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| """ | |
| Enhanced chain of thought and reasoning capabilities for the AI agent. | |
| """ | |
| import logging | |
| from typing import Dict, Any, List, Optional | |
| from dataclasses import dataclass | |
| from enum import Enum | |
| logger = logging.getLogger(__name__) | |
| class ReasoningType(Enum): | |
| """Types of reasoning approaches.""" | |
| LINEAR = "linear" # Standard step-by-step reasoning | |
| TREE = "tree" # Tree of thoughts with branching | |
| SELF_CONSISTENT = "self_consistent" # Multiple reasoning paths | |
| LAYERED = "layered" # Multi-layer verification | |
| class ReasoningStep: | |
| """A single step in the reasoning process.""" | |
| step_number: int | |
| description: str | |
| tool_name: Optional[str] | |
| tool_input: Optional[Dict[str, Any]] | |
| output: Optional[Any] | |
| confidence: float | |
| verification_status: bool | |
| class ReasoningPath: | |
| """A complete reasoning path with steps.""" | |
| steps: List[ReasoningStep] | |
| final_answer: str | |
| confidence: float | |
| verification_status: bool | |
| class AdvancedReasoning: | |
| """Enhanced reasoning system with multiple approaches.""" | |
| def __init__(self): | |
| self.reasoning_history = [] | |
| self.verification_threshold = 0.8 | |
| self.max_verification_steps = 4 | |
| def generate_reasoning_plan(self, query: str, reasoning_type: ReasoningType) -> List[ReasoningStep]: | |
| """Generate a reasoning plan based on the query and reasoning type.""" | |
| if reasoning_type == ReasoningType.LINEAR: | |
| return self._generate_linear_plan(query) | |
| elif reasoning_type == ReasoningType.TREE: | |
| return self._generate_tree_plan(query) | |
| elif reasoning_type == ReasoningType.SELF_CONSISTENT: | |
| return self._generate_self_consistent_plan(query) | |
| elif reasoning_type == ReasoningType.LAYERED: | |
| return self._generate_layered_plan(query) | |
| else: | |
| raise ValueError(f"Unknown reasoning type: {reasoning_type}") | |
| def _generate_linear_plan(self, query: str) -> List[ReasoningStep]: | |
| """Generate a linear, step-by-step reasoning plan.""" | |
| # This would typically use an LLM to break down the query | |
| # For now, return a placeholder plan | |
| return [ | |
| ReasoningStep( | |
| step_number=1, | |
| description="Analyze query and identify key components", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=2, | |
| description="Determine required tools and information", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=3, | |
| description="Execute tool calls and gather information", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=4, | |
| description="Synthesize information into final answer", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ) | |
| ] | |
| def _generate_tree_plan(self, query: str) -> List[ReasoningStep]: | |
| """Generate a tree-based reasoning plan with branching paths.""" | |
| # This would use an LLM to generate multiple possible approaches | |
| # For now, return a placeholder plan | |
| return [ | |
| ReasoningStep( | |
| step_number=1, | |
| description="Generate multiple reasoning paths", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=2, | |
| description="Evaluate each path's potential", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=3, | |
| description="Select and execute best path", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ) | |
| ] | |
| def _generate_self_consistent_plan(self, query: str) -> List[ReasoningStep]: | |
| """Generate a self-consistent reasoning plan with multiple paths.""" | |
| # This would use an LLM to generate multiple independent solutions | |
| # For now, return a placeholder plan | |
| return [ | |
| ReasoningStep( | |
| step_number=1, | |
| description="Generate multiple independent solutions", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=2, | |
| description="Compare solutions for consistency", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=3, | |
| description="Select most consistent solution", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ) | |
| ] | |
| def _generate_layered_plan(self, query: str) -> List[ReasoningStep]: | |
| """Generate a layered reasoning plan with verification at each step.""" | |
| # This would use an LLM to generate a plan with built-in verification | |
| # For now, return a placeholder plan | |
| return [ | |
| ReasoningStep( | |
| step_number=1, | |
| description="Initial reasoning and tool selection", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=2, | |
| description="First layer verification", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=3, | |
| description="Second layer verification", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ), | |
| ReasoningStep( | |
| step_number=4, | |
| description="Final synthesis and verification", | |
| tool_name=None, | |
| tool_input=None, | |
| output=None, | |
| confidence=0.0, | |
| verification_status=False | |
| ) | |
| ] | |
| def verify_step(self, step: ReasoningStep) -> bool: | |
| """Verify a single reasoning step.""" | |
| if not step.output: | |
| return False | |
| # Check confidence threshold | |
| if step.confidence < self.verification_threshold: | |
| return False | |
| # Verify tool output if applicable | |
| if step.tool_name and step.tool_input: | |
| # This would typically use an LLM to verify the tool's output | |
| # For now, return True if we have output | |
| return bool(step.output) | |
| return True | |
| def verify_path(self, path: ReasoningPath) -> bool: | |
| """Verify an entire reasoning path.""" | |
| # Check if all steps are verified | |
| if not all(step.verification_status for step in path.steps): | |
| return False | |
| # Check final confidence | |
| if path.confidence < self.verification_threshold: | |
| return False | |
| # Verify final answer | |
| if not path.final_answer: | |
| return False | |
| return True | |
| def record_reasoning(self, path: ReasoningPath): | |
| """Record a reasoning path for future reference.""" | |
| self.reasoning_history.append(path) | |
| def get_reasoning_history(self) -> List[ReasoningPath]: | |
| """Get the history of reasoning paths.""" | |
| return self.reasoning_history.copy() | |
| def analyze_reasoning_patterns(self) -> Dict[str, Any]: | |
| """Analyze patterns in reasoning history.""" | |
| if not self.reasoning_history: | |
| return {} | |
| # Calculate success rate | |
| success_count = sum(1 for path in self.reasoning_history if path.verification_status) | |
| success_rate = success_count / len(self.reasoning_history) | |
| # Calculate average confidence | |
| avg_confidence = sum(path.confidence for path in self.reasoning_history) / len(self.reasoning_history) | |
| # Calculate average steps per path | |
| avg_steps = sum(len(path.steps) for path in self.reasoning_history) / len(self.reasoning_history) | |
| return { | |
| "success_rate": success_rate, | |
| "average_confidence": avg_confidence, | |
| "average_steps": avg_steps, | |
| "total_paths": len(self.reasoning_history) | |
| } |