src/model
Databricks Model Serving resolver for Mastra agents.
Each agent step calls buildModel with the active
RequestContext. The user stamped by MastraServer carries an
AppKit WorkspaceClient; we ask it for the workspace host and a
fresh bearer header, then construct a request-scoped Vercel AI SDK provider
for the endpoint’s native Databricks serving protocol.
This module only adds the Mastra-specific glue. The actual model
selection - listing the workspace catalogue and resolving an
explicit name / class / fallback chain to a real endpoint id - lives
in @dbx-tools/model (selectModel) so non-Mastra consumers
(e.g. a job that just needs a model name) can reuse it. Here we
assemble the explicit ask from Mastra’s request context (the
per-request override under MASTRA_MODEL_OVERRIDE_KEY, the
agent / plugin modelId, or DATABRICKS_SERVING_ENDPOINT_NAME),
pass the plugin’s fuzzy / class / fallback knobs through, and wrap
the resolved id in the ProviderV4 model Mastra accepts. Catalogue fetches fail loud: network / auth errors
propagate so callers see the real SDK message.