Building long context RAG with RAPTOR from scratch
3 min read
4 months ago
Published on May 16, 2024
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Table of Contents
Building Long Context RAG with RAPTOR: Step-by-Step Tutorial
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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.
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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.
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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.
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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.
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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.
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Creating a Document Index:
- Index the raw documents and their summaries using a simple indexing tool like Chroma.
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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.
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Analyzing Results:
- Review the retrieved information, including raw documents and higher-level summaries, to understand the effectiveness of the retrieval process.
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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.
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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.