mirror of
https://github.com/arc53/DocsGPT
synced 2024-11-03 23:15:37 +00:00
69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
import os
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import pickle
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import dotenv
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import datetime
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from flask import Flask, request, render_template
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# os.environ["LANGCHAIN_HANDLER"] = "langchain"
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import faiss
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from langchain import OpenAI
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from langchain.chains import VectorDBQAWithSourcesChain
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from langchain.prompts import PromptTemplate
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# Redirect PosixPath to WindowsPath on Windows
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import platform
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if platform.system() == "Windows":
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import pathlib
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temp = pathlib.PosixPath
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pathlib.PosixPath = pathlib.WindowsPath
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# loading the .env file
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dotenv.load_dotenv()
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# loading the index and the store and the prompt template
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index = faiss.read_index("docs.index")
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with open("combine_prompt.txt", "r") as f:
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template = f.read()
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with open("faiss_store.pkl", "rb") as f:
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store = pickle.load(f)
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app = Flask(__name__)
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@app.route("/")
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def home():
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return render_template("index.html")
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@app.route("/api/answer", methods=["POST"])
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def api_answer():
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data = request.get_json()
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question = data["question"]
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store.index = index
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# create a prompt template
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c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template)
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# create a chain with the prompt template and the store
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chain = VectorDBQAWithSourcesChain.from_llm(llm=OpenAI(temperature=0), vectorstore=store, combine_prompt=c_prompt)
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# fetch the answer
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result = chain({"question": question})
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# some formatting for the frontend
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result['answer'] = result['answer'].replace("\\n", "<br>")
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result['answer'] = result['answer'].replace("SOURCES:", "")
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return result
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# handling CORS
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@app.after_request
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def after_request(response):
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response.headers.add('Access-Control-Allow-Origin', '*')
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response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
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response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
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return response
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if __name__ == "__main__":
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app.run(debug=True)
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