chunk-content

To chunk markdown for embedding, run the chunking script on long-form Markdown to break it into smaller slides, sections, or headings. This produces smaller embeddable pieces mapped under a dedicated chunked output directory for memory indexing.

The best way to prepare large knowledge bases for agent retrieval is chunking long-form Markdown into compact sections. This ensures an agent can quickly fetch relevant, smaller pieces of information on demand instead of scanning entire documents.

You split long-form tutorials by applying chunking logic that breaks the Markdown into slides, sections, or headings. The process mirrors the input structure under a chunked output directory while preserving source metadata in comments.

Yes, chunking markdown preserves visual context by copying referenced images directly into the chunked output directory. It also retains source metadata by embedding it within comments inside each generated chunk file.

You should re-run the markdown chunking pipeline when chunks need refreshing or when the chunking specification changes. It processes previously converted Markdown to ensure downstream embedding inputs remain up to date.

Yes, this chunking approach applies to long-form docs, tutorials, and manuals. It breaks down extensive Markdown content into smaller, structured pieces specifically optimized for embedding and on-demand retrieval.