alvis
b3cf588f2f
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>
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
from __future__ import annotations
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from collections import Counter
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from datetime import datetime, timezone
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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_SEVEN_DAYS_S = 7 * 86_400
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class RecentPatternsAgent(BaseAgent):
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"""Surfaces the user's reaction pattern from the last 7 days of feedback."""
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agent_id: ClassVar[str] = "recent-patterns"
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ttl_seconds: ClassVar[int] = 86_400 # 24h
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version: ClassVar[str] = "1.0.0"
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def compute(self, inp: AgentInput) -> AgentOutput:
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now_ts = inp.now.timestamp()
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recent = [
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f for f in inp.feedback_history
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if self._age_s(f.get("created_at", ""), now_ts) <= _SEVEN_DAYS_S
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]
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counts: Counter[str] = Counter(f.get("action") for f in recent)
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total = len(recent)
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dwell_ms = inp.profile.get("mean_dwell_ms_30d")
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if total == 0:
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prompt = "No tip reactions recorded in the last 7 days."
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else:
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done = counts.get("done", 0)
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dismissed = counts.get("dismiss", 0)
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snoozed = counts.get("snooze", 0)
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parts = [
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f"Last 7 days: {total} tip reaction{'s' if total != 1 else ''} — "
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f"{done} completed, {dismissed} dismissed, {snoozed} snoozed."
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]
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if dwell_ms is not None:
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dwell_s = round(dwell_ms / 1000)
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if dwell_s < 15:
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parts.append(
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"Average dwell is very short — user may be acting on auto-pilot; vary tip content."
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)
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elif dwell_s < 60:
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parts.append(f"Average dwell {dwell_s}s — tips are being read.")
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else:
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parts.append(
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f"Average dwell {dwell_s}s — user deliberates; prefer tips that reward reflection."
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)
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prompt = " ".join(parts)
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snapshot = {
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"recent_total": total,
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"action_counts": dict(counts),
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"mean_dwell_ms_30d": dwell_ms,
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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 _age_s(iso: str, now_ts: float) -> float:
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if not iso:
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return float("inf")
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try:
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dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return now_ts - dt.timestamp()
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except Exception:
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return float("inf")
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