130 lines
4.3 KiB
Python
130 lines
4.3 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.auth import AuthCredentials
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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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from langchain.vectorstores import Weaviate as WeaviateLangchain
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from langchain.embeddings.openai 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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OPENAI_API_KEY="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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=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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# client3 = weaviate.connect_to_weaviate_cloud(
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# cluster_url="s4x71b3lt32z3md1ya0yw.c0.asia-southeast1.gcp.weaviate.cloud", # 예: "http://183.111.96.67:32668"
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# auth_credentials=Auth.api_key("0ojxypfXy2Y1rKw3KQdBUtwcWSrYPWitE7Qu")
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# )
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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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# auth_key = AuthCredentials.api_key("01js3q6y7twaxccm5dbh3se9bt") # 없으면 None
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# auth_cre= AuthCredentials.api_key("01js3q6y7twaxccm5dbh3se9bt")
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# 커넥션 설정 (http, grpc 둘 다)
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# auth_client_secret=Auth.api_key("01js3q6y7twaxccm5dbh3se9bt")
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# connection_params = ConnectionParams.from_params(
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# http_host="183.111.96.67",
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# http_port=32668,
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# http_secure=False,
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# grpc_host="183.111.96.67",
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# grpc_port=32619, # 예시 포트, 실제 grpc 포트 넣어야 함
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# grpc_secure=False
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# )
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# client4 = WeaviateClient(connection_params=connection_params, auth_client_secret=auth_client_secret)
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# if client4.is_ready():
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# print("Weaviate 4 연결 성공!")
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# else:
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# print("연결 실패 4. 서버 상태를 확인하세요.")
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# import weaviate as legacy_weaviate
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# legacy_client = legacy_weaviate.Client(connection_params=client4.connection_params) # langchain용
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client = weaviate.Client(
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url="http://183.111.96.67:32668", # 예: http://localhost:8080
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auth_client_secret=weaviate.AuthApiKey(api_key="01js3q6y7twaxccm5dbh3se9bt"),
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additional_headers={
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"X-OpenAI-Api-Key": OPENAI_API_KEY
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}
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)
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vectorstore = WeaviateLangchain(client=client, index_name="LangDocs", text_key="text", embedding=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY))
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# 2. 벡터스토어
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# vectorstore = Weaviate(
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# client=client,
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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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