fix: make worker-delegated embedding survive the shipped deployments

Query embedding moved to the Celery worker, but nothing that ships was
updated to consume the queue it dispatches to.

- Add `embeddings` to every worker `-Q` list (compose x3, k8s, devcontainer,
  sandbox README). Without it a search blocked for EMBEDDINGS_DELEGATE_TIMEOUT
  and then answered with no retrieved context, because classic_rag swallows the
  dispatch error and skips the source -- bad answers, not an error.

- Skip the task_postrun heap reclaim for the embed task. The full gc.collect()
  was written for docling/torch parses; on a worker holding the ONNX model it
  measured ~86ms against ~8ms for the embed itself, a 9x slowdown of the round
  trip for a task that allocates a few kilobytes.

- Resolve the installation pin in the re-embed script. It never imports
  application.app, so an install pinned in app_metadata with no EMBEDDINGS_NAME
  set -- every stock k8s deployment, whose manifests carry no embedding config
  -- would rewrite its whole index with the legacy default and stamp
  sources.model to match, then be told by the boot warning to run it again.

- Fail fast for 30s after a failed dispatch. fanout.embed_questions falls back
  to letting each store embed its own query, so one dead-worker retrieval paid
  the timeout once in the fan-out and again per source.

- Forget the task result. Nothing reads it back: the key is per-dispatch UUID,
  not content-addressed, so a repeated query mints another. Left alone every
  search leaked ~17KB for result_expires (7 days) into the Redis the broker
  shares -- on the bundled k8s manifest (1Gi, no maxmemory policy) that is an
  OOMKill that takes the broker with it.

- Release the model ensure_vector_schema loads to read the width of an
  unregistered model, in a process that delegates and would never call it.
  The width still comes from the model, not the table, so the mismatch check
  the hook exists for keeps working.

- Correct the docs that said otherwise: embeddings.md claimed the standard
  deployment worked unchanged, upgrading.mdx said no action was needed, and
  the settings table listed none of the three delegation settings.

docs/content/Models/embeddings.md

@@ -95,7 +95,9 @@ A local embedding model costs a few hundred megabytes of resident memory per pro
 
 `EMBEDDINGS_DELEGATE_TO_WORKER` (on by default) moves that work to the Celery worker: the API sends the text over the broker and gets the vector back, holding no model. Measured on a default install, the API process drops from ~657 MB to ~284 MB, and query embedding costs one broker round trip (~60 ms on a prefork worker).
 
-Retrieval then depends on a worker consuming `EMBEDDINGS_QUEUE` (`embeddings` by default). A bare `celery worker` with no `-Q` consumes it along with everything else, so the standard deployment works unchanged — but its concurrency is shared with ingest, so a query can queue behind a long parse. Run a dedicated worker to isolate query latency:
+Retrieval then depends on a worker consuming `EMBEDDINGS_QUEUE` (`embeddings` by default). A bare `celery worker` with no `-Q` consumes it along with everything else. **A worker started with an explicit `-Q` must list it** — the bundled Compose and Kubernetes manifests run `-Q docsgpt,parsing,embeddings` for exactly this reason. Omit it and every search blocks for `EMBEDDINGS_DELEGATE_TIMEOUT` and then returns an answer with no retrieved context, without raising.
+
+Sharing one worker also shares its concurrency with ingest, so a query can queue behind a long parse. Run a dedicated worker to isolate query latency:
 
 ```bash
 celery -A application.app.celery worker -Q embeddings