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| """Bayesian reasoning implementation.""" | |
| import logging | |
| from typing import Dict, Any, List | |
| import json | |
| import re | |
| from datetime import datetime | |
| from .base import ReasoningStrategy | |
| 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 | |