space/agentic.py

"""Specialized reasoning strategies for Agentic Workflow."""

import logging
from typing import Dict, Any, List, Optional, Set, Union, Tuple
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import asyncio
from collections import defaultdict

from .base import ReasoningStrategy

class TaskType(Enum):
    """Types of tasks in agentic workflow."""
    CODE_GENERATION = "code_generation"
    CODE_MODIFICATION = "code_modification"
    CODE_REVIEW = "code_review"
    DEBUGGING = "debugging"
    ARCHITECTURE = "architecture"
    OPTIMIZATION = "optimization"
    DOCUMENTATION = "documentation"
    TESTING = "testing"

class ResourceType(Enum):
    """Types of resources in agentic workflow."""
    CODE_CONTEXT = "code_context"
    SYSTEM_CONTEXT = "system_context"
    USER_CONTEXT = "user_context"
    TOOLS = "tools"
    APIS = "apis"
    DOCUMENTATION = "documentation"
    DEPENDENCIES = "dependencies"
    HISTORY = "history"

@dataclass
class TaskComponent:
    """Component of a decomposed task."""
    id: str
    type: TaskType
    description: str
    dependencies: List[str]
    resources: Dict[ResourceType, Any]
    constraints: List[str]
    priority: float
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class ResourceAllocation:
    """Resource allocation for a task."""
    resource_type: ResourceType
    quantity: Union[int, float]
    priority: float
    constraints: List[str]
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class ExecutionStep:
    """Step in task execution."""
    id: str
    task_id: str
    action: str
    resources: Dict[ResourceType, Any]
    status: str
    result: Optional[Dict[str, Any]]
    feedback: List[str]
    timestamp: datetime = field(default_factory=datetime.now)

class TaskDecompositionStrategy(ReasoningStrategy):
    """
    Advanced task decomposition strategy that:
    1. Analyzes task complexity and dependencies
    2. Breaks down tasks into manageable components
    3. Identifies resource requirements
    4. Establishes execution order
    5. Manages constraints and priorities
    """
    
    def __init__(self, max_components: int = 10):
        self.max_components = max_components
        self.components: Dict[str, TaskComponent] = {}
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Decompose task into components."""
        try:
            # Analyze task
            task_analysis = await self._analyze_task(query, context)
            
            # Generate components
            components = await self._generate_components(task_analysis, context)
            
            # Establish dependencies
            dependency_graph = await self._establish_dependencies(components, context)
            
            # Determine execution order
            execution_order = await self._determine_execution_order(
                components, dependency_graph, context)
            
            return {
                "success": True,
                "components": [self._component_to_dict(c) for c in components],
                "dependency_graph": dependency_graph,
                "execution_order": execution_order,
                "metadata": {
                    "total_components": len(components),
                    "complexity_score": task_analysis.get("complexity_score", 0.0),
                    "resource_requirements": task_analysis.get("resource_requirements", {})
                }
            }
        except Exception as e:
            logging.error(f"Error in task decomposition: {str(e)}")
            return {"success": False, "error": str(e)}

class ResourceManagementStrategy(ReasoningStrategy):
    """
    Advanced resource management strategy that:
    1. Tracks available resources
    2. Allocates resources to tasks
    3. Handles resource constraints
    4. Optimizes resource utilization
    5. Manages resource dependencies
    """
    
    def __init__(self):
        self.allocations: Dict[str, ResourceAllocation] = {}
        self.utilization_history: List[Dict[str, Any]] = []
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Manage resource allocation."""
        try:
            # Analyze resource requirements
            requirements = await self._analyze_requirements(query, context)
            
            # Check resource availability
            availability = await self._check_availability(requirements, context)
            
            # Generate allocation plan
            allocation_plan = await self._generate_allocation_plan(
                requirements, availability, context)
            
            # Optimize allocations
            optimized_plan = await self._optimize_allocations(allocation_plan, context)
            
            return {
                "success": True,
                "allocation_plan": optimized_plan,
                "resource_metrics": {
                    "utilization": self._calculate_utilization(),
                    "efficiency": self._calculate_efficiency(),
                    "constraints_satisfied": self._check_constraints(optimized_plan)
                }
            }
        except Exception as e:
            logging.error(f"Error in resource management: {str(e)}")
            return {"success": False, "error": str(e)}

class ContextualPlanningStrategy(ReasoningStrategy):
    """
    Advanced contextual planning strategy that:
    1. Analyzes multiple context types
    2. Generates context-aware plans
    3. Handles context changes
    4. Maintains context consistency
    5. Optimizes for context constraints
    """
    
    def __init__(self):
        self.context_history: List[Dict[str, Any]] = []
        self.plan_adaptations: List[Dict[str, Any]] = []
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate context-aware plan."""
        try:
            # Analyze contexts
            context_analysis = await self._analyze_contexts(query, context)
            
            # Generate base plan
            base_plan = await self._generate_base_plan(context_analysis, context)
            
            # Adapt to contexts
            adapted_plan = await self._adapt_to_contexts(base_plan, context_analysis)
            
            # Validate plan
            validation = await self._validate_plan(adapted_plan, context)
            
            return {
                "success": True,
                "plan": adapted_plan,
                "context_impact": context_analysis.get("impact_assessment", {}),
                "adaptations": self.plan_adaptations,
                "validation_results": validation
            }
        except Exception as e:
            logging.error(f"Error in contextual planning: {str(e)}")
            return {"success": False, "error": str(e)}

class AdaptiveExecutionStrategy(ReasoningStrategy):
    """
    Advanced adaptive execution strategy that:
    1. Monitors execution progress
    2. Adapts to changes and feedback
    3. Handles errors and exceptions
    4. Optimizes execution flow
    5. Maintains execution state
    """
    
    def __init__(self):
        self.execution_steps: List[ExecutionStep] = []
        self.adaptation_history: List[Dict[str, Any]] = []
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Execute task adaptively."""
        try:
            # Initialize execution
            execution_state = await self._initialize_execution(query, context)
            
            # Monitor and adapt
            while not self._is_execution_complete(execution_state):
                # Execute step
                step_result = await self._execute_step(execution_state, context)
                
                # Process feedback
                feedback = await self._process_feedback(step_result, context)
                
                # Adapt execution
                execution_state = await self._adapt_execution(
                    execution_state, feedback, context)
                
                # Record step
                self._record_step(step_result, feedback)
            
            return {
                "success": True,
                "execution_trace": [self._step_to_dict(s) for s in self.execution_steps],
                "adaptations": self.adaptation_history,
                "final_state": execution_state
            }
        except Exception as e:
            logging.error(f"Error in adaptive execution: {str(e)}")
            return {"success": False, "error": str(e)}

class FeedbackIntegrationStrategy(ReasoningStrategy):
    """
    Advanced feedback integration strategy that:
    1. Collects multiple types of feedback
    2. Analyzes feedback patterns
    3. Generates improvement suggestions
    4. Tracks feedback implementation
    5. Measures feedback impact
    """
    
    def __init__(self):
        self.feedback_history: List[Dict[str, Any]] = []
        self.improvement_history: List[Dict[str, Any]] = []
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Integrate and apply feedback."""
        try:
            # Collect feedback
            feedback = await self._collect_feedback(query, context)
            
            # Analyze patterns
            patterns = await self._analyze_patterns(feedback, context)
            
            # Generate improvements
            improvements = await self._generate_improvements(patterns, context)
            
            # Implement changes
            implementation = await self._implement_improvements(improvements, context)
            
            # Measure impact
            impact = await self._measure_impact(implementation, context)
            
            return {
                "success": True,
                "feedback_analysis": patterns,
                "improvements": improvements,
                "implementation_status": implementation,
                "impact_metrics": impact
            }
        except Exception as e:
            logging.error(f"Error in feedback integration: {str(e)}")
            return {"success": False, "error": str(e)}

    async def _collect_feedback(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Collect feedback from multiple sources."""
        prompt = f"""
        Collect feedback from:
        Query: {query}
        Context: {json.dumps(context)}
        
        Consider:
        1. User feedback
        2. System metrics
        3. Code analysis
        4. Performance data
        5. Error patterns
        
        Format as:
        [Feedback]
        Source: ...
        Type: ...
        Content: ...
        Priority: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_feedback(response["answer"])

    def _parse_feedback(self, response: str) -> List[Dict[str, Any]]:
        """Parse feedback from response."""
        feedback_items = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[Feedback]'):
                if current:
                    feedback_items.append(current)
                current = {
                    "source": "",
                    "type": "",
                    "content": "",
                    "priority": 0.0
                }
            elif current:
                if line.startswith('Source:'):
                    current["source"] = line[7:].strip()
                elif line.startswith('Type:'):
                    current["type"] = line[5:].strip()
                elif line.startswith('Content:'):
                    current["content"] = line[8:].strip()
                elif line.startswith('Priority:'):
                    try:
                        current["priority"] = float(line[9:].strip())
                    except:
                        pass
        
        if current:
            feedback_items.append(current)
        
        return feedback_items