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>
50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
from __future__ import annotations
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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class MomentumAgent(BaseAgent):
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"""Characterises the user's recent engagement trend from profile features."""
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agent_id: ClassVar[str] = "momentum"
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ttl_seconds: ClassVar[int] = 21600 # 6h
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version: ClassVar[str] = "1.0.0"
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def compute(self, inp: AgentInput) -> AgentOutput:
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completion = inp.profile.get("completion_rate_30d")
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dismiss = inp.profile.get("dismiss_rate_30d")
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volume = inp.profile.get("tip_volume_30d")
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parts: list[str] = []
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if completion is not None:
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pct = round(completion * 100)
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if pct >= 50:
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parts.append(f"The user completes {pct}% of tips (strong engagement).")
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elif pct >= 25:
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parts.append(f"The user completes {pct}% of tips (moderate engagement).")
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else:
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parts.append(
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f"The user completes {pct}% of tips "
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f"(low engagement — prefer simple, immediately actionable tips)."
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)
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else:
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parts.append("No completion-rate data yet (new user).")
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if dismiss is not None:
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dpct = round(dismiss * 100)
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if dpct >= 40:
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parts.append(f"Dismiss rate is high ({dpct}%) — avoid repetitive or irrelevant tips.")
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elif dpct <= 10:
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parts.append(f"Dismiss rate is low ({dpct}%).")
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if volume is not None and int(volume) < 5:
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parts.append("Very few tips served so far — this is an early-stage user.")
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prompt = " ".join(parts) if parts else "No engagement data available yet."
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snapshot = {
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"completion_rate_30d": completion,
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"dismiss_rate_30d": dismiss,
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"tip_volume_30d": volume,
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}
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return self._make_output(inp, prompt, snapshot)
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