query_faiss.py

import os
from dotenv import load_dotenv
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings, OpenAI
from langchain.chains.retrieval import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain import hub


def main():
    # Load environment variables from a .env file
    load_dotenv()

    embeddings = OpenAIEmbeddings()
    new_vectorstore = FAISS.load_local(
        "faiss_index_react", embeddings, allow_dangerous_deserialization=True
    )

    retrieval_qa_chat_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
    combine_docs_chain = create_stuff_documents_chain(
        OpenAI(), retrieval_qa_chat_prompt
    )
    retrieval_chain = create_retrieval_chain(
        new_vectorstore.as_retriever(), combine_docs_chain
    )

    res = retrieval_chain.invoke({"input": "Give me the gist of ReAct in 3 sentences."})
    print(res["answer"])


if __name__ == "__main__":
    main()