a6674a1e76
- 将业务无关输入细分为 greeting / meaningless / off_business 三类场景 - 新增 StewardOffTopicAgent，用 function calling 生成管家语气引导回复 - steward endpoint 与 user_agent_application 串联 off_topic 引导话术 - 补充 planner 与 user agent 的 off_topic 覆盖测试
158 lines
6.4 KiB
Python
158 lines
6.4 KiB
Python
"""小财管家业务无关输入的引导生成 agent。
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当用户的输入被识别为与财务任务无关时（问候、纯数字、闲聊等），
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由该 agent 用 function calling 让主模型生成一句管家对主人语气的引导回复。
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LLM 不可用或调用失败时，由调用方 fallback 到规则模板。
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from typing import Any
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from app.schemas.steward import StewardPlanRequest
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from app.services.runtime_chat import RuntimeChatService
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STEWARD_OFF_TOPIC_FUNCTION_NAME = "submit_steward_off_topic_response"
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STEWARD_OFF_TOPIC_SCENARIO_PROMPTS: dict[str, str] = {
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"greeting": (
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"用户发起了礼貌问候，例如「你好」「您好」「早上好」。"
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"请像管家一样礼貌地回应主人的问候，温和询问主人今天要办理什么业务，"
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"并顺势说明小财管家能帮主人整理费用申请和费用报销两类事项。"
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"可以再提示主人试试用具体的话术来表达需求。"
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),
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"off_business": (
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"用户说了有意义的话但与财务无关，例如问天气、聊生活、聊工作日常。"
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"请温和地告诉主人：这句话里没有识别到财务事项，"
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"小财管家目前只能帮主人整理费用申请和费用报销。"
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"再请主人用具体的话术告诉小财管家他/她想办什么业务。"
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),
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"meaningless": (
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"用户输入了与财务无关且难以理解的内容，例如纯数字、纯标点、重复字符。"
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"请温和地告诉主人：这句话里好像没有出现费用申请、报销、出差、交通、招待这些关键词，"
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"请主人换种说法再告诉小财管家一次。"
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),
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}
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@dataclass(frozen=True, slots=True)
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class StewardOffTopicAgentResult:
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response_text: str
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model_call_traces: list[dict[str, Any]]
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class StewardOffTopicAgent:
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"""使用大模型 function calling 生成小财管家对业务无关输入的多样化引导。"""
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def __init__(self, runtime_chat_service: RuntimeChatService) -> None:
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self.runtime_chat_service = runtime_chat_service
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self.last_call_traces: list[dict[str, Any]] = []
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def generate(
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self,
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request: StewardPlanRequest,
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*,
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scenario: str,
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) -> StewardOffTopicAgentResult | None:
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messages = self._build_messages(request, scenario=scenario)
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try:
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result = self.runtime_chat_service.complete_with_tool_call(
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messages,
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tools=[self._build_tool_schema()],
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tool_choice={
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"type": "function",
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"function": {"name": STEWARD_OFF_TOPIC_FUNCTION_NAME},
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},
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max_tokens=400,
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temperature=0.7,
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timeout_seconds=15,
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max_attempts=1,
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)
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except Exception:
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return None
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self.last_call_traces = result.calls_as_dicts()
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if result.tool_call is None or result.tool_call.name != STEWARD_OFF_TOPIC_FUNCTION_NAME:
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return None
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arguments = result.tool_call.arguments or {}
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response_text = str(arguments.get("response_text") or "").strip()
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if not response_text:
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return None
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return StewardOffTopicAgentResult(
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response_text=response_text,
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model_call_traces=self.last_call_traces,
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)
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@staticmethod
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def _build_messages(
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request: StewardPlanRequest,
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*,
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scenario: str,
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) -> list[dict[str, Any]]:
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scenario_hint = STEWARD_OFF_TOPIC_SCENARIO_PROMPTS.get(
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scenario,
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STEWARD_OFF_TOPIC_SCENARIO_PROMPTS["off_business"],
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)
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return [
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{
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"role": "system",
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"content": (
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"你是 X-Financial 的小财管家，要像一位贴身的管家那样为主人服务。"
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"用户输入的内容与财务任务无关，你需要生成一段温和、尊敬的引导回复。"
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"要求：\n"
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"1. 始终称呼用户为「主人」「您」，不要用「你」。\n"
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"2. 语气尊敬、温和、主动，体现管家的服务意识。\n"
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"3. 不要每次都用相同的句式，要根据用户的输入和当前场景变化表达。\n"
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"4. 控制在 2-4 句，不要过长。\n"
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"5. 必须使用 function calling 输出 response_text，不要返回普通文本。\n"
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"6. response_text 使用 Markdown，第一行用 ### 标题，正文与引导句之间留空行。\n"
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"7. 如果主人是在问候，先礼貌回应再引导；"
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"如果主人说的是非财务话题，温和说明小财管家能做什么；"
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"如果主人的内容没有意义，请他/她换种说法。\n"
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f"当前场景：{scenario_hint}"
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),
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},
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{
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"role": "user",
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"content": json.dumps(
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{
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"message": request.message,
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"scenario": scenario,
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},
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ensure_ascii=False,
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),
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},
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]
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@staticmethod
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def _build_tool_schema() -> dict[str, Any]:
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return {
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"type": "function",
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"function": {
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"name": STEWARD_OFF_TOPIC_FUNCTION_NAME,
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"description": (
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"提交小财管家对业务无关输入的引导回复。"
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"response_text 是完整 Markdown 文本："
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"第一行 ### 标题（基于场景），空行，正文（歉意或回应 + 能力范围 + 引导句）。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"response_text": {
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"type": "string",
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"description": (
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"面向用户的引导回复 Markdown 文本。"
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"称呼用户为「主人」「您」，不要用「你」。"
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),
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},
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},
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"required": ["response_text"],
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},
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},
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}
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