dosubot[bot]
commented
6 months ago 🤖
Hey there, @deepak-habilelabs! Good to see you working with LangChain again. Hope your project is coming along smoothly. Let's dive into your issue.
Based on the information you've provided, it seems like you're trying to implement the RetrievalQA class with a RAG (Retrieval-Augmented Generation) setup. The output you're seeing is the representation of the RetrievalQA instance you've created.
The RetrievalQA class in the LangChain framework is used for creating a question-answering system. It retrieves relevant information from a given set of documents based on the question asked. The from_chain_type method in the RetrievalQA class is a class method that initializes an instance of the BaseRetrievalQA class using a specified chain type.
The StuffDocumentsChain class you're using seems to be a custom implementation, as it's not directly mentioned in the LangChain repository. It appears to be a class that combines documents in a certain way before they're processed by the language model. However, without more specific information about this class, it's hard to provide a more detailed explanation.
The return_source_documents=True argument in your RetrievalQA instance indicates that the source documents used for answering the question will be returned along with the answer. This can be useful for debugging or for understanding how the model is coming up with its answers.
The memory=ConversationBufferMemory(...) part is setting up a memory buffer for the conversation, which can be used to store and retrieve past interactions in the conversation.
If you're experiencing issues with the RetrievalQA not returning the expected results, it could be due to a number of factors. It could be related to the specific documents you're using, the way you're splitting and embedding the documents, the specific question you're asking, or the parameters you're using for the language model or the retriever.
If you could provide more specific details about the issue you're experiencing, such as the specific documents you're using, the question you're asking, and the answer you're getting vs. the answer you're expecting, it would be easier to help diagnose the issue.
For more information, you can refer to the LangChain repository and the specific files mentioned in the context:
- libs/langchain/tests/integration_tests/chains/test_retrieval_qa.py
- libs/langchain/langchain/chains/retrieval_qa/base.py
I hope this helps! If you have any more questions or need further clarification, feel free to ask.
-Dosu
Sources
- libs/langchain/tests/integration_tests/chains/test_retrieval_qa.py
- libs/langchain/langchain/chains/retrieval_qa/base.py
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qa = RetrievalQA.from_chain_type(llm=llm, chain_type='stuff', retriever=retriever, return_source_documents=True, chain_type_kwargs={'prompt': prompt}, memory=memory, ) print(qa) below is my result when I am printing qa , I am implementing RAG memory=ConversationBufferMemory(output_key='result', input_key='query', return_messages=True, memory_key='chat_history') combine_documents_chain=StuffDocumentsChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['context', 'question'], template="Use the following pieces of information to answer the user's question.\n If you don't know the answer, just say that you don't know, don't try to make up an answer.\n Context: {context}\n Question: {question}\n Only return the helpful answer below and nothing else.\n Helpful answer:\n "), llm=ChatOpenAI(client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, model_name='gpt-3.5-turbo-16k', temperature=0.1, openai_api_key='sk-??', openai_proxy='')), document_variable_name='context') return_source_documents=True retriever=VectorStoreRetriever(tags=['Chroma', 'OpenAIEmbeddings'], vectorstore=<langchain_community.vectorstores.chroma.Chroma object at 0x7fbc6a8bfd60>, search_kwargs={'k': 2})