alvis
ad6747c242
Step 4 — /api/profile read-through API:
GET /api/profile → { user, prefs, consents, contexts }
PATCH /api/profile/prefs/:scope upsert user_preferences (source='user')
PATCH /api/profile/consents grant / revoke consent keys
PATCH /api/profile/contexts create / activate / deactivate contexts
Legacy consentGiven bit folded in as data:core fallback.
Step 5 — registry-driven eligibility filter:
fetchRegistry() exported from agent-registry.ts.
profile/eligibility.ts: getEligibleAgentIds(userId) — filters by required
consents, silenced_in_contexts, and user_preferences[enabled=false].
fetchOrchestratorTip filters agent_outputs to eligible set before calling
ml/serving /recommend. Fail-closed: registry unavailable → empty set.
Step 6 — shared context-inference framework (#111) + time-of-day proof (#112):
ml/agents/inference/: UserHistory, FeedbackEvent, run_inference().
Framework: cold-start, min_history gating, error fallback, structured logs.
TimeOfDayAgent v1.1.0: inferred_params=[preferred_hour]; also reads
quiet_start/quiet_end from agent_prefs. agent_prefs injected by TS caller.
AgentInput gains agent_prefs field.
ml/serving: POST /agents/{agent_id}/infer endpoint.
agent-outputs.ts computeAndStore: loads prefs before compute, calls /infer
after, persists results (source='inferred'); user overrides never touched.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
10 lines
363 B
Python
10 lines
363 B
Python
"""Shared context-inference framework (ADR-0014 §3, issue #111).
|
|
|
|
Each agent's manifest declares InferredParams; this package owns the
|
|
scheduling contract, history data model, and write path to user_preferences.
|
|
"""
|
|
from .framework import run_inference
|
|
from .history import FeedbackEvent, UserHistory
|
|
|
|
__all__ = ["run_inference", "FeedbackEvent", "UserHistory"]
|