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>
132 lines
4.6 KiB
Python
132 lines
4.6 KiB
Python
from __future__ import annotations
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from collections import Counter
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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from .inference.history import UserHistory
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from .manifest import AgentManifest, InferredParam
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_DOW_NAMES = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
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def _infer_preferred_hour(history: UserHistory) -> int:
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"""Mode hour of day across all 'done' feedback events; falls back to 9."""
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done_hours = [e.hour for e in history.events if e.action == "done"]
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if not done_hours:
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return 9
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return Counter(done_hours).most_common(1)[0][0]
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MANIFEST = AgentManifest(
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id="time-of-day",
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version="1.1.0", # bumped: inferred_params added (ADR-0014 §3, #112)
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description="Frames the current moment relative to the user's productive peak and quiet hours.",
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pref_schema={
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"type": "object",
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"additionalProperties": False,
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"properties": {
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"quiet_start": {
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"type": "string",
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"pattern": "^([01][0-9]|2[0-3]):[0-5][0-9]$",
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"description": "HH:MM start of quiet hours (24h, user's local TZ).",
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},
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"quiet_end": {
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"type": "string",
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"pattern": "^([01][0-9]|2[0-3]):[0-5][0-9]$",
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"description": "HH:MM end of quiet hours.",
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},
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},
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},
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context_schema=["profile.features"],
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required_consents=["data:core", "agent:time-of-day"],
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output_contract={"type": "snippet", "format": "free_text"},
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ttl_sec=900,
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inferred_params=[
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InferredParam(
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key="preferred_hour",
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ttl_sec=3_600, # recompute hourly
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cold_start_default=None,
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min_history=10, # need at least 10 feedback events to be meaningful
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infer=_infer_preferred_hour,
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),
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],
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)
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class TimeOfDayAgent(BaseAgent):
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"""Frames the current moment relative to the user's productive peak."""
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agent_id: ClassVar[str] = MANIFEST.id
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ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
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version: ClassVar[str] = MANIFEST.version
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def compute(self, inp: AgentInput) -> AgentOutput:
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hour = inp.now.hour
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dow = inp.now.weekday() # 0=Monday … 6=Sunday
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is_weekend = dow >= 5
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# agent_prefs (inferred or user-set) take precedence over ML profile features.
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preferred_raw = inp.agent_prefs.get("preferred_hour", inp.profile.get("preferred_hour"))
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preferred = int(preferred_raw) if preferred_raw is not None else None
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quiet_start: str | None = inp.agent_prefs.get("quiet_start")
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quiet_end: str | None = inp.agent_prefs.get("quiet_end")
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in_quiet = self._in_quiet_window(hour, quiet_start, quiet_end)
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parts = [f"It is {hour:02d}:00 on {_DOW_NAMES[dow]} ({self._label(hour)})."]
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if is_weekend:
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parts.append("Weekend context — prefer personal or reflective tips over work tasks.")
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if in_quiet:
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parts.append(
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f"User is in their quiet window ({quiet_start}–{quiet_end}) — "
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"avoid urgent or demanding tips."
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)
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if preferred is not None:
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delta = min(abs(hour - preferred), 24 - abs(hour - preferred))
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if delta == 0:
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parts.append(
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f"This is the user's peak productivity hour ({preferred:02d}:00) — "
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"a high-impact tip is appropriate."
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)
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elif delta <= 2:
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parts.append(f"Approaching the user's peak productivity window ({preferred:02d}:00).")
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else:
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parts.append("No preferred-hour data yet.")
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prompt = " ".join(parts)
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snapshot = {
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"hour": hour,
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"day_of_week": dow,
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"preferred_hour": preferred,
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"quiet_start": quiet_start,
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"quiet_end": quiet_end,
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}
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return self._make_output(inp, prompt, snapshot)
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@staticmethod
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def _in_quiet_window(hour: int, start: str | None, end: str | None) -> bool:
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if not start or not end:
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return False
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try:
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sh = int(start.split(":")[0])
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eh = int(end.split(":")[0])
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except (ValueError, IndexError):
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return False
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if sh <= eh:
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return sh <= hour < eh
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# wraps midnight e.g. 22:00–07:00
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return hour >= sh or hour < eh
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@staticmethod
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def _label(hour: int) -> str:
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if 5 <= hour < 12:
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return "morning"
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if 12 <= hour < 17:
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return "afternoon"
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if 17 <= hour < 21:
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return "evening"
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return "night"
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