Spaces:
Running
Running
File size: 3,583 Bytes
ba907cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
"""
Chat API routes.
This module handles chat and conversation endpoints.
"""
import traceback
from fastapi import HTTPException
from langchain.memory import ConversationBufferMemory
from configs.config import Config, ErrorMessages
from models.models import ChatRequest, ChatResponse
from services import session_manager, rag_service
from utils import retrieve_similar_chunks
async def chat_handler(request: ChatRequest) -> ChatResponse:
"""
Handle chat requests with document context.
Args:
request: Chat request containing query and session info
Returns:
Chat response with answer and context
Raises:
HTTPException: If processing fails
"""
# Validate query
if not request.query or not request.query.strip():
raise HTTPException(status_code=400, detail=ErrorMessages.EMPTY_QUERY)
if len(request.query.strip()) < 3:
raise HTTPException(status_code=400, detail=ErrorMessages.QUERY_TOO_SHORT)
# Get session data
session_data, found = session_manager.get_session(request.session_id, request.model_name)
if not found:
raise HTTPException(status_code=404, detail=ErrorMessages.SESSION_EXPIRED)
try:
# Validate session data integrity
is_valid, missing_keys = session_manager.validate_session(request.session_id)
if not is_valid:
raise HTTPException(status_code=500, detail=ErrorMessages.SESSION_INCOMPLETE)
# Prepare agent memory with chat history
agent_memory = ConversationBufferMemory(
memory_key="chat_history",
input_key="input",
return_messages=True
)
for entry in session_data.get("chat_history", []):
agent_memory.chat_memory.add_user_message(entry["user"])
agent_memory.chat_memory.add_ai_message(entry["assistant"])
# Retrieve initial similar chunks for context
initial_similar_chunks = retrieve_similar_chunks(
request.query,
session_data["index"],
session_data["chunks"],
session_data["model"],
k=Config.INITIAL_CONTEXT_CHUNKS
)
# Generate response using RAG service
response = rag_service.generate_response(
llm=session_data["llm"],
query=request.query,
context_chunks=initial_similar_chunks,
faiss_index=session_data["index"],
document_chunks=session_data["chunks"],
embedding_model=session_data["model"],
memory=agent_memory,
use_tavily=request.use_search
)
response_output = response.get("output", ErrorMessages.RESPONSE_GENERATION_ERROR)
# Save chat history
session_manager.add_chat_entry(
request.session_id,
request.query,
response_output
)
return ChatResponse(
status="success",
answer=response_output,
context_used=[
{
"text": chunk,
"score": float(score),
"metadata": meta
}
for chunk, score, meta in initial_similar_chunks
]
)
except HTTPException:
# Re-raise HTTP exceptions as-is
raise
except Exception as e:
raise HTTPException(
status_code=500,
detail=ErrorMessages.PROCESSING_ERROR.format(error=str(e))
)
|