from dotenv import load_dotenv load_dotenv('environment.env') from fastapi import FastAPI, Request, HTTPException, status, Body # from fastapi.templating import Jinja2Templates from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import FileResponse from fastapi import Depends from contextlib import asynccontextmanager from pydantic import BaseModel from typing import List, Optional import uvicorn from typing import List, Optional import sqlparse from sqlalchemy import create_engine import pandas as pd #from retrying import retry import datetime import json from json import loads import pandas as pd import time from langchain.callbacks import get_openai_callback from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings from RAG_strategy import multi_query, naive_rag, naive_rag_for_qapairs from Indexing_Split import create_retriever as split_retriever from Indexing_Split import gen_doc_from_database, gen_doc_from_history import os from langchain_community.vectorstores import SupabaseVectorStore from langchain_openai import OpenAIEmbeddings from supabase.client import Client, create_client from add_vectordb import GetVectorStore from langchain_community.cache import RedisSemanticCache # 更新导入路径 from langchain_core.prompts import PromptTemplate import openai openai_api_key = os.getenv("OPENAI_API_KEY") openai.api_key = openai_api_key URI = os.getenv("SUPABASE_URI") global_retriever = None @asynccontextmanager async def lifespan(app: FastAPI): global global_retriever global vector_store start = time.time() # global_retriever = split_retriever(path='./Documents', extension="docx") # global_retriever = raptor_retriever(path='../Documents', extension="txt") # global_retriever = unstructured_retriever(path='../Documents') supabase_url = os.getenv("SUPABASE_URL") supabase_key = os.getenv("SUPABASE_KEY") document_table = "documents" supabase: Client = create_client(supabase_url, supabase_key) embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) vector_store = GetVectorStore(embeddings, supabase, document_table) global_retriever = vector_store.as_retriever(search_kwargs={"k": 4}) print(time.time() - start) yield def get_retriever(): return global_retriever def get_vector_store(): return vector_store app = FastAPI(lifespan=lifespan) # templates = Jinja2Templates(directory="temp") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @app.get("/answer2") def multi_query_answer(question, retriever=Depends(get_retriever)): start = time.time() with get_openai_callback() as cb: # qa_doc = gen_doc_from_database() # qa_history_doc = gen_doc_from_history() # qa_doc.extend(qa_history_doc) # vectorstore = Chroma.from_documents(documents=qa_doc, embedding=OpenAIEmbeddings(), collection_name="qa_pairs") # retriever_qa = vectorstore.as_retriever(search_kwargs={"k": 3}) # final_answer, reference_docs = naive_rag_for_qapairs(question, retriever_qa) final_answer = 'False' if final_answer == 'False': final_answer, reference_docs = multi_query(question, retriever, chat_history=[]) # print(CHAT_HISTORY) # with get_openai_callback() as cb: # final_answer, reference_docs = multi_query(question, retriever) processing_time = time.time() - start print(processing_time) save_history(question, final_answer, reference_docs, cb, processing_time) return {"Answer": final_answer} class ChatHistoryItem(BaseModel): q: str a: str @app.post("/answer_with_history") def multi_query_answer(question: Optional[str] = '', chat_history: List[ChatHistoryItem] = Body(...), retriever=Depends(get_retriever)): start = time.time() chat_history = [(item.q, item.a) for item in chat_history if item.a != ""] print(chat_history) # TODO: similarity search with get_openai_callback() as cb: final_answer, reference_docs = multi_query(question, retriever, chat_history) processing_time = time.time() - start print(processing_time) save_history(question, final_answer, reference_docs, cb, processing_time) return {"Answer": final_answer} @app.post("/answer_with_history2") def multi_query_answer(question: Optional[str] = '', extension: Optional[str] = 'pdf', chat_history: List[ChatHistoryItem] = Body(...), retriever=Depends(get_retriever)): start = time.time() retriever = vector_store.as_retriever(search_kwargs={"k": 4, 'filter': {'extension':extension}}) chat_history = [(item.q, item.a) for item in chat_history if item.a != ""] print(chat_history) # TODO: similarity search with get_openai_callback() as cb: final_answer, reference_docs = multi_query(question, retriever, chat_history) processing_time = time.time() - start print(processing_time) save_history(question, final_answer, reference_docs, cb, processing_time) return {"Answer": final_answer} def save_history(question, answer, reference, cb, processing_time): # reference = [doc.dict() for doc in reference] record = { 'Question': [question], 'Answer': [answer], 'Total_Tokens': [cb.total_tokens], 'Total_Cost': [cb.total_cost], 'Processing_time': [processing_time], 'Contexts': [str(reference)] } df = pd.DataFrame(record) engine = create_engine(URI) df.to_sql(name='systex_records', con=engine, index=False, if_exists='append') class history_output(BaseModel): Question: str Answer: str Contexts: str Total_Tokens: int Total_Cost: float Processing_time: float Time: datetime.datetime @app.get('/history', response_model=List[history_output]) async def get_history(): engine = create_engine(URI, echo=True) df = pd.read_sql_table("systex_records", engine.connect()) df.fillna('', inplace=True) result = df.to_json(orient='index', force_ascii=False) result = loads(result) return result.values() @app.get("/") def read_root(): return {"message": "Welcome to the SYSTEX API"} if __name__ == "__main__": uvicorn.run("RAG_app:app", host='127.0.0.1', port=8081, reload=True) # if __name__ == "__main__": # uvicorn.run("RAG_app:app", host='cmm.ai', port=8081, reload=True, ssl_keyfile="/etc/letsencrypt/live/cmm.ai/privkey.pem", # ssl_certfile="/etc/letsencrypt/live/cmm.ai/fullchain.pem")