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from langchain_core.runnables import Runnable
from langchain_core.callbacks import BaseCallbackHandler
from fastapi import FastAPI, Request, Depends
from sse_starlette.sse import EventSourceResponse
from langserve.serialization import WellKnownLCSerializer
from typing import List
from sqlalchemy.orm import Session
from app.chains import simple_chain, formatted_chain, history_chain
import app.crud as crud
import app.models as models
import app.schemas as schemas
from app.database import SessionLocal, engine
from app.callbacks import LogResponseCallback
from app.prompts import format_chat_history
models.Base.metadata.create_all(bind=engine)
app = FastAPI()
def get_db():
db = SessionLocal()
try:
yield db
finally:
db.close()
async def generate_stream(input_data: schemas.BaseModel, runnable: Runnable, callbacks: List[BaseCallbackHandler]=[]):
for output in runnable.stream(input_data.dict(), config={"callbacks": callbacks}):
data = WellKnownLCSerializer().dumps(output).decode("utf-8")
yield {'data': data, "event": "data"}
yield {"event": "end"}
@app.post("/simple/stream")
async def simple_stream(request: Request):
data = await request.json()
user_question = schemas.UserQuestion(**data['input'])
return EventSourceResponse(generate_stream(user_question, simple_chain))
@app.post("/formatted/stream")
async def formatted_stream(request: Request):
# TODO: use the formatted_chain to implement the "/formatted/stream" endpoint.
data = await request.json()
user_question = schemas.UserQuestion(**data['input'])
return EventSourceResponse(generate_stream(user_question, formatted_chain))
@app.post("/history/stream")
async def history_stream(request: Request, db: Session = Depends(get_db)):
# TODO: Let's implement the "/history/stream" endpoint. The endpoint should follow those steps:
# - The endpoint receives the request
# - The request is parsed into a user request
# - The user request is used to pull the chat history of the user
# - We add as part of the user history the current question by using add_message.
# - We create an instance of HistoryInput by using format_chat_history.
# - We use the history input within the history chain.
data = await request.json()
history_input = schemas.HistoryInput(**data['input'])
user_name = history_input.username
crud.add_message(db, schemas.MessageBase(message=history_input.question, type="user"), user_name)
chat_history = crud.get_user_chat_history(db, user_name)
chat_history_str = format_chat_history(chat_history)
history_input.chat_history = chat_history_str
return EventSourceResponse(generate_stream(history_input, history_chain))
@app.post("/rag/stream")
async def rag_stream(request: Request, db: Session = Depends(get_db)):
# TODO: Let's implement the "/rag/stream" endpoint. The endpoint should follow those steps:
# - The endpoint receives the request
# - The request is parsed into a user request
# - The user request is used to pull the chat history of the user
# - We add as part of the user history the current question by using add_message.
# - We create an instance of HistoryInput by using format_chat_history.
# - We use the history input within the rag chain.
raise NotImplemented
@app.post("/filtered_rag/stream")
async def filtered_rag_stream(request: Request, db: Session = Depends(get_db)):
# TODO: Let's implement the "/filtered_rag/stream" endpoint. The endpoint should follow those steps:
# - The endpoint receives the request
# - The request is parsed into a user request
# - The user request is used to pull the chat history of the user
# - We add as part of the user history the current question by using add_message.
# - We create an instance of HistoryInput by using format_chat_history.
# - We use the history input within the filtered rag chain.
raise NotImplemented
if __name__ == "__main__":
import uvicorn
uvicorn.run("main:app", host="localhost", reload=True, port=8000) |