MCP Chat Analysis Server

  • src
  • mcp_chat_analysis
from typing import Dict, List, Any, Optional import logging from datetime import datetime import asyncio from qdrant_client import QdrantClient from qdrant_client.http import models as qdrant_models from neo4j import AsyncGraphDatabase from .models import ( ConversationData, SearchQuery, MetricsRequest, ConceptRequest, Message, Conversation, SearchResult, ConceptNode, MetricsResult, MetricType ) from .embeddings import EmbeddingGenerator logger = logging.getLogger(__name__) class ConversationProcessor: """Processes conversations for analysis and storage""" def __init__( self, qdrant: QdrantClient, neo4j: AsyncGraphDatabase, embedding_generator: EmbeddingGenerator ): self.qdrant = qdrant self.neo4j = neo4j self.embedding_generator = embedding_generator self.collection_name = "chat_embeddings" # Ensure Qdrant collection exists self._ensure_collection() def _ensure_collection(self): """Ensure Qdrant collection exists with correct schema""" try: self.qdrant.get_collection(self.collection_name) except: self.qdrant.create_collection( collection_name=self.collection_name, vectors_config=qdrant_models.VectorParams( size=self.embedding_generator.embedding_dim, distance=qdrant_models.Distance.COSINE ) ) logger.info(f"Created Qdrant collection: {self.collection_name}") async def process_conversations( self, data: ConversationData ) -> Dict[str, Any]: """ Process conversations from import data Args: data: Conversation import data Returns: Processing statistics """ stats = { "start_time": datetime.now(), "conversations_processed": 0, "messages_processed": 0, "vectors_created": 0, "concepts_extracted": 0 } # TODO: Implement conversation parsing based on format conversations = await self._parse_conversations(data) for conv in conversations: # Store in Neo4j await self._store_conversation_graph(conv) stats["conversations_processed"] += 1 # Process messages for msg in conv.messages: # Generate and store embeddings embedding = await self.embedding_generator.generate_single( msg.content ) await self._store_message_vector( msg, conv.id, embedding ) stats["messages_processed"] += 1 stats["vectors_created"] += 1 # Extract and store concepts concepts = await self._extract_message_concepts(conv) stats["concepts_extracted"] += len(concepts) stats["end_time"] = datetime.now() stats["duration"] = stats["end_time"] - stats["start_time"] return stats async def search( self, query: SearchQuery ) -> List[SearchResult]: """ Perform semantic search Args: query: Search parameters Returns: List of search results """ # Generate query embedding embedding = await self.embedding_generator.generate_single( query.query ) # Search Qdrant results = self.qdrant.search( collection_name=self.collection_name, query_vector=embedding, limit=query.limit, score_threshold=query.min_score ) # Format results search_results = [] for res in results: # Get full message context from Neo4j msg_data = await self._get_message_context( res.payload["message_id"] ) if msg_data: search_results.append(SearchResult( message=msg_data["message"], conversation=msg_data["conversation"], score=res.score, context=msg_data.get("context", {}) )) return search_results async def analyze_metrics( self, request: MetricsRequest ) -> MetricsResult: """ Analyze conversation metrics Args: request: Metrics analysis request Returns: Analysis results """ metrics = {} for metric_type in request.metrics: if metric_type == MetricType.MESSAGE_FREQUENCY: metrics[metric_type] = await self._analyze_message_frequency( request.conversation_id, request.time_window ) elif metric_type == MetricType.RESPONSE_TIMES: metrics[metric_type] = await self._analyze_response_times( request.conversation_id ) elif metric_type == MetricType.TOPIC_DIVERSITY: metrics[metric_type] = await self._analyze_topic_diversity( request.conversation_id ) elif metric_type == MetricType.CONVERSATION_DEPTH: metrics[metric_type] = await self._analyze_conversation_depth( request.conversation_id ) elif metric_type == MetricType.INTERACTION_PATTERNS: metrics[metric_type] = await self._analyze_interaction_patterns( request.conversation_id ) return MetricsResult( conversation_id=request.conversation_id, time_window=request.time_window, metrics=metrics ) async def extract_concepts( self, request: ConceptRequest ) -> List[ConceptNode]: """ Extract concepts from conversation Args: request: Concept extraction request Returns: List of extracted concepts """ # Get conversation conv = await self._get_conversation(request.conversation_id) if not conv: return [] # Extract concepts concepts = await self._extract_message_concepts( conv, min_relevance=request.min_relevance, max_concepts=request.max_concepts ) return concepts async def _parse_conversations( self, data: ConversationData ) -> List[Conversation]: """Parse conversations from import data""" # TODO: Implement format-specific parsing raise NotImplementedError async def _store_conversation_graph( self, conversation: Conversation ): """Store conversation in Neo4j""" # TODO: Implement Neo4j storage raise NotImplementedError async def _store_message_vector( self, message: Message, conversation_id: str, embedding: List[float] ): """Store message vector in Qdrant""" # TODO: Implement Qdrant storage raise NotImplementedError async def _extract_message_concepts( self, conversation: Conversation, min_relevance: float = 0.5, max_concepts: int = 10 ) -> List[ConceptNode]: """Extract concepts from messages""" # TODO: Implement concept extraction raise NotImplementedError async def _get_message_context( self, message_id: str ) -> Optional[Dict]: """Get full message context from Neo4j""" # TODO: Implement context retrieval raise NotImplementedError async def _get_conversation( self, conversation_id: str ) -> Optional[Conversation]: """Get conversation from Neo4j""" # TODO: Implement conversation retrieval raise NotImplementedError async def _analyze_message_frequency( self, conversation_id: str, time_window: Optional[str] ) -> Dict: """Analyze message frequency""" # TODO: Implement frequency analysis raise NotImplementedError async def _analyze_response_times( self, conversation_id: str ) -> Dict: """Analyze response times""" # TODO: Implement response time analysis raise NotImplementedError async def _analyze_topic_diversity( self, conversation_id: str ) -> Dict: """Analyze topic diversity""" # TODO: Implement topic diversity analysis raise NotImplementedError async def _analyze_conversation_depth( self, conversation_id: str ) -> Dict: """Analyze conversation depth""" # TODO: Implement depth analysis raise NotImplementedError async def _analyze_interaction_patterns( self, conversation_id: str ) -> Dict: """Analyze interaction patterns""" # TODO: Implement pattern analysis raise NotImplementedError