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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 | |