# rag_chain.py
import os
import weaviate
from weaviate import Client
# from weaviate import WeaviateClient
# from weaviate.connect import ConnectionParams
from weaviate.auth import AuthApiKey
from weaviate.auth import AuthCredentials
# 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
from langchain.vectorstores import Weaviate as WeaviateLangchain
from langchain.embeddings.openai import OpenAIEmbeddings

def build_rag_chain():
    # 1. Weaviate 클라이언트
    auth = weaviate.AuthApiKey(api_key="01js3q6y7twaxccm5dbh3se9bt")

    OPENAI_API_KEY="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"
    
    # http_config = {
    #     "host": "183.111.96.67",
    #     "port": 32668,
    #     "secure": False
    # }
    # grpc_config = {
    #     "host": "183.111.96.67",
    #     "port": 32619,
    #     "secure": False
    # }

    # connection_params = ConnectionParams(
    #     http=http_config,
    #     grpc=grpc_config,
    #     auth_credentials=auth
    # )

    # client = WeaviateClient(connection_params=connection_params)
  
    # client2 = 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=auth,  # 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.")

    # if client2.is_ready():
    #     print("Weaviate 2 연결 성공!")
    # else:
    #     print("연결 실패 2. 서버 상태를 확인하세요.")

  
    # client3 = weaviate.connect_to_weaviate_cloud(
    #     cluster_url="s4x71b3lt32z3md1ya0yw.c0.asia-southeast1.gcp.weaviate.cloud",  # 예: "http://183.111.96.67:32668"
    #     auth_credentials=Auth.api_key("0ojxypfXy2Y1rKw3KQdBUtwcWSrYPWitE7Qu")  
    # )
  
    # if client3.is_ready():
    #     print("Weaviate 3 연결 성공!")
    # else:
    #     print("연결 실패 3. 서버 상태를 확인하세요.")


    # auth_key = AuthCredentials.api_key("01js3q6y7twaxccm5dbh3se9bt")  # 없으면 None
    # auth_cre= AuthCredentials.api_key("01js3q6y7twaxccm5dbh3se9bt")

    # 커넥션 설정 (http, grpc 둘 다)
    # auth_client_secret=Auth.api_key("01js3q6y7twaxccm5dbh3se9bt")
    # connection_params = ConnectionParams.from_params(
    #     http_host="183.111.96.67",
    #     http_port=32668,
    #     http_secure=False,
    #     grpc_host="183.111.96.67",
    #     grpc_port=32619,  # 예시 포트, 실제 grpc 포트 넣어야 함
    #     grpc_secure=False
    # )

    # client4 = WeaviateClient(connection_params=connection_params, auth_client_secret=auth_client_secret)

    # if client4.is_ready():
    #     print("Weaviate 4 연결 성공!")
    # else:
    #     print("연결 실패 4. 서버 상태를 확인하세요.")

    # import weaviate as legacy_weaviate
    # legacy_client = legacy_weaviate.Client(connection_params=client4.connection_params)  # langchain용


    client = weaviate.Client(
        url="http://183.111.96.67:32401",  # 예: http://localhost:8080
        auth_client_secret=weaviate.AuthApiKey(api_key="01js3q6y7twaxccm5dbh3se9bt"),
        additional_headers={
            "X-OpenAI-Api-Key": OPENAI_API_KEY
        }
    )

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

    vectorstore = WeaviateLangchain(client=client, index_name="LangDocs", text_key="text", embedding=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY))
    print("WeaviateLangchain")

    # 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)
    print("llm")

    retriever = vectorstore.as_retriever()
    print("retriever")

    # 4. RetrievalQA chain 구성

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

    # client.close()
    return qa_chain