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from langchain.chains import RetrievalQA |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.vectorstores import Chroma |
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from data import prepare_data |
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path = './llm' |
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persist_directory = "vector_db" |
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llm = prepare_data(db_path = persist_directory, llm_path = path) |
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embeddings = HuggingFaceEmbeddings() |
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vectordb = Chroma(persist_directory = persist_directory, embedding_function = embeddings) |
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doc_retriever = vectordb.as_retriever() |
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shakespeare_qa = RetrievalQA.from_chain_type(llm = llm, chain_type = "stuff", retriever = doc_retriever) |
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if __name__ == "__main__": |
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import gradio as gr |
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def make_inference(query): |
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shakespeare_qa.run(query) |
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demo = gr.Interface(fn = make_inference, inputs = "text", outputs = "text", |
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title = "Answer to the question about Shakespeare", |
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description = "This is a demo of the LangChain library.", ) |
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demo.launch() |
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