What it is, how to run it, how configuration works, the module layout,
the seven steps to add an endpoint, the standing surprises, and a
Kubernetes cheat-sheet — each pointing at the detailed doc. Plus a
backend-developer section in SCAN_TO_ORDER.md: file map, local try-out,
tests, tuning knobs, and how to change the embedding model or add a
provider.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
POST /v1/mob/scan/lookup label + customer → catalogue match, sizes, and
every registered store that sells it with live
stock, in-stock first / nearest first, one
recommended
POST /v1/mob/scan/confirm chosen store + size + qty → re-read the ledger;
ok, or the next-nearest store with enough of the
same product
GET /v1/mob/scan/stores registered stores nearest first
Recognition is pgvector cosine search over every brand_* table (each
with its own index, merged) plus a word match that settles near-ties
and works alone when no model is configured. The embedder is chosen by
EMBEDDING_PROVIDER (OpenAI-compatible or Gemini) and must be the model
that indexed the catalogue: verified 2026-09-15 as all-MiniLM-L6-v2 over
search_query, served by the cluster's Ollama as `all-minilm`; the first
search refuses a width mismatch by name.
Customer, stores and catalogue are read concurrently under a 5 s cap; a
slow model degrades to a text answer. Vectors and ranked hits are cached
in Redis and in-process; live stock never is. Availability uses the same
rules as the customer catalogue (approve, publishedat, ledger balance,
outlet price else retail). No stock reservation: confirm re-reads.
scratch/cataloguedims reports the catalogue's embedding width and fill.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
