# rag_chain.py
import os
import weaviate
from weaviate import Client
from weaviate.connect import ConnectionParams
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="01js3q6y7twaxccm5dbh3se9bt")

    # client = weaviate.connect_to_weaviate_cloud(cluster_url="http://183.111.96.67:32668", 
    #                          auth_credentials=Auth.api_key("01js3q6y7twaxccm5dbh3se9bt"),
    #                         headers={
    #     "X-OpenAI-Api-Key": "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"  # 필요할 경우
    # })
    OPENAI_API_KEY="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"
    
    # client = weaviate.Client(
    #     url="http://183.111.96.67:32668",  # 예: "http://183.111.96.67:32668"
    #     auth_client_secret=Auth.api_key("01js3q6y7twaxccm5dbh3se9bt"),  # 필요 없으면 제거
    #     additional_headers={
    #         "X-OpenAI-Api-Key": "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"  # 필요할 경우
    #     }  
    # )
    client = Client(
        connection_params=ConnectionParams.from_http(
            host="183.111.96.67",   # 도메인 or IP
            port=32668,             # 포트
            secure=False,            # HTTP면 False, HTTPS면 True
            auth_credentials=auth_config
        )
    )

    connection_params = ConnectionParams(
        http_host="183.111.96.67",
        http_port=32668,
        http_secure=False,
        grpc_host="183.111.96.67",
        grpc_port=32619,
        grpc_secure=False,
        auth_credentials=auth_config
    )

    client = WeaviateClient(connection_params)
  
    # client = weaviate.connect_to_custom(
    #     http_host="183.111.96.67",
    #     http_port=32668,
    #     grpc_host="183.111.96.67",
    #     http_secure=False,
    #     grpc_port=32619,
    #     grpc_secure=False,
    #     auth_credentials=AuthApiKey("01js3q6y7twaxccm5dbh3se9bt"),  # 인증이 필요 없으면 생략 가능
    #     headers={"X-OpenAI-Api-Key": OPENAI_API_KEY}  # 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(openai_api_key=OPENAI_API_KEY)
    )

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

    retriever = vectorstore.as_retriever()

    # 4. RetrievalQA chain 구성

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

    client.close()
    return qa_chain