alvis
bc71dc203d
Replaces the coarse density-bucket window_days with three InferredParams (all TTL=24h): - lookback_days: min window containing ≥30 done events, capped at 30d (min_history=5) - weekly_cycle: per-DOW peak-to-mean strength list (min_history=21, ≥3 weeks of signal) - daily_cycle: per-hour peak-to-mean strength list (min_history=14) compute() renders cycle hints when strength > 0.5: "User tends to complete tips on Tuesdays and Saturdays." "User is most active around 8pm." Legacy window_days pref key still accepted as a fallback. - window_days pref renamed lookback_days; backward-compat fallback in compute() - Agent bumped to v1.2.0 - 19 new tests: weekend-warrior, weekday-only, evening-person, no-pattern, legacy compat, snippet rendering with strong/weak signals Closes #116 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ml/
Python. Owns models, features, training, online scoring.
| Dir | Role | Phase |
|---|---|---|
serving/ |
FastAPI online scorer (/score, /generate) + LiteLLM gateway + prompt registry (prompts.py) + JetStream consumers for signals.> / feedback.>, called by recommender |
1–2 |
features/ |
context assembler (context.py): signals → PromptContext; profile-feature schema mirror (profile_schema.py); Feast adapter later |
2 |
pipelines/ |
batch feature + training scripts | 4 |
registry/ |
MLflow-backed model registry integration | 4 |
experiments/ |
A/B assignment + multi-armed bandit policies | 4 |
notebooks/ |
research; never imported by production code | — |
Principles
- Every model has a model card in
registry/describing inputs, offline metrics, fairness checks, and rollout history. - Online inference must be stateless and < 50ms p99.
- Training reads from the offline feature store; serving reads from the online feature store; definitions are shared (no train/serve skew).
- Shadow deploys before any policy change that affects real users.
Feature contract
Profile features (batched)
User-level features (completion rate, preferred hour, tip volume…) are computed
by the TypeScript recommender and shipped to ml/serving on every /score and
/generate call as profile_features: dict | None. The Python mirror in
features/profile_schema.py documents each feature's name, dtype, TTL, source,
and null fallback — keep it in sync with services/api/src/profile/registry.ts
(a CI-style test asserts names and ttlSec values match). See ADR-0011.
Context features (JIT)
Request-time signals assembled by features/context.py (hour_of_day,
day_of_week, task list). These are never cached — they are derived from the
system clock and the live Todoist feed at the moment of the score call.
CONTEXT_FEATURES in context.py declares freshness, source, and fallback for
each field (issue #61).
Prompt registry
serving/prompts.py keys tip-generation prompts by stable version string. Adding a new variant means adding an entry — no caller changes. Selection precedence: POST /generate body's prompt_version field → env DEFAULT_PROMPT_VERSION → "v1". The TypeScript recommender drives selection via TIP_PROMPT_VERSION (single value or comma-separated rotation); the version actually used flows back in the response and is persisted to tip_scores.prompt_version so the admin reward-analytics dashboard can bucket reactions per variant.