How to combine an LLMChain with a RAG (with memory)

[guilhermelirio]

I am building an application where the chatbot can respond both via RAG (with the documents already embedded) and can respond to another prompt with functions (search via API and bring the result).

How could I build this application?

[dosubot[bot]]

Hey there, @guilhermelirio! I'm here to help you out with any coding conundrums or questions you might have. Let's tackle this challenge together!

To combine an LLMChain with a RAG setup that includes memory, you can follow these steps:

1. Initialize a Conversation Buffer: Use a data structure to store the conversation history, which will help maintain context across interactions.

2. Set Up the RAG System: Use a retriever to fetch relevant documents based on the user's query. Ensure these documents are embedded and can be accessed efficiently.

3. Integrate LLMChain: Create a chain that can handle both RAG responses and function-based responses. You can use a routing mechanism to decide whether to use the RAG or call an API function based on the user's input.

4. Combine with Memory: Incorporate the conversation buffer into your chain. This can be done by passing the conversation history as part of the input to your prompt or chain, ensuring that the context of previous interactions is considered.

Here's a basic outline of how you might implement this:

# Initialize conversation buffer
conversation_buffer = []

# Define a function to update the buffer
def update_conversation(user_input, model_response):
    conversation_buffer.append({"user": user_input, "model": model_response})

# Set up the RAG system
def retrieve_documents(query):
    # Implement your document retrieval logic here
    pass

# Define the LLMChain with routing
def llm_chain(input):
    if should_use_rag(input):
        documents = retrieve_documents(input)
        # Process documents and generate response
    else:
        # Call API function and generate response
        pass

# Use the conversation buffer in the chain
def process_input(user_input):
    # Include conversation history in the input
    input_with_history = {"input": user_input, "history": conversation_buffer}
    response = llm_chain(input_with_history)
    update_conversation(user_input, response)
    return response

For more detailed examples and discussions on similar topics, you might find these discussions helpful:

• Combining RAG and Function calling ^[1]
• How to do RAG + Conversational Memory? ^[2]

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[feijoes]

@guilhermelirio this might be useful: https://medium.com/@pani.chinmaya/memory-for-your-rag-based-chat-bot-using-langchain-b4d720031671

[guilhermelirio]

@feijoes I will read it, thank you!

EDIT:
I read it here and it talks more about the types of memory.
The issue is not the memory itself. It is knowing when to use LLMChain (chatbot) instead of RAG to bring the answer.

Remember that in the Chatbot, I need to make some API calls.

[guilhermelirio]

@dosu How would I do item 3? Do you have an example?

Integrate LLMChain: Create a chain that can handle both RAG responses and function-based responses. You can use a routing mechanism to decide whether to use the RAG or call an API function based on the user's input.

[dosubot[bot]]

Could you provide more details about the specific routing mechanism you are considering or any particular framework or library you are using for this integration? This will help in providing a more tailored example.

To continue talking to Dosu, mention @dosu.

[guilhermelirio]

@dosu yes!

It is knowing when to use LLMChain (chatbot) instead of RAG to bring the answer.

Remember that in the Chatbot, I need to make some API calls.

[dosubot[bot]]

Could you share more about the criteria or conditions you want to use to decide between using the LLMChain and RAG? Additionally, are there specific types of user inputs or scenarios where you know you will need to make API calls? This information will help in designing a suitable routing mechanism.

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