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SToneX 7fc6ee3f83 feat(find): semantic channel 
v2's recall gains its second channel. infra/embed.py is an
OpenAI-compatible /embeddings client that never raises and caches a
query's vector in-process by model and folded text. application/
find_index.py builds one card matrix per catalog in the background on
the first v2 find: vectors are read from the archive's object store
under find-vectors/<model>/<card sha256>, only missing cards are
embedded, and those are written back. Without a store they live in the
process; without the API the channel stays off and the build is retried
later. The judged event and the searchlog row record the query's
embedding time and error.

The object store gains named objects for these vectors only, each body
carrying its own card hash and size. numpy joins the server extra for the
matrix product. Settings find_embed_api_key (falls back to treg's
OpenRouter key on the OpenRouter URL), find_embed_model, find_embed_url,
find_embed_timeout_s.

The bench builds the vectors before scoring when a key is set, and its
judge cache key now includes the job question's wording.
2026-09-30 18:19:49 +08:00

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