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import langchain |
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from langchain import HuggingFaceHub |
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from langchain.embeddings import HuggingFaceHubEmbeddings |
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from langchain.document_loaders import PyPDFLoader |
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from langchain.vectorstores import FAISS |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain.chains.question_answering import load_qa_chain |
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def llm_conv(filename): |
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document_loader = PyPDFLoader(filename) |
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chunks = document_loader.load_and_split() |
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embeddings = HuggingFaceHubEmbeddings() |
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db = FAISS.from_documents(chunks, embeddings) |
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return db, chunks |
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def similarity(filename, repo_id, model_kwargs, query): |
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db, chunks = llm_conv(filename) |
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docs = db.similarity_search(query) |
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chain = load_qa_chain( |
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HuggingFaceHub( |
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repo_id=repo_id, |
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model_kwargs=model_kwargs |
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), |
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chain_type="stuff" |
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) |
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question = f"<|system|>\nYou are a intelligent chatbot and expertise in {chunks[0].page_content}.</s>\n<|user|>\n{query}.\n<|assistant|>" |
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result = chain.run( |
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input_documents=docs, |
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question=question |
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) |
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return result |
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