diff --git "a/reasoning.py" "b/reasoning.py" new file mode 100644--- /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: