Convert ml-serving from isolated MLflow runs to nested traces using
mlflow.start_span_no_context(). The recommend endpoint now emits a full
span tree: recommend (CHAIN) → build_context (TOOL), agent:* (AGENT) ×N,
llm_orchestrator (LLM). Compute and infer endpoints each emit a single span.
Supporting changes:
- mlflow-skinny>=3.1.0 added to requirements
- MLflow configured with --serve-artifacts + mlflow-artifacts:/ default root
for cross-container artifact proxy (spans now persist from ml-serving)
- --allowed-hosts extended to include mlflow:5000 (SDK includes port in Host)
- science_destiny slider wired through prompts.py and recommend endpoint
- Config page exposes science/destiny slider (0=data-driven, 100=intuitive)
- Tip page shows rationale inline on tap
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- TS: pino + pino-http; every HTTP request log includes traceId from
W3C traceparent header (generated if absent); forwarded to ml/serving
on all /score, /generate, /reward, and /api/ml proxy calls
- Python: structlog JSON; FastAPI middleware binds trace_id via
contextvars so every log line within a request carries it
- Sentry: optional SENTRY_DSN init in both runtimes (no-op if unset)
- Replace all console.* calls across services/api with pino logger
- Update tests to spy on logger instead of console
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds a NATS JetStream consumer to ml/serving so the feature pipeline
can react to events without the API triggering every read.
- nats_consumer.py: durable push consumers for signals.> and feedback.>
streams; acks on success, naks for redeliver, up to NATS_MAX_DELIVER
attempts; per-consumer health state (last_msg_ts, processed, errors)
- main.py: FastAPI lifespan wires start/stop; /health exposes nats state
- requirements.txt: adds nats-py>=2.9.0
- Dockerfile.ml: copy all *.py from ml/serving (was missing prompts.py)
Handled subjects:
signals.task.synced → writes per-user sync metadata to STATE_DIR
signals.tip.feedback → logged for observability (reward via HTTP path)
Config: NATS_URL (empty = disabled), NATS_DURABLE_PREFIX, NATS_MAX_DELIVER
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ML serving:
- LinUCB contextual bandit (disjoint, d=5 features: hour_sin/cos, is_overdue, task_age, priority)
- /score endpoint replaces stub random; /reward endpoint for online learning
- Per-user model state persisted to disk as JSON (survives restarts)
- venv at ml/serving/.venv; start with pnpm dev from ml/serving
Recommender:
- Todoist fetch now extracts features (is_overdue, task_age_days, priority)
- RemotePolicy calls ml/serving with 3s timeout; falls back to RandomPolicy
- Reward sent to /reward on feedback (done=+1, snooze=0, dismiss=-1)
Web Push:
- VAPID keys in config; push_subscriptions table in DB
- POST/DELETE /api/push/subscribe; GET /api/push/vapid-public-key
- Service worker (public/sw.js): push → showNotification, notificationclick → focus/open
- "notify me" button on tip page; registers SW + subscribes on permission grant
Event bus:
- services/api/src/events/bus.ts: typed EventEmitter wrapper
- Subjects: signals.tip.served, signals.tip.feedback, signals.task.synced
- Same publish/subscribe API NATS JetStream will implement — swap is mechanical
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
