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>
47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
from __future__ import annotations
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from collections import defaultdict
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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class FocusAreaAgent(BaseAgent):
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"""Identifies the most congested project/area in the user's task list."""
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agent_id: ClassVar[str] = "focus-area"
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ttl_seconds: ClassVar[int] = 43_200 # 12h
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version: ClassVar[str] = "1.0.0"
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def compute(self, inp: AgentInput) -> AgentOutput:
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by_project: dict[str, list[dict]] = defaultdict(list)
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for task in inp.tasks:
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project = task.get("project_id") or task.get("project") or "default"
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by_project[project].append(task)
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if not by_project:
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prompt = "No tasks available to identify a focus area."
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return self._make_output(inp, prompt, {"project_count": 0})
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# Score each project: overdue tasks count double
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def score(tasks: list[dict]) -> float:
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return sum(2.0 if t.get("is_overdue") else 1.0 for t in tasks)
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top_project, top_tasks = max(by_project.items(), key=lambda kv: score(kv[1]))
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overdue_in_top = sum(1 for t in top_tasks if t.get("is_overdue"))
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label = "the default project" if top_project == "default" else f'"{top_project}"'
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n = len(top_tasks)
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parts = [
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f"The user's most congested area is {label} "
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f"({n} task{'s' if n != 1 else ''}, {overdue_in_top} overdue)."
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]
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if overdue_in_top >= 3:
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parts.append("Consider surfacing an action from this area.")
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prompt = " ".join(parts)
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snapshot = {
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"top_project": top_project,
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"top_task_count": n,
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"top_overdue_count": overdue_in_top,
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"project_count": len(by_project),
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}
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return self._make_output(inp, prompt, snapshot)
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