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"""
Simplified cognitive processor for Codette
"""
from typing import List, Dict, Any, Optional
try:
import numpy as np
except Exception:
np = None
from datetime import datetime
class CognitiveProcessor:
"""Core processing engine for Codette responses"""
MODES = {
"logical": {
"name": "Logical Analysis",
"process": lambda q: f"Analyzing {q} systematically...",
"weight": 0.9
},
"creative": {
"name": "Creative Insight",
"process": lambda q: f"Exploring creative approaches to {q}...",
"weight": 0.8
},
"practical": {
"name": "Practical Application",
"process": lambda q: f"Considering practical implementations for {q}...",
"weight": 0.85
}
}
def __init__(self):
"""Initialize cognitive processor with default modes"""
self.active_modes = self.MODES.copy()
self.processing_history = []
def process(self,
query: str,
route_node: Optional[str] = None,
confidence: float = 0.5) -> Dict[str, Any]:
"""
Process query using active modes and routing information
Args:
query: Input text to process
route_node: Optional BioKinetic mesh node
confidence: Routing confidence score
Returns:
Dict with response and insights
"""
try:
# Generate insights from each mode
insights = []
weighted_responses = []
for mode_name, mode_info in self.active_modes.items():
# Apply confidence to weight
effective_weight = mode_info["weight"] * confidence
if effective_weight > 0.3: # Minimum threshold
response = mode_info["process"](query)
weighted_responses.append((response, effective_weight))
insights.append(f"{mode_info['name']}")
# Combine responses based on weights
if weighted_responses:
# Sort by weight
weighted_responses.sort(key=lambda x: x[1], reverse=True)
# Take top response
main_response = weighted_responses[0][0]
else:
main_response = f"Processing query: {query}"
# Record processing
self.processing_history.append({
"timestamp": str(datetime.now()),
"query": query,
"response": main_response,
"route_node": route_node,
"confidence": confidence
})
# Prune history if too long
if len(self.processing_history) > 10:
self.processing_history = self.processing_history[-10:]
return {
"response": main_response,
"insights": insights[:3] # Top 3 insights
}
except Exception as e:
print(f"Error processing query: {e}")
return {
"response": f"I apologize, but I encountered an error processing your query.",
"insights": ["Error recovery activated"]
}
def get_metrics(self) -> Dict[str, Any]:
"""Get processor metrics and status"""
try:
return {
"active_modes": list(self.active_modes.keys()),
"mode_weights": {mode: info["weight"] for mode, info in self.active_modes.items()},
"processing_history_length": len(self.processing_history)
}
except Exception as e:
print(f"Error getting metrics: {e}")
return {} |