Building long context RAG with RAPTOR from scratch

From the video description

The rise of long context LLMs and embeddings will change RAG pipeline design. Instead of splitting docs and indexing doc chunks, it will become feasible to index full documents. RAG approaches will need to flexibly answer lower-level questions from single documents or higher-level questions that require information across many documents. RAPTOR (Sarthi et al) is one approach to tackle this by building a tree of document summaries: docs are clustered and clusters are summarized to capture higher-level information across similar docs.