Drop all four Airflow containers (db, init, webserver, scheduler) from the
mlops compose profile, leaving MLflow as the sole mlops service. Remove
AIRFLOW_* env vars, config fields, health-check entries, DAG trigger code
in admin/bench routes, the airflow_dag_run_id schema column, Airflow nav
links and DAG-run links in the admin UI, the two Airflow DAG files
(bench_dag.py, sim_dag.py), and all related docs/ADR references.
Simulations now run exclusively via the subprocess path.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New DAG (`ml/pipelines/bench_dag.py`) with three linked tasks:
1. collect.py — generates candidates, logs to MLflow
2. export_for_judge — exports pending runs for Claude Code scoring
3. compare — generates leaderboard by (model, prompt) cell
Config via dag_run.conf supports all collect.py options (models, prompts,
n_tips, n_scenarios, temperature, experiment name, max_model_b).
New admin API endpoints (`services/api/src/routes/bench.ts`):
- GET /api/bench/experiments — list tip-bench-* experiments
- POST /api/bench/run — trigger DAG with custom config
- GET /api/bench/runs/:experiment — list runs in experiment
- GET /api/bench/leaderboard/:experiment — leaderboard by (model, prompt)
All endpoints require admin auth. Human judge (Claude Code) scores are
applied manually post-export; future enhancement: add webhook to DAG.
Admin UI can now trigger and monitor benchmarks from a dashboard panel.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
