RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) builds hierarchical summaries for multi-level retrieval. Problem: Standard RAG retrieves leaf chunks, missing high-level context. Long documents need both summary understanding and detail access. Architecture: Split documents into chunks → summarize groups of chunks → summarize summaries → build tree hierarchy. Each level provides different granularity. Retrieval strategy: Can retrieve at any level - high-level for overview questions, leaf level for details, or combine levels. Tree traversal for focused retrieval. Construction: Bottom-up clustering and summarization, typically 3-5 levels depending on document size. Summarization: LLM generates abstractive summaries capturing key information at each cluster. Query routing: Match query against nodes at different levels, retrieve from appropriate granularity. Benefits: Handles both "what is this about" and "what was the specific number" queries. Better for long documents. Costs: Expensive construction (many LLM calls for summaries), storage for tree, query complexity. Use cases: Books, long reports, documentation sites, research paper collections.