Source URL: https://simonwillison.net/2024/Dec/6/roaming-rag/#atom-everything
Source: Simon Willison’s Weblog
Title: Roaming RAG – make the model find the answers
Feedly Summary: Roaming RAG – make the model find the answers
Neat new RAG technique (with a snappy name) from John Berryman:
The big idea of Roaming RAG is to craft a simple LLM application so that the LLM assistant is able to read a hierarchical outline of a document, and then rummage though the document (by opening sections) until it finds and answer to the question at hand. Since Roaming RAG directly navigates the text of the document, there is no need to set up retrieval infrastructure, and fewer moving parts means less things you can screw up!
John includes an example which works by collapsing a Markdown document down to just the headings, each with an instruction comment that says .
An expand_section() tool is then provided with the following tool description:
Expand a section of the markdown document to reveal its contents.
– Expand the most specific (lowest-level) relevant section first
– Multiple sections can be expanded in parallel
– You can expand any section regardless of parent section state (e.g. parent sections do not need to be expanded to view subsection content)
I’ve explored both vector search and full-text search RAG in the past, but this is the first convincing sounding technique I’ve seen that skips search entirely and instead leans into allowing the model to directly navigate large documents via their headings.
Via @jnbrymn.bsky.social
Tags: prompt-engineering, generative-ai, ai, rag, llms
AI Summary and Description: Yes
Summary: The text introduces a novel technique called Roaming RAG, which enhances how Large Language Models (LLMs) can access and retrieve information from document hierarchies. By enabling direct navigation of document headings without the need for complex retrieval infrastructures, it presents a significant advancement in generative AI applications, particularly for professionals in AI and document processing.
Detailed Description: The Roaming RAG method, presented by John Berryman, is a promising approach to streamline information retrieval for LLM applications. It specifically addresses the challenges associated with document navigation and retrieval systems. The following points outline its significance and functionality:
– **Concept of Roaming RAG**:
– It simplifies the LLM’s ability to read a hierarchical document structure rather than depending on traditional search mechanisms.
– By allowing the model to navigate directly through the headings, it reduces the complexity and potential points of failure often inherent in retrieval systems.
– **Functionality**:
– The technique involves compressing Markdown documents to focus only on headings, which streamlines the retrieval process.
– An ‘expand_section()’ tool is implemented to allow users to reveal specific contents of the document as needed, facilitating targeted navigation.
– **Execution**:
– Users can expand the most relevant sections first, which optimizes the search for specific information.
– The flexibility of expanding multiple sections simultaneously without the requirement of expanding parent sections means that users can gain access to information much more quickly and efficiently.
– **Comparative Advantage**:
– This approach contrasts with traditional vector or full-text searches that require constructing complex infrastructures.
– By eliminating the search step, Roaming RAG could lead to faster information retrieval and improved user experience in applications relying on generative AI.
Overall, Roaming RAG represents a significant innovation in the context of LLM application development, particularly for professionals involved in AI and information retrieval, as it offers a more straightforward and efficient method to access structured information within documents.