45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
# rag_chain.py
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import os
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import weaviate
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from langchain.embeddings import HuggingFaceEmbeddings # 필요 시 HuggingFaceEmbeddings로 교체 가능
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from langchain.vectorstores import Weaviate
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from langchain.chains import RetrievalQA
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from langchain.llms import HuggingFaceHub
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def build_rag_chain():
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# 1. Weaviate 클라이언트
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auth_config = weaviate.AuthApiKey(api_key="01jryrcctd8c8vxbj4bs2ywrgs")
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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("01jryrcctd8c8vxbj4bs2ywrgs"),
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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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# 2. 벡터스토어
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vectorstore = Weaviate(
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client=client,
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index_name="Test",
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text_key="text",
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embedding= HuggingFaceEmbeddings(
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model_name="sentence-transformers/all-MiniLM-L6-v2"
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)
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)
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# 3. HuggingFace LLM (예: mistralai/Mistral-7B-Instruct-v0.2)
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llm = HuggingFaceHub(
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repo_id="mistralai/Mistral-7B-Instruct-v0.2",
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model_kwargs={
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"temperature": 0.1,
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"max_new_tokens": 512,
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"top_p": 0.95,
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}
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)
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retriever = vectorstore.as_retriever()
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# 4. RetrievalQA chain 구성
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return RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type="stuff")
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