Intelligent RAG, Fetch Surrounding Chunks

In the realm of Retrieval-Augmented Generation (RAG), one persistent challenge is finding the optimal amount of data to feed into a Large Language Model (LLM). Too little data results in insufficient or inaccurate responses, while too much data leads to vague answers. This delicate balance inspired me to develop a notebook focusing on intelligent chunking and leveraging Elasticsearch vector database.

The Motivation

The primary motivation behind building this notebook was to demonstrate a refined approach to RAG by addressing the challenge of data chunking. Traditional methods often fall short in dynamically adjusting the data size fed to LLMs, either overwhelming the model with too much context or starving it with too little. This notebook aims to strike the right balance, providing just enough information for the LLM to generate precise and contextually relevant responses. However, it must be noted that there is no one-size-fits-all solution.

This method works especially well with books and similar texts where content flows within longer sections or chapters. However, it may require adaptation for texts structured into shorter, distinct sections, such as research papers or articles, where each segment might cover a different topic. In such cases, additional strategies may be necessary to effectively chunk and retrieve related content.

The Methodology

Fetch Surrounding Chunks

The core idea is to partition the source text into manageable chunks, ensuring each chunk contains just the right amount of information. For this demonstration, I used text from "Harry Potter and the Sorcerer's Stone." The text was partitioned into chapters, and each chapter was further divided into smaller chunks. These chunks, along with their dense and sparse (ELSER) vector representations, were indexed in the Elasticsearch vector database.

architecture

Assigning Numbers to Chunks

Each chunk within a chapter was assigned a sequential integer, allowing us to identify its position. When a matching chunk is found, the chapter number and chunk number are used to retrieve surrounding chunks, providing additional context for the LLM.

Vector Database in Elasticsearch

These chunks and their vector representations were ingested into an Elasticsearch Cloud instance. Elasticsearch's robust vector search capabilities make it ideal for hosting these chunks, allowing for efficient retrieval of the most relevant chunks based on the semantic content or text match of a user's query.

To retrieve the relevant chunks, I employed a hybrid search strategy using dense vector comparisons, sparse vector comparisons, and text search in parallel. This multi-faceted approach ensures that the search results are both semantically rich and contextually accurate. A query is issued to find the matched chunk, which returns the chunk number and chapter. Surrounding chunks for that chapter are then fetched based on the matched chunk.

architecture

The RAG Pattern

When a query is made, the search flow performs the following steps:

  1. Query Analysis: The user's query is translated into dense and sparse vectors to retrieve the most relevant chunks from the Elasticsearch index.
  2. Chunk Retrieval: Using the AI search strategy, the system retrieves the top relevant chunks.
  3. Contextual Expansion: Adjacent chunks (n-1 and n+1) are also retrieved to provide a more comprehensive context. If the chunk is the last in the chapter, it fetches n-1 and n-2; if it's the first, it fetches n+1 and n+2.
  4. LLM Response: These intelligently selected chunks are then fed into the LLM, ensuring it receives the optimal amount of information to generate a precise and contextually relevant response.

Why This Matters

This approach addresses a critical aspect of RAG by optimizing the input data fed to LLMs. By leveraging intelligent chunking and hybrid semantic search, this method enhances the accuracy and relevance of the responses generated by LLMs. It showcases a pattern that can be widely applied in various applications within the RAG space, from customer support to content generation and beyond.

Conclusion

This notebook underscores the importance of intelligent data chunking in the RAG framework and demonstrates how Elasticsearch vector database can be leveraged to achieve optimal results. By ensuring the LLM receives just the right amount of information, this methodology paves the way for more accurate and contextually rich responses, enhancing the overall effectiveness of RAG systems.

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Looking to build RAG into your apps? Want to try different LLMs with a vector database?
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