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… failures from retryable runtime errors so RQ Retry only requeues blips.
Keep the JobStore API but store lifecycle and artifact metadata in proseqgo_jobs instead of SQLite.
Centralize Queue.enqueue(process_job), job_timeout, Retry, and the failure callback for durable job ids.
Create the Postgres row, enqueue to Redis, and expose queue metrics; remove startup/shutdown of the polling daemon.
Run embedding/training work under RQ, update Postgres progress/status, and raise transient errors for retry instead of polling SQLite.
Starts a Prometheus metrics HTTP server, requeues orphaned started jobs (from a previous SIGKILL), then runs an RQ worker with graceful SIGTERM.
Describe the API→Postgres→Redis→worker flow, crash recovery, and env vars after the queue migration.
Expose JOBS_DATABASE_URL, REDIS_URL, queue name, timeouts, and artifact-root overrides for the async worker stack.
Separate non-retryable failures from retryable runtime errors so RQ Retry only requeues blips.
Keep the JobStore API but store lifecycle and artifact metadata in proseqgo_jobs instead of SQLite.
Write queued rows to Postgres, enqueue process_job, refresh queue metrics, and drop the in-process daemon loop.
Centralize Queue.enqueue with job_timeout, Retry, and the failure callback for durable job ids.
Serve worker metrics, requeue started jobs after SIGKILL, and run the embedding-jobs RQ worker.
Drive embedding work from RQ, update Postgres progress/status, and raise transient errors for retry.
Describe API→Postgres→Redis→worker flow, crash recovery, and storage layout after the queue migration.
Create the durable job-history DB on first Postgres volume init, separate from MLflow's mlflow database.
Mount the jobs DB init script, run Redis with AOF, split embedding/training APIs from GPU workers, and wire JOBS_DATABASE_URL plus Redis for async dispatch.
Add Prometheus targets for embedding-worker, trainer-worker, and Redis so queue and broker health are visible beside the APIs.
Extend target-down coverage to workers/redis_exporter and add critical rules for embedding-worker down with a non-empty RQ queue.
Show cafa5_rq_queue_length and redis_memory_used_bytes on the domain pipelines dashboard after the Redis/RQ migration.
Relocate embedding/MLflow smoke scripts out of scripts/ so pipeline CLIs stay separate from acceptance checks.
Add the smoke/acceptance directory to the repo tree so verification scripts are easy to find.
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