generated from kgod/ai-review-template
76 lines
3.3 KiB
Python
76 lines
3.3 KiB
Python
"""Chat intent registry: taxonomy, descriptions, few-shots, and output schema.
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Single source of truth for Builder free-text intents. The LLM classifier may only
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choose from this registry; handlers are bound deterministically in code (LLM
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proposes, code disposes). Bump CHAT_INTENT_REGISTRY_VERSION on any taxonomy change.
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"""
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from __future__ import annotations
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from enum import StrEnum
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from typing import Literal
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from pydantic import Field
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from .llm_services import StrictSchema
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CHAT_INTENT_REGISTRY_VERSION = "1"
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class ChatIntent(StrEnum):
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PROVIDE_FACTS = "provide_facts"
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NEW_ENTRY = "new_entry"
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EDIT_ENTRY = "edit_entry"
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EDIT_IDENTITY = "edit_identity"
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REVISE_PROPOSAL = "revise_proposal"
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NO_INFO = "no_info"
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ASK_QUESTION = "ask_question"
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CHITCHAT = "chitchat"
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UNCLEAR = "unclear"
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INTENT_DESCRIPTIONS: dict[ChatIntent, str] = {
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ChatIntent.PROVIDE_FACTS: "在给当前草稿补充事实（含回答追问）：动作、方法、工具、规模、结果等",
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ChatIntent.NEW_ENTRY: "想新增另一段经历（教育/工作/实习/项目/校园），不是在改当前这段",
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ChatIntent.EDIT_ENTRY: "想修改某段已确认写入简历的经历，可提到公司/学校/项目名",
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ChatIntent.EDIT_IDENTITY: "想改当前这段的基础信息：学校、公司、职位、时间等",
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ChatIntent.REVISE_PROPOSAL: "对当前候选优化稿的调整指令：保留原文、不要跳过、再专业一点等",
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ChatIntent.NO_INFO: "明确表示没有、无、暂无，是对追问的否定回答",
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ChatIntent.ASK_QUESTION: "在提问：关于简历怎么写、流程、建议等；不是在提供事实",
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ChatIntent.CHITCHAT: "寒暄、感谢、好的、可以等纯应答，不含新事实",
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ChatIntent.UNCLEAR: "无法判断意图，需要向用户澄清",
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}
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INTENT_FEWSHOTS: tuple[dict[str, object], ...] = (
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{"message": "负责后端接口开发，使用 Python 和 FastAPI，覆盖 3 个业务流程", "intent": ChatIntent.PROVIDE_FACTS},
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{"message": "新增一段教育经历", "intent": ChatIntent.NEW_ENTRY},
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{"message": "修改一下我之前写的那个 AI Career Copilot 项目经历", "intent": ChatIntent.EDIT_ENTRY},
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{"message": "把学校名字改成东莞城市学院", "intent": ChatIntent.EDIT_IDENTITY},
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{"message": "保留原文，不要用这版优化稿", "intent": ChatIntent.REVISE_PROPOSAL},
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{"message": "没有", "intent": ChatIntent.NO_INFO},
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{"message": "这段经历你觉得怎么写比较好？", "intent": ChatIntent.ASK_QUESTION},
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{"message": "好的，谢谢", "intent": ChatIntent.CHITCHAT},
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{"message": "嗯……那个嘛", "intent": ChatIntent.UNCLEAR},
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)
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class ExtractedFact(StrictSchema):
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"""One atomic fact copied or tightly paraphrased from the user's own message."""
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text: str
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kind: Literal["action", "method", "tool", "scale", "result", "other"] = "other"
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class ChatTurnClassification(StrictSchema):
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"""Structured output of the chat intent classifier (one call per message)."""
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intent: ChatIntent
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confidence: float = Field(default=0.5, ge=0, le=1)
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target_section: str | None = None
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target_entry_hint: str | None = None
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facts: list[ExtractedFact] = Field(default_factory=list)
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identity_updates: dict[str, str] | None = None
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revision_instruction: str | None = None
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user_question: str | None = None
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reason: str = ""
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