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Author	SHA1	Message	Date
alvis	4267e6ac68	feat(ml/serving): inject profile features + sort tasks in tip prompt (#79 ) - prompts.py: sort tasks overdue-first → priority desc → age desc before rendering into the LLM prompt (same ordering as ml/features/context.py) - prompts.py: render User profile summary line (completion_rate, dismiss_rate, preferred_hour) when profile_features are present - main.py: add profile_features field to PromptContext; plumb from GenerateRequest into the prompt builder via model_copy - logging_config.py: drop add_logger_name processor (incompatible with PrintLoggerFactory — caused test ordering failures) - test_generate.py: 6 new tests covering sort order, profile rendering, partial fields, empty profile, and end-to-end plumbing through /generate Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-04-27 13:46:16 +00:00
alvis	430804e9a5	feat(ml): prompt registry + per-request variant selection Replaces the hardcoded "v1" label with a real prompt registry: ml/serving/prompts.py — keyed by version: v1 (baseline), v2-mentor (calm/specific persona), v3-few-shot (v1 persona + curated examples) ml/serving/main.py — POST /generate accepts optional prompt_version, 422 on unknown, echoes the version actually used back in the response services/api/src/config.ts — TIP_PROMPT_VERSION: empty / single / comma-list (uniform random per request) services/api/src/routes/recommender.ts — pickPromptVersion() drives selection; the response's prompt_version (not a stale TS constant) is what lands in tip_scores so the #92 reward-analytics dashboard shows real per-variant reaction rates Closes #84. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>	2026-04-24 15:44:04 +00:00
alvis	ffdf70733f	feat: M2 AI tips — LiteLLM gateway, context assembler, end-to-end generation pipeline 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

Author

SHA1

Message

Date

alvis

4267e6ac68

feat(ml/serving): inject profile features + sort tasks in tip prompt (#79 )

- prompts.py: sort tasks overdue-first → priority desc → age desc before
  rendering into the LLM prompt (same ordering as ml/features/context.py)
- prompts.py: render User profile summary line (completion_rate, dismiss_rate,
  preferred_hour) when profile_features are present
- main.py: add profile_features field to PromptContext; plumb from
  GenerateRequest into the prompt builder via model_copy
- logging_config.py: drop add_logger_name processor (incompatible with
  PrintLoggerFactory — caused test ordering failures)
- test_generate.py: 6 new tests covering sort order, profile rendering,
  partial fields, empty profile, and end-to-end plumbing through /generate

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-04-27 13:46:16 +00:00

alvis

430804e9a5

feat(ml): prompt registry + per-request variant selection

Replaces the hardcoded "v1" label with a real prompt registry:

  ml/serving/prompts.py       — keyed by version: v1 (baseline),
                                v2-mentor (calm/specific persona),
                                v3-few-shot (v1 persona + curated examples)
  ml/serving/main.py          — POST /generate accepts optional prompt_version,
                                422 on unknown, echoes the version actually used
                                back in the response
  services/api/src/config.ts  — TIP_PROMPT_VERSION: empty / single / comma-list
                                (uniform random per request)
  services/api/src/routes/recommender.ts
                              — pickPromptVersion() drives selection; the
                                response's prompt_version (not a stale TS
                                constant) is what lands in tip_scores so the
                                #92 reward-analytics dashboard shows real
                                per-variant reaction rates

Closes #84.

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

2026-04-24 15:44:04 +00:00

alvis

ffdf70733f

feat: M2 AI tips — LiteLLM gateway, context assembler, end-to-end generation pipeline

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

3 Commits