🦖RAPTOR🦖 for Advanced RAG
“Retrieval-augmented language models can better adapt to changes in world state and incorporate long-tail knowledge.”
Yet, the majority of existing RAG methodologies only retrieve short, contiguous chunks from a retrieval corpus, which limits the holistic understanding of the document's overall context.
Today, we introduce a new technique that marries clustering with traditional RAG approach.
The concept is straightforward: cluster similar chunks to enrich the context. However, it goes beyond just that, it actually involves "recursively embedding, clustering, and summarizing chunks of text, constructing a tree with differing levels of summarization from the bottom up."
🚀Why RAPTOR?
During inference, the RAPTOR model leverages this tree architecture, weaving together information from extensive documents across different abstraction levels.
As detailed in the original paper, "RAPTOR: RECURSIVE ABSTRACTIVE PROCESSING FOR TREE-ORGANIZED RETRIEVAL," controlled experiments have illustrated that retrieval with recursive summaries significantly outperforms traditional retrieval-augmented generation methods across a variety of tasks.
In question-answering tasks that require complex, multi-step reasoning, this approach has demonstrated unparalleled results. For instance, integrating RAPTOR retrieval with GPT-4 usage has elevated the best performance on the QuALITY benchmark by a remarkable 20% in absolute accuracy.
Curious to delve deeper into this?
Join Professor Mehdi as he explains the concept of RAPTOR and leads us through a discussion of the technique, including pros and cons for production, in the video below!👇
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Source of images/quotes:
🖼️ Blogpost for today: Ladder of Abstraction:
🗞️Paper: RAPTOR: RECURSIVE ABSTRACTIVE PROCESSING FOR TREE-ORGANIZED RETRIEVAL: https://arxiv.org/pdf/2401.18059.pdf
🔨 Implementation: Coming next week!
📚 Also if you'd like to learn more about RAG systems, check out our book on the RAG system:
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