alvis
04212ff318
Replaces snooze-rate heuristic with p50 of actual task lateness (completedAt − dueAt).
Adds project_realness inference: projects with chronic lateness get realness < 1 and
the agent softens its snippet language from "overdue" to "past target date".
- TaskCompletion added to UserHistory with lateness_days computed property
- _infer_lateness_tolerance: p50 of task_completions, clipped at 0, float
- _infer_project_realness: per-project median lateness normalised by global median
- Both InferredParams use 7d TTL; cold_start = 0.0 / {}
- AgentInferRequest accepts task_completions; endpoint wires them through
- 12 new tests covering punctual/chronic/mixed users and language softening
- Agent bumped to v1.2.0
Closes #115
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.