[Question]: Can we let the LLM automatically identify whether RAG should be used in different senarios?

[waltonfuture]

Question Validation

• [X] I have searched both the documentation and discord for an answer.

Question

For every question, using RAG is time-consuming. I wonder if we can let the LLM automatically identify whether RAG should be used in different senarios.

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[ravi03071991]

@waltonfuture Yes, you can use a router that routes your query to RAG or the LLM itself can answer it. Esentially the router itself is an LLM.

[dosubot[bot]]

Based on the context provided, it seems you're looking to implement a router that can automatically decide whether to use the Retrieval-Augmented Generation (RAG) or the Language Model (LLM) itself to answer queries in different scenarios. This can be achieved by creating a decision-making function that analyzes the query and decides whether to use RAG or LLM. This function can then be integrated into the handle_question method to route the query to either RAG or LLM based on the decision made.

Here's an example of how you can implement this:

async def handle_question(self, question: str) -> None:
    if self.query_pipeline is None:
        raise ValueError("query_pipeline is not defined.")

    # Decision-making function to determine whether to use RAG or LLM
    def should_use_rag(question: str) -> bool:
        # Example heuristic: Use RAG if the query is complex or specific, otherwise use LLM
        # This is a placeholder, replace with your own logic
        return len(question.split()) > 5

    query_pipeline = cast(QueryPipeline, self.query_pipeline)
    query_pipeline.verbose = self.verbose
    chat_engine = cast(CondenseQuestionChatEngine, self.chat_engine)

    if should_use_rag(question):
        # Use RAG
        response = chat_engine.chat(question)
    else:
        # Directly use LLM without RAG
        response = self.llm.generate_response(question)  # Assuming LLM has a method like this

    if isinstance(response, StreamingResponse):
        response.print_response_stream()
    else:
        response = cast(Response, response)
        print(response)

In this example, the should_use_rag function is a placeholder for your decision-making logic. You need to replace it with your own logic that determines whether a query should be answered using RAG or directly by the LLM. The handle_question method is then modified to use this function to decide which method to use for answering the query.

Please note, the self.llm.generate_response(question) part is hypothetical and assumes that your LLM class or object has a method to directly generate responses to queries. You might need to adjust this part based on the actual capabilities and methods available in your LLM implementation.

You can refer to the LlamaIndex documentation and the Qdrant client code for more details.

This approach is based on a similar issue in the LlamaIndex repository: [Question]: How can I create a 'all-in-one' RAG pipeline?. The solution suggested in this issue is to implement a query classifier that determines the complexity of the query and routes it to the appropriate query engine. For simple queries, a naive top K retriever approach is suggested, which can be implemented using the search or search_batch methods of the Qdrant client in LlamaIndex. For complex queries, the SubQuestionQueryEngine is suggested.

I hope this helps! If you have any further questions, feel free to ask.

Sources

• [Question]: How can I create a 'all-in-one' RAG pipeline?
• llama-index-cli/llama_index/cli/rag/base.py
• llama-index-core/llama_index/core/base/query_pipeline/query.py

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[waltonfuture]

@waltonfuture Yes, you can use a router that routes your query to RAG or the LLM itself can answer it. Esentially the router itself is an LLM.

Thanks for your advice! But how can the router automatically judge whether my input need RAG or not without human control?

[durga0201]

@waltonfuture Routers are modules that take in a user query and a set of “choices” (defined by metadata), and returns one or more selected choices.

They can be used on their own (as “selector modules”), or used as a query engine or retriever (e.g. on top of other query engines/retrievers).

They are simple but powerful modules that use LLMs for decision making capabilities. They can be used for the following use cases and more:

Selecting the right data source among a diverse range of data sources

Deciding whether to do summarization (e.g. using summary index query engine) or semantic search (e.g. using vector index query engine)

Deciding whether to “try” out a bunch of choices at once and combine the results (using multi-routing capabilities).

The core router modules exist in the following forms:

LLM selectors put the choices as a text dump into a prompt and use LLM text completion endpoint to make decisions

Pydantic selectors pass choices as Pydantic schemas into a function calling endpoint, and return Pydantic objects