128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
# rag_chain.py
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import os
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import weaviate
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from weaviate import Client
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from weaviate import WeaviateClient
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from weaviate.connect import ConnectionParams
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from weaviate.auth import AuthApiKey
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from weaviate.classes.init import Auth
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from langchain.vectorstores import Weaviate
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from langchain.chains import RetrievalQA
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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def build_rag_chain():
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# 1. Weaviate 클라이언트
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auth = weaviate.AuthApiKey(api_key="01js3q6y7twaxccm5dbh3se9bt")
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# client = weaviate.connect_to_weaviate_cloud(cluster_url="http://183.111.96.67:32668",
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# auth_credentials=Auth.api_key("01js3q6y7twaxccm5dbh3se9bt"),
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# headers={
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# "X-OpenAI-Api-Key": "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" # 필요할 경우
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# })
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OPENAI_API_KEY="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"
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client3 = weaviate.Client(
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url="http://183.111.96.67:32668", # 예: "http://183.111.96.67:32668"
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auth_client_secret=auth
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)
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# client = Client(
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# connection_params=ConnectionParams.from_http(
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# host="183.111.96.67", # 도메인 or IP
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# port=32668, # 포트
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# secure=False, # HTTP면 False, HTTPS면 True
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# auth_credentials=auth
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# )
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# )
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# connection_params = {
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# "http": {
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# "host": "183.111.96.67",
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# "port": 32668,
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# "secure": False
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# },
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# "grpc": {
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# "host": "183.111.96.67",
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# "port": 32619,
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# "secure": False
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# },
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# "auth_credentials": auth
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# }
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http_config = {
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"host": "183.111.96.67",
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"port": 32668,
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"secure": False
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}
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grpc_config = {
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"host": "183.111.96.67",
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"port": 32619,
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"secure": False
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}
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# connection_params = ConnectionParams(
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# http=HttpConfig(
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# host="183.111.96.67",
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# port=32668,
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# secure=False),
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# grpc=GrpcConfig(
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# host="183.111.96.67",
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# port=32619,
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# secure=False),
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# auth_credentials=auth_config
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# )
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connection_params = ConnectionParams(
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http=http_config,
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grpc=grpc_config,
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auth_credentials=auth
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)
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client = WeaviateClient(connection_params=connection_params)
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client2 = weaviate.connect_to_custom(
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http_host="183.111.96.67",
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http_port=32668,
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grpc_host="183.111.96.67",
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http_secure=False,
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grpc_port=32619,
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grpc_secure=False,
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auth_credentials=auth, # 인증이 필요 없으면 생략 가능
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headers={"X-OpenAI-Api-Key": OPENAI_API_KEY} # 필요시
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)
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if client.is_ready():
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print("Weaviate 연결 성공!")
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else:
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print("연결 실패. 서버 상태를 확인하세요.")
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if client2.is_ready():
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print("Weaviate 2 연결 성공!")
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else:
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print("연결 실패 2. 서버 상태를 확인하세요.")
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if client3.is_ready():
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print("Weaviate 3 연결 성공!")
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else:
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print("연결 실패 3. 서버 상태를 확인하세요.")
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# 2. 벡터스토어
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vectorstore = Weaviate(
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client=client3,
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index_name="LangDocs",
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text_key="text",
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embedding=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
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)
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# 3. HuggingFace LLM (예: mistralai/Mistral-7B-Instruct-v0.2)
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llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
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retriever = vectorstore.as_retriever()
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# 4. RetrievalQA chain 구성
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qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
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client.close()
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return qa_chain
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