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RAPTOR

Tree-organized retrieval with recursive summaries

MIT

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Flat chunk retrieval misses high-level themes in long documents because embeddings only see local passages. RAPTOR clusters chunks, summarizes them recursively into a tree, and retrieves at multiple levels so RAG can answer both detail and overview questions.

INTEGRATION GUIDE

1. Build a summary tree over long documents for multi-level RAG 2. Retrieve both fine-grained chunks and high-level abstracts 3. Improve long-document QA where flat vector search loses theme

TAGS

pythonragretrievalsummarizationtreelong-contextresearch
RAPTOR — AI Tool | Agentic AI For Good