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"""Neurosymbolic reasoning implementation."""
import logging
from typing import Dict, Any, List, Tuple
import json
from .base import ReasoningStrategy
class NeurosymbolicReasoning(ReasoningStrategy):
"""Implements neurosymbolic reasoning combining neural and symbolic approaches."""
async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
try:
# Neural processing
neural_features = await self._neural_processing(query, context)
# Symbolic reasoning
symbolic_rules = await self._symbolic_reasoning(neural_features, context)
# Integration
integrated = await self._neurosymbolic_integration(neural_features, symbolic_rules, context)
# Final inference
conclusion = await self._final_inference(integrated, context)
return {
"success": True,
"answer": conclusion["answer"],
"neural_features": neural_features,
"symbolic_rules": symbolic_rules,
"integrated_reasoning": integrated,
"confidence": conclusion["confidence"],
"explanation": conclusion["explanation"]
}
except Exception as e:
return {"success": False, "error": str(e)}
async def _neural_processing(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
prompt = f"""
Extract neural features from query:
Query: {query}
Context: {json.dumps(context)}
For each feature:
1. [Type]: Feature type
2. [Value]: Extracted value
3. [Confidence]: Extraction confidence
4. [Relations]: Related concepts
Format as:
[F1]
Type: ...
Value: ...
Confidence: ...
Relations: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_features(response["answer"])
async def _symbolic_reasoning(self, features: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
prompt = f"""
Generate symbolic rules from features:
Features: {json.dumps(features)}
Context: {json.dumps(context)}
For each rule:
1. [Condition]: Rule condition
2. [Implication]: What it implies
3. [Certainty]: Rule certainty
4. [Source]: Derivation source
Format as:
[R1]
Condition: ...
Implication: ...
Certainty: ...
Source: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_rules(response["answer"])
async def _neurosymbolic_integration(self, features: List[Dict[str, Any]], rules: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
prompt = f"""
Integrate neural and symbolic components:
Features: {json.dumps(features)}
Rules: {json.dumps(rules)}
Context: {json.dumps(context)}
For each integration:
1. [Components]: What is being integrated
2. [Method]: How they are combined
3. [Result]: Integration outcome
4. [Confidence]: Integration confidence
Format as:
[I1]
Components: ...
Method: ...
Result: ...
Confidence: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_integration(response["answer"])
async def _final_inference(self, integrated: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
prompt = f"""
Draw final conclusions from integrated reasoning:
Integrated: {json.dumps(integrated)}
Context: {json.dumps(context)}
Provide:
1. Final answer/conclusion
2. Confidence level (0-1)
3. Explanation of reasoning
4. Key factors considered
"""
response = await context["groq_api"].predict(prompt)
return self._parse_conclusion(response["answer"])
def _parse_features(self, response: str) -> List[Dict[str, Any]]:
"""Parse neural features from response."""
features = []
current = None
for line in response.split('\n'):
line = line.strip()
if not line:
continue
if line.startswith('[F'):
if current:
features.append(current)
current = {
"type": "",
"value": "",
"confidence": 0.0,
"relations": []
}
elif current:
if line.startswith('Type:'):
current["type"] = line[5:].strip()
elif line.startswith('Value:'):
current["value"] = line[6:].strip()
elif line.startswith('Confidence:'):
try:
current["confidence"] = float(line[11:].strip())
except:
pass
elif line.startswith('Relations:'):
current["relations"] = [r.strip() for r in line[10:].split(',')]
if current:
features.append(current)
return features
def _parse_rules(self, response: str) -> List[Dict[str, Any]]:
"""Parse symbolic rules from response."""
rules = []
current = None
for line in response.split('\n'):
line = line.strip()
if not line:
continue
if line.startswith('[R'):
if current:
rules.append(current)
current = {
"condition": "",
"implication": "",
"certainty": 0.0,
"source": ""
}
elif current:
if line.startswith('Condition:'):
current["condition"] = line[10:].strip()
elif line.startswith('Implication:'):
current["implication"] = line[12:].strip()
elif line.startswith('Certainty:'):
try:
current["certainty"] = float(line[10:].strip())
except:
pass
elif line.startswith('Source:'):
current["source"] = line[7:].strip()
if current:
rules.append(current)
return rules
def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
"""Parse integration results from response."""
integrations = []
current = None
for line in response.split('\n'):
line = line.strip()
if not line:
continue
if line.startswith('[I'):
if current:
integrations.append(current)
current = {
"components": "",
"method": "",
"result": "",
"confidence": 0.0
}
elif current:
if line.startswith('Components:'):
current["components"] = line[11:].strip()
elif line.startswith('Method:'):
current["method"] = line[7:].strip()
elif line.startswith('Result:'):
current["result"] = line[7:].strip()
elif line.startswith('Confidence:'):
try:
current["confidence"] = float(line[11:].strip())
except:
pass
if current:
integrations.append(current)
return integrations
def _parse_conclusion(self, response: str) -> Dict[str, Any]:
"""Parse final conclusion from response."""
conclusion = {
"answer": "",
"confidence": 0.0,
"explanation": "",
"factors": []
}
mode = None
for line in response.split('\n'):
line = line.strip()
if not line:
continue
if line.startswith('Answer:'):
conclusion["answer"] = line[7:].strip()
elif line.startswith('Confidence:'):
try:
conclusion["confidence"] = float(line[11:].strip())
except:
conclusion["confidence"] = 0.5
elif line.startswith('Explanation:'):
conclusion["explanation"] = line[12:].strip()
elif line.startswith('Factors:'):
mode = "factors"
elif mode == "factors" and line.startswith('- '):
conclusion["factors"].append(line[2:].strip())
return conclusion
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