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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
@dataclass
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
@dataclass
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)
} |