aa4bdd8f09
feat(admin): LLM tip quality dashboard — per-model/prompt/kind breakdowns
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/admin/reward-analytics now surfaces served count, reaction rate, and avg
reward grouped by llm_model, prompt_version, and tip_kind — closing the
loop so model/prompt iterations in M2 are legible next to the bandit
policy view. Data comes from the tip_scores columns added in ffdf707Closes #92 .
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com >
2026-04-24 15:24:52 +00:00
bb879c5f0f
refactor(admin): drop simulations/experiments/models pages; group nav into sections
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Removes the in-shell MLOps pages (experiments, models, simulations) and their
client API helpers in favour of external MLflow/Airflow links. Nav is regrouped
into Signals / Recommender status / Ops sections for clarity.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com >
2026-04-18 14:41:17 +00:00
85367aeaa0
feat: MLOps external services, AI stack planning, admin MLOps hub
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Infrastructure:
- Add `mlops` compose profile: MLflow (basic-auth, /mlflow path) + Airflow (LocalExecutor, /airflow path) + airflow-db
- infra/mlflow/basic_auth.ini for MLflow auth config
- Caddy routes /mlflow* and /airflow* inside existing o.alogins.net block (see agap_git)
- Dockerfile.admin: NEXT_PUBLIC_MLFLOW_URL / NEXT_PUBLIC_AIRFLOW_URL build args (default /mlflow, /airflow)
Admin panel:
- /admin/models: replace MLflow iframe with external link cards
- /admin/experiments: replace LinUCB stats with MLOps hub (links to MLflow experiments/models + Airflow DAGs/datasets)
- AdminShell: external nav links for MLflow ↗ and Airflow ↗ under MLOps section
Docs & planning:
- README: new AI stack section (Ollama/LiteLLM/OpenWebUI three-tier, tip generation pipeline, model aliases)
- README: Phase 2 expanded with AI infra issues (#86-#93) and granular pipeline breakdown
- README: Phase 4 expanded with LLM MLOps items (#94-#97)
- CLAUDE.md: AI stack section, updated current phase (M1 shipped / M2 in progress), compose profiles, updated What NOT to do
- docs/architecture/overview.md: AI stack section, updated decision flow diagram for Phase 2 LLM pipeline
- ADR-0006: updated to reflect external services (path-based, not embedded)
- Gitea issues #86-#97 created (M2: AI infra + pipeline; M4: LLM MLOps)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com >
2026-04-17 08:20:44 +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
