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main.py
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main.py
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from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
from dotenv import load_dotenv
import pickle
import os
from langchain_utils import search_doc
from langchain.llms import OpenAI
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, HypotheticalDocumentEmbedder
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
load_dotenv()
# def load_docsearch(pth):
# with open(pth, "rb") as f:
# return pickle.load(f)
def load_docsearch(pth, fname):
with open(os.path.join(pth, f"{fname}.pkl"), "rb") as f:
docsearch = pickle.load(f)
docsearch.load_local(os.path.join(pth, f"{fname}.idx"))
return docsearch
docsearch = load_docsearch("data", "GOOG_hype")
app = FastAPI()
class Msg(BaseModel):
msg: str
@app.get("/")
async def root():
return {"message": "Hello World. Welcome to FastAPI! Changed!"}
@app.get("/path")
async def demo_get():
return {"message": "This is /path endpoint, use a post request to transform the text to uppercase"}
@app.post("/path")
async def demo_post(inp: Msg):
return {"message": inp.msg.upper()}
@app.get("/path/{query}")
async def query(query: str):
answer = search_doc(query, docsearch, return_only_outputs=True)["output_text"]
return {"response": f"{answer}"}
def start():
"""Launched with `poetry run start` at root level"""
uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)