# rag_chain.py
import os
import weaviate
from weaviate.auth import AuthApiKey
from weaviate.classes.init import Auth
from langchain.vectorstores import Weaviate
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings

def build_rag_chain():
    # 1. Weaviate 클라이언트
    auth_config = weaviate.AuthApiKey(api_key="01jryrcctd8c8vxbj4bs2ywrgs")

    # client = weaviate.connect_to_weaviate_cloud(cluster_url="http://183.111.96.67:32668", 
    #                          auth_credentials=Auth.api_key("01jryrcctd8c8vxbj4bs2ywrgs"),
    #                         headers={
    #     "X-OpenAI-Api-Key": "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"  # 필요할 경우
    # })
    # client = weaviate.Client(
    #     url="http://183.111.96.67:32668",  # 예: "http://183.111.96.67:32668"
    #     auth_client_secret=Auth.api_key("01jryrcctd8c8vxbj4bs2ywrgs"),  # 필요 없으면 제거
    #     additional_headers={
    #         "X-OpenAI-Api-Key": "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"  # 필요할 경우
    #     }  
    # )

    client = weaviate.connect_to_custom(
        http_host="183.111.96.67",
        http_port=32668,
        auth_credentials=AuthApiKey("01jryrcctd8c8vxbj4bs2ywrgs"),  # 인증이 필요 없으면 생략 가능
        headers={"X-OpenAI-Api-Key": "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"}  # 필요시
    )

    if client.is_ready():
        print("Weaviate 연결 성공!")
    else:
        print("연결 실패. 서버 상태를 확인하세요.")


    # 2. 벡터스토어
    vectorstore = Weaviate(
        client=client,
        index_name="LangDocs",
        text_key="text",
        embedding=OpenAIEmbeddings()
    )

    # 3. HuggingFace LLM (예: mistralai/Mistral-7B-Instruct-v0.2)
    llm = ChatOpenAI(temperature=0)

    retriever = vectorstore.as_retriever()

    # 4. RetrievalQA chain 구성

    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever)