Building long context RAG with RAPTOR from scratch

Building Long Context RAG with RAPTOR: Step-by-Step Tutorial

1. Introduction to Long Context LLMs and RAPTOR:

   • In this tutorial, we will explore the concept of long context LLMs and a new method called RAPTOR. Long context LLMs like Gemini and RAPTOR are being used for various projects, such as code assistants.

2. Understanding the Need for Retrieval Strategies:

   • Evaluate the limitations of using long context LLMs without retrieval strategies, such as latency and cost considerations.
   • Explore the idea of utilizing lightweight retrieval strategies to address these limitations while still leveraging long context models effectively.

3. Exploring RAPTOR Method:

   • Discuss the RAPTOR method, which involves clustering and summarizing information from a set of documents recursively to create abstract summaries.
   • Understand how RAPTOR can be beneficial for long context retrieval by integrating information from different parts of documents.

4. Implementing RAPTOR for Long Context Retrieval:

   • Set up a Python environment to run the RAPTOR method.
   • Retrieve a set of documents related to a specific topic, such as LangChain expressions.

5. Clustering and Summarizing Documents:

   • Use a Gaussian Mixture Model to cluster the documents based on their content.
   • Apply a summarization prompt to generate abstract summaries for each cluster.

6. Creating a Document Index:

   • Index the raw documents and their summaries using a simple indexing tool like Chroma.

7. Setting up a Retrieval Chain:

   • Establish a retrieval chain using the document index containing both raw web pages and summaries.
   • Run the retrieval process to fetch relevant information based on the query.

8. Analyzing Results:

   • Review the retrieved information, including raw documents and higher-level summaries, to understand the effectiveness of the retrieval process.

9. Considerations for Scalability:

   • Discuss the scalability of the approach and how it can be applied to larger corpora of documents.
   • Highlight the benefits of using RAPTOR for cases where individual documents do not exceed the context limit of the LLM.

10. Experimentation and Further Exploration:

    • Encourage experimentation with the RAPTOR method and document indexing for different use cases.
    • Emphasize the availability of code for users to explore and implement the long context retrieval process with RAPTOR.

By following these steps, you can build a long context retrieval system using the RAPTOR method from scratch, enabling effective information retrieval from a set of documents with complex content structures.