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23 lines
840 B
23 lines
840 B
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import os
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from langchain.vectorstores import Chroma
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.chat_models import ChatOpenAI
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from langchain.chains import RetrievalQA
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from langchain.vectorstores.base import VectorStoreRetriever
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base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
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# Load and process the text
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embedding = OpenAIEmbeddings()
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persist_directory = 'db'
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# Now we can load the persisted database from disk, and use it as normal.
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llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_base=base_path)
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vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
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retriever = VectorStoreRetriever(vectorstore=vectordb)
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qa = RetrievalQA.from_llm(llm=llm, retriever=retriever)
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query = "What the president said about taxes ?"
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print(qa.run(query))
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