michi-chat

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Knowledge base ​

Each tenant can have a set of markdown documents the bot searches when a visitor asks a factual question - hours, policies, FAQs, product details. Retrieval is grounded: when nothing relevant is found, the bot says it doesn't know instead of guessing.

Adding documents ​

Tenant → Knowledge base. Paste markdown with a title, save - the document is chunked, embedded and searchable immediately. Saving under an existing title replaces that document.

Two rules make retrieval precise:

  1. Structure with headings. Chunking is heading-aware: each ## section becomes a retrieval unit with its breadcrumb attached. One fact per section beats one wall of text.
  2. Only counter-safe facts. Everything here can reach a visitor. Nothing internal, no wholesale prices, nothing you would not say out loud at the counter.

Wiring it to the bot ​

Enable the search_kb tool pack on the tenant page. Without it, the documents exist but the bot never reads them.

Checking quality ​

The table shows a chunk count per document - one giant chunk or fifty tiny ones is a sign the headings need work. Developers with a repo clone can go further:

bash
docker compose exec app npm run kb:ingest -- <tenant-slug>     # bulk-ingest kb/<slug>/*.md
docker compose exec app npm run kb:eval -- <tenant-slug>       # recall@k: does the right doc come back?
docker compose exec app npm run eval:answers -- <tenant-slug>  # judge-graded: is the final ANSWER right?

The recall eval measures retrieval; the answers eval sends real chat turns and has a second model grade each reply for faithfulness (nothing invented) and completeness (the required facts arrive) - numbers, not feelings.

Backup and bulk editing (CSV) ​

Export CSV on the Knowledge base page downloads every document as a two-column spreadsheet (title, content). Edit it in Excel or Google Sheets - fix facts, add rows - then Import from CSV brings it back: rows match by title, changed documents are re-embedded, unchanged ones are skipped. Export before big edits and you always have a restore point.

Instant repeat answers ​

Common opening questions ("what time do you open?") are served from a semantic cache: an answer already given for a near-identical first message returns instantly, at zero model cost. The cache clears itself whenever you save or delete knowledge or tenant settings, so editing a fact never leaves a stale cached answer behind.