LLM Wiki app Chunker – transform documents into navigable knowledge trees
The Chunker app transforms documents into navigable knowledge trees by processing them into self-sufficient chunks and multi-level summaries. This approach allows users to explore documents progressively, starting from high-level overviews and drilling down into details without loading the entire text. By utilizing intelligent chunking and bottom-up aggregation, Chunker preserves the document's natural structure and enhances navigation.
- ▪Chunker processes documents into a hierarchy of self-sufficient chunks and multi-level summaries.
- ▪It uses an LLM to find semantically complete split points for chunking, ensuring each chunk is a complete thought.
- ▪The app builds a navigable tree where every node is self-contained, allowing for efficient exploration of document content.
Opening excerpt (first ~120 words) tap to expand
Chunker Transform documents into navigable knowledge trees. Chunker processes a document into a hierarchy of self-sufficient chunks and multi-level summaries, producing a set of linked markdown files that an AI model (or a human) can explore through progressive disclosure -- starting from a high-level overview and drilling into details on demand, without ever loading the entire document. The Problem When an AI model needs to work with a long document, the standard approaches are wasteful: Full context loading feeds the entire document into a prompt. This burns tokens, dilutes attention, and hits context window limits. Naive chunking (split every N tokens) produces fragments that start and end mid-thought.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.