RAG Text Chunker

Retrieval-Augmented Generation (RAG) and long-context LLM workflows both depend on splitting documents into chunks that are small enough to embed or rank, yet big enough to keep their meaning. Naive word-count splitters cut a sentence or a code block in half and destroy context. The RAG Text Chunker splits intelligently: a Markdown-header strategy breaks the document at heading boundaries (preserving the heading hierarchy as each chunk's source path), while fixed-word and fixed-character strategies give you deterministic sizes with configurable overlap so adjacent chunks share context at the edges. Every chunk carries metadata — its index, the source heading it came from, and an estimated token count — and you can copy a single chunk, copy them all, or export the full set as JSON ready for an embeddings pipeline. Chunking runs entirely locally in your browser. Have raw HTML? Convert it to Markdown first with the HTML to Markdown tool, then chunk it here.