focus-area now recomputes at most once per hour, and only if the task list
actually changed since the last compute.
- focus-area TTL: 43200s → 3600s; version bumped to 2.1.0
- computeAndStore hashes sorted task contents (MD5) and checks the stored
_task_hash in the existing snapshot; skips the ml-serving call when the
hash matches and the output isn't expired
- ml-serving injects _task_hash into the snapshot so the next cycle can compare
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each unique task title is now enriched by LiteLLM once and cached in the DB.
Subsequent agent compute cycles (every 12h) fetch the cache before calling
ml-serving; only new titles hit the tip-generator.
- DB: task_enrichments(content_hash PK, description, model, created_at)
- TS: fetchEnrichmentCache / persistEnrichments helpers in agent-outputs.ts;
enrichment_cache passed in compute request, new_enrichments persisted from response
- Python: AgentComputeRequest.enrichment_cache / AgentComputeResponse.new_enrichments;
AgentInput.enrichment_cache; _enrich_batch returns (descriptions, new_entries);
cluster_tasks returns (clusters, new_enrichments)
- FocusAreaAgent stashes new_enrichments in signals_snapshot under _new_enrichments;
compute_agent endpoint pops it before storing the snapshot
Closes part of #129
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ported from taskpile experiments/clustering_eval (prompt v1, qwen2.5:1.5b).
The experiment showed ARI 0.22→0.77 and AUROC 0.76→0.91 on synthetic tasks
when embedding LLM-expanded descriptions instead of raw titles.
- Expand each task title via LiteLLM tip-generator before embedding
- Prefix with "clustering: " (nomic-embed-text task instruction prefix)
- Cache expansions in-memory by content hash within a compute cycle
- Falls back to raw title if enrichment fails; no change to fallback behaviour
Fixes#129
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The old code called Ollama's /api/embeddings one task at a time, which caused
silent fallback to project-based grouping when host.docker.internal:11434 was
unreachable from the ml-serving container.
- Switch to LiteLLM /embeddings (model alias "embedder") as primary path
- Batch all task contents in one request instead of N serial calls
- Fall back to Ollama /api/embed (updated to current API) when LITELLM_URL is absent
- Update tests to mock _embed_batch instead of the removed _embed
Fixes#123
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
OAuth2 flow with all 11 Google Fitness scopes (activity, body, sleep,
heart rate, nutrition, location, blood glucose/pressure/temperature,
oxygen saturation, reproductive health). Stores access + refresh tokens;
auto-refreshes on expiry.
GoogleHealthSignalSource fetches steps, sleep sessions, active minutes,
calories, and heart rate from the Fit aggregate + sessions APIs. Signals
flow into both the tip orchestrator and the health-vitals pre-compute
agent, which generates prompt snippets about step progress, sleep
deficit, sedentary time, and elevated heart rate.
Signal.kind extended with 'health'; IntegrationProvider extended with
'google-health'. Agent compute signal mapping enriched to include source,
kind, and all features so health-vitals can filter its own signals.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds four InferredParams (all TTL=24h, min_history=50 except preferred_hour=10):
- quiet_start / quiet_end: longest contiguous below-baseline hour run (HH:MM)
- peak_hours: top-quartile done-event hours, sorted ascending
- tz: cold-start only ("UTC"); populated from auth provider, no inference function
compute() updated:
- in_quiet check (quiet window) takes precedence over peak hours
- in_peak emits "peak productivity hour" language when current hour is in peak_hours
- approaching peak (within 2h) surfaces for orchestrator timing
- tz surfaced in snippet header when not UTC
- snapshot adds peak_hours, in_quiet, in_peak, tz
- Agent bumped to v1.2.0
- 21 new tests: night-owl, early-bird, shift-worker, quiet/peak snippet rendering
- Fixed test_snapshot_keys in test_agents.py to include new snapshot fields
Closes#112
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
Adds two InferredParams (TTL=7d) computed from 28-day rolling daily done counts:
- baseline_completions_per_day: mean done events/day over the window
- stdev: stdev of daily counts (floored at 0.1 to avoid division by zero)
MomentumAgent.compute() now calculates a z-score from recent done events in
inp.feedback_history vs the inferred baseline. Snippet language switches to
z-score framing ("above your usual pace", "slowing down") when |z| >= 1.0,
falling back to engagement_trend labels when in the normal range.
- engagement_trend InferredParam preserved for backward compatibility
- momentum_window pref added (default 7, user-overridable)
- 14 new tests covering power user, casual user, returning-from-break, and
relative stdev comparison; engagement_trend tests updated for z-score priority
- Agent bumped to v1.2.0
Closes#114
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
All four agents bumped to v1.1.0.
momentum (#114): infers engagement_trend ('up'|'stable'|'down') by comparing
done-rate in the last 7 days vs the prior 7 days. Agent surfaces the trend
in its snippet ("trending up — build on the momentum").
overdue-task (#115): infers lateness_tolerance_days (0/1/2) from snooze rate.
Agent now filters tasks against the tolerance so low-urgency users aren't
nagged about tasks that are only hours overdue.
recent-patterns (#116): infers window_days (7/14/30) from feedback event
density — sparse users get a wider window so the snippet isn't always empty.
focus-area (#113): no inferred params (project-level feedback linkage needed,
tracked under #78). preferred_areas pref was declared but ignored; agent now
honours it as a tiebreaker and mentions it in the snippet.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each agent now exports a module-level MANIFEST declaring id, version,
pref_schema, required_consents, ttl_sec, and silenced_in_contexts. The
registry surfaces both the agent and its manifest, and rejects on
mismatch so the two cannot drift.
ml/serving exposes GET /agents/registry; services/api proxies it as
GET /api/agents/registry with a 60s in-process cache so admin pageviews
don't hammer upstream. Failures aren't cached.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds ml/agents/ — five specialised sub-agents (overdue_task, momentum,
time_of_day, recent_patterns, focus_area) each producing a prompt snippet
from user signals. A registry wires them up; the orchestrator prompt in
ml/serving/prompts.py synthesises their outputs into one tip via LiteLLM.
Also wires /api/agents route in the API and updates the Dockerfile to copy
the full ml/ tree with PYTHONPATH=/app so agent imports resolve correctly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
