serving
Live Databricks Model Serving catalogue access.
Lists the workspace’s /serving-endpoints once per host and caches the
result with a TTL via AppKit’s CacheManager, with concurrent callers
sharing one in-flight promise (the coalescing pattern of Python’s
cachetools-async). Surfaces each endpoint as a stable
ServingEndpointSummary - including the Foundation Model API
quality / speed / cost profile when present, the classified
ModelClass, and (for embedding endpoints) the measured vector
dimension - and snaps loose, human-typed names to real endpoint ids
through fuse.js extended search so tokens like "claude sonnet" resolve to
databricks-claude-sonnet-4-6.
The class stamp and embedding dimension are computed once per cache load:
every embedding endpoint is “pinged” in parallel and the resulting vector
length recorded, so the cost is paid on a cache miss, not per read. The ping
is best-effort - a failure logs at debug and leaves dimension unset rather
than failing the whole listing.