DocsGPT/application/app.py
2023-02-07 22:42:59 +00:00

110 lines
3.3 KiB
Python

import os
import pickle
import dotenv
import datetime
from flask import Flask, request, render_template
# os.environ["LANGCHAIN_HANDLER"] = "langchain"
import faiss
from langchain import OpenAI
from langchain.chains import VectorDBQAWithSourcesChain
from langchain.prompts import PromptTemplate
import requests
# Redirect PosixPath to WindowsPath on Windows
import platform
if platform.system() == "Windows":
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
# loading the .env file
dotenv.load_dotenv()
with open("combine_prompt.txt", "r") as f:
template = f.read()
app = Flask(__name__)
@app.route("/")
def home():
return render_template("index.html")
@app.route("/api/answer", methods=["POST"])
def api_answer():
data = request.get_json()
question = data["question"]
api_key = data["api_key"]
# check if the vectorstore is set
if "active_docs" in data:
vectorstore = "vectors/" + data["active_docs"]
if data['active_docs'] == "default":
vectorstore = ""
else:
vectorstore = ""
# loading the index and the store and the prompt template
index = faiss.read_index(f"{vectorstore}docs.index")
with open(f"{vectorstore}faiss_store.pkl", "rb") as f:
store = pickle.load(f)
store.index = index
# create a prompt template
c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template)
# create a chain with the prompt template and the store
chain = VectorDBQAWithSourcesChain.from_llm(llm=OpenAI(openai_api_key=api_key, temperature=0), vectorstore=store, combine_prompt=c_prompt)
# fetch the answer
result = chain({"question": question})
# some formatting for the frontend
result['answer'] = result['answer'].replace("\\n", "<br>")
result['answer'] = result['answer'].replace("SOURCES:", "")
# mock result
# result = {
# "answer": "The answer is 42",
# "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
# }
return result
@app.route("/api/docs_check", methods=["POST"])
def check_docs():
# check if docs exist in a vectorstore folder
data = request.get_json()
vectorstore = "vectors/" + data["docs"]
base_path = 'https://raw.githubusercontent.com/arc53/DocsHUB/main/'
#
if os.path.exists(vectorstore):
return {"status": 'exists'}
else:
r = requests.get(base_path + vectorstore + "docs.index")
# save to vectors directory
# check if the directory exists
if not os.path.exists(vectorstore):
os.makedirs(vectorstore)
with open(vectorstore + "docs.index", "wb") as f:
f.write(r.content)
# download the store
r = requests.get(base_path + vectorstore + "faiss_store.pkl")
with open(vectorstore + "faiss_store.pkl", "wb") as f:
f.write(r.content)
return {"status": 'loaded'}
# handling CORS
@app.after_request
def after_request(response):
response.headers.add('Access-Control-Allow-Origin', '*')
response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
return response
if __name__ == "__main__":
app.run(debug=True)