alvis/oO - oO - AgapGit

alvis/oO

Author SHA1 Message Date

Author	SHA1	Message	Date
alvis	aa4bdd8f09	feat(admin): LLM tip quality dashboard — per-model/prompt/kind breakdowns /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 `ffdf707` and tip_feedback.reward_milli; bandit-only tips show as "(bandit-only)". Closes #92. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>	2026-04-24 15:24:52 +00:00
alvis	bb879c5f0f	refactor(admin): drop simulations/experiments/models pages; group nav into sections 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
alvis	faf44c18fc	feat: ε-greedy v1 as active policy; dwell-time reward inference; offline sim framework - 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

alvis

aa4bdd8f09

feat(admin): LLM tip quality dashboard — per-model/prompt/kind breakdowns

/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 ffdf707 and
tip_feedback.reward_milli; bandit-only tips show as "(bandit-only)".

Closes #92.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

2026-04-24 15:24:52 +00:00

alvis

bb879c5f0f

refactor(admin): drop simulations/experiments/models pages; group nav into sections

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

alvis

faf44c18fc

feat: ε-greedy v1 as active policy; dwell-time reward inference; offline sim framework

- 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

3 Commits