raptor

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Basic Information

This repository is the official implementation of RAPTOR, short for Recursive Abstractive Processing for Tree-Organized Retrieval. It serves as a reference codebase implementing the RAPTOR method and is intended to support researchers and engineers who work on retrieval tasks that exploit hierarchical or tree-structured document organization. The project focuses on combining recursive processing with abstractive summarization steps to improve retrieval over tree-organized data. Its purpose is to enable reproduction of the RAPTOR approach, to facilitate experimentation with recursive and hierarchical retrieval strategies, and to provide an implementation that can be studied, adapted, or integrated into retrieval-augmented systems and research workflows.

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Features
Reference implementation of the RAPTOR algorithm emphasizing recursive abstractive processing for tree-organized retrieval. Code-level realization of mechanisms to traverse and process tree-structured documents with recursive summarization or abstraction steps. Aimed at enabling experiments that combine retrieval and abstractive processing in hierarchical contexts. Provides a consolidated codebase labeled as the official implementation to support study and reuse of the RAPTOR method. Designed to be a starting point for reproducing results, comparing approaches, and integrating tree-based retrieval logic into larger retrieval-augmented systems.
Use Cases
Helps researchers reproduce and validate the RAPTOR method and its reported behaviors on tree-organized retrieval tasks. Supports prototyping of hierarchical retrieval pipelines that require recursive abstraction or summarization stages. Enables comparative evaluation of retrieval strategies that leverage document trees and structured organization. Assists engineers who want to adapt the RAPTOR approach into retrieval-augmented generation systems or other applications needing structured retrieval. Serves as a technical reference for understanding how recursive abstractive processing can be applied to tree-structured information.

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