Owusu Samuel Blessing
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
import faiss
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from langchain_community.docstore.in_memory import InMemoryDocstore
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from langchain_community.vectorstores import FAISS
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.chains import create_retrieval_chain
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain_openai import OpenAIEmbeddings, ChatOpenAI
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import openai
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from openai import OpenAI
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from langchain_community.document_loaders.csv_loader import CSVLoader
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from pathlib import Path
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from langchain_openai import ChatOpenAI,OpenAIEmbeddings
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import os
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from dotenv import load_dotenv
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from scripts.rag import get_answer, response_agent
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# Load environment variables from a .env file
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# Initialize FastAPI app
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app = FastAPI()
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# Define a request model
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class QuestionRequest(BaseModel):
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question: str
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@app.post("/ask")
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def ask_question(request: QuestionRequest):
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try:
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# Use the RAG system to get the answer
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calculation_data = get_answer(rag_chain, request.question)
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final_response = response_agent(request.question, calculation_data)
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return {"answer": final_response}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=5079) |