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| from langchain.chains import RetrievalQA | |
| def generate_response(llm, vector_store, question, relevant_docs): | |
| # Create a retrieval-based question-answering chain using the relevant documents | |
| qa_chain = RetrievalQA.from_chain_type( | |
| llm=llm, | |
| retriever=vector_store.as_retriever(), | |
| return_source_documents=True | |
| ) | |
| try: | |
| result = qa_chain.invoke(question, documents=relevant_docs) | |
| response = result['result'] | |
| source_docs = result['source_documents'] | |
| return response, source_docs | |
| except Exception as e: | |
| print(f"Error during QA chain invocation: {e}") | |
| raise e | |