Update 2026-05-13 16:43:53
This commit is contained in:
@@ -0,0 +1,92 @@
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"""Post-run evaluation for background tasks (heartbeat & cron).
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After the agent executes a background task, this module makes a lightweight
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LLM call to decide whether the result warrants notifying the user.
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from loguru import logger
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if TYPE_CHECKING:
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from nanobot.providers.base import LLMProvider
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_EVALUATE_TOOL = [
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{
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"type": "function",
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"function": {
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"name": "evaluate_notification",
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"description": "Decide whether the user should be notified about this background task result.",
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"parameters": {
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"type": "object",
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"properties": {
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"should_notify": {
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"type": "boolean",
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"description": "true = result contains actionable/important info the user should see; false = routine or empty, safe to suppress",
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},
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"reason": {
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"type": "string",
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"description": "One-sentence reason for the decision",
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},
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},
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"required": ["should_notify"],
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},
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},
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}
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]
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_SYSTEM_PROMPT = (
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"You are a notification gate for a background agent. "
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"You will be given the original task and the agent's response. "
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"Call the evaluate_notification tool to decide whether the user "
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"should be notified.\n\n"
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"Notify when the response contains actionable information, errors, "
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"completed deliverables, or anything the user explicitly asked to "
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"be reminded about.\n\n"
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"Suppress when the response is a routine status check with nothing "
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"new, a confirmation that everything is normal, or essentially empty."
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)
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async def evaluate_response(
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response: str,
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task_context: str,
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provider: LLMProvider,
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model: str,
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) -> bool:
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"""Decide whether a background-task result should be delivered to the user.
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Uses a lightweight tool-call LLM request (same pattern as heartbeat
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``_decide()``). Falls back to ``True`` (notify) on any failure so
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that important messages are never silently dropped.
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"""
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try:
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llm_response = await provider.chat_with_retry(
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messages=[
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{"role": "system", "content": _SYSTEM_PROMPT},
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{"role": "user", "content": (
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f"## Original task\n{task_context}\n\n"
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f"## Agent response\n{response}"
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)},
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],
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tools=_EVALUATE_TOOL,
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model=model,
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max_tokens=256,
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temperature=0.0,
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)
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if not llm_response.has_tool_calls:
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logger.warning("evaluate_response: no tool call returned, defaulting to notify")
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return True
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args = llm_response.tool_calls[0].arguments
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should_notify = args.get("should_notify", True)
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reason = args.get("reason", "")
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logger.info("evaluate_response: should_notify={}, reason={}", should_notify, reason)
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return bool(should_notify)
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except Exception:
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logger.exception("evaluate_response failed, defaulting to notify")
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return True
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