reasoning/quantum.py

"""Quantum-inspired reasoning implementations."""

import logging
from typing import Dict, Any, List
import json

from .base import ReasoningStrategy

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)}