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"""
Enhanced chat service with Redis caching integration
"""
from typing import Optional, Dict, Any, AsyncIterator
import asyncio
from src.services.chat_service import ChatService, IntentDetector, Intent
from src.services.cache_service import cache_service
from src.core import get_logger
from src.api.models.pagination import ChatMessagePagination, CursorPaginationResponse
logger = get_logger(__name__)
class CachedChatService(ChatService):
"""Chat service with Redis caching for improved performance"""
def __init__(self):
super().__init__()
self.intent_detector = IntentDetector()
async def process_message(
self,
message: str,
session_id: str,
user_id: Optional[str] = None,
context: Optional[Dict[str, Any]] = None,
stream: bool = False
) -> Dict[str, Any]:
"""
Process a chat message with caching support.
Args:
message: User message
session_id: Session identifier
user_id: Optional user ID
context: Optional context
stream: Whether to stream response
Returns:
Chat response dictionary
"""
# Get or create session
session = await self.get_or_create_session(session_id, user_id)
# Save user message
await self.save_message(session_id, "user", message)
# Detect intent
intent = self.intent_detector.detect(message)
# Check cache for common responses (only for non-streaming)
if not stream and intent.confidence > 0.8:
cached_response = await cache_service.get_cached_chat_response(
message,
intent.type.value
)
if cached_response:
logger.info(f"Returning cached response for: {message[:50]}...")
# Save cached response to history
await self.save_message(
session_id,
"assistant",
cached_response.get("message", ""),
cached_response.get("agent_id")
)
return cached_response
# Get appropriate agent
agent = await self.get_agent_for_intent(intent)
try:
# Process with agent
if stream:
# For streaming, return async generator
return self._stream_agent_response(
agent, message, intent, session, session_id
)
else:
# Regular response
response = await self._get_agent_response(
agent, message, intent, session
)
# Save agent response
await self.save_message(
session_id,
"assistant",
response["message"],
response["agent_id"]
)
# Cache successful responses with high confidence
if intent.confidence > 0.8 and response.get("confidence", 0) > 0.7:
await cache_service.cache_chat_response(
message,
response,
intent.type.value
)
# Update session with any investigation ID
if "investigation_id" in response:
await self.update_session_investigation(
session_id,
response["investigation_id"]
)
# Save session state to cache
await cache_service.save_session_state(session_id, {
"last_message": message,
"last_intent": intent.dict(),
"last_agent": response["agent_id"],
"investigation_id": session.current_investigation_id,
"message_count": len(self.messages.get(session_id, []))
})
return response
except Exception as e:
logger.error(f"Error processing message: {e}")
error_response = {
"session_id": session_id,
"agent_id": "system",
"agent_name": "Sistema",
"message": "Desculpe, ocorreu um erro ao processar sua mensagem. Por favor, tente novamente.",
"confidence": 0.0,
"error": True
}
await self.save_message(
session_id,
"assistant",
error_response["message"],
"system"
)
return error_response
async def _get_agent_response(
self,
agent,
message: str,
intent: Intent,
session
) -> Dict[str, Any]:
"""Get response from agent"""
# Create agent context
context = {
"session_id": session.id,
"intent": intent.dict(),
"entities": intent.entities,
"investigation_id": session.current_investigation_id,
"history": await self.get_session_messages(session.id, limit=10)
}
# Check agent context cache
cached_context = await cache_service.get_agent_context(
agent.agent_id,
session.id
)
if cached_context:
context.update(cached_context)
# Execute agent
result = await agent.execute({
"message": message,
"context": context
})
# Save agent context for future use
if result.get("context_update"):
await cache_service.save_agent_context(
agent.agent_id,
session.id,
result["context_update"]
)
# Format response
return {
"session_id": session.id,
"agent_id": agent.agent_id,
"agent_name": agent.name,
"message": result.get("response", ""),
"confidence": result.get("confidence", 0.5),
"suggested_actions": result.get("suggested_actions", []),
"requires_input": result.get("requires_input"),
"metadata": {
"intent_type": intent.type.value,
"processing_time": result.get("processing_time", 0),
"is_demo_mode": not bool(intent.entities.get("api_key")),
"timestamp": session.last_activity.isoformat()
}
}
async def _stream_agent_response(
self,
agent,
message: str,
intent: Intent,
session,
session_id: str
) -> AsyncIterator[Dict[str, Any]]:
"""Stream response from agent"""
# Initial chunks
yield {
"type": "start",
"timestamp": session.last_activity.isoformat()
}
yield {
"type": "detecting",
"message": "Analisando sua mensagem..."
}
yield {
"type": "intent",
"intent": intent.type.value,
"confidence": intent.confidence
}
yield {
"type": "agent_selected",
"agent_id": agent.agent_id,
"agent_name": agent.name
}
# Simulate streaming response
# In production, this would stream from the LLM
response = await self._get_agent_response(
agent, message, intent, session
)
# Stream response in chunks
message_text = response["message"]
words = message_text.split()
for i in range(0, len(words), 3):
chunk = " ".join(words[i:i+3])
yield {
"type": "chunk",
"content": chunk + " "
}
await asyncio.sleep(0.05) # Simulate typing
# Save complete message
await self.save_message(
session_id,
"assistant",
message_text,
response["agent_id"]
)
# Final completion
yield {
"type": "complete",
"suggested_actions": response.get("suggested_actions", [])
}
async def restore_session_from_cache(
self,
session_id: str
) -> Optional[Dict[str, Any]]:
"""Restore session state from cache"""
cached_state = await cache_service.get_session_state(session_id)
if cached_state:
# Restore session
session = await self.get_or_create_session(session_id)
if cached_state.get("investigation_id"):
session.current_investigation_id = cached_state["investigation_id"]
logger.info(f"Restored session {session_id} from cache")
return cached_state
return None
async def get_cache_stats(self) -> Dict[str, Any]:
"""Get cache statistics for monitoring"""
return await cache_service.get_cache_stats()
async def get_session_messages_paginated(
self,
session_id: str,
cursor: Optional[str] = None,
limit: int = 50,
direction: str = "prev"
) -> CursorPaginationResponse[Dict[str, Any]]:
"""
Get paginated messages for a session using cursor pagination.
Args:
session_id: Session identifier
cursor: Pagination cursor
limit: Number of messages per page
direction: "next" or "prev" (default: "prev" for chat)
Returns:
Paginated response with messages and cursors
"""
# Get all messages for session
messages = self.messages.get(session_id, [])
# Add unique IDs if missing
for i, msg in enumerate(messages):
if "id" not in msg:
msg["id"] = f"{session_id}-{i}"
# Paginate using cursor
return ChatMessagePagination.paginate_messages(
messages=messages,
cursor=cursor,
limit=limit,
direction=direction
)
# Export the enhanced service
# Use lazy initialization to avoid import-time errors
_chat_service_instance = None
def get_chat_service():
"""Get or create the chat service instance"""
global _chat_service_instance
if _chat_service_instance is None:
_chat_service_instance = CachedChatService()
return _chat_service_instance
# For backward compatibility
chat_service = None # Will be replaced by getter |