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