e3ca3ba733
feat: SignalSource abstraction — generalize signal ingestion beyond Todoist ( #78 )
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- Add Signal + SignalSource interfaces to packages/shared-types
- TipCandidate.features widened to Record<string,number|boolean> to match Signal
- TodoistSignalSource: encapsulates fetch, cache, 401 handling, bus events, and act()
- SignalAggregator: parallel fan-out across sources with per-source failure isolation
- Recommender refactored to consume Signal[] via aggregator; source action dispatch via aggregator.act()
- ADR-0009: signal normalization strategy
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com >
2026-04-18 01:11:56 +00:00
ffdf70733f
feat: M2 AI tips — LiteLLM gateway, context assembler, end-to-end generation pipeline
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Issues closed : #86 , #87 , #88 , #89 , #90 , #91 , #79 , #80 , #82
infra:
- docker-compose `ai` profile: Ollama + LiteLLM services
- infra/litellm/litellm_config.yaml: tip-generator / embedder / judge aliases
- .env.example: LITELLM_URL, LITELLM_MASTER_KEY, OLLAMA_URL
ml/serving:
- POST /generate: calls LiteLLM tip-generator alias, returns TipCandidate[]
- JSON retry loop (2 retries with correction prompt on malformed response)
- _parse_llm_json strips markdown fences
ml/features:
- context.py: build_context() assembles user signals → PromptContext
(sorts overdue/high-priority tasks first for LLM prompt quality)
shared-types:
- TipKind, TipSource, TipCandidate types
- Tip gains kind + rationale fields
services/api:
- recommender: 3-stage pipeline (assemble → score → serve)
Stage 1: Todoist tasks + LLM candidates fetched in parallel
Stage 2: egreedy bandit scores merged candidate pool
Stage 3: serve + log with prompt_version, llm_model, tip_kind
- tip_scores: prompt_version, llm_model, tip_kind columns + migrations
- config: LITELLM_URL added
- integrations: surface token_status in /integrations response
tests:
- ml/serving/tests/test_generate.py: 13 tests (retry, 502/503, fence variants)
- ml/features/test_context.py: 9 tests (sorting, edge cases)
- services/api recommender.unit.test.ts: 16 pure-function tests (inferReward, dueAgeDays)
- services/api recommender.test.ts: 4 integration tests (tip_scores columns, LLM fallback)
- shared-types: TipCandidate, rationale, full TipFeedback action set
docs:
- ADR-0008: LiteLLM AI gateway decision
- overview.md: M2 pipeline description updated
- ml/README.md: serving + features roles updated
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com >
2026-04-17 14:09:02 +00:00
faf44c18fc
feat: ε-greedy v1 as active policy; dwell-time reward inference; offline sim framework
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- Promote egreedy-v1 to active serving policy (ADR-0007): /score/egreedy + /reward/egreedy
replaces linucb-v1 endpoints after offline sim shows +10.7% mean reward (−0.548 vs −0.606)
- Replace explicit helpful/not_helpful feedback with dwell-time inferred reward (inferReward):
dismiss=−1.0, snooze=+0.1, done<15s=−0.3, done 15s–2min=+1.0, done 2–10min=+0.6, done>10min=+0.3
- Add ml/serving ε-greedy endpoints: /score/egreedy, /reward/egreedy, /stats/egreedy/{user_id}
with d=7 feature vector (base 5 + sin/cos day-of-week encoding)
- Add offline simulation framework (ml/experiments/sim): rule/LLM/claude-code judges,
two-phase score+reward, synthetic personas, task generator; results stored in sim_runs/sim_events
- Add /admin/simulations page: start runs, live-poll status, reward curve SVG, action/persona tables
- Fix egreedy day_of_week training skew: reward endpoint now uses actual dow instead of hardcoded 0
- Fix runner.py proxy bypass: httpx.Client(trust_env=False) for localhost ML calls
- Add dwellMs to TipFeedbackEvent contract and bus.test.ts fixture
- Schema: sim_runs, sim_events tables; tip_feedback gains dwell_ms, reward_milli columns
- ADR-0006: admin console framework; ADR-0007: egreedy-v1 policy selection rationale
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com >
2026-04-16 07:44:37 +00:00
e62c726ea4
feat: M1 admin console — all 10 remaining pages + signal/quality/ops infrastructure
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Admin console (issues #63–72):
- Event stream viewer: live-tail ring buffer (500 events) with subject/user filters
- Feature store browser: per-user feature vector history from ml/serving
- Model registry panel: MLflow embed at /admin/models
- Experiment dashboard: LinUCB per-user stats (pulls, reward, θ) + bandit reset
- Recommendation log: per-tip explainability (policy, score, features, latency)
- Reward analytics: daily reaction breakdown + per-policy compare
- Data quality widget: missing-feature rate, stale-token rate, daily completeness
- Ops actions: replay-signal, policy enable/disable; user actions link to Users page
- SQL runner: read-only SELECT runner with saved queries
- Health rollup: fan-out to api/ml/sqlite/event-bus with auto-refresh
Backend:
- tip_scores table: logs features+policy+score+latency at every scoring call (#67 )
- saved_queries table: per-admin saved SQL (#71 )
- Event bus: 500-event ring buffer + tail() API (#63 )
- Admin routes: /events, /tips, /reward-analytics, /data-quality, /health,
/policies, /replay-signal, /sql, /saved-queries endpoints
- /api/ml/* admin-gated proxy to ml/serving (#64 , #66 )
- Shadow-policy registry in recommender (#56 )
ML serving:
- /reset/{user_id}: clear bandit state + feature history (#66 )
- /stats/{user_id}: pulls, cumulative reward, estimated mean, θ (#66 )
- /features/{user_id}: last 100 feature vectors logged at scoring time (#64 )
- Meta (pulls, rewards) persisted alongside A/b matrices
Web:
- Tip action sheet adds Helpful / Not helpful buttons (#62 )
- TipFeedback type extended with helpful/not_helpful actions
- Rewards mapped: helpful=+0.5, not_helpful=−0.5
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com >
2026-04-16 03:56:48 +00:00
65218762be
feat: Phase 0 walking skeleton — monorepo, API, web, ML stub
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Sets up the full Phase 0 foundation:
- pnpm workspaces + turbo build graph; native module build approval
- packages/shared-types: HTTP contracts (Tip, Auth, Integrations, User)
- services/api: Express modular monolith with better-sqlite3/drizzle
- auth: Google OAuth2 + PKCE via openid-client v6, cookie sessions
- integrations: Todoist OAuth2 connect/disconnect, token vault
- recommender: RandomPolicy over Todoist tasks, feedback sink
- user: profile, consent capture, full account deletion (GDPR)
- apps/web: Next.js 15, three pages (sign-in → connect → tip)
- tip page: black canvas, hold-to-act gesture, action sheet
- PWA manifest + theme
- ml/serving: FastAPI stub implementing the POST /score contract
- infra: docker-compose (core/full profiles), Dockerfiles, CI skeleton
- .env.example with all required vars documented
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com >
2026-04-14 12:41:24 +00:00
cf4c7a0eb4
chore: scaffold oO monorepo with architecture, roadmap, and module stubs
2026-04-13 14:19:56 +00:00
