When fetchOrchestratorTip returned null (LiteLLM timeout, bad JSON, etc.)
the recommender silently fell back to randomPolicy, serving a raw Todoist
task with no rationale — explaining both reported symptoms.
- Remove randomPolicy/signalToCandidate; return 204 when orchestrator fails
so the UI shows "All clear" instead of a confusing Todoist task
- Pass recent_tip through the stack (frontend → POST /recommend →
fetchOrchestratorTip → ml/serving RecommendRequest → build_orchestrator_messages)
so after snooze the LLM is instructed not to repeat the snoozed content
Fixes#122
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Convert ml-serving from isolated MLflow runs to nested traces using
mlflow.start_span_no_context(). The recommend endpoint now emits a full
span tree: recommend (CHAIN) → build_context (TOOL), agent:* (AGENT) ×N,
llm_orchestrator (LLM). Compute and infer endpoints each emit a single span.
Supporting changes:
- mlflow-skinny>=3.1.0 added to requirements
- MLflow configured with --serve-artifacts + mlflow-artifacts:/ default root
for cross-container artifact proxy (spans now persist from ml-serving)
- --allowed-hosts extended to include mlflow:5000 (SDK includes port in Host)
- science_destiny slider wired through prompts.py and recommend endpoint
- Config page exposes science/destiny slider (0=data-driven, 100=intuitive)
- Tip page shows rationale inline on tap
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Small models (qwen2.5:1.5b) mirror the language of task title content
in the prompt. Adding an explicit English note to snippets that embed
raw task titles (focus-area, overdue-task) prevents language bleed.
Also added the instruction to the orchestrator system prompt and user
message as belt-and-suspenders.
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>
- 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>
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>
