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"""Response management module for metadata generation and file I/O operations - Phase G (Steps 17-20)."""
import time
import json
import logging
from typing import Dict, List, Any
from dataclasses import dataclass
from datetime import datetime
from .setup import GraphRAGSetup
from .query_preprocessing import QueryAnalysis, DriftRoutingResult, VectorizedQuery
from .answer_synthesis import SynthesisResult
@dataclass
class ResponseMetadata:
"""Complete response metadata structure."""
query_type: str
search_strategy: str
complexity_score: float
total_time_seconds: float
phases_completed: List[str]
status: str
phase_details: Dict[str, Any]
database_stats: Dict[str, Any]
class ResponseManager:
def __init__(self, setup: GraphRAGSetup):
self.setup = setup
self.config = setup.config
self.logger = logging.getLogger(self.__class__.__name__)
def generate_comprehensive_metadata(self,
analysis: QueryAnalysis,
routing: DriftRoutingResult,
vectorization: VectorizedQuery,
community_results: Dict[str, Any],
follow_up_results: Dict[str, Any],
augmentation_results: Any,
synthesis_results: SynthesisResult,
total_time: float) -> Dict[str, Any]:
"""
Generate comprehensive metadata for query response.
Consolidates all phase results into structured metadata format.
"""
try:
communities = community_results.get('communities', [])
metadata = {
# Execution Summary
"query_type": analysis.query_type.value,
"search_strategy": routing.search_strategy.value,
"complexity_score": analysis.complexity_score,
"total_time_seconds": round(total_time, 2),
"phases_completed": ["A-Init", "B-Preprocess", "C-Communities", "D-Followup", "E-Vector", "F-Synthesis"],
"status": "success",
# Phase A: Initialization
"phase_a": self._generate_phase_a_metadata(),
# Phase B: Query Preprocessing
"phase_b": self._generate_phase_b_metadata(analysis, vectorization, routing),
# Phase C: Community Search
"phase_c": self._generate_phase_c_metadata(communities, community_results),
# Phase D: Follow-up Search
"phase_d": self._generate_phase_d_metadata(follow_up_results),
# Phase E: Vector Augmentation
"phase_e": self._generate_phase_e_metadata(augmentation_results),
# Phase F: Answer Synthesis
"phase_f": self._generate_phase_f_metadata(synthesis_results),
# Database Statistics
"database_stats": self._generate_database_stats(follow_up_results, communities, augmentation_results)
}
self.logger.info("Generated comprehensive metadata with all phase details")
return metadata
except Exception as e:
self.logger.error(f"Failed to generate metadata: {e}")
return self._generate_fallback_metadata(str(e))
def _generate_phase_a_metadata(self) -> Dict[str, Any]:
"""Generate Phase A initialization metadata."""
from my_config import MY_CONFIG
return {
"neo4j_connected": bool(self.setup.neo4j_conn),
"vector_db_ready": bool(self.setup.query_engine),
"llm_model": getattr(MY_CONFIG, 'LLM_MODEL', 'unknown'),
"embedding_model": getattr(MY_CONFIG, 'EMBEDDING_MODEL', 'unknown'),
"drift_config_loaded": bool(self.setup.drift_config)
}
def _generate_phase_b_metadata(self, analysis: QueryAnalysis, vectorization: VectorizedQuery, routing: DriftRoutingResult) -> Dict[str, Any]:
"""Generate Phase B query preprocessing metadata."""
return {
"entities_extracted": len(analysis.entities_mentioned),
"semantic_keywords": len(vectorization.semantic_keywords),
"embedding_dimensions": len(vectorization.embedding),
"similarity_threshold": vectorization.similarity_threshold,
"routing_confidence": round(routing.confidence, 3)
}
def _generate_phase_c_metadata(self, communities: List[Any], community_results: Dict[str, Any]) -> Dict[str, Any]:
"""Generate Phase C community search metadata."""
return {
"communities_found": len(communities),
"community_ids": [c.community_id for c in communities[:5]],
"similarities": [round(c.similarity_score, 3) for c in communities[:5]],
"entities_extracted": len(community_results.get('extracted_data', {}).get('entities', [])),
"relationships_extracted": len(community_results.get('extracted_data', {}).get('relationships', []))
}
def _generate_phase_d_metadata(self, follow_up_results: Dict[str, Any]) -> Dict[str, Any]:
"""Generate Phase D follow-up search metadata."""
intermediate_answers = follow_up_results.get('intermediate_answers', [])
avg_confidence = 0.0
if intermediate_answers:
avg_confidence = sum(a.confidence for a in intermediate_answers) / len(intermediate_answers)
return {
"questions_generated": len(follow_up_results.get('follow_up_questions', [])),
"graph_traversals": len(follow_up_results.get('local_search_results', [])),
"entities_found": len(follow_up_results.get('detailed_entities', [])),
"intermediate_answers": len(intermediate_answers),
"avg_confidence": round(avg_confidence, 3)
}
def _generate_phase_e_metadata(self, augmentation_results: Any) -> Dict[str, Any]:
"""Generate Phase E vector augmentation metadata."""
if not augmentation_results:
return {"vector_results_count": 0, "augmentation_confidence": 0.0}
vector_files = []
if hasattr(augmentation_results, 'vector_results'):
for i, result in enumerate(augmentation_results.vector_results):
file_info = {
"file_id": i + 1,
"file_path": getattr(result, 'file_path', 'unknown'),
"similarity": round(result.similarity_score, 3),
"content_length": len(result.content),
"relevance": round(getattr(result, 'relevance_score', 0.0), 3)
}
vector_files.append(file_info)
return {
"vector_results_count": len(augmentation_results.vector_results) if hasattr(augmentation_results, 'vector_results') else 0,
"augmentation_confidence": round(augmentation_results.augmentation_confidence, 3) if hasattr(augmentation_results, 'augmentation_confidence') else 0.0,
"execution_time": round(augmentation_results.execution_time, 2) if hasattr(augmentation_results, 'execution_time') else 0.0,
"similarity_threshold": 0.75,
"vector_files": vector_files
}
def _generate_phase_f_metadata(self, synthesis_results: SynthesisResult) -> Dict[str, Any]:
"""Generate Phase F answer synthesis metadata."""
return {
"synthesis_confidence": round(synthesis_results.confidence_score, 3),
"sources_integrated": len(synthesis_results.source_evidence),
"final_answer_length": len(synthesis_results.final_answer),
"synthesis_method": getattr(synthesis_results, 'synthesis_method', 'comprehensive_fusion')
}
def _generate_database_stats(self, follow_up_results: Dict[str, Any], communities: List[Any], augmentation_results: Any) -> Dict[str, Any]:
"""Generate database statistics metadata."""
vector_docs_used = 0
if augmentation_results and hasattr(augmentation_results, 'vector_results'):
vector_docs_used = len(augmentation_results.vector_results)
return {
"total_nodes": self.setup.graph_stats.get('node_count', 0),
"total_relationships": self.setup.graph_stats.get('relationship_count', 0),
"total_communities": self.setup.graph_stats.get('community_count', 0),
"nodes_accessed": len(follow_up_results.get('detailed_entities', [])),
"communities_searched": len(communities),
"vector_docs_used": vector_docs_used
}
def _generate_fallback_metadata(self, error: str) -> Dict[str, Any]:
"""Generate minimal metadata when full generation fails."""
return {
"status": "metadata_generation_error",
"error": error,
"phases_completed": "incomplete",
"total_time_seconds": 0.0
}
def save_response_to_files(self, user_query: str, result: Dict[str, Any]) -> None:
"""
Save query response and metadata to separate files.
Handles file I/O operations for response persistence.
"""
try:
timestamp = time.strftime('%Y-%m-%d %H:%M:%S')
# Save response to response file
self._save_response_file(user_query, result, timestamp)
# Save metadata to metadata file
self._save_metadata_file(user_query, result, timestamp)
self.logger.info(f"Saved response and metadata for query: {user_query[:50]}...")
except Exception as e:
self.logger.error(f"Failed to save response files: {e}")
def _save_response_file(self, user_query: str, result: Dict[str, Any], timestamp: str) -> None:
"""Save response content to response file."""
try:
with open('logs/graphrag_query/graphrag_responses.txt', 'a', encoding='utf-8') as f:
f.write(f"\n{'='*80}\n")
f.write(f"QUERY [{timestamp}]: {user_query}\n")
f.write(f"{'='*80}\n")
f.write(f"RESPONSE: {result['answer']}\n")
f.write(f"{'='*80}\n\n")
except Exception as e:
self.logger.error(f"Failed to save response file: {e}")
def _save_metadata_file(self, user_query: str, result: Dict[str, Any], timestamp: str) -> None:
"""Save metadata to metadata file."""
try:
with open('logs/graphrag_query/graphrag_metadata.txt', 'a', encoding='utf-8') as f:
f.write(f"\n{'='*80}\n")
f.write(f"METADATA [{timestamp}]: {user_query}\n")
f.write(f"{'='*80}\n")
f.write(json.dumps(result['metadata'], indent=2, default=str))
f.write(f"\n{'='*80}\n\n")
except Exception as e:
self.logger.error(f"Failed to save metadata file: {e}")
def format_error_response(self, error_message: str) -> Dict[str, Any]:
"""
Generate standardized error response with metadata.
Creates consistent error format for failed queries.
"""
return {
"answer": f"Sorry, I encountered an error: {error_message}",
"metadata": {
"status": "error",
"error_message": error_message,
"phases_completed": "incomplete",
"neo4j_connected": bool(self.setup.neo4j_conn) if self.setup.neo4j_conn else False,
"vector_engine_ready": bool(self.setup.query_engine) if self.setup.query_engine else False,
"timestamp": datetime.now().isoformat()
}
}
# Exports
__all__ = ['ResponseManager', 'ResponseMetadata'] |