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| import logging | |
| from model import llm, vectorstore, splitter, embedding, QA_PROMPT | |
| # Chain for Web | |
| from langchain.chains import RetrievalQA | |
| bsic_chain = RetrievalQA.from_chain_type( | |
| llm=llm, | |
| chain_type="stuff", | |
| retriever = vectorstore.as_retriever(search_kwargs={"k": 4}), | |
| return_source_documents= True, | |
| input_key="question", | |
| chain_type_kwargs={"prompt": QA_PROMPT}, | |
| ) | |
| from MultiQueryRetriever import MultiQueryRetriever | |
| retriever_from_llm = MultiQueryRetriever.from_llm( | |
| retriever=vectorstore.as_retriever(search_kwargs={"k": 3}), | |
| llm=llm, | |
| ) | |
| multiQuery_chain = RetrievalQA.from_chain_type( | |
| llm=llm, | |
| chain_type="stuff", | |
| retriever = retriever_from_llm, | |
| return_source_documents= True, | |
| input_key="question", | |
| chain_type_kwargs={"prompt": QA_PROMPT}, | |
| ) |