dataclasses import dataclass, field +from enum import Enum +import json +import asyncio +from datetime import datetime +import uuid +from concurrent.futures import ThreadPoolExecutor +import networkx as nx +from collections import defaultdict +import numpy as np + +from reasoning import UnifiedReasoningEngine as ReasoningEngine, StrategyType as ReasoningMode +from reasoning.meta_learning import MetaLearningStrategy + +T = TypeVar('T') + +class AgentRole(Enum): + """Different roles an agent can take.""" + PLANNER = "planner" + EXECUTOR = "executor" + MONITOR = "monitor" + COORDINATOR = "coordinator" + LEARNER = "learner" + +class AgentState(Enum): + """Possible states of an agent.""" + IDLE = "idle" + BUSY = "busy" + ERROR = "error" + LEARNING = "learning" + TERMINATED = "terminated" + +class TaskPriority(Enum): + """Task priority levels.""" + LOW = 0 + MEDIUM = 1 + HIGH = 2 + CRITICAL = 3 + +@dataclass +class AgentMetadata: + """Metadata about an agent.""" + id: str + role: AgentRole + capabilities: List[str] + state: AgentState + load: float + last_active: datetime + metrics: Dict[str, float] + +@dataclass +class Task: + """Represents a task in the system.""" + id: str + description: str + priority: TaskPriority + dependencies: List[str] + assigned_to: Optional[str] + state: str + created_at: datetime + deadline: Optional[datetime] + metadata: Dict[str, Any] + +class AgentOrchestrator: + """Advanced orchestrator for managing agentic system.""" + + def __init__(self, config: Dict[str, Any] = None): + self.config = config or {} + + # Core components + self.agents: Dict[str, AgentMetadata] = {} + self.tasks: Dict[str, Task] = {} + self.task_graph = nx.DiGraph() + + # State management + self.state_history: List[Dict[str, Any]] = [] + self.global_state: Dict[str, Any] = {} + + # Resource management + self.resource_pool: Dict[str, Any] = {} + self.resource_locks: Dict[str, asyncio.Lock] = {} + + # Communication + self.message_queue = asyncio.Queue() + self.event_bus = asyncio.Queue() + + # Performance monitoring + self.metrics = defaultdict(list) + self.performance_log = [] + + # Error handling + self.error_handlers: Dict[str, callable] = {} + self.recovery_strategies: Dict[str, callable] = {} + + # Async support + self.executor = ThreadPoolExecutor(max_workers=4) + self.lock = asyncio.Lock() + + # Logging + self.logger = logging.getLogger(__name__) + + # Initialize components + self._init_components() + + def _init_components(self): + """Initialize orchestrator components.""" + # Initialize reasoning engine + self.reasoning_engine = ReasoningEngine( + min_confidence=0.7, + parallel_threshold=5, + learning_rate=0.1, + strategy_weights={ + "LOCAL_LLM": 2.0, + "CHAIN_OF_THOUGHT": 1.0, + "TREE_OF_THOUGHTS": 1.0, + "META_LEARNING": 1.5 + } + ) + + # Initialize meta-learning + self.meta_learning = MetaLearningStrategy() + + # Register basic error handlers + self._register_error_handlers() + + async def register_agent( + self, + role: AgentRole, + capabilities: List[str] + ) -> str: + """Register a new agent with the orchestrator.""" + agent_id = str(uuid.uuid4()) + + agent = AgentMetadata( + id=agent_id, + role=role, + capabilities=capabilities, + state=AgentState.IDLE, + load=0.0, + last_active=datetime.now(), + metrics={} + ) + + async with self.lock: + self.agents[agent_id] = agent + self.logger.info(f"Registered new agent: {agent_id} with role {role}") + + return agent_id + + async def submit_task( + self, + description: str, + priority: TaskPriority = TaskPriority.MEDIUM, + dependencies: List[str] = None, + deadline: Optional[datetime] = None, + metadata: Dict[str, Any] = None + ) -> str: + """Submit a new task to the orchestrator.""" + task_id = str(uuid.uuid4()) + + task = Task( + id=task_id, + description=description, + priority=priority, + dependencies=dependencies or [], + assigned_to=None, + state="pending", + created_at=datetime.now(), + deadline=deadline, + metadata=metadata or {} + ) + + async with self.lock: + self.tasks[task_id] = task + self._update_task_graph(task) + + # Trigger task planning + await self._plan_task_execution(task_id) + + return task_id + + async def _plan_task_execution(self, task_id: str) -> None: + """Plan the execution of a task.""" + task = self.tasks[task_id] + + # Check dependencies + if not await self._check_dependencies(task): + self.logger.info(f"Task {task_id} waiting for dependencies") + return + + # Find suitable agent + agent_id = await self._find_suitable_agent(task) + if not agent_id: + self.logger.warning(f"No suitable agent found for task {task_id}") + return + + # Assign task + await self._assign_task(task_id, agent_id) + + async def _check_dependencies(self, task: Task) -> bool: + """Check if all task dependencies are satisfied.""" + for dep_id in task.dependencies: + if dep_id not in self.tasks: + return False + if self.tasks[dep_id].state != "completed": + return False + return True + + async def _find_suitable_agent(self, task: Task) -> Optional[str]: + """Find the most suitable agent for a task.""" + best_agent = None + best_score = float('-inf') + + for agent_id, agent in self.agents.items(): + if agent.state != AgentState.IDLE: + continue + + score = await self._calculate_agent_suitability(agent, task) + if score > best_score: + best_score = score + best_agent = agent_id + + return best_agent + + async def _calculate_agent_suitability( + self, + agent: AgentMetadata, + task: Task + ) -> float: + """Calculate how suitable an agent is for a task.""" + # Base score on capabilities match + capability_score = sum( + 1 for cap in task.metadata.get("required_capabilities", []) + if cap in agent.capabilities + ) + + # Consider agent load + load_score = 1 - agent.load + + # Consider agent's recent performance + performance_score = sum(agent.metrics.values()) / len(agent.metrics) if agent.metrics else 0.5 + + # Weighted combination + weights = self.config.get("agent_selection_weights", { + "capabilities": 0.5, + "load": 0.3, + "performance": 0.2 + }) + + return ( + weights["capabilities"] * capability_score + + weights["load"] * load_score + + weights["performance"] * performance_score + ) + + async def _assign_task(self, task_id: str, agent_id: str) -> None: + """Assign a task to an agent.""" + async with self.lock: + task = self.tasks[task_id] + agent = self.agents[agent_id] + + task.assigned_to = agent_id + task.state = "assigned" + agent.state = AgentState.BUSY + agent.load += 1 + agent.last_active = datetime.now() + + self.logger.info(f"Assigned task {task_id} to agent {agent_id}") + + # Notify agent + await self.message_queue.put({ + "type": "task_assignment", + "task_id": task_id, + "agent_id": agent_id, + "timestamp": datetime.now() + }) + + def _update_task_graph(self, task: Task) -> None: + """Update the task dependency graph.""" + self.task_graph.add_node(task.id, task=task) + for dep_id in task.dependencies: + self.task_graph.add_edge(dep_id, task.id) + + async def _monitor_system_state(self): + """Monitor overall system state.""" + while True: + try: + # Collect agent states + agent_states = { + agent_id: { + "state": agent.state, + "load": agent.load, + "metrics": agent.metrics + } + for agent_id, agent in self.agents.items() + } + + # Collect task states + task_states = { + task_id: { + "state": task.state, + "assigned_to": task.assigned_to, + "deadline": task.deadline + } + for task_id, task in self.tasks.items() + } + + # Update global state + self.global_state = { + "timestamp": datetime.now(), + "agents": agent_states, + "tasks": task_states, + "resource_usage": self._get_resource_usage(), + "performance_metrics": self._calculate_performance_metrics() + } + + # Archive state + self.state_history.append(self.global_state.copy()) + + # Trim history if too long + if len(self.state_history) > 1000: + self.state_history = self.state_history[-1000:] + + # Check for anomalies + await self._check_anomalies() + + await asyncio.sleep(1) # Monitor frequency + + except Exception as e: + self.logger.error(f"Error in system monitoring: {e}") + await self._handle_error("monitoring_error", e) + + def _get_resource_usage(self) -> Dict[str, float]: + """Get current resource usage statistics.""" + return { + "cpu_usage": sum(agent.load for agent in self.agents.values()) / len(self.agents), + "memory_usage": len(self.state_history) * 1000, # Rough estimate + "queue_size": self.message_queue.qsize() + } + + def _calculate_performance_metrics(self) -> Dict[str, float]: + """Calculate current performance metrics.""" + metrics = {} + + # Task completion rate + completed_tasks = sum(1 for task in self.tasks.values() if task.state == "completed") + total_tasks = len(self.tasks) + metrics["task_completion_rate"] = completed_tasks / max(1, total_tasks) + + # Average task duration + durations = [] + for task in self.tasks.values(): + if task.state == "completed" and "completion_time" in task.metadata: + duration = (task.metadata["completion_time"] - task.created_at).total_seconds() + durations.append(duration) + metrics["avg_task_duration"] = sum(durations) / len(durations) if durations else 0 + + # Agent utilization + metrics["agent_utilization"] = sum(agent.load for agent in self.agents.values()) / len(self.agents) + + return metrics + + async def _check_anomalies(self): + """Check for system anomalies.""" + # Check for overloaded agents + for agent_id, agent in self.agents.items(): + if agent.load > 0.9: # 90% load threshold + await self._handle_overload(agent_id) + + # Check for stalled tasks + now = datetime.now() + for task_id, task in self.tasks.items(): + if task.state == "assigned": + duration = (now - task.created_at).total_seconds() + if duration > 3600: # 1 hour threshold + await self._handle_stalled_task(task_id) + + # Check for missed deadlines + for task_id, task in self.tasks.items(): + if task.deadline and now > task.deadline and task.state != "completed": + await self._handle_missed_deadline(task_id) + + async def _handle_overload(self, agent_id: str): + """Handle an overloaded agent.""" + agent = self.agents[agent_id] + + # Try to redistribute tasks + assigned_tasks = [ + task_id for task_id, task in self.tasks.items() + if task.assigned_to == agent_id and task.state == "assigned" + ] + + for task_id in assigned_tasks: + # Find another suitable agent + new_agent_id = await self._find_suitable_agent(self.tasks[task_id]) + if new_agent_id: + await self._reassign_task(task_id, new_agent_id) + + async def _handle_stalled_task(self, task_id: str): + """Handle a stalled task.""" + task = self.tasks[task_id] + + # First, try to ping the assigned agent + if task.assigned_to: + agent = self.agents[task.assigned_to] + if agent.state == AgentState.ERROR: + # Agent is in error state, reassign task + await self._reassign_task(task_id, None) + else: + # Request status update from agent + await self.message_queue.put({ + "type": "status_request", + "task_id": task_id, + "agent_id": task.assigned_to, + "timestamp": datetime.now() + }) + + async def _handle_missed_deadline(self, task_id: str): + """Handle a missed deadline.""" + task = self.tasks[task_id] + + # Log the incident + self.logger.warning(f"Task {task_id} missed deadline: {task.deadline}") + + # Update task priority to CRITICAL + task.priority = TaskPriority.CRITICAL + + # If task is assigned, try to speed it up + if task.assigned_to: + await self.message_queue.put({ + "type": "expedite_request", + "task_id": task_id, + "agent_id": task.assigned_to, + "timestamp": datetime.now() + }) + else: + # If not assigned, try to assign to fastest available agent + await self._plan_task_execution(task_id) + + async def _reassign_task(self, task_id: str, new_agent_id: Optional[str] = None): + """Reassign a task to a new agent.""" + task = self.tasks[task_id] + old_agent_id = task.assigned_to + + if old_agent_id: + # Update old agent + old_agent = self.agents[old_agent_id] + old_agent.load -= 1 + if old_agent.load <= 0: + old_agent.state = AgentState.IDLE + + if new_agent_id is None: + # Find new suitable agent + new_agent_id = await self._find_suitable_agent(task) + + if new_agent_id: + # Assign to new agent + await self._assign_task(task_id, new_agent_id) + else: + # No suitable agent found, mark task as pending + task.state = "pending" + task.assigned_to = None + + def _register_error_handlers(self): + """Register basic error handlers.""" + self.error_handlers.update({ + "monitoring_error": self._handle_monitoring_error, + "agent_error": self._handle_agent_error, + "task_error": self._handle_task_error, + "resource_error": self._handle_resource_error + }) + + self.recovery_strategies.update({ + "agent_recovery": self._recover_agent, + "task_recovery": self._recover_task, + "resource_recovery": self._recover_resource + }) + + async def _handle_error(self, error_type: str, error: Exception): + """Handle an error using registered handlers.""" + handler = self.error_handlers.get(error_type) + if handler: + try: + await handler(error) + except Exception as e: + self.logger.error(f"Error in error handler: {e}") + else: + self.logger.error(f"No handler for error type: {error_type}") + self.logger.error(f"Error: {error}") + + async def _handle_monitoring_error(self, error: Exception): + """Handle monitoring system errors.""" + self.logger.error(f"Monitoring error: {error}") + # Implement recovery logic + pass + + async def _handle_agent_error(self, error: Exception): + """Handle agent-related errors.""" + self.logger.error(f"Agent error: {error}") + # Implement recovery logic + pass + + async def _handle_task_error(self, error: Exception): + """Handle task-related errors.""" + self.logger.error(f"Task error: {error}") + # Implement recovery logic + pass + + async def _handle_resource_error(self, error: Exception): + """Handle resource-related errors.""" + self.logger.error(f"Resource error: {error}") + # Implement recovery logic + pass + + async def _recover_agent(self, agent_id: str): + """Recover a failed agent.""" + try: + agent = self.agents[agent_id] + + # Log recovery attempt + self.logger.info(f"Attempting to recover agent {agent_id}") + + # Reset agent state + agent.state = AgentState.IDLE + agent.load = 0 + agent.last_active = datetime.now() + + # Reassign any tasks that were assigned to this agent + for task_id, task in self.tasks.items(): + if task.assigned_to == agent_id: + await self._reassign_task(task_id) + + # Update metrics + agent.metrics["recovery_attempts"] = agent.metrics.get("recovery_attempts", 0) + 1 + + self.logger.info(f"Successfully recovered agent {agent_id}") + return True + + except Exception as e: + self.logger.error(f"Failed to recover agent {agent_id}: {e}") + return False + + async def _recover_task(self, task_id: str): + """Recover a failed task.""" + try: + task = self.tasks[task_id] + + # Log recovery attempt + self.logger.info(f"Attempting to recover task {task_id}") + + # Reset task state + task.state = "pending" + task.assigned_to = None + + # Try to reassign the task + await self._reassign_task(task_id) + + self.logger.info(f"Successfully recovered task {task_id}") + return True + + except Exception as e: + self.logger.error(f"Failed to recover task {task_id}: {e}") + return False + + async def _recover_resource(self, resource_id: str): + """Recover a failed resource.""" + try: + # Log recovery attempt + self.logger.info(f"Attempting to recover resource {resource_id}") + + # Release any locks on the resource + if resource_id in self.resource_locks: + lock = self.resource_locks[resource_id] + if lock.locked(): + lock.release() + + # Reset resource state + if resource_id in self.resource_pool: + self.resource_pool[resource_id] = { + "state": "available", + "last_error": None, + "last_recovery": datetime.now() + } + + self.logger.info(f"Successfully recovered resource {resource_id}") + return True + + except Exception as e: + self.logger.error(f"Failed to recover resource {resource_id}: {e}") + return False + + async def create_agent(self, role: AgentRole, capabilities: List[str]) -> str: + """Create a new agent with specified role and capabilities.""" + agent_id = str(uuid.uuid4()) + + agent_metadata = AgentMetadata( + id=agent_id, + role=role, + capabilities=capabilities, + state=AgentState.IDLE, + load=0.0, + last_active=datetime.now(), + metrics={ + "tasks_completed": 0, + "success_rate": 1.0, + "avg_response_time": 0.0, + "resource_usage": 0.0 + } + ) + + self.agents[agent_id] = agent_metadata + self.logger.info(f"Created new agent {agent_id} with role {role}") + + return agent_id diff --git a/space/portfolio_optimization.py b/space/portfolio_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..41e373b65daf4a18dbb89dd8bfce3bc2d78959d8 --- /dev/null +++ b/space/portfolio_optimization.py @@ -0,0 +1,549 @@ +"""Advanced portfolio optimization for venture strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class VentureMetrics: + """Venture performance metrics.""" + revenue: float + profit: float + growth_rate: float + risk_score: float + resource_usage: Dict[str, float] + synergy_score: float + +@dataclass +class ResourceAllocation: + """Resource allocation configuration.""" + venture_id: str + resources: Dict[str, float] + constraints: List[str] + dependencies: List[str] + priority: float + +class PortfolioOptimizer: + """ + Advanced portfolio optimization that: + 1. Optimizes venture mix + 2. Allocates resources + 3. Manages risks + 4. Maximizes synergies + 5. Balances growth + """ + + def __init__(self): + self.ventures: Dict[str, VentureMetrics] = {} + self.allocations: Dict[str, ResourceAllocation] = {} + + async def optimize_portfolio(self, + ventures: List[str], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize venture portfolio.""" + try: + # Analyze ventures + analysis = await self._analyze_ventures(ventures, context) + + # Optimize allocation + allocation = await self._optimize_allocation(analysis, context) + + # Risk optimization + risk = await self._optimize_risk(allocation, context) + + # Synergy optimization + synergy = await self._optimize_synergies(risk, context) + + # Performance projections + projections = await self._project_performance(synergy, context) + + return { + "success": projections["annual_profit"] >= 1_000_000, + "analysis": analysis, + "allocation": allocation, + "risk": risk, + "synergy": synergy, + "projections": projections + } + except Exception as e: + logging.error(f"Error in portfolio optimization: {str(e)}") + return {"success": False, "error": str(e)} + + async def _analyze_ventures(self, + ventures: List[str], + context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze venture characteristics.""" + prompt = f""" + Analyze ventures: + Ventures: {json.dumps(ventures)} + Context: {json.dumps(context)} + + Analyze: + 1. Performance metrics + 2. Resource requirements + 3. Risk factors + 4. Growth potential + 5. Synergy opportunities + + Format as: + [Venture1] + Metrics: ... + Resources: ... + Risks: ... + Growth: ... + Synergies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_venture_analysis(response["answer"]) + + async def _optimize_allocation(self, + analysis: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize resource allocation.""" + prompt = f""" + Optimize resource allocation: + Analysis: {json.dumps(analysis)} + Context: {json.dumps(context)} + + Optimize for: + 1. Resource efficiency + 2. Growth potential + 3. Risk balance + 4. Synergy capture + 5. Constraint satisfaction + + Format as: + [Allocation1] + Venture: ... + Resources: ... + Constraints: ... + Dependencies: ... + Priority: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_allocation_optimization(response["answer"]) + + async def _optimize_risk(self, + allocation: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize risk management.""" + prompt = f""" + Optimize risk management: + Allocation: {json.dumps(allocation)} + Context: {json.dumps(context)} + + Optimize for: + 1. Risk diversification + 2. Exposure limits + 3. Correlation management + 4. Hedging strategies + 5. Contingency planning + + Format as: + [Risk1] + Type: ... + Exposure: ... + Mitigation: ... + Contingency: ... + Impact: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_risk_optimization(response["answer"]) + + async def _optimize_synergies(self, + risk: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize portfolio synergies.""" + prompt = f""" + Optimize synergies: + Risk: {json.dumps(risk)} + Context: {json.dumps(context)} + + Optimize for: + 1. Resource sharing + 2. Knowledge transfer + 3. Market leverage + 4. Technology reuse + 5. Customer cross-sell + + Format as: + [Synergy1] + Type: ... + Ventures: ... + Potential: ... + Requirements: ... + Timeline: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_synergy_optimization(response["answer"]) + + async def _project_performance(self, + synergy: Dict[str, Any], + context: Dict[str, Any]) -> Dict[str, Any]: + """Project portfolio performance.""" + prompt = f""" + Project performance: + Synergy: {json.dumps(synergy)} + Context: {json.dumps(context)} + + Project: + 1. Revenue growth + 2. Profit margins + 3. Resource utilization + 4. Risk metrics + 5. Synergy capture + + Format as: + [Projections] + Revenue: ... + Profit: ... + Resources: ... + Risk: ... + Synergies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_performance_projections(response["answer"]) + + def _calculate_portfolio_metrics(self) -> Dict[str, float]: + """Calculate comprehensive portfolio metrics.""" + if not self.ventures: + return { + "total_revenue": 0.0, + "total_profit": 0.0, + "avg_growth": 0.0, + "avg_risk": 0.0, + "resource_efficiency": 0.0, + "synergy_capture": 0.0 + } + + metrics = { + "total_revenue": sum(v.revenue for v in self.ventures.values()), + "total_profit": sum(v.profit for v in self.ventures.values()), + "avg_growth": np.mean([v.growth_rate for v in self.ventures.values()]), + "avg_risk": np.mean([v.risk_score for v in self.ventures.values()]), + "resource_efficiency": self._calculate_resource_efficiency(), + "synergy_capture": np.mean([v.synergy_score for v in self.ventures.values()]) + } + + return metrics + + def _calculate_resource_efficiency(self) -> float: + """Calculate resource utilization efficiency.""" + if not self.ventures or not self.allocations: + return 0.0 + + total_resources = defaultdict(float) + used_resources = defaultdict(float) + + # Sum up total and used resources + for venture_id, allocation in self.allocations.items(): + for resource, amount in allocation.resources.items(): + total_resources[resource] += amount + if venture_id in self.ventures: + used_resources[resource] += ( + amount * self.ventures[venture_id].resource_usage.get(resource, 0) + ) + + # Calculate efficiency for each resource + efficiencies = [] + for resource in total_resources: + if total_resources[resource] > 0: + efficiency = used_resources[resource] / total_resources[resource] + efficiencies.append(efficiency) + + return np.mean(efficiencies) if efficiencies else 0.0 + + def get_portfolio_insights(self) -> Dict[str, Any]: + """Get comprehensive portfolio insights.""" + metrics = self._calculate_portfolio_metrics() + + return { + "portfolio_metrics": metrics, + "venture_metrics": { + venture_id: { + "revenue": v.revenue, + "profit": v.profit, + "growth_rate": v.growth_rate, + "risk_score": v.risk_score, + "synergy_score": v.synergy_score + } + for venture_id, v in self.ventures.items() + }, + "resource_allocation": { + venture_id: { + "resources": a.resources, + "priority": a.priority, + "constraints": len(a.constraints), + "dependencies": len(a.dependencies) + } + for venture_id, a in self.allocations.items() + }, + "risk_profile": { + "portfolio_risk": metrics["avg_risk"], + "risk_concentration": self._calculate_risk_concentration(), + "risk_correlation": self._calculate_risk_correlation() + }, + "optimization_opportunities": self._identify_optimization_opportunities() + } + + def _calculate_risk_concentration(self) -> float: + """Calculate risk concentration in portfolio.""" + if not self.ventures: + return 0.0 + + risk_weights = [v.risk_score for v in self.ventures.values()] + return np.std(risk_weights) if len(risk_weights) > 1 else 0.0 + + def _calculate_risk_correlation(self) -> float: + """Calculate risk correlation between ventures.""" + if len(self.ventures) < 2: + return 0.0 + + # Create correlation matrix of risk scores and resource usage + venture_metrics = [ + [v.risk_score] + list(v.resource_usage.values()) + for v in self.ventures.values() + ] + + correlation_matrix = np.corrcoef(venture_metrics) + return np.mean(correlation_matrix[np.triu_indices_from(correlation_matrix, k=1)]) + + def _identify_optimization_opportunities(self) -> List[Dict[str, Any]]: + """Identify portfolio optimization opportunities.""" + opportunities = [] + + # Resource optimization opportunities + resource_efficiency = self._calculate_resource_efficiency() + if resource_efficiency < 0.8: + opportunities.append({ + "type": "resource_optimization", + "potential": 1.0 - resource_efficiency, + "description": "Improve resource utilization efficiency" + }) + + # Risk optimization opportunities + risk_concentration = self._calculate_risk_concentration() + if risk_concentration > 0.2: + opportunities.append({ + "type": "risk_diversification", + "potential": risk_concentration, + "description": "Reduce risk concentration" + }) + + # Synergy optimization opportunities + avg_synergy = np.mean([v.synergy_score for v in self.ventures.values()]) if self.ventures else 0 + if avg_synergy < 0.7: + opportunities.append({ + "type": "synergy_capture", + "potential": 1.0 - avg_synergy, + "description": "Increase synergy capture" + }) + + return opportunities + +class PortfolioOptimizationStrategy(ReasoningStrategy): + """ + Advanced portfolio optimization strategy that: + 1. Analyzes venture metrics + 2. Optimizes resource allocation + 3. Balances risk-reward + 4. Maximizes portfolio synergies + 5. Provides actionable recommendations + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize portfolio optimization strategy.""" + super().__init__() + self.config = config or {} + self.optimizer = PortfolioOptimizer() + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Generate portfolio optimization strategy based on query and context. + + Args: + query: The portfolio optimization query + context: Additional context and parameters + + Returns: + Dict containing optimization strategy and confidence scores + """ + try: + # Extract portfolio parameters + params = self._extract_parameters(query, context) + + # Optimize portfolio + optimization_result = self.optimizer.optimize_portfolio( + ventures=params.get('ventures', []), + constraints=params.get('constraints', []), + objectives=params.get('objectives', []) + ) + + # Get metrics + metrics = self.optimizer.get_portfolio_metrics() + + # Generate recommendations + recommendations = self._generate_recommendations( + optimization_result, + metrics + ) + + return { + 'answer': self._format_strategy(optimization_result, metrics, recommendations), + 'confidence': self._calculate_confidence(optimization_result), + 'optimization': optimization_result, + 'metrics': metrics, + 'recommendations': recommendations + } + + except Exception as e: + logging.error(f"Portfolio optimization failed: {str(e)}") + return { + 'error': f"Portfolio optimization failed: {str(e)}", + 'confidence': 0.0 + } + + def _extract_parameters(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Extract optimization parameters from query and context.""" + params = {} + + # Extract ventures + if 'ventures' in context: + params['ventures'] = context['ventures'] + else: + # Default empty portfolio + params['ventures'] = [] + + # Extract constraints + if 'constraints' in context: + params['constraints'] = context['constraints'] + else: + # Default constraints + params['constraints'] = [ + 'budget_limit', + 'risk_tolerance', + 'resource_capacity' + ] + + # Extract objectives + if 'objectives' in context: + params['objectives'] = context['objectives'] + else: + # Default objectives + params['objectives'] = [ + 'maximize_returns', + 'minimize_risk', + 'maximize_synergies' + ] + + return params + + def _generate_recommendations( + self, + optimization_result: Dict[str, Any], + metrics: Dict[str, Any] + ) -> List[str]: + """Generate actionable recommendations.""" + recommendations = [] + + # Portfolio composition recommendations + if 'allocation' in optimization_result: + allocation = optimization_result['allocation'] + recommendations.extend([ + f"Allocate {alloc['percentage']:.1f}% to {alloc['venture']}" + for alloc in allocation + ]) + + # Risk management recommendations + if 'risk_analysis' in metrics: + risk = metrics['risk_analysis'] + if risk.get('total_risk', 0) > 0.7: + recommendations.append( + "Consider reducing exposure to high-risk ventures" + ) + if risk.get('correlation', 0) > 0.8: + recommendations.append( + "Increase portfolio diversification to reduce correlation" + ) + + # Performance optimization recommendations + if 'performance' in metrics: + perf = metrics['performance'] + if perf.get('sharpe_ratio', 0) < 1.0: + recommendations.append( + "Optimize risk-adjusted returns through better venture selection" + ) + if perf.get('efficiency', 0) < 0.8: + recommendations.append( + "Improve resource allocation efficiency across ventures" + ) + + return recommendations + + def _calculate_confidence(self, optimization_result: Dict[str, Any]) -> float: + """Calculate confidence score based on optimization quality.""" + # Base confidence + confidence = 0.5 + + # Adjust based on optimization completeness + if optimization_result.get('allocation'): + confidence += 0.1 + if optimization_result.get('risk_analysis'): + confidence += 0.1 + if optimization_result.get('performance_metrics'): + confidence += 0.1 + + # Adjust based on solution quality + if optimization_result.get('convergence_status') == 'optimal': + confidence += 0.2 + elif optimization_result.get('convergence_status') == 'suboptimal': + confidence += 0.1 + + return min(confidence, 1.0) + + def _format_strategy( + self, + optimization_result: Dict[str, Any], + metrics: Dict[str, Any], + recommendations: List[str] + ) -> str: + """Format optimization strategy into readable text.""" + sections = [] + + # Portfolio allocation + if 'allocation' in optimization_result: + allocation = optimization_result['allocation'] + sections.append("Portfolio Allocation:") + for alloc in allocation: + sections.append( + f"- {alloc['venture']}: {alloc['percentage']:.1f}%" + ) + + # Key metrics + if metrics: + sections.append("\nKey Metrics:") + for key, value in metrics.items(): + if isinstance(value, (int, float)): + sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}") + else: + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Recommendations + if recommendations: + sections.append("\nKey Recommendations:") + for rec in recommendations: + sections.append(f"- {rec}") + + return "\n".join(sections) diff --git a/space/quantum.py b/space/quantum.py new file mode 100644 index 0000000000000000000000000000000000000000..9aaeeef25bc2043a389706d7f876385f69f21fd6 --- /dev/null +++ b/space/quantum.py @@ -0,0 +1,372 @@ +"""Quantum-inspired reasoning implementations.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +@dataclass +class QuantumState: + """Quantum state with superposition and entanglement.""" + name: str + amplitude: complex + phase: float + entangled_states: List[str] = field(default_factory=list) + +class QuantumReasoning(ReasoningStrategy): + """ + Advanced quantum reasoning that: + 1. Creates quantum states + 2. Applies quantum operations + 3. Measures outcomes + 4. Handles superposition + 5. Models entanglement + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize quantum reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Configure quantum parameters + self.num_qubits = self.config.get('num_qubits', 3) + self.measurement_threshold = self.config.get('measurement_threshold', 0.1) + self.decoherence_rate = self.config.get('decoherence_rate', 0.01) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply quantum reasoning to analyze complex decisions. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Initialize quantum states + states = await self._initialize_states(query, context) + + # Apply quantum operations + evolved_states = await self._apply_operations(states, context) + + # Measure outcomes + measurements = await self._measure_states(evolved_states, context) + + # Generate analysis + analysis = await self._generate_analysis(measurements, context) + + return { + 'answer': self._format_analysis(analysis), + 'confidence': self._calculate_confidence(measurements), + 'states': states, + 'evolved_states': evolved_states, + 'measurements': measurements, + 'analysis': analysis + } + + except Exception as e: + logging.error(f"Quantum reasoning failed: {str(e)}") + return { + 'error': f"Quantum reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _initialize_states( + self, + query: str, + context: Dict[str, Any] + ) -> List[QuantumState]: + """Initialize quantum states.""" + states = [] + + # Extract key terms for state initialization + terms = set(query.lower().split()) + + # Create quantum states based on terms + for i, term in enumerate(terms): + if i >= self.num_qubits: + break + + # Calculate initial amplitude and phase + amplitude = 1.0 / np.sqrt(len(terms[:self.num_qubits])) + phase = 2 * np.pi * i / len(terms[:self.num_qubits]) + + states.append(QuantumState( + name=term, + amplitude=complex(amplitude * np.cos(phase), amplitude * np.sin(phase)), + phase=phase + )) + + # Create entangled states if specified + if context.get('entangle', False): + self._entangle_states(states) + + return states + + async def _apply_operations( + self, + states: List[QuantumState], + context: Dict[str, Any] + ) -> List[QuantumState]: + """Apply quantum operations to states.""" + evolved_states = [] + + # Get operation parameters + rotation = context.get('rotation', 0.0) + phase_shift = context.get('phase_shift', 0.0) + + for state in states: + # Apply rotation + rotated_amplitude = state.amplitude * np.exp(1j * rotation) + + # Apply phase shift + shifted_phase = (state.phase + phase_shift) % (2 * np.pi) + + # Apply decoherence + decohered_amplitude = rotated_amplitude * (1 - self.decoherence_rate) + + evolved_states.append(QuantumState( + name=state.name, + amplitude=decohered_amplitude, + phase=shifted_phase, + entangled_states=state.entangled_states.copy() + )) + + return evolved_states + + async def _measure_states( + self, + states: List[QuantumState], + context: Dict[str, Any] + ) -> Dict[str, float]: + """Measure quantum states.""" + measurements = {} + + # Calculate total probability + total_probability = sum( + abs(state.amplitude) ** 2 + for state in states + ) + + if total_probability > 0: + # Normalize and store measurements + for state in states: + probability = (abs(state.amplitude) ** 2) / total_probability + if probability > self.measurement_threshold: + measurements[state.name] = probability + + return measurements + + def _entangle_states(self, states: List[QuantumState]) -> None: + """Create entanglement between states.""" + if len(states) < 2: + return + + # Simple entanglement: connect adjacent states + for i in range(len(states) - 1): + states[i].entangled_states.append(states[i + 1].name) + states[i + 1].entangled_states.append(states[i].name) + + async def _generate_analysis( + self, + measurements: Dict[str, float], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Generate quantum analysis.""" + # Sort states by measurement probability + ranked_states = sorted( + measurements.items(), + key=lambda x: x[1], + reverse=True + ) + + # Calculate quantum statistics + amplitudes = list(measurements.values()) + mean = np.mean(amplitudes) if amplitudes else 0 + std = np.std(amplitudes) if amplitudes else 0 + + # Calculate quantum entropy + entropy = -sum( + p * np.log2(p) if p > 0 else 0 + for p in measurements.values() + ) + + return { + 'top_state': ranked_states[0][0] if ranked_states else '', + 'probability': ranked_states[0][1] if ranked_states else 0, + 'alternatives': [ + {'name': name, 'probability': prob} + for name, prob in ranked_states[1:] + ], + 'statistics': { + 'mean': mean, + 'std': std, + 'entropy': entropy + } + } + + def _format_analysis(self, analysis: Dict[str, Any]) -> str: + """Format analysis into readable text.""" + sections = [] + + # Top quantum state + if analysis['top_state']: + sections.append( + f"Most probable quantum state: {analysis['top_state']} " + f"(probability: {analysis['probability']:.2%})" + ) + + # Alternative states + if analysis['alternatives']: + sections.append("\nAlternative quantum states:") + for alt in analysis['alternatives']: + sections.append( + f"- {alt['name']}: {alt['probability']:.2%}" + ) + + # Quantum statistics + stats = analysis['statistics'] + sections.append("\nQuantum statistics:") + sections.append(f"- Mean amplitude: {stats['mean']:.2%}") + sections.append(f"- Standard deviation: {stats['std']:.2%}") + sections.append(f"- Quantum entropy: {stats['entropy']:.2f} bits") + + return "\n".join(sections) + + def _calculate_confidence(self, measurements: Dict[str, float]) -> float: + """Calculate overall confidence score.""" + if not measurements: + return 0.0 + + # Base confidence + confidence = 0.5 + + # Adjust based on measurement distribution + probs = list(measurements.values()) + + # Strong leading measurement increases confidence + max_prob = max(probs) + if max_prob > 0.8: + confidence += 0.3 + elif max_prob > 0.6: + confidence += 0.2 + elif max_prob > 0.4: + confidence += 0.1 + + # Low entropy (clear distinction) increases confidence + entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs) + max_entropy = -np.log2(1/len(probs)) # Maximum possible entropy + + if entropy < 0.3 * max_entropy: + confidence += 0.2 + elif entropy < 0.6 * max_entropy: + confidence += 0.1 + + return min(confidence, 1.0) + + +class QuantumInspiredStrategy(ReasoningStrategy): + """Implements Quantum-Inspired reasoning.""" + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + try: + # Create a clean context for serialization + clean_context = {k: v for k, v in context.items() if k != "groq_api"} + + prompt = f""" + You are a meta-learning reasoning system that adapts its approach based on problem characteristics. + + Problem Type: + Query: {query} + Context: {json.dumps(clean_context)} + + Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows: + + PROBLEM ANALYSIS: + - [First key aspect or complexity factor] + - [Second key aspect or complexity factor] + - [Third key aspect or complexity factor] + + SOLUTION PATHS: + - Path 1: [Specific solution approach] + - Path 2: [Alternative solution approach] + - Path 3: [Another alternative approach] + + META INSIGHTS: + - Learning 1: [Key insight about the problem space] + - Learning 2: [Key insight about solution approaches] + - Learning 3: [Key insight about trade-offs] + + CONCLUSION: + [Final synthesized solution incorporating meta-learnings] + """ + + response = await context["groq_api"].predict(prompt) + + if not response["success"]: + return response + + # Parse response into components + lines = response["answer"].split("\n") + problem_analysis = [] + solution_paths = [] + meta_insights = [] + conclusion = "" + + section = None + for line in lines: + line = line.strip() + if not line: + continue + + if "PROBLEM ANALYSIS:" in line: + section = "analysis" + elif "SOLUTION PATHS:" in line: + section = "paths" + elif "META INSIGHTS:" in line: + section = "insights" + elif "CONCLUSION:" in line: + section = "conclusion" + elif line.startswith("-"): + content = line.lstrip("- ").strip() + if section == "analysis": + problem_analysis.append(content) + elif section == "paths": + solution_paths.append(content) + elif section == "insights": + meta_insights.append(content) + elif section == "conclusion": + conclusion += line + " " + + return { + "success": True, + "problem_analysis": problem_analysis, + "solution_paths": solution_paths, + "meta_insights": meta_insights, + "conclusion": conclusion.strip(), + # Add standard fields for compatibility + "reasoning_path": problem_analysis + solution_paths + meta_insights, + "conclusion": conclusion.strip() + } + + except Exception as e: + return {"success": False, "error": str(e)} diff --git a/space/recursive.py b/space/recursive.py new file mode 100644 index 0000000000000000000000000000000000000000..f2c784f28a2d21d7ab62927e0ea4bfec05cc52fc --- /dev/null +++ b/space/recursive.py @@ -0,0 +1,576 @@ +"""Recursive reasoning implementation with advanced decomposition and synthesis.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Tuple, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy + +class SubproblemType(Enum): + """Types of subproblems in recursive reasoning.""" + ATOMIC = "atomic" + COMPOSITE = "composite" + PARALLEL = "parallel" + SEQUENTIAL = "sequential" + CONDITIONAL = "conditional" + ITERATIVE = "iterative" + +class SolutionStatus(Enum): + """Status of subproblem solutions.""" + PENDING = "pending" + IN_PROGRESS = "in_progress" + SOLVED = "solved" + FAILED = "failed" + BLOCKED = "blocked" + OPTIMIZING = "optimizing" + +@dataclass +class Subproblem: + """Represents a subproblem in recursive reasoning.""" + id: str + type: SubproblemType + query: str + context: Dict[str, Any] + parent_id: Optional[str] + children: List[str] + status: SolutionStatus + solution: Optional[Dict[str, Any]] + confidence: float + dependencies: List[str] + metadata: Dict[str, Any] = field(default_factory=dict) + +@dataclass +class RecursiveStep: + """Represents a step in recursive reasoning.""" + id: str + subproblem_id: str + action: str + timestamp: datetime + result: Optional[Dict[str, Any]] + metrics: Dict[str, float] + metadata: Dict[str, Any] = field(default_factory=dict) + +class RecursiveReasoning(ReasoningStrategy): + """ + Advanced Recursive Reasoning implementation with: + - Dynamic problem decomposition + - Parallel subproblem solving + - Solution synthesis + - Cycle detection + - Optimization strategies + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize recursive reasoning.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Recursive reasoning specific parameters + self.max_depth = self.config.get('max_depth', 5) + self.optimization_rounds = self.config.get('optimization_rounds', 2) + + # Problem tracking + self.subproblems: Dict[str, Subproblem] = {} + self.steps: List[RecursiveStep] = [] + self.solution_cache: Dict[str, Dict[str, Any]] = {} + self.cycle_detection: Set[str] = set() + + # Performance metrics + self.depth_distribution: Dict[int, int] = defaultdict(int) + self.type_distribution: Dict[SubproblemType, int] = defaultdict(int) + self.success_rate: Dict[SubproblemType, float] = defaultdict(float) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing recursive reasoning.""" + try: + # Initialize root problem + root = await self._initialize_problem(query, context) + self.subproblems[root.id] = root + + # Recursively solve + solution = await self._solve_recursive(root.id, depth=0) + + # Optimize solution + optimized = await self._optimize_solution(solution, root, context) + + # Update metrics + self._update_metrics(root.id) + + return { + "success": True, + "answer": optimized["answer"], + "confidence": optimized["confidence"], + "decomposition": self._get_problem_tree(root.id), + "solution_trace": self._get_solution_trace(root.id), + "performance_metrics": self._get_performance_metrics(), + "meta_insights": optimized["meta_insights"] + } + except Exception as e: + logging.error(f"Error in recursive reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _initialize_problem(self, query: str, context: Dict[str, Any]) -> Subproblem: + """Initialize the root problem.""" + prompt = f""" + Initialize recursive reasoning problem: + Query: {query} + Context: {json.dumps(context)} + + Analyze for: + 1. Problem type classification + 2. Initial decomposition strategy + 3. Key dependencies + 4. Solution approach + + Format as: + [Problem] + Type: ... + Strategy: ... + Dependencies: ... + Approach: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_problem_init(response["answer"], query, context) + + async def _decompose_problem(self, problem: Subproblem, context: Dict[str, Any]) -> List[Subproblem]: + """Decompose a problem into subproblems.""" + prompt = f""" + Decompose problem into subproblems: + Problem: {json.dumps(self._problem_to_dict(problem))} + Context: {json.dumps(context)} + + For each subproblem specify: + 1. [Type]: {" | ".join([t.value for t in SubproblemType])} + 2. [Query]: Specific question + 3. [Dependencies]: Required solutions + 4. [Approach]: Solution strategy + + Format as: + [S1] + Type: ... + Query: ... + Dependencies: ... + Approach: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_subproblems(response["answer"], problem.id, context) + + async def _solve_recursive(self, problem_id: str, depth: int) -> Dict[str, Any]: + """Recursively solve a problem and its subproblems.""" + if depth > self.max_depth: + return {"success": False, "error": "Maximum recursion depth exceeded"} + + if problem_id in self.cycle_detection: + return {"success": False, "error": "Cycle detected in recursive solving"} + + problem = self.subproblems[problem_id] + self.cycle_detection.add(problem_id) + self.depth_distribution[depth] += 1 + + try: + # Check cache + cache_key = f"{problem.query}:{json.dumps(problem.context)}" + if cache_key in self.solution_cache: + return self.solution_cache[cache_key] + + # Check if atomic + if problem.type == SubproblemType.ATOMIC: + solution = await self._solve_atomic(problem) + else: + # Decompose + subproblems = await self._decompose_problem(problem, problem.context) + for sub in subproblems: + self.subproblems[sub.id] = sub + problem.children.append(sub.id) + + # Solve subproblems + if problem.type == SubproblemType.PARALLEL and len(subproblems) >= self.parallel_threshold: + # Solve in parallel + tasks = [self._solve_recursive(sub.id, depth + 1) for sub in subproblems] + subsolutions = await asyncio.gather(*tasks) + else: + # Solve sequentially + subsolutions = [] + for sub in subproblems: + subsolution = await self._solve_recursive(sub.id, depth + 1) + subsolutions.append(subsolution) + + # Synthesize solutions + solution = await self._synthesize_solutions(subsolutions, problem, problem.context) + + # Cache solution + self.solution_cache[cache_key] = solution + problem.solution = solution + problem.status = SolutionStatus.SOLVED if solution["success"] else SolutionStatus.FAILED + + return solution + + finally: + self.cycle_detection.remove(problem_id) + + async def _solve_atomic(self, problem: Subproblem) -> Dict[str, Any]: + """Solve an atomic problem.""" + prompt = f""" + Solve atomic problem: + Problem: {json.dumps(self._problem_to_dict(problem))} + + Provide: + 1. Direct solution + 2. Confidence level + 3. Supporting evidence + 4. Alternative approaches + + Format as: + [Solution] + Answer: ... + Confidence: ... + Evidence: ... + Alternatives: ... + """ + + response = await problem.context["groq_api"].predict(prompt) + solution = self._parse_atomic_solution(response["answer"]) + + self._record_step(RecursiveStep( + id=f"step_{len(self.steps)}", + subproblem_id=problem.id, + action="atomic_solve", + timestamp=datetime.now(), + result=solution, + metrics={"confidence": solution.get("confidence", 0.0)}, + metadata={} + )) + + return solution + + async def _synthesize_solutions(self, subsolutions: List[Dict[str, Any]], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]: + """Synthesize solutions from subproblems.""" + prompt = f""" + Synthesize solutions: + Problem: {json.dumps(self._problem_to_dict(problem))} + Solutions: {json.dumps(subsolutions)} + Context: {json.dumps(context)} + + Provide: + 1. Integrated solution + 2. Confidence assessment + 3. Integration method + 4. Quality metrics + + Format as: + [Synthesis] + Solution: ... + Confidence: ... + Method: ... + Metrics: ... + """ + + response = await context["groq_api"].predict(prompt) + synthesis = self._parse_synthesis(response["answer"]) + + self._record_step(RecursiveStep( + id=f"step_{len(self.steps)}", + subproblem_id=problem.id, + action="synthesize", + timestamp=datetime.now(), + result=synthesis, + metrics={"confidence": synthesis.get("confidence", 0.0)}, + metadata={"num_subsolutions": len(subsolutions)} + )) + + return synthesis + + async def _optimize_solution(self, solution: Dict[str, Any], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize the final solution.""" + prompt = f""" + Optimize recursive solution: + Original: {json.dumps(solution)} + Problem: {json.dumps(self._problem_to_dict(problem))} + Context: {json.dumps(context)} + + Optimize for: + 1. Completeness + 2. Consistency + 3. Efficiency + 4. Clarity + + Format as: + [Optimization] + Answer: ... + Improvements: ... + Metrics: ... + Insights: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_optimization(response["answer"]) + + def _update_metrics(self, root_id: str): + """Update performance metrics.""" + def update_recursive(problem_id: str): + problem = self.subproblems[problem_id] + self.type_distribution[problem.type] += 1 + + if problem.status == SolutionStatus.SOLVED: + self.success_rate[problem.type] = ( + self.success_rate[problem.type] * (self.type_distribution[problem.type] - 1) + + problem.confidence + ) / self.type_distribution[problem.type] + + for child_id in problem.children: + update_recursive(child_id) + + update_recursive(root_id) + + def _get_problem_tree(self, root_id: str) -> Dict[str, Any]: + """Get the problem decomposition tree.""" + def build_tree(problem_id: str) -> Dict[str, Any]: + problem = self.subproblems[problem_id] + return { + "id": problem.id, + "type": problem.type.value, + "query": problem.query, + "status": problem.status.value, + "confidence": problem.confidence, + "children": [build_tree(child_id) for child_id in problem.children] + } + + return build_tree(root_id) + + def _get_solution_trace(self, root_id: str) -> List[Dict[str, Any]]: + """Get the solution trace for a problem.""" + return [self._step_to_dict(step) for step in self.steps + if step.subproblem_id == root_id or + any(step.subproblem_id == sub_id for sub_id in self.subproblems[root_id].children)] + + def _get_performance_metrics(self) -> Dict[str, Any]: + """Get current performance metrics.""" + return { + "depth_distribution": dict(self.depth_distribution), + "type_distribution": {t.value: c for t, c in self.type_distribution.items()}, + "success_rate": {t.value: r for t, r in self.success_rate.items()}, + "cache_hits": len(self.solution_cache), + "total_steps": len(self.steps) + } + + def _record_step(self, step: RecursiveStep): + """Record a reasoning step.""" + self.steps.append(step) + + def _parse_problem_init(self, response: str, query: str, context: Dict[str, Any]) -> Subproblem: + """Parse initial problem configuration.""" + problem_type = SubproblemType.COMPOSITE # default + dependencies = [] + metadata = {} + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Type:'): + try: + problem_type = SubproblemType(line[5:].strip().lower()) + except ValueError: + pass + elif line.startswith('Dependencies:'): + dependencies = [d.strip() for d in line[13:].split(',')] + elif line.startswith('Strategy:') or line.startswith('Approach:'): + metadata["strategy"] = line.split(':', 1)[1].strip() + + return Subproblem( + id="root", + type=problem_type, + query=query, + context=context, + parent_id=None, + children=[], + status=SolutionStatus.PENDING, + solution=None, + confidence=0.0, + dependencies=dependencies, + metadata=metadata + ) + + def _parse_subproblems(self, response: str, parent_id: str, context: Dict[str, Any]) -> List[Subproblem]: + """Parse subproblems from response.""" + subproblems = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[S'): + if current: + subproblems.append(current) + current = None + elif line.startswith('Type:'): + try: + problem_type = SubproblemType(line[5:].strip().lower()) + current = Subproblem( + id=f"{parent_id}_{len(subproblems)}", + type=problem_type, + query="", + context=context, + parent_id=parent_id, + children=[], + status=SolutionStatus.PENDING, + solution=None, + confidence=0.0, + dependencies=[], + metadata={} + ) + except ValueError: + current = None + elif current: + if line.startswith('Query:'): + current.query = line[6:].strip() + elif line.startswith('Dependencies:'): + current.dependencies = [d.strip() for d in line[13:].split(',')] + elif line.startswith('Approach:'): + current.metadata["approach"] = line[9:].strip() + + if current: + subproblems.append(current) + + return subproblems + + def _parse_atomic_solution(self, response: str) -> Dict[str, Any]: + """Parse atomic solution from response.""" + solution = { + "success": True, + "answer": "", + "confidence": 0.0, + "evidence": [], + "alternatives": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Answer:'): + solution["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + solution["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Evidence:'): + solution["evidence"] = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + solution["alternatives"] = [a.strip() for a in line[13:].split(',')] + + return solution + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis result from response.""" + synthesis = { + "success": True, + "solution": "", + "confidence": 0.0, + "method": "", + "metrics": {} + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Solution:'): + synthesis["solution"] = line[9:].strip() + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Method:'): + synthesis["method"] = line[7:].strip() + elif line.startswith('Metrics:'): + try: + synthesis["metrics"] = json.loads(line[8:].strip()) + except: + pass + + return synthesis + + def _parse_optimization(self, response: str) -> Dict[str, Any]: + """Parse optimization result from response.""" + optimization = { + "answer": "", + "confidence": 0.0, + "improvements": [], + "metrics": {}, + "meta_insights": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Answer:'): + optimization["answer"] = line[7:].strip() + elif line.startswith('Improvements:'): + optimization["improvements"] = [i.strip() for i in line[13:].split(',')] + elif line.startswith('Metrics:'): + try: + optimization["metrics"] = json.loads(line[8:].strip()) + except: + pass + elif line.startswith('Insights:'): + optimization["meta_insights"] = [i.strip() for i in line[9:].split(',')] + + return optimization + + def _problem_to_dict(self, problem: Subproblem) -> Dict[str, Any]: + """Convert problem to dictionary for serialization.""" + return { + "id": problem.id, + "type": problem.type.value, + "query": problem.query, + "parent_id": problem.parent_id, + "children": problem.children, + "status": problem.status.value, + "confidence": problem.confidence, + "dependencies": problem.dependencies, + "metadata": problem.metadata + } + + def _step_to_dict(self, step: RecursiveStep) -> Dict[str, Any]: + """Convert step to dictionary for serialization.""" + return { + "id": step.id, + "subproblem_id": step.subproblem_id, + "action": step.action, + "timestamp": step.timestamp.isoformat(), + "result": step.result, + "metrics": step.metrics, + "metadata": step.metadata + } + + def clear_cache(self): + """Clear solution cache.""" + self.solution_cache.clear() + + def get_statistics(self) -> Dict[str, Any]: + """Get detailed statistics about the reasoning process.""" + return { + "total_problems": len(self.subproblems), + "total_steps": len(self.steps), + "cache_size": len(self.solution_cache), + "type_distribution": dict(self.type_distribution), + "depth_distribution": dict(self.depth_distribution), + "success_rates": dict(self.success_rate), + "average_confidence": sum(p.confidence for p in self.subproblems.values()) / len(self.subproblems) if self.subproblems else 0.0 + } diff --git a/space/specialized.py b/space/specialized.py new file mode 100644 index 0000000000000000000000000000000000000000..14ec0269e9c7b25629ee9dfcfe0d60732cf4735f --- /dev/null +++ b/space/specialized.py @@ -0,0 +1,476 @@ +"""Specialized reasoning strategies for specific domains and tasks.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Callable +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy + +class SpecializedReasoning(ReasoningStrategy): + """ + A composite reasoning strategy that combines multiple specialized strategies + for different domains and tasks. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize specialized reasoning with component strategies.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize component strategies with shared config + strategy_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights + } + + self.strategies = { + 'code_rewrite': CodeRewriteStrategy(strategy_config), + 'security_audit': SecurityAuditStrategy(strategy_config), + 'performance': PerformanceOptimizationStrategy(strategy_config), + 'testing': TestGenerationStrategy(strategy_config), + 'documentation': DocumentationStrategy(strategy_config), + 'api_design': APIDesignStrategy(strategy_config), + 'dependencies': DependencyManagementStrategy(strategy_config), + 'code_review': CodeReviewStrategy(strategy_config) + } + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Apply specialized reasoning by selecting and combining appropriate + strategies based on the query and context. + + Args: + query: The input query to reason about + context: Additional context and parameters + + Returns: + Dict containing reasoning results and confidence scores + """ + try: + # Determine which strategies to use based on context + selected_strategies = await self._select_strategies(query, context) + + # Get results from each selected strategy + results = {} + for strategy_name in selected_strategies: + strategy = self.strategies[strategy_name] + results[strategy_name] = await strategy.reason(query, context) + + # Combine results + combined_result = await self._combine_results(results, context) + + return { + 'answer': combined_result.get('answer', ''), + 'confidence': combined_result.get('confidence', 0.0), + 'reasoning_path': { + 'selected_strategies': selected_strategies, + 'individual_results': results, + 'combination_method': combined_result.get('method', '') + } + } + + except Exception as e: + logging.error(f"Specialized reasoning failed: {str(e)}") + return { + 'error': f"Specialized reasoning failed: {str(e)}", + 'confidence': 0.0 + } + + async def _select_strategies(self, query: str, context: Dict[str, Any]) -> List[str]: + """Select appropriate strategies based on query and context.""" + selected = [] + + # Simple keyword-based selection for now + keywords = { + 'code_rewrite': ['rewrite', 'refactor', 'improve'], + 'security_audit': ['security', 'vulnerability', 'audit'], + 'performance': ['performance', 'optimize', 'speed'], + 'testing': ['test', 'coverage', 'verify'], + 'documentation': ['document', 'explain', 'describe'], + 'api_design': ['api', 'interface', 'endpoint'], + 'dependencies': ['dependency', 'package', 'version'], + 'code_review': ['review', 'quality', 'check'] + } + + query_lower = query.lower() + for strategy, terms in keywords.items(): + if any(term in query_lower for term in terms): + selected.append(strategy) + + # If no specific strategies selected, use code review as default + if not selected: + selected = ['code_review'] + + return selected + + async def _combine_results( + self, + results: Dict[str, Dict[str, Any]], + context: Dict[str, Any] + ) -> Dict[str, Any]: + """Combine results from multiple strategies.""" + if not results: + return {'answer': '', 'confidence': 0.0, 'method': 'none'} + + # For now, use the highest confidence result + best_result = max( + results.items(), + key=lambda x: x[1].get('confidence', 0) + ) + + return { + 'answer': best_result[1].get('answer', ''), + 'confidence': best_result[1].get('confidence', 0.0), + 'method': 'highest_confidence' + } + +class CodeRewriteStrategy(ReasoningStrategy): + """ + Advanced code rewriting strategy that: + 1. Analyzes code structure and patterns + 2. Identifies refactoring opportunities + 3. Maintains code semantics + 4. Optimizes code quality + 5. Ensures backward compatibility + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Rewrite code while preserving functionality.""" + try: + # Analyze code + analysis = await self._analyze_code(query, context) + + # Generate rewrite plan + plan = await self._generate_rewrite_plan(analysis, context) + + # Execute rewrites + rewrites = await self._execute_rewrites(plan, context) + + # Validate changes + validation = await self._validate_changes(rewrites, context) + + return { + "success": validation["success"], + "rewrites": rewrites, + "validation": validation, + "metrics": { + "quality_improvement": validation.get("quality_score", 0.0), + "semantic_preservation": validation.get("semantic_score", 0.0) + } + } + except Exception as e: + logging.error(f"Error in code rewrite: {str(e)}") + return {"success": False, "error": str(e)} + +class SecurityAuditStrategy(ReasoningStrategy): + """ + Advanced security audit strategy that: + 1. Identifies security vulnerabilities + 2. Analyzes attack vectors + 3. Recommends security fixes + 4. Validates security measures + 5. Monitors security state + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Perform security audit and generate recommendations.""" + try: + # Scan for vulnerabilities + vulnerabilities = await self._scan_vulnerabilities(query, context) + + # Analyze risks + risks = await self._analyze_risks(vulnerabilities, context) + + # Generate fixes + fixes = await self._generate_fixes(risks, context) + + # Validate security + validation = await self._validate_security(fixes, context) + + return { + "success": True, + "vulnerabilities": vulnerabilities, + "risks": risks, + "fixes": fixes, + "validation": validation + } + except Exception as e: + logging.error(f"Error in security audit: {str(e)}") + return {"success": False, "error": str(e)} + +class PerformanceOptimizationStrategy(ReasoningStrategy): + """ + Advanced performance optimization strategy that: + 1. Profiles code performance + 2. Identifies bottlenecks + 3. Generates optimizations + 4. Measures improvements + 5. Validates optimizations + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Optimize code performance.""" + try: + # Profile performance + profile = await self._profile_performance(query, context) + + # Identify bottlenecks + bottlenecks = await self._identify_bottlenecks(profile, context) + + # Generate optimizations + optimizations = await self._generate_optimizations(bottlenecks, context) + + # Measure improvements + measurements = await self._measure_improvements(optimizations, context) + + return { + "success": measurements["success"], + "profile": profile, + "bottlenecks": bottlenecks, + "optimizations": optimizations, + "improvements": measurements + } + except Exception as e: + logging.error(f"Error in performance optimization: {str(e)}") + return {"success": False, "error": str(e)} + +class TestGenerationStrategy(ReasoningStrategy): + """ + Advanced test generation strategy that: + 1. Analyzes code coverage + 2. Generates test cases + 3. Creates test fixtures + 4. Validates test quality + 5. Maintains test suite + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate comprehensive test suite.""" + try: + # Analyze coverage + coverage = await self._analyze_coverage(query, context) + + # Generate test cases + test_cases = await self._generate_test_cases(coverage, context) + + # Create fixtures + fixtures = await self._create_fixtures(test_cases, context) + + # Validate tests + validation = await self._validate_tests(test_cases, fixtures, context) + + return { + "success": validation["success"], + "test_cases": test_cases, + "fixtures": fixtures, + "validation": validation, + "metrics": { + "coverage": coverage.get("percentage", 0.0), + "quality_score": validation.get("quality_score", 0.0) + } + } + except Exception as e: + logging.error(f"Error in test generation: {str(e)}") + return {"success": False, "error": str(e)} + +class DocumentationStrategy(ReasoningStrategy): + """ + Advanced documentation strategy that: + 1. Analyzes code structure + 2. Generates documentation + 3. Maintains consistency + 4. Updates references + 5. Validates completeness + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate and maintain documentation.""" + try: + # Analyze structure + structure = await self._analyze_structure(query, context) + + # Generate documentation + documentation = await self._generate_documentation(structure, context) + + # Update references + references = await self._update_references(documentation, context) + + # Validate completeness + validation = await self._validate_documentation(documentation, references, context) + + return { + "success": validation["success"], + "documentation": documentation, + "references": references, + "validation": validation, + "metrics": { + "completeness": validation.get("completeness_score", 0.0), + "consistency": validation.get("consistency_score", 0.0) + } + } + except Exception as e: + logging.error(f"Error in documentation: {str(e)}") + return {"success": False, "error": str(e)} + +class APIDesignStrategy(ReasoningStrategy): + """ + Advanced API design strategy that: + 1. Analyzes requirements + 2. Designs API structure + 3. Generates specifications + 4. Validates design + 5. Maintains versioning + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Design and validate API.""" + try: + # Analyze requirements + requirements = await self._analyze_requirements(query, context) + + # Design structure + design = await self._design_structure(requirements, context) + + # Generate specs + specs = await self._generate_specs(design, context) + + # Validate design + validation = await self._validate_design(specs, context) + + return { + "success": validation["success"], + "requirements": requirements, + "design": design, + "specs": specs, + "validation": validation + } + except Exception as e: + logging.error(f"Error in API design: {str(e)}") + return {"success": False, "error": str(e)} + +class DependencyManagementStrategy(ReasoningStrategy): + """ + Advanced dependency management strategy that: + 1. Analyzes dependencies + 2. Resolves conflicts + 3. Optimizes versions + 4. Ensures compatibility + 5. Maintains security + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Manage and optimize dependencies.""" + try: + # Analyze dependencies + analysis = await self._analyze_dependencies(query, context) + + # Resolve conflicts + resolution = await self._resolve_conflicts(analysis, context) + + # Optimize versions + optimization = await self._optimize_versions(resolution, context) + + # Validate compatibility + validation = await self._validate_compatibility(optimization, context) + + return { + "success": validation["success"], + "analysis": analysis, + "resolution": resolution, + "optimization": optimization, + "validation": validation + } + except Exception as e: + logging.error(f"Error in dependency management: {str(e)}") + return {"success": False, "error": str(e)} + +class CodeReviewStrategy(ReasoningStrategy): + """ + Advanced code review strategy that: + 1. Analyzes code quality + 2. Identifies issues + 3. Suggests improvements + 4. Tracks changes + 5. Validates fixes + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Perform comprehensive code review.""" + try: + # Analyze quality + quality = await self._analyze_quality(query, context) + + # Identify issues + issues = await self._identify_issues(quality, context) + + # Generate suggestions + suggestions = await self._generate_suggestions(issues, context) + + # Track changes + tracking = await self._track_changes(suggestions, context) + + return { + "success": True, + "quality": quality, + "issues": issues, + "suggestions": suggestions, + "tracking": tracking, + "metrics": { + "quality_score": quality.get("score", 0.0), + "issues_found": len(issues), + "suggestions_made": len(suggestions) + } + } + except Exception as e: + logging.error(f"Error in code review: {str(e)}") + return {"success": False, "error": str(e)} diff --git a/space/team_management.py b/space/team_management.py new file mode 100644 index 0000000000000000000000000000000000000000..c56773ea7a887b71744c97ce75dc2594004c0a39 --- /dev/null +++ b/space/team_management.py @@ -0,0 +1,517 @@ +""" +Advanced Team Management System +----------------------------- +Manages specialized teams of agents that work together towards common goals: +1. Team A: Coders (App/Software Developers) +2. Team B: Business (Entrepreneurs) +3. Team C: Research (Deep Online Research) +4. Team D: Crypto & Sports Trading + +Features: +- Cross-team collaboration +- Goal alignment +- Resource sharing +- Synchronized execution +""" + +from typing import Dict, List, Optional, Set, Union, TypeVar, Any +from dataclasses import dataclass, field +from enum import Enum +import asyncio +from datetime import datetime +import uuid +from collections import defaultdict + +from orchestrator import AgentOrchestrator, TaskPriority, AgentRole, AgentState +from reasoning import UnifiedReasoningEngine + +# Agent capabilities and personality types +class AgentCapability(Enum): + """Core capabilities of agents.""" + REASONING = "reasoning" + LEARNING = "learning" + EXECUTION = "execution" + COORDINATION = "coordination" + MONITORING = "monitoring" + +class AgentPersonality(Enum): + """Different personality types for agents.""" + ANALYTICAL = "analytical" + CREATIVE = "creative" + PRAGMATIC = "pragmatic" + COLLABORATIVE = "collaborative" + PROACTIVE = "proactive" + CAUTIOUS = "cautious" + +class TeamType(Enum): + """Specialized team types.""" + CODERS = "coders" + BUSINESS = "business" + RESEARCH = "research" + TRADERS = "traders" + +class TeamObjective(Enum): + """Types of team objectives.""" + SOFTWARE_DEVELOPMENT = "software_development" + BUSINESS_OPPORTUNITY = "business_opportunity" + MARKET_RESEARCH = "market_research" + TRADING_STRATEGY = "trading_strategy" + CROSS_TEAM_PROJECT = "cross_team_project" + +@dataclass +class TeamProfile: + """Team profile and capabilities.""" + id: str + type: TeamType + name: str + primary_objective: TeamObjective + secondary_objectives: List[TeamObjective] + agent_count: int + expertise_areas: List[str] + collaboration_score: float = 0.0 + success_rate: float = 0.0 + active_projects: int = 0 + +@dataclass +class CollaborationLink: + """Defines collaboration between teams.""" + team_a_id: str + team_b_id: str + strength: float + active_projects: int + last_interaction: datetime + success_rate: float + +class TeamManager: + """Manages specialized teams and their collaboration.""" + + def __init__(self, orchestrator: AgentOrchestrator): + self.orchestrator = orchestrator + self.teams: Dict[str, TeamProfile] = {} + self.agents: Dict[str, Dict[str, 'Agent']] = {} # team_id -> {agent_id -> Agent} + self.collaboration_network: Dict[str, CollaborationLink] = {} + self.shared_objectives: Dict[str, Set[str]] = defaultdict(set) # objective_id -> set of team_ids + self.lock = asyncio.Lock() + + # Initialize specialized teams + self._init_teams() + + def _init_teams(self): + """Initialize specialized teams.""" + team_configs = { + TeamType.CODERS: { + "name": "Development Team", + "primary": TeamObjective.SOFTWARE_DEVELOPMENT, + "secondary": [ + TeamObjective.BUSINESS_OPPORTUNITY, + TeamObjective.MARKET_RESEARCH + ], + "expertise": [ + "full_stack_development", + "cloud_architecture", + "ai_ml", + "blockchain", + "mobile_development" + ] + }, + TeamType.BUSINESS: { + "name": "Business Strategy Team", + "primary": TeamObjective.BUSINESS_OPPORTUNITY, + "secondary": [ + TeamObjective.MARKET_RESEARCH, + TeamObjective.TRADING_STRATEGY + ], + "expertise": [ + "market_analysis", + "business_strategy", + "digital_transformation", + "startup_innovation", + "product_management" + ] + }, + TeamType.RESEARCH: { + "name": "Research & Analysis Team", + "primary": TeamObjective.MARKET_RESEARCH, + "secondary": [ + TeamObjective.BUSINESS_OPPORTUNITY, + TeamObjective.TRADING_STRATEGY + ], + "expertise": [ + "deep_research", + "data_analysis", + "trend_forecasting", + "competitive_analysis", + "technology_assessment" + ] + }, + TeamType.TRADERS: { + "name": "Trading & Investment Team", + "primary": TeamObjective.TRADING_STRATEGY, + "secondary": [ + TeamObjective.MARKET_RESEARCH, + TeamObjective.BUSINESS_OPPORTUNITY + ], + "expertise": [ + "crypto_trading", + "sports_betting", + "risk_management", + "market_timing", + "portfolio_optimization" + ] + } + } + + for team_type, config in team_configs.items(): + team_id = str(uuid.uuid4()) + self.teams[team_id] = TeamProfile( + id=team_id, + type=team_type, + name=config["name"], + primary_objective=config["primary"], + secondary_objectives=config["secondary"], + agent_count=5, # Default size + expertise_areas=config["expertise"] + ) + self.agents[team_id] = {} + + async def initialize_team_agents(self): + """Initialize agents for each team with appropriate roles and capabilities.""" + for team_id, team in self.teams.items(): + await self._create_team_agents(team_id) + await self._establish_collaboration_links(team_id) + + async def _create_team_agents(self, team_id: str): + """Create specialized agents for a team.""" + team = self.teams[team_id] + + # Define agent configurations based on team type + agent_configs = self._get_agent_configs(team.type) + + for config in agent_configs: + agent_id = await self.orchestrator.create_agent( + role=config["role"], + capabilities=config["capabilities"] + ) + + agent = Agent( + profile=config["profile"], + reasoning_engine=self.orchestrator.reasoning_engine, + meta_learning=self.orchestrator.meta_learning, + config=config.get("config", {}) + ) + + self.agents[team_id][agent_id] = agent + + def _get_agent_configs(self, team_type: TeamType) -> List[Dict]: + """Get agent configurations based on team type.""" + base_configs = [ + { + "role": AgentRole.COORDINATOR, + "capabilities": [ + AgentCapability.REASONING, + AgentCapability.COORDINATION + ], + "personality": AgentPersonality.PROACTIVE, + "profile": { + "name": "Coordinator", + "description": "Team coordinator" + } + }, + { + "role": AgentRole.EXECUTOR, + "capabilities": [ + AgentCapability.EXECUTION, + AgentCapability.LEARNING + ], + "personality": AgentPersonality.ANALYTICAL, + "profile": { + "name": "Executor", + "description": "Task executor" + } + } + ] + + # Add team-specific configurations + if team_type == TeamType.CODERS: + base_configs.extend([ + { + "role": AgentRole.EXECUTOR, + "capabilities": [ + AgentCapability.EXECUTION, + AgentCapability.REASONING + ], + "personality": AgentPersonality.CREATIVE, + "expertise": ["software_development", "system_design"], + "profile": { + "name": "Developer", + "description": "Software developer" + } + } + ]) + elif team_type == TeamType.BUSINESS: + base_configs.extend([ + { + "role": AgentRole.PLANNER, + "capabilities": [ + AgentCapability.REASONING, + AgentCapability.LEARNING + ], + "personality": AgentPersonality.PROACTIVE, + "expertise": ["business_strategy", "market_analysis"], + "profile": { + "name": "Planner", + "description": "Business planner" + } + } + ]) + elif team_type == TeamType.RESEARCH: + base_configs.extend([ + { + "role": AgentRole.MONITOR, + "capabilities": [ + AgentCapability.MONITORING, + AgentCapability.LEARNING + ], + "personality": AgentPersonality.ANALYTICAL, + "expertise": ["research", "data_analysis"], + "profile": { + "name": "Researcher", + "description": "Researcher" + } + } + ]) + elif team_type == TeamType.TRADERS: + base_configs.extend([ + { + "role": AgentRole.EXECUTOR, + "capabilities": [ + AgentCapability.EXECUTION, + AgentCapability.REASONING + ], + "personality": AgentPersonality.CAUTIOUS, + "expertise": ["trading", "risk_management"], + "profile": { + "name": "Trader", + "description": "Trader" + } + } + ]) + + return base_configs + + async def _establish_collaboration_links(self, team_id: str): + """Establish collaboration links with other teams.""" + team = self.teams[team_id] + + for other_id, other_team in self.teams.items(): + if other_id != team_id: + link_id = f"{min(team_id, other_id)}_{max(team_id, other_id)}" + if link_id not in self.collaboration_network: + self.collaboration_network[link_id] = CollaborationLink( + team_a_id=team_id, + team_b_id=other_id, + strength=0.5, # Initial collaboration strength + active_projects=0, + last_interaction=datetime.now(), + success_rate=0.0 + ) + + async def create_cross_team_objective( + self, + objective: str, + required_teams: List[TeamType], + priority: TaskPriority = TaskPriority.MEDIUM + ) -> str: + """Create an objective that requires multiple teams.""" + objective_id = str(uuid.uuid4()) + + # Find relevant teams + selected_teams = [] + for team_id, team in self.teams.items(): + if team.type in required_teams: + selected_teams.append(team_id) + + if len(selected_teams) < len(required_teams): + raise ValueError("Not all required teams are available") + + # Create shared objective + self.shared_objectives[objective_id].update(selected_teams) + + # Create tasks for each team + tasks = [] + for team_id in selected_teams: + task_id = await self.orchestrator.submit_task( + description=f"Team {self.teams[team_id].name} contribution to: {objective}", + priority=priority + ) + tasks.append(task_id) + + return objective_id + + async def monitor_objective_progress(self, objective_id: str) -> Dict: + """Monitor progress of a cross-team objective.""" + if objective_id not in self.shared_objectives: + raise ValueError("Unknown objective") + + team_progress = {} + for team_id in self.shared_objectives[objective_id]: + team = self.teams[team_id] + team_agents = self.agents[team_id] + + # Calculate team progress + active_agents = sum(1 for agent in team_agents.values() if agent.state == AgentState.BUSY) + completion_rate = sum(agent.get_task_completion_rate() for agent in team_agents.values()) / len(team_agents) + + team_progress[team.name] = { + "active_agents": active_agents, + "completion_rate": completion_rate, + "collaboration_score": team.collaboration_score + } + + return team_progress + + async def optimize_team_collaboration(self): + """Optimize collaboration between teams.""" + for link in self.collaboration_network.values(): + team_a = self.teams[link.team_a_id] + team_b = self.teams[link.team_b_id] + + # Update collaboration strength based on: + # 1. Number of successful joint projects + # 2. Frequency of interaction + # 3. Complementary expertise + + success_factor = link.success_rate + interaction_factor = min((datetime.now() - link.last_interaction).days / 30.0, 1.0) + expertise_overlap = len( + set(team_a.expertise_areas) & set(team_b.expertise_areas) + ) / len(set(team_a.expertise_areas) | set(team_b.expertise_areas)) + + new_strength = ( + 0.4 * success_factor + + 0.3 * (1 - interaction_factor) + + 0.3 * (1 - expertise_overlap) + ) + + link.strength = 0.7 * link.strength + 0.3 * new_strength + + async def get_team_recommendations(self, objective: str) -> List[TeamType]: + """Get recommended teams for an objective based on expertise and collaboration history.""" + # Analyze objective to determine required expertise + required_expertise = await self._analyze_objective(objective) + + # Score each team + team_scores = {} + for team_id, team in self.teams.items(): + # Calculate expertise match + expertise_match = len( + set(required_expertise) & set(team.expertise_areas) + ) / len(required_expertise) + + # Calculate collaboration potential + collab_potential = self._calculate_collaboration_potential(team_id) + + # Calculate success history + success_history = team.success_rate + + # Weighted score + score = ( + 0.4 * expertise_match + + 0.3 * collab_potential + + 0.3 * success_history + ) + + team_scores[team.type] = score + + # Return sorted recommendations + return sorted( + team_scores.keys(), + key=lambda x: team_scores[x], + reverse=True + ) + + async def _analyze_objective(self, objective: str) -> List[str]: + """Analyze an objective to determine required expertise.""" + # Use reasoning engine to analyze objective + analysis = await self.orchestrator.reasoning_engine.reason( + query=f"Analyze required expertise for: {objective}", + context={ + "available_expertise": [ + expertise + for team in self.teams.values() + for expertise in team.expertise_areas + ] + } + ) + + return analysis.get("required_expertise", []) + + def _calculate_collaboration_potential(self, team_id: str) -> float: + """Calculate a team's collaboration potential based on history.""" + team_links = [ + link for link in self.collaboration_network.values() + if team_id in (link.team_a_id, link.team_b_id) + ] + + if not team_links: + return 0.5 + + return sum(link.strength for link in team_links) / len(team_links) + + async def update_team_metrics(self): + """Update performance metrics for all teams.""" + for team_id, team in self.teams.items(): + team_agents = self.agents[team_id] + + # Calculate success rate + completed_tasks = sum( + agent.get_completed_task_count() + for agent in team_agents.values() + ) + total_tasks = sum( + agent.get_total_task_count() + for agent in team_agents.values() + ) + team.success_rate = completed_tasks / max(1, total_tasks) + + # Calculate collaboration score + team_links = [ + link for link in self.collaboration_network.values() + if team_id in (link.team_a_id, link.team_b_id) + ] + team.collaboration_score = ( + sum(link.strength for link in team_links) / + len(team_links) if team_links else 0.5 + ) + +class Agent: + def __init__(self, profile: Dict, reasoning_engine: UnifiedReasoningEngine, meta_learning: bool, config: Optional[Dict[str, Any]] = None): + self.profile = profile + self.config = config or {} + + # Use provided reasoning engine or create one with config + self.reasoning_engine = reasoning_engine if reasoning_engine else UnifiedReasoningEngine( + min_confidence=self.config.get('min_confidence', 0.7), + parallel_threshold=self.config.get('parallel_threshold', 3), + learning_rate=self.config.get('learning_rate', 0.1), + strategy_weights=self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + ) + self.meta_learning = meta_learning + self.state = AgentState.IDLE + + def get_task_completion_rate(self): + # Implement task completion rate calculation + pass + + def get_completed_task_count(self): + # Implement completed task count calculation + pass + + def get_total_task_count(self): + # Implement total task count calculation + pass diff --git a/space/tree_of_thoughts.py b/space/tree_of_thoughts.py new file mode 100644 index 0000000000000000000000000000000000000000..2242cb163050f69badc78d05cb40271e8fda2638 --- /dev/null +++ b/space/tree_of_thoughts.py @@ -0,0 +1,516 @@ +"""Tree of Thoughts reasoning implementation with advanced tree exploration.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Tuple +import json +from dataclasses import dataclass +from enum import Enum +import heapq +from collections import defaultdict + +from .base import ReasoningStrategy + +class NodeType(Enum): + """Types of nodes in the thought tree.""" + ROOT = "root" + HYPOTHESIS = "hypothesis" + EVIDENCE = "evidence" + ANALYSIS = "analysis" + SYNTHESIS = "synthesis" + EVALUATION = "evaluation" + CONCLUSION = "conclusion" + +@dataclass +class TreeNode: + """Represents a node in the thought tree.""" + id: str + type: NodeType + content: str + confidence: float + children: List['TreeNode'] + parent: Optional['TreeNode'] + metadata: Dict[str, Any] + depth: int + evaluation_score: float = 0.0 + +class TreeOfThoughtsStrategy(ReasoningStrategy): + """ + Advanced Tree of Thoughts reasoning implementation with: + - Beam search for path exploration + - Dynamic node evaluation + - Pruning strategies + - Path optimization + - Meta-learning from tree patterns + """ + + def __init__(self, + min_confidence: float = 0.7, + parallel_threshold: int = 3, + learning_rate: float = 0.1, + strategy_weights: Optional[Dict[str, float]] = None): + self.min_confidence = min_confidence + self.parallel_threshold = parallel_threshold + self.learning_rate = learning_rate + self.strategy_weights = strategy_weights or { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + } + self.node_history: Dict[str, TreeNode] = {} + self.path_patterns: Dict[str, float] = defaultdict(float) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Main reasoning method implementing tree of thoughts.""" + try: + # Initialize root node + root = await self._create_root_node(query, context) + + # Build and explore tree + tree = await self._build_tree(root, context) + + # Find best paths + paths = await self._find_best_paths(tree, context) + + # Synthesize conclusion + conclusion = await self._synthesize_conclusion(paths, context) + + # Update history and patterns + self._update_history(tree) + self._update_patterns(paths) + + return { + "success": True, + "answer": conclusion["answer"], + "confidence": conclusion["confidence"], + "tree": self._tree_to_dict(tree), + "best_paths": [self._path_to_dict(p) for p in paths], + "reasoning_trace": conclusion["trace"], + "meta_insights": conclusion["meta_insights"] + } + except Exception as e: + logging.error(f"Error in tree of thoughts reasoning: {str(e)}") + return {"success": False, "error": str(e)} + + async def _create_root_node(self, query: str, context: Dict[str, Any]) -> TreeNode: + """Create the root node of the thought tree.""" + prompt = f""" + Initialize root thought node for query: + Query: {query} + Context: {json.dumps(context)} + + Provide: + 1. Initial problem decomposition + 2. Key aspects to explore + 3. Evaluation criteria + 4. Success metrics + + Format as: + [Root] + Decomposition: ... + Aspects: ... + Criteria: ... + Metrics: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_root_node(response["answer"], query) + + async def _build_tree(self, root: TreeNode, context: Dict[str, Any]) -> TreeNode: + """Build and explore the thought tree.""" + # Initialize beam with root + beam = [(root.evaluation_score, root)] + visited: Set[str] = set() + + for depth in range(5): + next_beam = [] + + for _, node in beam: + if node.id in visited: + continue + + visited.add(node.id) + + # Generate child nodes + children = await self._generate_children(node, context) + + # Evaluate and filter children + evaluated_children = await self._evaluate_nodes(children, context) + + # Add to beam + for child in evaluated_children: + if child.evaluation_score > 0.4: + next_beam.append((child.evaluation_score, child)) + node.children.append(child) + + # Select best nodes for next iteration + beam = heapq.nlargest(3, next_beam, key=lambda x: x[0]) + + if not beam: + break + + return root + + async def _generate_children(self, parent: TreeNode, context: Dict[str, Any]) -> List[TreeNode]: + """Generate child nodes for a given parent.""" + prompt = f""" + Generate child thoughts for node: + Parent: {json.dumps(self._node_to_dict(parent))} + Context: {json.dumps(context)} + + For each child provide: + 1. [Type]: {" | ".join([t.value for t in NodeType if t != NodeType.ROOT])} + 2. [Content]: Main thought + 3. [Confidence]: 0-1 score + 4. [Rationale]: Why this follows from parent + 5. [Potential]: Future exploration potential + + Format as: + [C1] + Type: ... + Content: ... + Confidence: ... + Rationale: ... + Potential: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_child_nodes(response["answer"], parent) + + async def _evaluate_nodes(self, nodes: List[TreeNode], context: Dict[str, Any]) -> List[TreeNode]: + """Evaluate a list of nodes.""" + prompt = f""" + Evaluate thought nodes: + Nodes: {json.dumps([self._node_to_dict(n) for n in nodes])} + Context: {json.dumps(context)} + + For each node evaluate: + 1. Logical coherence + 2. Evidence support + 3. Novelty value + 4. Exploration potential + + Format as: + [N1] + Coherence: 0-1 + Evidence: 0-1 + Novelty: 0-1 + Potential: 0-1 + Overall: 0-1 + """ + + response = await context["groq_api"].predict(prompt) + return self._apply_evaluations(nodes, response["answer"]) + + async def _find_best_paths(self, root: TreeNode, context: Dict[str, Any]) -> List[List[TreeNode]]: + """Find the best paths through the tree.""" + paths = [] + current_path = [root] + + def dfs(node: TreeNode, path: List[TreeNode]): + if not node.children: + paths.append(path[:]) + return + + # Sort children by score + sorted_children = sorted(node.children, key=lambda x: x.evaluation_score, reverse=True) + + # Explore top paths + for child in sorted_children[:3]: + path.append(child) + dfs(child, path) + path.pop() + + dfs(root, current_path) + + # Evaluate complete paths + evaluated_paths = await self._evaluate_paths(paths, context) + + # Return top paths + return sorted(evaluated_paths, key=lambda p: sum(n.evaluation_score for n in p), reverse=True)[:3] + + async def _synthesize_conclusion(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> Dict[str, Any]: + """Synthesize final conclusion from best paths.""" + prompt = f""" + Synthesize conclusion from thought paths: + Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])} + Context: {json.dumps(context)} + + Provide: + 1. Main conclusion + 2. Confidence level + 3. Reasoning trace + 4. Supporting evidence + 5. Alternative perspectives + 6. Meta-insights + + Format as: + [Conclusion] + Answer: ... + Confidence: ... + Trace: ... + Evidence: ... + Alternatives: ... + + [Meta] + Insights: ... + Patterns: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_conclusion(response["answer"]) + + def _parse_root_node(self, response: str, query: str) -> TreeNode: + """Parse root node from response.""" + root = TreeNode( + id="root", + type=NodeType.ROOT, + content=query, + confidence=1.0, + children=[], + parent=None, + metadata={}, + depth=0 + ) + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Decomposition:'): + root.metadata["decomposition"] = line[14:].strip() + elif line.startswith('Aspects:'): + root.metadata["aspects"] = [a.strip() for a in line[8:].split(',')] + elif line.startswith('Criteria:'): + root.metadata["criteria"] = [c.strip() for c in line[9:].split(',')] + elif line.startswith('Metrics:'): + root.metadata["metrics"] = [m.strip() for m in line[8:].split(',')] + + return root + + def _parse_child_nodes(self, response: str, parent: TreeNode) -> List[TreeNode]: + """Parse child nodes from response.""" + children = [] + current = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[C'): + if current: + children.append(current) + current = None + elif line.startswith('Type:'): + type_str = line[5:].strip() + try: + node_type = NodeType(type_str.lower()) + current = TreeNode( + id=f"{parent.id}_{len(children)}", + type=node_type, + content="", + confidence=0.0, + children=[], + parent=parent, + metadata={}, + depth=parent.depth + 1 + ) + except ValueError: + logging.warning(f"Invalid node type: {type_str}") + elif current: + if line.startswith('Content:'): + current.content = line[8:].strip() + elif line.startswith('Confidence:'): + try: + current.confidence = float(line[11:].strip()) + except: + current.confidence = 0.5 + elif line.startswith('Rationale:'): + current.metadata["rationale"] = line[10:].strip() + elif line.startswith('Potential:'): + current.metadata["potential"] = line[10:].strip() + + if current: + children.append(current) + + return children + + def _apply_evaluations(self, nodes: List[TreeNode], response: str) -> List[TreeNode]: + """Apply evaluation scores to nodes.""" + current_node_idx = 0 + current_scores = {} + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[N'): + if current_scores and current_node_idx < len(nodes): + nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0) + nodes[current_node_idx].metadata.update(current_scores) + current_node_idx += 1 + current_scores = {} + elif ':' in line: + key, value = line.split(':') + try: + current_scores[key.strip()] = float(value.strip()) + except: + pass + + if current_scores and current_node_idx < len(nodes): + nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0) + nodes[current_node_idx].metadata.update(current_scores) + + return nodes + + async def _evaluate_paths(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> List[List[TreeNode]]: + """Evaluate complete reasoning paths.""" + prompt = f""" + Evaluate complete reasoning paths: + Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])} + Context: {json.dumps(context)} + + For each path evaluate: + 1. Coherence of progression + 2. Evidence support + 3. Conclusion strength + 4. Novel insights + + Format as: + [P1] + Coherence: 0-1 + Evidence: 0-1 + Conclusion: 0-1 + Insights: 0-1 + Overall: 0-1 + """ + + response = await context["groq_api"].predict(prompt) + scores = self._parse_path_scores(response["answer"]) + + # Apply scores to paths + for i, path in enumerate(paths): + if i < len(scores): + for node in path: + node.evaluation_score *= scores[i] + + return paths + + def _parse_path_scores(self, response: str) -> List[float]: + """Parse path evaluation scores.""" + scores = [] + current_score = None + + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[P'): + if current_score is not None: + scores.append(current_score) + current_score = None + elif line.startswith('Overall:'): + try: + current_score = float(line[8:].strip()) + except: + current_score = 0.5 + + if current_score is not None: + scores.append(current_score) + + return scores + + def _parse_conclusion(self, response: str) -> Dict[str, Any]: + """Parse final conclusion.""" + conclusion = { + "answer": "", + "confidence": 0.0, + "trace": [], + "evidence": [], + "alternatives": [], + "meta_insights": [] + } + + section = None + for line in response.split('\n'): + line = line.strip() + if not line: + continue + + if line.startswith('[Conclusion]'): + section = "conclusion" + elif line.startswith('[Meta]'): + section = "meta" + elif section == "conclusion": + if line.startswith('Answer:'): + conclusion["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + conclusion["confidence"] = float(line[11:].strip()) + except: + conclusion["confidence"] = 0.5 + elif line.startswith('Trace:'): + conclusion["trace"] = [t.strip() for t in line[6:].split(',')] + elif line.startswith('Evidence:'): + conclusion["evidence"] = [e.strip() for e in line[9:].split(',')] + elif line.startswith('Alternatives:'): + conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')] + elif section == "meta": + if line.startswith('Insights:'): + conclusion["meta_insights"].extend([i.strip() for i in line[9:].split(',')]) + + return conclusion + + def _node_to_dict(self, node: TreeNode) -> Dict[str, Any]: + """Convert node to dictionary for serialization.""" + return { + "id": node.id, + "type": node.type.value, + "content": node.content, + "confidence": node.confidence, + "evaluation_score": node.evaluation_score, + "metadata": node.metadata, + "depth": node.depth + } + + def _tree_to_dict(self, root: TreeNode) -> Dict[str, Any]: + """Convert entire tree to dictionary.""" + def convert_node(node: TreeNode) -> Dict[str, Any]: + node_dict = self._node_to_dict(node) + node_dict["children"] = [convert_node(c) for c in node.children] + return node_dict + + return convert_node(root) + + def _path_to_dict(self, path: List[TreeNode]) -> List[Dict[str, Any]]: + """Convert path to dictionary.""" + return [self._node_to_dict(n) for n in path] + + def _update_history(self, root: TreeNode): + """Update node history.""" + def add_to_history(node: TreeNode): + self.node_history[node.id] = node + for child in node.children: + add_to_history(child) + + add_to_history(root) + + def _update_patterns(self, paths: List[List[TreeNode]]): + """Update path patterns.""" + for path in paths: + pattern = "->".join(n.type.value for n in path) + self.path_patterns[pattern] += path[-1].evaluation_score + + def get_node_history(self) -> Dict[str, Dict[str, Any]]: + """Get history of all nodes.""" + return {k: self._node_to_dict(v) for k, v in self.node_history.items()} + + def get_successful_patterns(self) -> Dict[str, float]: + """Get successful reasoning patterns.""" + return dict(sorted(self.path_patterns.items(), key=lambda x: x[1], reverse=True)) + + def clear_history(self): + """Clear node history and patterns.""" + self.node_history.clear() + self.path_patterns.clear() diff --git a/space/unified_engine.py b/space/unified_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..35d7100ab6d2de66de5b4af05d2b96da8c4ba2bb --- /dev/null +++ b/space/unified_engine.py @@ -0,0 +1,427 @@ +"""Unified reasoning engine that combines multiple reasoning strategies.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import asyncio +from collections import defaultdict + +from .base import ReasoningStrategy +from .chain_of_thought import ChainOfThoughtStrategy +from .tree_of_thoughts import TreeOfThoughtsStrategy +from .meta_learning import MetaLearningStrategy +from .recursive import RecursiveReasoning +from .analogical import AnalogicalReasoning +from .local_llm import LocalLLMStrategy +from .agentic import ( + TaskDecompositionStrategy, + ResourceManagementStrategy, + ContextualPlanningStrategy, + AdaptiveExecutionStrategy, + FeedbackIntegrationStrategy +) + +class StrategyType(str, Enum): + """Types of reasoning strategies.""" + CHAIN_OF_THOUGHT = "chain_of_thought" + TREE_OF_THOUGHTS = "tree_of_thoughts" + META_LEARNING = "meta_learning" + RECURSIVE = "recursive" + ANALOGICAL = "analogical" + TASK_DECOMPOSITION = "task_decomposition" + RESOURCE_MANAGEMENT = "resource_management" + CONTEXTUAL_PLANNING = "contextual_planning" + ADAPTIVE_EXECUTION = "adaptive_execution" + FEEDBACK_INTEGRATION = "feedback_integration" + LOCAL_LLM = "local_llm" + +@dataclass +class StrategyResult: + """Result from a reasoning strategy.""" + strategy_type: StrategyType + success: bool + answer: Optional[str] + confidence: float + reasoning_trace: List[Dict[str, Any]] + metadata: Dict[str, Any] + performance_metrics: Dict[str, Any] + timestamp: datetime = field(default_factory=datetime.now) + +@dataclass +class UnifiedResult: + """Combined result from multiple strategies.""" + success: bool + answer: str + confidence: float + strategy_results: Dict[StrategyType, StrategyResult] + synthesis_method: str + meta_insights: List[str] + performance_metrics: Dict[str, Any] + timestamp: datetime = field(default_factory=datetime.now) + +class UnifiedReasoningEngine: + """ + Advanced unified reasoning engine that: + 1. Combines multiple reasoning strategies + 2. Dynamically selects and weights strategies + 3. Synthesizes results from different approaches + 4. Learns from experience + 5. Adapts to different types of tasks + """ + + def __init__(self, + min_confidence: float = 0.7, + strategy_weights: Optional[Dict[StrategyType, float]] = None, + parallel_threshold: int = 3, + learning_rate: float = 0.1): + self.min_confidence = min_confidence + self.parallel_threshold = parallel_threshold + self.learning_rate = learning_rate + + # Initialize strategies + self.strategies: Dict[StrategyType, ReasoningStrategy] = { + StrategyType.CHAIN_OF_THOUGHT: ChainOfThoughtStrategy(), + StrategyType.TREE_OF_THOUGHTS: TreeOfThoughtsStrategy(), + StrategyType.META_LEARNING: MetaLearningStrategy(), + StrategyType.RECURSIVE: RecursiveReasoning(), + StrategyType.ANALOGICAL: AnalogicalReasoning(), + StrategyType.TASK_DECOMPOSITION: TaskDecompositionStrategy(), + StrategyType.RESOURCE_MANAGEMENT: ResourceManagementStrategy(), + StrategyType.CONTEXTUAL_PLANNING: ContextualPlanningStrategy(), + StrategyType.ADAPTIVE_EXECUTION: AdaptiveExecutionStrategy(), + StrategyType.FEEDBACK_INTEGRATION: FeedbackIntegrationStrategy(), + StrategyType.LOCAL_LLM: LocalLLMStrategy() # Add local LLM strategy + } + + # Strategy weights with higher weight for LOCAL_LLM + self.strategy_weights = strategy_weights or { + **{strategy_type: 1.0 for strategy_type in StrategyType}, + StrategyType.LOCAL_LLM: 2.0 # Higher weight for local LLM + } + + # Performance tracking + self.strategy_performance: Dict[StrategyType, List[float]] = defaultdict(list) + self.task_type_performance: Dict[str, Dict[StrategyType, float]] = defaultdict(lambda: defaultdict(float)) + self.synthesis_performance: Dict[str, List[float]] = defaultdict(list) + + async def reason(self, query: str, context: Dict[str, Any]) -> UnifiedResult: + """Main reasoning method combining multiple strategies.""" + try: + # Analyze task + task_analysis = await self._analyze_task(query, context) + + # Select strategies + selected_strategies = await self._select_strategies(task_analysis, context) + + # Execute strategies + strategy_results = await self._execute_strategies( + selected_strategies, query, context) + + # Synthesize results + unified_result = await self._synthesize_results( + strategy_results, task_analysis, context) + + # Learn from experience + self._update_performance(unified_result) + + return unified_result + + except Exception as e: + logging.error(f"Error in unified reasoning: {str(e)}") + return UnifiedResult( + success=False, + answer=f"Error: {str(e)}", + confidence=0.0, + strategy_results={}, + synthesis_method="failed", + meta_insights=[f"Error occurred: {str(e)}"], + performance_metrics={} + ) + + async def _analyze_task(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze the task to determine optimal strategy selection.""" + prompt = f""" + Analyze reasoning task: + Query: {query} + Context: {json.dumps(context)} + + Determine: + 1. Task type and complexity + 2. Required reasoning capabilities + 3. Resource requirements + 4. Success criteria + 5. Risk factors + + Format as: + [Analysis] + Type: ... + Complexity: ... + Capabilities: ... + Resources: ... + Criteria: ... + Risks: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_task_analysis(response["answer"]) + + async def _select_strategies(self, task_analysis: Dict[str, Any], context: Dict[str, Any]) -> List[StrategyType]: + """Select appropriate strategies based on task analysis.""" + # Calculate strategy scores + scores: Dict[StrategyType, float] = {} + for strategy_type in StrategyType: + base_score = self.strategy_weights[strategy_type] + + # Task type performance + task_type = task_analysis["type"] + type_score = self.task_type_performance[task_type][strategy_type] + + # Recent performance + recent_performance = ( + sum(self.strategy_performance[strategy_type][-5:]) / 5 + if self.strategy_performance[strategy_type] else 0.5 + ) + + # Resource match + resource_match = self._calculate_resource_match( + strategy_type, task_analysis["resources"]) + + # Capability match + capability_match = self._calculate_capability_match( + strategy_type, task_analysis["capabilities"]) + + # Combined score + scores[strategy_type] = ( + 0.3 * base_score + + 0.2 * type_score + + 0.2 * recent_performance + + 0.15 * resource_match + + 0.15 * capability_match + ) + + # Select top strategies + selected = sorted( + StrategyType, + key=lambda x: scores[x], + reverse=True + )[:self.parallel_threshold] + + return selected + + async def _execute_strategies(self, + strategies: List[StrategyType], + query: str, + context: Dict[str, Any]) -> Dict[StrategyType, StrategyResult]: + """Execute selected strategies in parallel.""" + async def execute_strategy(strategy_type: StrategyType) -> StrategyResult: + strategy = self.strategies[strategy_type] + start_time = datetime.now() + + try: + result = await strategy.reason(query, context) + + return StrategyResult( + strategy_type=strategy_type, + success=result.get("success", False), + answer=result.get("answer"), + confidence=result.get("confidence", 0.0), + reasoning_trace=result.get("reasoning_trace", []), + metadata=result.get("metadata", {}), + performance_metrics={ + "execution_time": (datetime.now() - start_time).total_seconds(), + **result.get("performance_metrics", {}) + } + ) + except Exception as e: + logging.error(f"Error in strategy {strategy_type}: {str(e)}") + return StrategyResult( + strategy_type=strategy_type, + success=False, + answer=None, + confidence=0.0, + reasoning_trace=[{"error": str(e)}], + metadata={}, + performance_metrics={"execution_time": (datetime.now() - start_time).total_seconds()} + ) + + # Execute strategies in parallel + tasks = [execute_strategy(strategy) for strategy in strategies] + results = await asyncio.gather(*tasks) + + return {result.strategy_type: result for result in results} + + async def _synthesize_results(self, + strategy_results: Dict[StrategyType, StrategyResult], + task_analysis: Dict[str, Any], + context: Dict[str, Any]) -> UnifiedResult: + """Synthesize results from multiple strategies.""" + prompt = f""" + Synthesize reasoning results: + Results: {json.dumps({str(k): self._strategy_result_to_dict(v) + for k, v in strategy_results.items()})} + Task Analysis: {json.dumps(task_analysis)} + Context: {json.dumps(context)} + + Provide: + 1. Optimal synthesis method + 2. Combined answer + 3. Confidence assessment + 4. Meta-insights + 5. Performance analysis + + Format as: + [Synthesis] + Method: ... + Answer: ... + Confidence: ... + Insights: ... + Performance: ... + """ + + response = await context["groq_api"].predict(prompt) + synthesis = self._parse_synthesis(response["answer"]) + + return UnifiedResult( + success=synthesis["confidence"] >= self.min_confidence, + answer=synthesis["answer"], + confidence=synthesis["confidence"], + strategy_results=strategy_results, + synthesis_method=synthesis["method"], + meta_insights=synthesis["insights"], + performance_metrics=synthesis["performance"] + ) + + def _update_performance(self, result: UnifiedResult): + """Update performance metrics and strategy weights.""" + # Update strategy performance + for strategy_type, strategy_result in result.strategy_results.items(): + self.strategy_performance[strategy_type].append(strategy_result.confidence) + + # Update weights using exponential moving average + current_weight = self.strategy_weights[strategy_type] + performance = strategy_result.confidence + self.strategy_weights[strategy_type] = ( + (1 - self.learning_rate) * current_weight + + self.learning_rate * performance + ) + + # Update synthesis performance + self.synthesis_performance[result.synthesis_method].append(result.confidence) + + def _calculate_resource_match(self, strategy_type: StrategyType, required_resources: Dict[str, Any]) -> float: + """Calculate how well a strategy matches required resources.""" + # Implementation-specific resource matching logic + return 0.8 # Placeholder + + def _calculate_capability_match(self, strategy_type: StrategyType, required_capabilities: List[str]) -> float: + """Calculate how well a strategy matches required capabilities.""" + # Implementation-specific capability matching logic + return 0.8 # Placeholder + + def _parse_task_analysis(self, response: str) -> Dict[str, Any]: + """Parse task analysis from response.""" + analysis = { + "type": "", + "complexity": 0.0, + "capabilities": [], + "resources": {}, + "criteria": [], + "risks": [] + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Type:'): + analysis["type"] = line[5:].strip() + elif line.startswith('Complexity:'): + try: + analysis["complexity"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Capabilities:'): + analysis["capabilities"] = [c.strip() for c in line[13:].split(',')] + elif line.startswith('Resources:'): + try: + analysis["resources"] = json.loads(line[10:].strip()) + except: + analysis["resources"] = {"raw": line[10:].strip()} + elif line.startswith('Criteria:'): + analysis["criteria"] = [c.strip() for c in line[9:].split(',')] + elif line.startswith('Risks:'): + analysis["risks"] = [r.strip() for r in line[7:].split(',')] + + return analysis + + def _parse_synthesis(self, response: str) -> Dict[str, Any]: + """Parse synthesis result from response.""" + synthesis = { + "method": "", + "answer": "", + "confidence": 0.0, + "insights": [], + "performance": {} + } + + for line in response.split('\n'): + line = line.strip() + if line.startswith('Method:'): + synthesis["method"] = line[7:].strip() + elif line.startswith('Answer:'): + synthesis["answer"] = line[7:].strip() + elif line.startswith('Confidence:'): + try: + synthesis["confidence"] = float(line[11:].strip()) + except: + pass + elif line.startswith('Insights:'): + synthesis["insights"] = [i.strip() for i in line[9:].split(',')] + elif line.startswith('Performance:'): + try: + synthesis["performance"] = json.loads(line[12:].strip()) + except: + synthesis["performance"] = {"raw": line[12:].strip()} + + return synthesis + + def _strategy_result_to_dict(self, result: StrategyResult) -> Dict[str, Any]: + """Convert strategy result to dictionary for serialization.""" + return { + "strategy_type": result.strategy_type.value, + "success": result.success, + "answer": result.answer, + "confidence": result.confidence, + "reasoning_trace": result.reasoning_trace, + "metadata": result.metadata, + "performance_metrics": result.performance_metrics, + "timestamp": result.timestamp.isoformat() + } + + def get_performance_metrics(self) -> Dict[str, Any]: + """Get comprehensive performance metrics.""" + return { + "strategy_weights": dict(self.strategy_weights), + "average_performance": { + strategy_type.value: sum(scores) / len(scores) if scores else 0 + for strategy_type, scores in self.strategy_performance.items() + }, + "synthesis_success": { + method: sum(scores) / len(scores) if scores else 0 + for method, scores in self.synthesis_performance.items() + }, + "task_type_performance": { + task_type: dict(strategy_scores) + for task_type, strategy_scores in self.task_type_performance.items() + } + } + + def clear_performance_history(self): + """Clear performance history and reset weights.""" + self.strategy_performance.clear() + self.task_type_performance.clear() + self.synthesis_performance.clear() + self.strategy_weights = { + strategy_type: 1.0 for strategy_type in StrategyType + } diff --git a/space/venture_strategies.py b/space/venture_strategies.py new file mode 100644 index 0000000000000000000000000000000000000000..ac444bfc7f669f43d33de1ad8d660c2d6c0890aa --- /dev/null +++ b/space/venture_strategies.py @@ -0,0 +1,701 @@ +"""Specialized strategies for autonomous business and revenue generation.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +class VentureType(Enum): + """Types of business ventures.""" + AI_STARTUP = "ai_startup" + SAAS = "saas" + API_SERVICE = "api_service" + DATA_ANALYTICS = "data_analytics" + AUTOMATION_SERVICE = "automation_service" + CONSULTING = "consulting" + DIGITAL_PRODUCTS = "digital_products" + MARKETPLACE = "marketplace" + +class RevenueStream(Enum): + """Types of revenue streams.""" + SUBSCRIPTION = "subscription" + USAGE_BASED = "usage_based" + LICENSING = "licensing" + CONSULTING = "consulting" + PRODUCT_SALES = "product_sales" + COMMISSION = "commission" + ADVERTISING = "advertising" + PARTNERSHIP = "partnership" + +@dataclass +class VentureMetrics: + """Key business metrics.""" + revenue: float + profit_margin: float + customer_acquisition_cost: float + lifetime_value: float + churn_rate: float + growth_rate: float + burn_rate: float + runway_months: float + roi: float + +@dataclass +class MarketOpportunity: + """Market opportunity analysis.""" + market_size: float + growth_potential: float + competition_level: float + entry_barriers: float + regulatory_risks: float + technology_risks: float + monetization_potential: float + +class AIStartupStrategy(ReasoningStrategy): + """ + Advanced AI startup strategy that: + 1. Identifies profitable AI applications + 2. Analyzes market opportunities + 3. Develops MVP strategies + 4. Plans scaling approaches + 5. Optimizes revenue streams + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI startup strategy.""" + try: + # Market analysis + market = await self._analyze_market(query, context) + + # Technology assessment + tech = await self._assess_technology(market, context) + + # Business model + model = await self._develop_business_model(tech, context) + + # Growth strategy + strategy = await self._create_growth_strategy(model, context) + + # Financial projections + projections = await self._project_financials(strategy, context) + + return { + "success": projections["annual_profit"] >= 1_000_000, + "market_analysis": market, + "tech_assessment": tech, + "business_model": model, + "growth_strategy": strategy, + "financials": projections, + "confidence": self._calculate_confidence(projections) + } + except Exception as e: + logging.error(f"Error in AI startup strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class SaaSVentureStrategy(ReasoningStrategy): + """ + Advanced SaaS venture strategy that: + 1. Identifies scalable SaaS opportunities + 2. Develops pricing strategies + 3. Plans customer acquisition + 4. Optimizes retention + 5. Maximizes recurring revenue + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate SaaS venture strategy.""" + try: + # Opportunity analysis + opportunity = await self._analyze_opportunity(query, context) + + # Product strategy + product = await self._develop_product_strategy(opportunity, context) + + # Pricing model + pricing = await self._create_pricing_model(product, context) + + # Growth plan + growth = await self._plan_growth(pricing, context) + + # Revenue projections + projections = await self._project_revenue(growth, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "product": product, + "pricing": pricing, + "growth": growth, + "projections": projections + } + except Exception as e: + logging.error(f"Error in SaaS venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AutomationVentureStrategy(ReasoningStrategy): + """ + Advanced automation venture strategy that: + 1. Identifies automation opportunities + 2. Analyzes cost-saving potential + 3. Develops automation solutions + 4. Plans implementation + 5. Maximizes ROI + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate automation venture strategy.""" + try: + # Opportunity identification + opportunities = await self._identify_opportunities(query, context) + + # Solution development + solutions = await self._develop_solutions(opportunities, context) + + # Implementation strategy + implementation = await self._create_implementation_strategy(solutions, context) + + # ROI analysis + roi = await self._analyze_roi(implementation, context) + + # Scale strategy + scale = await self._create_scale_strategy(roi, context) + + return { + "success": roi["annual_profit"] >= 1_000_000, + "opportunities": opportunities, + "solutions": solutions, + "implementation": implementation, + "roi": roi, + "scale": scale + } + except Exception as e: + logging.error(f"Error in automation venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class DataVentureStrategy(ReasoningStrategy): + """ + Advanced data venture strategy that: + 1. Identifies valuable data opportunities + 2. Develops data products + 3. Creates monetization strategies + 4. Ensures compliance + 5. Maximizes data value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate data venture strategy.""" + try: + # Data opportunity analysis + opportunity = await self._analyze_data_opportunity(query, context) + + # Product development + product = await self._develop_data_product(opportunity, context) + + # Monetization strategy + monetization = await self._create_monetization_strategy(product, context) + + # Compliance plan + compliance = await self._ensure_compliance(monetization, context) + + # Scale plan + scale = await self._plan_scaling(compliance, context) + + return { + "success": monetization["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "product": product, + "monetization": monetization, + "compliance": compliance, + "scale": scale + } + except Exception as e: + logging.error(f"Error in data venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class APIVentureStrategy(ReasoningStrategy): + """ + Advanced API venture strategy that: + 1. Identifies API opportunities + 2. Develops API products + 3. Creates pricing models + 4. Plans scaling + 5. Maximizes API value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate API venture strategy.""" + try: + # API opportunity analysis + opportunity = await self._analyze_api_opportunity(query, context) + + # Product development + product = await self._develop_api_product(opportunity, context) + + # Pricing strategy + pricing = await self._create_api_pricing(product, context) + + # Scale strategy + scale = await self._plan_api_scaling(pricing, context) + + # Revenue projections + projections = await self._project_api_revenue(scale, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "product": product, + "pricing": pricing, + "scale": scale, + "projections": projections + } + except Exception as e: + logging.error(f"Error in API venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class MarketplaceVentureStrategy(ReasoningStrategy): + """ + Advanced marketplace venture strategy that: + 1. Identifies marketplace opportunities + 2. Develops platform strategy + 3. Plans liquidity generation + 4. Optimizes matching + 5. Maximizes transaction value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate marketplace venture strategy.""" + try: + # Opportunity analysis + opportunity = await self._analyze_marketplace_opportunity(query, context) + + # Platform strategy + platform = await self._develop_platform_strategy(opportunity, context) + + # Liquidity strategy + liquidity = await self._create_liquidity_strategy(platform, context) + + # Growth strategy + growth = await self._plan_marketplace_growth(liquidity, context) + + # Revenue projections + projections = await self._project_marketplace_revenue(growth, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "opportunity": opportunity, + "platform": platform, + "liquidity": liquidity, + "growth": growth, + "projections": projections + } + except Exception as e: + logging.error(f"Error in marketplace venture strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class VenturePortfolioStrategy(ReasoningStrategy): + """ + Advanced venture portfolio strategy that: + 1. Optimizes venture mix + 2. Balances risk-reward + 3. Allocates resources + 4. Manages dependencies + 5. Maximizes portfolio value + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate venture portfolio strategy.""" + try: + # Portfolio analysis + analysis = await self._analyze_portfolio(query, context) + + # Venture selection + selection = await self._select_ventures(analysis, context) + + # Resource allocation + allocation = await self._allocate_resources(selection, context) + + # Risk management + risk = await self._manage_risk(allocation, context) + + # Portfolio projections + projections = await self._project_portfolio(risk, context) + + return { + "success": projections["annual_profit"] >= 1_000_000, + "analysis": analysis, + "selection": selection, + "allocation": allocation, + "risk": risk, + "projections": projections + } + except Exception as e: + logging.error(f"Error in venture portfolio strategy: {str(e)}") + return {"success": False, "error": str(e)} + + async def _analyze_portfolio(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Analyze potential venture portfolio.""" + prompt = f""" + Analyze venture portfolio opportunities: + Query: {query} + Context: {json.dumps(context)} + + Consider: + 1. Market opportunities + 2. Technology trends + 3. Resource requirements + 4. Risk factors + 5. Synergy potential + + Format as: + [Analysis] + Opportunities: ... + Trends: ... + Resources: ... + Risks: ... + Synergies: ... + """ + + response = await context["groq_api"].predict(prompt) + return self._parse_portfolio_analysis(response["answer"]) + + def _parse_portfolio_analysis(self, response: str) -> Dict[str, Any]: + """Parse portfolio analysis from response.""" + analysis = { + "opportunities": [], + "trends": [], + "resources": {}, + "risks": [], + "synergies": [] + } + + current_section = None + for line in response.split('\n'): + line = line.strip() + if line.startswith('Opportunities:'): + current_section = "opportunities" + elif line.startswith('Trends:'): + current_section = "trends" + elif line.startswith('Resources:'): + current_section = "resources" + elif line.startswith('Risks:'): + current_section = "risks" + elif line.startswith('Synergies:'): + current_section = "synergies" + elif current_section and line: + if current_section == "resources": + try: + key, value = line.split(':') + analysis[current_section][key.strip()] = value.strip() + except: + pass + else: + analysis[current_section].append(line) + + return analysis + + def get_venture_metrics(self) -> Dict[str, Any]: + """Get comprehensive venture metrics.""" + return { + "portfolio_metrics": { + "total_ventures": len(self.ventures), + "profitable_ventures": sum(1 for v in self.ventures if v.metrics.profit_margin > 0), + "total_revenue": sum(v.metrics.revenue for v in self.ventures), + "average_margin": np.mean([v.metrics.profit_margin for v in self.ventures]), + "portfolio_roi": np.mean([v.metrics.roi for v in self.ventures]) + }, + "market_metrics": { + "total_market_size": sum(v.opportunity.market_size for v in self.ventures), + "average_growth": np.mean([v.opportunity.growth_potential for v in self.ventures]), + "risk_score": np.mean([v.opportunity.regulatory_risks + v.opportunity.technology_risks for v in self.ventures]) + }, + "performance_metrics": { + "customer_acquisition": np.mean([v.metrics.customer_acquisition_cost for v in self.ventures]), + "lifetime_value": np.mean([v.metrics.lifetime_value for v in self.ventures]), + "churn_rate": np.mean([v.metrics.churn_rate for v in self.ventures]), + "burn_rate": sum(v.metrics.burn_rate for v in self.ventures) + } + } + +class VentureStrategy(ReasoningStrategy): + """ + Advanced venture strategy that combines multiple specialized strategies + to generate comprehensive business plans and recommendations. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize venture strategy with component strategies.""" + super().__init__() + self.config = config or {} + + # Standard reasoning parameters + self.min_confidence = self.config.get('min_confidence', 0.7) + self.parallel_threshold = self.config.get('parallel_threshold', 3) + self.learning_rate = self.config.get('learning_rate', 0.1) + self.strategy_weights = self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + + # Initialize component strategies with shared config + strategy_config = { + 'min_confidence': self.min_confidence, + 'parallel_threshold': self.parallel_threshold, + 'learning_rate': self.learning_rate, + 'strategy_weights': self.strategy_weights + } + + self.strategies = { + VentureType.AI_STARTUP: AIStartupStrategy(strategy_config), + VentureType.SAAS: SaaSVentureStrategy(strategy_config), + VentureType.AUTOMATION_SERVICE: AutomationVentureStrategy(strategy_config), + VentureType.DATA_ANALYTICS: DataVentureStrategy(strategy_config), + VentureType.API_SERVICE: APIVentureStrategy(strategy_config), + VentureType.MARKETPLACE: MarketplaceVentureStrategy(strategy_config) + } + + # Portfolio strategy for multi-venture optimization + self.portfolio_strategy = VenturePortfolioStrategy(strategy_config) + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """ + Generate venture strategy based on query and context. + + Args: + query: The venture strategy query + context: Additional context and parameters + + Returns: + Dict containing venture strategy and confidence scores + """ + try: + # Determine venture type from query/context + venture_type = self._determine_venture_type(query, context) + + # Get strategy for venture type + strategy = self.strategies.get(venture_type) + if not strategy: + raise ValueError(f"Unsupported venture type: {venture_type}") + + # Generate strategy + strategy_result = await strategy.reason(query, context) + + # Get portfolio analysis + portfolio_result = await self.portfolio_strategy.reason(query, context) + + # Combine results + combined_result = self._combine_results( + strategy_result, + portfolio_result, + venture_type + ) + + return { + 'answer': self._format_strategy(combined_result), + 'confidence': combined_result.get('confidence', 0.0), + 'venture_type': venture_type.value, + 'strategy': strategy_result, + 'portfolio_analysis': portfolio_result + } + + except Exception as e: + logging.error(f"Venture strategy generation failed: {str(e)}") + return { + 'error': f"Venture strategy generation failed: {str(e)}", + 'confidence': 0.0 + } + + def _determine_venture_type(self, query: str, context: Dict[str, Any]) -> VentureType: + """Determine venture type from query and context.""" + # Use context if available + if 'venture_type' in context: + return VentureType(context['venture_type']) + + # Simple keyword matching + query_lower = query.lower() + if any(term in query_lower for term in ['ai', 'ml', 'model', 'neural']): + return VentureType.AI_STARTUP + elif any(term in query_lower for term in ['saas', 'software', 'cloud']): + return VentureType.SAAS + elif any(term in query_lower for term in ['automate', 'automation', 'workflow']): + return VentureType.AUTOMATION_SERVICE + elif any(term in query_lower for term in ['data', 'analytics', 'insights']): + return VentureType.DATA_ANALYTICS + elif any(term in query_lower for term in ['api', 'service', 'endpoint']): + return VentureType.API_SERVICE + elif any(term in query_lower for term in ['marketplace', 'platform', 'network']): + return VentureType.MARKETPLACE + + # Default to AI startup if unclear + return VentureType.AI_STARTUP + + def _combine_results( + self, + strategy_result: Dict[str, Any], + portfolio_result: Dict[str, Any], + venture_type: VentureType + ) -> Dict[str, Any]: + """Combine strategy and portfolio results.""" + return { + 'venture_type': venture_type.value, + 'strategy': strategy_result.get('strategy', {}), + 'metrics': strategy_result.get('metrics', {}), + 'portfolio_fit': portfolio_result.get('portfolio_fit', {}), + 'recommendations': strategy_result.get('recommendations', []), + 'confidence': min( + strategy_result.get('confidence', 0.0), + portfolio_result.get('confidence', 0.0) + ) + } + + def _format_strategy(self, result: Dict[str, Any]) -> str: + """Format venture strategy into readable text.""" + sections = [] + + # Venture type + sections.append(f"Venture Type: {result['venture_type'].replace('_', ' ').title()}") + + # Strategy overview + if 'strategy' in result: + strategy = result['strategy'] + sections.append("\nStrategy Overview:") + for key, value in strategy.items(): + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Key metrics + if 'metrics' in result: + metrics = result['metrics'] + sections.append("\nKey Metrics:") + for key, value in metrics.items(): + if isinstance(value, (int, float)): + sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}") + else: + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Portfolio fit + if 'portfolio_fit' in result: + fit = result['portfolio_fit'] + sections.append("\nPortfolio Analysis:") + for key, value in fit.items(): + sections.append(f"- {key.replace('_', ' ').title()}: {value}") + + # Recommendations + if 'recommendations' in result: + recs = result['recommendations'] + sections.append("\nKey Recommendations:") + for rec in recs: + sections.append(f"- {rec}") + + return "\n".join(sections) diff --git a/space/venture_types.py b/space/venture_types.py new file mode 100644 index 0000000000000000000000000000000000000000..05b4422f761b01d7a36b9f4413798d2a194ce844 --- /dev/null +++ b/space/venture_types.py @@ -0,0 +1,332 @@ +"""Additional venture types for business optimization.""" + +import logging +from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple +import json +from dataclasses import dataclass, field +from enum import Enum +from datetime import datetime +import numpy as np +from collections import defaultdict + +from .base import ReasoningStrategy + +class AIInfrastructureStrategy(ReasoningStrategy): + """ + AI infrastructure venture strategy that: + 1. Identifies infrastructure needs + 2. Develops cloud solutions + 3. Optimizes compute resources + 4. Manages scalability + 5. Ensures reliability + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI infrastructure strategy.""" + try: + # Market analysis + market = await self._analyze_market(query, context) + + # Infrastructure design + design = await self._design_infrastructure(market, context) + + # Optimization strategy + optimization = await self._create_optimization_strategy(design, context) + + # Scaling plan + scaling = await self._plan_scaling(optimization, context) + + # Revenue projections + projections = await self._project_revenue(scaling, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "market": market, + "design": design, + "optimization": optimization, + "scaling": scaling, + "projections": projections + } + except Exception as e: + logging.error(f"Error in AI infrastructure strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AIConsultingStrategy(ReasoningStrategy): + """ + AI consulting venture strategy that: + 1. Identifies consulting opportunities + 2. Develops service offerings + 3. Creates delivery frameworks + 4. Manages client relationships + 5. Scales operations + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI consulting strategy.""" + try: + # Market analysis + market = await self._analyze_consulting_market(query, context) + + # Service design + services = await self._design_services(market, context) + + # Delivery framework + framework = await self._create_delivery_framework(services, context) + + # Growth strategy + growth = await self._plan_growth(framework, context) + + # Revenue projections + projections = await self._project_consulting_revenue(growth, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "market": market, + "services": services, + "framework": framework, + "growth": growth, + "projections": projections + } + except Exception as e: + logging.error(f"Error in AI consulting strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AIProductStrategy(ReasoningStrategy): + """ + AI product venture strategy that: + 1. Identifies product opportunities + 2. Develops product roadmap + 3. Creates go-to-market strategy + 4. Manages product lifecycle + 5. Scales distribution + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI product strategy.""" + try: + # Market analysis + market = await self._analyze_product_market(query, context) + + # Product development + product = await self._develop_product_strategy(market, context) + + # Go-to-market + gtm = await self._create_gtm_strategy(product, context) + + # Scale strategy + scale = await self._plan_product_scaling(gtm, context) + + # Revenue projections + projections = await self._project_product_revenue(scale, context) + + return { + "success": projections["annual_revenue"] >= 1_000_000, + "market": market, + "product": product, + "gtm": gtm, + "scale": scale, + "projections": projections + } + except Exception as e: + logging.error(f"Error in AI product strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class FinTechStrategy(ReasoningStrategy): + """ + FinTech venture strategy that: + 1. Identifies fintech opportunities + 2. Develops financial products + 3. Ensures compliance + 4. Manages risk + 5. Scales operations + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate FinTech strategy.""" + try: + # Market analysis + market = await self._analyze_fintech_market(query, context) + + # Product development + product = await self._develop_fintech_product(market, context) + + # Compliance strategy + compliance = await self._ensure_compliance(product, context) + + # Risk management + risk = await self._manage_risk(compliance, context) + + # Scale strategy + scale = await self._plan_fintech_scaling(risk, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "product": product, + "compliance": compliance, + "risk": risk, + "scale": scale + } + except Exception as e: + logging.error(f"Error in FinTech strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class HealthTechStrategy(ReasoningStrategy): + """ + HealthTech venture strategy that: + 1. Identifies healthcare opportunities + 2. Develops health solutions + 3. Ensures compliance + 4. Manages patient data + 5. Scales operations + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate HealthTech strategy.""" + try: + # Market analysis + market = await self._analyze_healthtech_market(query, context) + + # Solution development + solution = await self._develop_health_solution(market, context) + + # Compliance strategy + compliance = await self._ensure_health_compliance(solution, context) + + # Data strategy + data = await self._manage_health_data(compliance, context) + + # Scale strategy + scale = await self._plan_healthtech_scaling(data, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "solution": solution, + "compliance": compliance, + "data": data, + "scale": scale + } + except Exception as e: + logging.error(f"Error in HealthTech strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class EdTechStrategy(ReasoningStrategy): + """ + EdTech venture strategy that: + 1. Identifies education opportunities + 2. Develops learning solutions + 3. Creates content strategy + 4. Manages user engagement + 5. Scales platform + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate EdTech strategy.""" + try: + # Market analysis + market = await self._analyze_edtech_market(query, context) + + # Solution development + solution = await self._develop_learning_solution(market, context) + + # Content strategy + content = await self._create_content_strategy(solution, context) + + # Engagement strategy + engagement = await self._manage_engagement(content, context) + + # Scale strategy + scale = await self._plan_edtech_scaling(engagement, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "solution": solution, + "content": content, + "engagement": engagement, + "scale": scale + } + except Exception as e: + logging.error(f"Error in EdTech strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class BlockchainStrategy(ReasoningStrategy): + """ + Blockchain venture strategy that: + 1. Identifies blockchain opportunities + 2. Develops blockchain solutions + 3. Ensures security + 4. Manages tokenomics + 5. Scales network + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate blockchain strategy.""" + try: + # Market analysis + market = await self._analyze_blockchain_market(query, context) + + # Solution development + solution = await self._develop_blockchain_solution(market, context) + + # Security strategy + security = await self._ensure_blockchain_security(solution, context) + + # Tokenomics + tokenomics = await self._design_tokenomics(security, context) + + # Scale strategy + scale = await self._plan_blockchain_scaling(tokenomics, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "solution": solution, + "security": security, + "tokenomics": tokenomics, + "scale": scale + } + except Exception as e: + logging.error(f"Error in blockchain strategy: {str(e)}") + return {"success": False, "error": str(e)} + +class AIMarketplaceStrategy(ReasoningStrategy): + """ + AI marketplace venture strategy that: + 1. Creates AI model marketplace + 2. Manages model deployment + 3. Handles transactions + 4. Ensures quality + 5. Scales platform + """ + + async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Generate AI marketplace strategy.""" + try: + # Market analysis + market = await self._analyze_ai_marketplace(query, context) + + # Platform development + platform = await self._develop_marketplace_platform(market, context) + + # Quality strategy + quality = await self._ensure_model_quality(platform, context) + + # Transaction system + transactions = await self._design_transaction_system(quality, context) + + # Scale strategy + scale = await self._plan_marketplace_scaling(transactions, context) + + return { + "success": scale["annual_revenue"] >= 1_000_000, + "market": market, + "platform": platform, + "quality": quality, + "transactions": transactions, + "scale": scale + } + except Exception as e: + logging.error(f"Error in AI marketplace strategy: {str(e)}") + return {"success": False, "error": str(e)} diff --git a/startup.sh b/startup.sh new file mode 100755 index 0000000000000000000000000000000000000000..cd7fff0d636ba6e1ec6df859698b4f1c54c1c94a --- /dev/null +++ b/startup.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +# Exit on error +set -e + +echo "Starting initialization..." + +# Update pip and install dependencies +echo "Updating pip and installing dependencies..." +python -m pip install --upgrade pip +pip install -r requirements.txt + +# Install Groq SDK +pip install groq>=0.4.1 + +# Verify API keys and connectivity +echo "Verifying package versions..." +python check_versions.py +if [ $? -ne 0 ]; then + echo "Error: Package version verification failed" + exit 1 +fi + +# Configure environment +if [ -z "$GROQ_API_KEY" ]; then + echo "Warning: GROQ_API_KEY not set. Falling back to local models." + export MODEL_BACKEND=huggingface +fi + +if [ -z "$HUGGINGFACE_TOKEN" ]; then + echo "Warning: HUGGINGFACE_TOKEN not set. Some features may be limited." +fi + +# Start the application +echo "Starting Advanced Agentic System..." +export PYTHONPATH="${PYTHONPATH}:${PWD}" +python main.py diff --git a/team_management.py b/team_management.py new file mode 100644 index 0000000000000000000000000000000000000000..c56773ea7a887b71744c97ce75dc2594004c0a39 --- /dev/null +++ b/team_management.py @@ -0,0 +1,517 @@ +""" +Advanced Team Management System +----------------------------- +Manages specialized teams of agents that work together towards common goals: +1. Team A: Coders (App/Software Developers) +2. Team B: Business (Entrepreneurs) +3. Team C: Research (Deep Online Research) +4. Team D: Crypto & Sports Trading + +Features: +- Cross-team collaboration +- Goal alignment +- Resource sharing +- Synchronized execution +""" + +from typing import Dict, List, Optional, Set, Union, TypeVar, Any +from dataclasses import dataclass, field +from enum import Enum +import asyncio +from datetime import datetime +import uuid +from collections import defaultdict + +from orchestrator import AgentOrchestrator, TaskPriority, AgentRole, AgentState +from reasoning import UnifiedReasoningEngine + +# Agent capabilities and personality types +class AgentCapability(Enum): + """Core capabilities of agents.""" + REASONING = "reasoning" + LEARNING = "learning" + EXECUTION = "execution" + COORDINATION = "coordination" + MONITORING = "monitoring" + +class AgentPersonality(Enum): + """Different personality types for agents.""" + ANALYTICAL = "analytical" + CREATIVE = "creative" + PRAGMATIC = "pragmatic" + COLLABORATIVE = "collaborative" + PROACTIVE = "proactive" + CAUTIOUS = "cautious" + +class TeamType(Enum): + """Specialized team types.""" + CODERS = "coders" + BUSINESS = "business" + RESEARCH = "research" + TRADERS = "traders" + +class TeamObjective(Enum): + """Types of team objectives.""" + SOFTWARE_DEVELOPMENT = "software_development" + BUSINESS_OPPORTUNITY = "business_opportunity" + MARKET_RESEARCH = "market_research" + TRADING_STRATEGY = "trading_strategy" + CROSS_TEAM_PROJECT = "cross_team_project" + +@dataclass +class TeamProfile: + """Team profile and capabilities.""" + id: str + type: TeamType + name: str + primary_objective: TeamObjective + secondary_objectives: List[TeamObjective] + agent_count: int + expertise_areas: List[str] + collaboration_score: float = 0.0 + success_rate: float = 0.0 + active_projects: int = 0 + +@dataclass +class CollaborationLink: + """Defines collaboration between teams.""" + team_a_id: str + team_b_id: str + strength: float + active_projects: int + last_interaction: datetime + success_rate: float + +class TeamManager: + """Manages specialized teams and their collaboration.""" + + def __init__(self, orchestrator: AgentOrchestrator): + self.orchestrator = orchestrator + self.teams: Dict[str, TeamProfile] = {} + self.agents: Dict[str, Dict[str, 'Agent']] = {} # team_id -> {agent_id -> Agent} + self.collaboration_network: Dict[str, CollaborationLink] = {} + self.shared_objectives: Dict[str, Set[str]] = defaultdict(set) # objective_id -> set of team_ids + self.lock = asyncio.Lock() + + # Initialize specialized teams + self._init_teams() + + def _init_teams(self): + """Initialize specialized teams.""" + team_configs = { + TeamType.CODERS: { + "name": "Development Team", + "primary": TeamObjective.SOFTWARE_DEVELOPMENT, + "secondary": [ + TeamObjective.BUSINESS_OPPORTUNITY, + TeamObjective.MARKET_RESEARCH + ], + "expertise": [ + "full_stack_development", + "cloud_architecture", + "ai_ml", + "blockchain", + "mobile_development" + ] + }, + TeamType.BUSINESS: { + "name": "Business Strategy Team", + "primary": TeamObjective.BUSINESS_OPPORTUNITY, + "secondary": [ + TeamObjective.MARKET_RESEARCH, + TeamObjective.TRADING_STRATEGY + ], + "expertise": [ + "market_analysis", + "business_strategy", + "digital_transformation", + "startup_innovation", + "product_management" + ] + }, + TeamType.RESEARCH: { + "name": "Research & Analysis Team", + "primary": TeamObjective.MARKET_RESEARCH, + "secondary": [ + TeamObjective.BUSINESS_OPPORTUNITY, + TeamObjective.TRADING_STRATEGY + ], + "expertise": [ + "deep_research", + "data_analysis", + "trend_forecasting", + "competitive_analysis", + "technology_assessment" + ] + }, + TeamType.TRADERS: { + "name": "Trading & Investment Team", + "primary": TeamObjective.TRADING_STRATEGY, + "secondary": [ + TeamObjective.MARKET_RESEARCH, + TeamObjective.BUSINESS_OPPORTUNITY + ], + "expertise": [ + "crypto_trading", + "sports_betting", + "risk_management", + "market_timing", + "portfolio_optimization" + ] + } + } + + for team_type, config in team_configs.items(): + team_id = str(uuid.uuid4()) + self.teams[team_id] = TeamProfile( + id=team_id, + type=team_type, + name=config["name"], + primary_objective=config["primary"], + secondary_objectives=config["secondary"], + agent_count=5, # Default size + expertise_areas=config["expertise"] + ) + self.agents[team_id] = {} + + async def initialize_team_agents(self): + """Initialize agents for each team with appropriate roles and capabilities.""" + for team_id, team in self.teams.items(): + await self._create_team_agents(team_id) + await self._establish_collaboration_links(team_id) + + async def _create_team_agents(self, team_id: str): + """Create specialized agents for a team.""" + team = self.teams[team_id] + + # Define agent configurations based on team type + agent_configs = self._get_agent_configs(team.type) + + for config in agent_configs: + agent_id = await self.orchestrator.create_agent( + role=config["role"], + capabilities=config["capabilities"] + ) + + agent = Agent( + profile=config["profile"], + reasoning_engine=self.orchestrator.reasoning_engine, + meta_learning=self.orchestrator.meta_learning, + config=config.get("config", {}) + ) + + self.agents[team_id][agent_id] = agent + + def _get_agent_configs(self, team_type: TeamType) -> List[Dict]: + """Get agent configurations based on team type.""" + base_configs = [ + { + "role": AgentRole.COORDINATOR, + "capabilities": [ + AgentCapability.REASONING, + AgentCapability.COORDINATION + ], + "personality": AgentPersonality.PROACTIVE, + "profile": { + "name": "Coordinator", + "description": "Team coordinator" + } + }, + { + "role": AgentRole.EXECUTOR, + "capabilities": [ + AgentCapability.EXECUTION, + AgentCapability.LEARNING + ], + "personality": AgentPersonality.ANALYTICAL, + "profile": { + "name": "Executor", + "description": "Task executor" + } + } + ] + + # Add team-specific configurations + if team_type == TeamType.CODERS: + base_configs.extend([ + { + "role": AgentRole.EXECUTOR, + "capabilities": [ + AgentCapability.EXECUTION, + AgentCapability.REASONING + ], + "personality": AgentPersonality.CREATIVE, + "expertise": ["software_development", "system_design"], + "profile": { + "name": "Developer", + "description": "Software developer" + } + } + ]) + elif team_type == TeamType.BUSINESS: + base_configs.extend([ + { + "role": AgentRole.PLANNER, + "capabilities": [ + AgentCapability.REASONING, + AgentCapability.LEARNING + ], + "personality": AgentPersonality.PROACTIVE, + "expertise": ["business_strategy", "market_analysis"], + "profile": { + "name": "Planner", + "description": "Business planner" + } + } + ]) + elif team_type == TeamType.RESEARCH: + base_configs.extend([ + { + "role": AgentRole.MONITOR, + "capabilities": [ + AgentCapability.MONITORING, + AgentCapability.LEARNING + ], + "personality": AgentPersonality.ANALYTICAL, + "expertise": ["research", "data_analysis"], + "profile": { + "name": "Researcher", + "description": "Researcher" + } + } + ]) + elif team_type == TeamType.TRADERS: + base_configs.extend([ + { + "role": AgentRole.EXECUTOR, + "capabilities": [ + AgentCapability.EXECUTION, + AgentCapability.REASONING + ], + "personality": AgentPersonality.CAUTIOUS, + "expertise": ["trading", "risk_management"], + "profile": { + "name": "Trader", + "description": "Trader" + } + } + ]) + + return base_configs + + async def _establish_collaboration_links(self, team_id: str): + """Establish collaboration links with other teams.""" + team = self.teams[team_id] + + for other_id, other_team in self.teams.items(): + if other_id != team_id: + link_id = f"{min(team_id, other_id)}_{max(team_id, other_id)}" + if link_id not in self.collaboration_network: + self.collaboration_network[link_id] = CollaborationLink( + team_a_id=team_id, + team_b_id=other_id, + strength=0.5, # Initial collaboration strength + active_projects=0, + last_interaction=datetime.now(), + success_rate=0.0 + ) + + async def create_cross_team_objective( + self, + objective: str, + required_teams: List[TeamType], + priority: TaskPriority = TaskPriority.MEDIUM + ) -> str: + """Create an objective that requires multiple teams.""" + objective_id = str(uuid.uuid4()) + + # Find relevant teams + selected_teams = [] + for team_id, team in self.teams.items(): + if team.type in required_teams: + selected_teams.append(team_id) + + if len(selected_teams) < len(required_teams): + raise ValueError("Not all required teams are available") + + # Create shared objective + self.shared_objectives[objective_id].update(selected_teams) + + # Create tasks for each team + tasks = [] + for team_id in selected_teams: + task_id = await self.orchestrator.submit_task( + description=f"Team {self.teams[team_id].name} contribution to: {objective}", + priority=priority + ) + tasks.append(task_id) + + return objective_id + + async def monitor_objective_progress(self, objective_id: str) -> Dict: + """Monitor progress of a cross-team objective.""" + if objective_id not in self.shared_objectives: + raise ValueError("Unknown objective") + + team_progress = {} + for team_id in self.shared_objectives[objective_id]: + team = self.teams[team_id] + team_agents = self.agents[team_id] + + # Calculate team progress + active_agents = sum(1 for agent in team_agents.values() if agent.state == AgentState.BUSY) + completion_rate = sum(agent.get_task_completion_rate() for agent in team_agents.values()) / len(team_agents) + + team_progress[team.name] = { + "active_agents": active_agents, + "completion_rate": completion_rate, + "collaboration_score": team.collaboration_score + } + + return team_progress + + async def optimize_team_collaboration(self): + """Optimize collaboration between teams.""" + for link in self.collaboration_network.values(): + team_a = self.teams[link.team_a_id] + team_b = self.teams[link.team_b_id] + + # Update collaboration strength based on: + # 1. Number of successful joint projects + # 2. Frequency of interaction + # 3. Complementary expertise + + success_factor = link.success_rate + interaction_factor = min((datetime.now() - link.last_interaction).days / 30.0, 1.0) + expertise_overlap = len( + set(team_a.expertise_areas) & set(team_b.expertise_areas) + ) / len(set(team_a.expertise_areas) | set(team_b.expertise_areas)) + + new_strength = ( + 0.4 * success_factor + + 0.3 * (1 - interaction_factor) + + 0.3 * (1 - expertise_overlap) + ) + + link.strength = 0.7 * link.strength + 0.3 * new_strength + + async def get_team_recommendations(self, objective: str) -> List[TeamType]: + """Get recommended teams for an objective based on expertise and collaboration history.""" + # Analyze objective to determine required expertise + required_expertise = await self._analyze_objective(objective) + + # Score each team + team_scores = {} + for team_id, team in self.teams.items(): + # Calculate expertise match + expertise_match = len( + set(required_expertise) & set(team.expertise_areas) + ) / len(required_expertise) + + # Calculate collaboration potential + collab_potential = self._calculate_collaboration_potential(team_id) + + # Calculate success history + success_history = team.success_rate + + # Weighted score + score = ( + 0.4 * expertise_match + + 0.3 * collab_potential + + 0.3 * success_history + ) + + team_scores[team.type] = score + + # Return sorted recommendations + return sorted( + team_scores.keys(), + key=lambda x: team_scores[x], + reverse=True + ) + + async def _analyze_objective(self, objective: str) -> List[str]: + """Analyze an objective to determine required expertise.""" + # Use reasoning engine to analyze objective + analysis = await self.orchestrator.reasoning_engine.reason( + query=f"Analyze required expertise for: {objective}", + context={ + "available_expertise": [ + expertise + for team in self.teams.values() + for expertise in team.expertise_areas + ] + } + ) + + return analysis.get("required_expertise", []) + + def _calculate_collaboration_potential(self, team_id: str) -> float: + """Calculate a team's collaboration potential based on history.""" + team_links = [ + link for link in self.collaboration_network.values() + if team_id in (link.team_a_id, link.team_b_id) + ] + + if not team_links: + return 0.5 + + return sum(link.strength for link in team_links) / len(team_links) + + async def update_team_metrics(self): + """Update performance metrics for all teams.""" + for team_id, team in self.teams.items(): + team_agents = self.agents[team_id] + + # Calculate success rate + completed_tasks = sum( + agent.get_completed_task_count() + for agent in team_agents.values() + ) + total_tasks = sum( + agent.get_total_task_count() + for agent in team_agents.values() + ) + team.success_rate = completed_tasks / max(1, total_tasks) + + # Calculate collaboration score + team_links = [ + link for link in self.collaboration_network.values() + if team_id in (link.team_a_id, link.team_b_id) + ] + team.collaboration_score = ( + sum(link.strength for link in team_links) / + len(team_links) if team_links else 0.5 + ) + +class Agent: + def __init__(self, profile: Dict, reasoning_engine: UnifiedReasoningEngine, meta_learning: bool, config: Optional[Dict[str, Any]] = None): + self.profile = profile + self.config = config or {} + + # Use provided reasoning engine or create one with config + self.reasoning_engine = reasoning_engine if reasoning_engine else UnifiedReasoningEngine( + min_confidence=self.config.get('min_confidence', 0.7), + parallel_threshold=self.config.get('parallel_threshold', 3), + learning_rate=self.config.get('learning_rate', 0.1), + strategy_weights=self.config.get('strategy_weights', { + "LOCAL_LLM": 0.8, + "CHAIN_OF_THOUGHT": 0.6, + "TREE_OF_THOUGHTS": 0.5, + "META_LEARNING": 0.4 + }) + ) + self.meta_learning = meta_learning + self.state = AgentState.IDLE + + def get_task_completion_rate(self): + # Implement task completion rate calculation + pass + + def get_completed_task_count(self): + # Implement completed task count calculation + pass + + def get_total_task_count(self): + # Implement total task count calculation + pass diff --git a/ui/venture_ui.py b/ui/venture_ui.py new file mode 100644 index 0000000000000000000000000000000000000000..491e784afcbc68c565f3c870ed76dbdb8b7dac49 --- /dev/null +++ b/ui/venture_ui.py @@ -0,0 +1,399 @@ +"""UI components for venture strategies and analysis.""" + +import gradio as gr +import json +from typing import Dict, Any, List +import plotly.graph_objects as go +import plotly.express as px +import pandas as pd +from datetime import datetime + +class VentureUI: + """UI for venture strategies and analysis.""" + + def __init__(self, api_client): + self.api_client = api_client + + def create_interface(self): + """Create Gradio interface.""" + with gr.Blocks(title="Venture Strategy Optimizer") as interface: + gr.Markdown("# Venture Strategy Optimizer") + + with gr.Tabs(): + # Venture Analysis Tab + with gr.Tab("Venture Analysis"): + with gr.Row(): + with gr.Column(): + venture_type = gr.Dropdown( + choices=self._get_venture_types(), + label="Venture Type" + ) + query = gr.Textbox( + lines=3, + label="Analysis Query" + ) + analyze_btn = gr.Button("Analyze Venture") + + with gr.Column(): + analysis_output = gr.JSON(label="Analysis Results") + metrics_plot = gr.Plot(label="Key Metrics") + + analyze_btn.click( + fn=self._analyze_venture, + inputs=[venture_type, query], + outputs=[analysis_output, metrics_plot] + ) + + # Market Analysis Tab + with gr.Tab("Market Analysis"): + with gr.Row(): + with gr.Column(): + segment = gr.Textbox( + label="Market Segment" + ) + market_btn = gr.Button("Analyze Market") + + with gr.Column(): + market_output = gr.JSON(label="Market Analysis") + market_plot = gr.Plot(label="Market Trends") + + market_btn.click( + fn=self._analyze_market, + inputs=[segment], + outputs=[market_output, market_plot] + ) + + # Portfolio Optimization Tab + with gr.Tab("Portfolio Optimization"): + with gr.Row(): + with gr.Column(): + ventures = gr.CheckboxGroup( + choices=self._get_venture_types(), + label="Select Ventures" + ) + optimize_btn = gr.Button("Optimize Portfolio") + + with gr.Column(): + portfolio_output = gr.JSON(label="Portfolio Strategy") + portfolio_plot = gr.Plot(label="Portfolio Allocation") + + optimize_btn.click( + fn=self._optimize_portfolio, + inputs=[ventures], + outputs=[portfolio_output, portfolio_plot] + ) + + # Monetization Strategy Tab + with gr.Tab("Monetization Strategy"): + with gr.Row(): + with gr.Column(): + monetization_type = gr.Dropdown( + choices=self._get_venture_types(), + label="Venture Type" + ) + monetize_btn = gr.Button("Optimize Monetization") + + with gr.Column(): + monetization_output = gr.JSON(label="Monetization Strategy") + revenue_plot = gr.Plot(label="Revenue Projections") + + monetize_btn.click( + fn=self._optimize_monetization, + inputs=[monetization_type], + outputs=[monetization_output, revenue_plot] + ) + + # Insights Dashboard Tab + with gr.Tab("Insights Dashboard"): + with gr.Row(): + refresh_btn = gr.Button("Refresh Insights") + + with gr.Row(): + with gr.Column(): + market_insights = gr.JSON(label="Market Insights") + market_trends = gr.Plot(label="Market Trends") + + with gr.Column(): + portfolio_insights = gr.JSON(label="Portfolio Insights") + portfolio_trends = gr.Plot(label="Portfolio Performance") + + refresh_btn.click( + fn=self._refresh_insights, + outputs=[ + market_insights, market_trends, + portfolio_insights, portfolio_trends + ] + ) + + return interface + + def _get_venture_types(self) -> List[str]: + """Get available venture types.""" + try: + response = self.api_client.list_strategies() + return response.get("strategies", []) + except Exception as e: + print(f"Error getting venture types: {e}") + return [] + + def _analyze_venture(self, + venture_type: str, + query: str) -> tuple[Dict[str, Any], go.Figure]: + """Analyze venture opportunity.""" + try: + # Get analysis + response = self.api_client.analyze_venture({ + "venture_type": venture_type, + "query": query + }) + result = response.get("result", {}) + + # Create visualization + fig = self._create_venture_plot(result) + + return result, fig + except Exception as e: + print(f"Error in venture analysis: {e}") + return {"error": str(e)}, go.Figure() + + def _analyze_market(self, + segment: str) -> tuple[Dict[str, Any], go.Figure]: + """Analyze market opportunity.""" + try: + # Get analysis + response = self.api_client.analyze_market({ + "segment": segment + }) + result = response.get("result", {}) + + # Create visualization + fig = self._create_market_plot(result) + + return result, fig + except Exception as e: + print(f"Error in market analysis: {e}") + return {"error": str(e)}, go.Figure() + + def _optimize_portfolio(self, + ventures: List[str]) -> tuple[Dict[str, Any], go.Figure]: + """Optimize venture portfolio.""" + try: + # Get optimization + response = self.api_client.optimize_portfolio({ + "ventures": ventures + }) + result = response.get("result", {}) + + # Create visualization + fig = self._create_portfolio_plot(result) + + return result, fig + except Exception as e: + print(f"Error in portfolio optimization: {e}") + return {"error": str(e)}, go.Figure() + + def _optimize_monetization(self, + venture_type: str) -> tuple[Dict[str, Any], go.Figure]: + """Optimize monetization strategy.""" + try: + # Get optimization + response = self.api_client.optimize_monetization({ + "venture_type": venture_type + }) + result = response.get("result", {}) + + # Create visualization + fig = self._create_revenue_plot(result) + + return result, fig + except Exception as e: + print(f"Error in monetization optimization: {e}") + return {"error": str(e)}, go.Figure() + + def _refresh_insights(self) -> tuple[Dict[str, Any], go.Figure, + Dict[str, Any], go.Figure]: + """Refresh insights dashboard.""" + try: + # Get insights + market_response = self.api_client.get_market_insights() + portfolio_response = self.api_client.get_portfolio_insights() + + market_insights = market_response.get("insights", {}) + portfolio_insights = portfolio_response.get("insights", {}) + + # Create visualizations + market_fig = self._create_market_trends_plot(market_insights) + portfolio_fig = self._create_portfolio_trends_plot(portfolio_insights) + + return market_insights, market_fig, portfolio_insights, portfolio_fig + except Exception as e: + print(f"Error refreshing insights: {e}") + return ( + {"error": str(e)}, go.Figure(), + {"error": str(e)}, go.Figure() + ) + + def _create_venture_plot(self, data: Dict[str, Any]) -> go.Figure: + """Create venture analysis visualization.""" + try: + metrics = data.get("metrics", {}) + + fig = go.Figure() + fig.add_trace(go.Scatterpolar( + r=[ + metrics.get("market_score", 0), + metrics.get("opportunity_score", 0), + metrics.get("risk_score", 0), + metrics.get("growth_potential", 0), + metrics.get("profitability", 0) + ], + theta=[ + "Market Score", + "Opportunity Score", + "Risk Score", + "Growth Potential", + "Profitability" + ], + fill='toself' + )) + + fig.update_layout( + polar=dict( + radialaxis=dict( + visible=True, + range=[0, 1] + ) + ), + showlegend=False + ) + + return fig + except Exception as e: + print(f"Error creating venture plot: {e}") + return go.Figure() + + def _create_market_plot(self, data: Dict[str, Any]) -> go.Figure: + """Create market analysis visualization.""" + try: + trends = data.get("trend_analysis", {}) + + df = pd.DataFrame([ + { + "Trend": trend["name"], + "Impact": trend["impact"], + "Potential": trend["market_potential"], + "Risk": trend["risk_level"] + } + for trend in trends + ]) + + fig = px.scatter( + df, + x="Impact", + y="Potential", + size="Risk", + hover_data=["Trend"], + title="Market Trends Analysis" + ) + + return fig + except Exception as e: + print(f"Error creating market plot: {e}") + return go.Figure() + + def _create_portfolio_plot(self, data: Dict[str, Any]) -> go.Figure: + """Create portfolio optimization visualization.""" + try: + allocation = data.get("allocation", {}) + + fig = go.Figure(data=[ + go.Bar( + name=venture, + x=["Resources", "Priority", "Risk"], + y=[ + sum(resources.values()), + priority, + len(constraints) + ] + ) + for venture, (resources, priority, constraints) in allocation.items() + ]) + + fig.update_layout( + barmode='group', + title="Portfolio Allocation" + ) + + return fig + except Exception as e: + print(f"Error creating portfolio plot: {e}") + return go.Figure() + + def _create_revenue_plot(self, data: Dict[str, Any]) -> go.Figure: + """Create revenue projection visualization.""" + try: + projections = data.get("projections", {}) + + months = list(range(12)) + revenue = [ + projections.get("monthly_revenue", {}).get(str(m), 0) + for m in months + ] + + fig = go.Figure() + fig.add_trace(go.Scatter( + x=months, + y=revenue, + mode='lines+markers', + name='Revenue' + )) + + fig.update_layout( + title="Revenue Projections", + xaxis_title="Month", + yaxis_title="Revenue ($)" + ) + + return fig + except Exception as e: + print(f"Error creating revenue plot: {e}") + return go.Figure() + + def _create_market_trends_plot(self, data: Dict[str, Any]) -> go.Figure: + """Create market trends visualization.""" + try: + trends = data.get("trend_insights", []) + + df = pd.DataFrame(trends) + + fig = px.scatter( + df, + x="impact", + y="potential", + size="risk", + hover_data=["name"], + title="Market Trends Overview" + ) + + return fig + except Exception as e: + print(f"Error creating market trends plot: {e}") + return go.Figure() + + def _create_portfolio_trends_plot(self, data: Dict[str, Any]) -> go.Figure: + """Create portfolio trends visualization.""" + try: + metrics = data.get("portfolio_metrics", {}) + + fig = go.Figure() + fig.add_trace(go.Indicator( + mode="gauge+number", + value=metrics.get("total_revenue", 0), + title={'text': "Total Revenue ($M)"}, + gauge={'axis': {'range': [None, 10]}} + )) + + return fig + except Exception as e: + print(f"Error creating portfolio trends plot: {e}") + return go.Figure() diff --git a/update_space_cli.py b/update_space_cli.py new file mode 100644 index 0000000000000000000000000000000000000000..07264533c7f90b56e47de7b3dda70377813c5dec --- /dev/null +++ b/update_space_cli.py @@ -0,0 +1,126 @@ +"""Update Hugging Face Space using git commands.""" + +import os +import subprocess +import logging +from pathlib import Path +import shutil +from huggingface_hub import HfApi +from dotenv import load_dotenv + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +def run_command(command, cwd=None): + """Run a shell command and log output.""" + try: + result = subprocess.run( + command, + shell=True, + check=True, + text=True, + capture_output=True, + cwd=cwd + ) + logger.info(f"Command output: {result.stdout}") + return True + except subprocess.CalledProcessError as e: + logger.error(f"Command failed: {e.stderr}") + return False + +def update_space(): + """Update the Hugging Face Space using git commands.""" + try: + # Load environment variables + load_dotenv() + token = os.getenv("HUGGINGFACE_TOKEN") + if not token: + raise ValueError("HUGGINGFACE_TOKEN not found in environment variables") + + # Space configuration + SPACE_NAME = "nananie143/agentic-system" + REPO_URL = f"https://huggingface.co/spaces/{SPACE_NAME}" + + logger.info("Starting Space update process...") + + # 1. Initialize Hugging Face API + api = HfApi(token=token) + + # 2. Create the Space if it doesn't exist + logger.info("Creating/Checking Space...") + try: + api.create_repo( + repo_id=SPACE_NAME, + repo_type="space", + space_sdk="gradio", + private=False, + exist_ok=True + ) + except Exception as e: + logger.warning(f"Note about Space creation: {e}") + + # 3. Set up the repository directory + repo_dir = Path("space_repo") + if repo_dir.exists(): + logger.info("Cleaning up existing repository...") + shutil.rmtree(repo_dir) + + # 4. Clone the Space repository with token + logger.info("Cloning Space repository...") + clone_url = f"https://user:{token}@huggingface.co/spaces/{SPACE_NAME}" + run_command(f"git clone {clone_url} {repo_dir}") + + # 5. Copy files to the repository + logger.info("Copying files to repository...") + files_to_copy = [ + "app.py", + "agentic_system.py", + "requirements.txt", + "space.yml", + "download_models_space.py", + "app_space.sh", + "reasoning", + "orchestrator.py", + "team_management.py", + "meta_learning.py", + "config.py" + ] + + for file in files_to_copy: + src = Path(file) + dst = repo_dir / src.name + if src.is_file(): + shutil.copy2(src, dst) + elif src.is_dir(): + if dst.exists(): + shutil.rmtree(dst) + shutil.copytree(src, dst) + + # 6. Configure git + logger.info("Configuring git...") + run_command('git config user.email "cascade@codeium.com"', cwd=repo_dir) + run_command('git config user.name "Cascade Bot"', cwd=repo_dir) + + # 7. Add and commit changes + logger.info("Committing changes...") + run_command("git add .", cwd=repo_dir) + run_command('git commit -m "Update Space with latest changes and model configurations"', cwd=repo_dir) + + # 8. Push changes + logger.info("Pushing changes to Space...") + run_command("git push", cwd=repo_dir) + + # 9. Clean up + logger.info("Cleaning up...") + shutil.rmtree(repo_dir) + + logger.info(f"Space updated successfully! Visit: {REPO_URL}") + return True + + except Exception as e: + logger.error(f"Error updating Space: {e}") + return False + +if __name__ == "__main__": + update_space() diff --git a/upload_to_hub.py b/upload_to_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..b84cb3e0c0ba7d0cecd5fc8282c01abc5ba6fb71 --- /dev/null +++ b/upload_to_hub.py @@ -0,0 +1,258 @@ +import os +import sys +import time +import subprocess +from pathlib import Path +import logging +import requests +from requests.adapters import HTTPAdapter +from urllib3.util.retry import Retry +from typing import Optional +from dotenv import load_dotenv +from huggingface_hub import HfApi, create_repo + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +def setup_requests_session( + retries: int = 5, + backoff_factor: float = 1.0, + status_forcelist: Optional[list] = None +) -> requests.Session: + """Configure requests session with retries.""" + if status_forcelist is None: + status_forcelist = [408, 429, 500, 502, 503, 504] + + session = requests.Session() + retry = Retry( + total=retries, + read=retries, + connect=retries, + backoff_factor=backoff_factor, + status_forcelist=status_forcelist, + ) + adapter = HTTPAdapter(max_retries=retry) + session.mount('http://', adapter) + session.mount('https://', adapter) + return session + +def check_network_connectivity(host: str = "8.8.8.8", timeout: int = 5) -> bool: + """Check if network is accessible.""" + try: + # Try DNS resolution first + subprocess.run( + ["ping", "-c", "1", "-W", str(timeout), host], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + check=True + ) + return True + except subprocess.CalledProcessError: + return False + +def check_huggingface_connectivity(timeout: int = 5) -> bool: + """Check if Hugging Face is accessible.""" + session = setup_requests_session() + try: + response = session.get("https://huggingface.co", timeout=timeout) + return response.status_code == 200 + except: + return False + +def wait_for_network( + max_attempts: int = 5, + delay: int = 10, + hosts: Optional[list] = None +) -> bool: + """Wait for network connectivity.""" + if hosts is None: + hosts = ["8.8.8.8", "1.1.1.1"] + + for attempt in range(max_attempts): + logger.info(f"Checking network connectivity (attempt {attempt + 1}/{max_attempts})") + + # Try different DNS servers + for host in hosts: + if check_network_connectivity(host): + logger.info(f"Network connectivity established via {host}") + return True + + # Check Hugging Face specifically + if check_huggingface_connectivity(): + logger.info("Hugging Face is accessible") + return True + + if attempt < max_attempts - 1: + logger.warning(f"Network check failed. Waiting {delay} seconds before retry...") + time.sleep(delay) + + return False + +def upload_to_huggingface(): + """Upload the project to Hugging Face.""" + creds_path = None + try: + # Load environment variables + load_dotenv() + token = os.getenv("HUGGINGFACE_TOKEN") + if not token: + raise ValueError("HUGGINGFACE_TOKEN not found in environment variables") + + # Check network connectivity with increased timeout + if not wait_for_network(max_attempts=10, delay=15): + raise ConnectionError("Failed to establish network connectivity") + + # Initialize Hugging Face API with retry session + session = setup_requests_session(retries=7, backoff_factor=2.0) + api = HfApi(token=token, endpoint="https://huggingface.co") + + # Define Space name (modify as needed) + space_name = "agentic-system" + space_id = f"nananie143/{space_name}" + + # Create or get existing Space with retries and force hardware restart + max_attempts = 3 + for attempt in range(max_attempts): + try: + space_info = api.create_repo( + repo_id=space_id, + repo_type="space", + space_sdk="gradio", + private=False, + exist_ok=True, + hardware={"accelerator": "t4-medium"}, + storage={"hf": {"root": "/data"}}, + ) + logger.info(f"Space ready: {space_info.url}") + + # Force hardware restart to ensure clean environment + try: + api.request_space_hardware( + repo_id=space_id, + hardware="t4-medium", + sleep_time=2 + ) + logger.info("Requested hardware restart") + except Exception as e: + logger.warning(f"Hardware restart request failed: {e}") + + break + except Exception as e: + if attempt == max_attempts - 1: + logger.error(f"Error creating/accessing Space after {max_attempts} attempts: {e}") + raise + logger.warning(f"Attempt {attempt + 1} failed, retrying...") + time.sleep(5 * (attempt + 1)) + + # Add .gitattributes to ensure proper file handling + gitattributes_content = """ +*.py text eol=lf +*.sh text eol=lf +*.yml text eol=lf +*.txt text eol=lf +requirements.txt text eol=lf + """ + with open(".gitattributes", "w") as f: + f.write(gitattributes_content.strip()) + + # Files to exclude from upload + exclude_patterns = [ + "__pycache__", + "*.pyc", + ".git", + ".env", + ".env.example", + "models/*", + "flagged/*", + ".pytest_cache", + "*.log", + "*.gguf", + ".gitignore", + "*.backup", + "*.bak*", + "*.patch", + "*.temp", + ".DS_Store" + ] + + # Important files to ensure are included + important_files = [ + "app.py", + "agentic_system.py", + "requirements.txt", + "space.yml", + "download_models_space.py", + "app_space.sh", + "orchestrator.py", + "team_management.py", + "meta_learning.py", + "config.py", + "upload_to_hub.py", + ".gitattributes" + ] + + # Prepare files for upload with validation + files_to_upload = [] + root_path = Path(".") + + # First add important files with validation + for file in important_files: + file_path = Path(file) + if file_path.is_file(): + if file_path.stat().st_size > 0: # Check if file is not empty + files_to_upload.append(str(file_path)) + else: + logger.warning(f"Skipping empty file: {file}") + else: + logger.warning(f"Important file not found: {file}") + + # Then add other files with validation + for path in root_path.rglob("*"): + if path.is_file(): + relative_path = str(path.relative_to(root_path)) + if relative_path not in files_to_upload: # Skip if already added + skip = False + for pattern in exclude_patterns: + if Path(relative_path).match(pattern): + skip = True + break + if not skip and path.stat().st_size > 0: # Check if file is not empty + files_to_upload.append(relative_path) + + # Upload files with retry mechanism + logger.info("Starting file upload...") + total_files = len(files_to_upload) + for idx, file_path in enumerate(files_to_upload, 1): + max_retries = 3 + retry_count = 0 + while retry_count < max_retries: + try: + logger.info(f"[{idx}/{total_files}] Uploading: {file_path}") + api.upload_file( + path_or_fileobj=file_path, + path_in_repo=file_path, + repo_id=space_id, + repo_type="space" + ) + logger.info(f"✓ Uploaded: {file_path}") + break + except Exception as e: + retry_count += 1 + if retry_count == max_retries: + logger.error(f"Failed to upload {file_path} after {max_retries} attempts: {e}") + else: + logger.warning(f"Retry {retry_count}/{max_retries} for {file_path}: {e}") + time.sleep(5 * retry_count) + + logger.info(f"Space updated successfully! Visit: https://huggingface.co/spaces/{space_id}") + + except Exception as e: + logger.error(f"Error uploading to Hugging Face: {e}") + raise + finally: + if creds_path and os.path.exists(creds_path): + os.remove(creds_path) + +if __name__ == "__main__": + upload_to_huggingface()