generated from kgod/ai-review-template
feat: builder 简历生成 + 轻度优化 + 简历导入交付副本
自内部仓库剥离深度优化与 RAG 知识库后的交付版本： - Builder 对话式简历生成（FSM + 意图路由 LLM 兜底增强） - 条目级轻度优化：事实覆盖门禁 + STAR/bullet 修复链，功能/简介/成果与技术栈同级保护 - 简历导入：DOCX/PDF 解析、结构归一、手机号脱敏 - PostgreSQL 运行时 + Alembic 迁移链 Co-Authored-By: Claude <noreply@anthropic.com>
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Claude
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"""Chat intent classifier: LLM primary, legacy keyword rules as degraded fallback.
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The LLM only *proposes* an intent from the fixed registry in chat_intents.py;
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handler binding stays deterministic in code. RuleBasedChatIntentClassifier mirrors
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the legacy keyword routing so the conversation keeps working when the LLM is down.
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"""
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from __future__ import annotations
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import logging
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import re
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from typing import Any, Protocol, runtime_checkable
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from .builder_conversation.predicates import (
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_is_no_information_reply,
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_is_revision_instruction,
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_requested_section,
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_requests_identity_change,
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_requests_new_entry,
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)
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from .chat_intents import (
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CHAT_INTENT_REGISTRY_VERSION,
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INTENT_DESCRIPTIONS,
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INTENT_FEWSHOTS,
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ChatIntent,
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ChatTurnClassification,
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ExtractedFact,
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)
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from .llm_services import OpenAICompatibleStructuredClient, log_ai_event
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from .settings import Settings
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_ENTRY_LABEL_KEYS = ("company", "project_name", "school", "organization", "title", "name", "position", "role")
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_CHITCHAT = {"可以", "好的", "好", "谢谢", "感谢", "继续", "没问题", "知道了", "嗯", "ok", "okay"}
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_QUESTION_TOKENS = ("?", "？", "吗", "怎么", "如何", "为什么", "哪", "能不能", "可以不可以")
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_EDIT_TOKENS = ("修改", "编辑", "调整", "改一下", "改下")
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@runtime_checkable
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class ChatIntentClassifier(Protocol):
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def classify(self, message: str, *, state_summary: dict[str, Any]) -> ChatTurnClassification: ...
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def build_chat_state_summary(profile: dict[str, Any], state: dict[str, Any] | None = None) -> dict[str, Any]:
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"""Compact, metadata-only snapshot fed to the classifier (never full resume text)."""
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state = state or {}
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entries: list[dict[str, str]] = []
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resume_content = profile.get("resume_content") or {}
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for section in resume_content.get("sections") or []:
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if not isinstance(section, dict):
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continue
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kind = str(section.get("kind") or "")
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for entry in section.get("items") or []:
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if not isinstance(entry, dict):
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continue
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label = next(
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(str(entry.get(key)).strip() for key in _ENTRY_LABEL_KEYS if str(entry.get(key) or "").strip()),
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"",
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)
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entries.append({"section": kind, "label": label})
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draft = state.get("draft") if isinstance(state.get("draft"), dict) else None
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return {
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"job_type": str(profile.get("job_type") or ""),
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"target_position": str(profile.get("target_position") or ""),
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"confirmed_entries": entries,
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"draft_section": str(draft.get("section") or "") if draft else None,
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}
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def _intent_system_prompt() -> str:
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lines = [
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"你是简历对话的意图分类器。根据用户消息与对话状态，从固定意图集合中选择唯一意图。",
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"facts 只能摘录或紧贴改写用户原话中的事实，不得编造用户没说过的内容。",
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f"注册表版本: {CHAT_INTENT_REGISTRY_VERSION}",
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"意图定义:",
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]
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lines += [f"- {intent.value}: {INTENT_DESCRIPTIONS[intent]}" for intent in ChatIntent]
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lines.append("示例:")
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lines += [f"- 消息: {shot['message']} → {shot['intent'].value}" for shot in INTENT_FEWSHOTS]
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return "\n".join(lines)
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_INTENT_SYSTEM_PROMPT = _intent_system_prompt()
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class RuleBasedChatIntentClassifier:
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"""Legacy keyword routing, kept verbatim as the degraded path."""
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def classify(self, message: str, *, state_summary: dict[str, Any]) -> ChatTurnClassification:
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content = message.strip()
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normalized = re.sub(r"[\s,，。；;！!？?]", "", content.casefold())
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if _is_no_information_reply(content):
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return self._result(ChatIntent.NO_INFO)
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if _is_revision_instruction(content):
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return self._result(ChatIntent.REVISE_PROPOSAL, revision_instruction=content)
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if _requests_identity_change(content):
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return self._result(ChatIntent.EDIT_IDENTITY)
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section = _requested_section(content)
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if section or _requests_new_entry(content):
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return self._result(ChatIntent.NEW_ENTRY, target_section=section)
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if any(token in normalized for token in _EDIT_TOKENS):
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hint = self._entry_hint(normalized, state_summary)
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return self._result(ChatIntent.EDIT_ENTRY, target_entry_hint=hint)
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if any(token in content for token in _QUESTION_TOKENS):
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return self._result(ChatIntent.ASK_QUESTION, user_question=content)
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parts = [part for part in re.split(r"[\s,，。；;！!？?]+", content.casefold()) if part]
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if parts and all(part in _CHITCHAT for part in parts):
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return self._result(ChatIntent.CHITCHAT)
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facts = [ExtractedFact(text=content)] if content else []
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return self._result(ChatIntent.PROVIDE_FACTS, facts=facts)
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@staticmethod
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def _entry_hint(normalized: str, state_summary: dict[str, Any]) -> str | None:
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for entry in state_summary.get("confirmed_entries") or []:
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label = str(entry.get("label") or "").strip()
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squashed = re.sub(r"[\s,，。；;！!？?]", "", label.casefold())
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if squashed and squashed in normalized:
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return label
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return None
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@staticmethod
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def _result(intent: ChatIntent, **fields: Any) -> ChatTurnClassification:
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return ChatTurnClassification(intent=intent, confidence=0.4, reason="rule_keyword", **fields)
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class LLMChatIntentClassifier:
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def __init__(self, client: OpenAICompatibleStructuredClient) -> None:
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self._client = client
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def classify(self, message: str, *, state_summary: dict[str, Any]) -> ChatTurnClassification:
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return self._client.complete(
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schema=ChatTurnClassification,
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schema_name="chat_intent_classification",
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system_prompt=_INTENT_SYSTEM_PROMPT,
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payload={
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"message": message,
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"state_summary": state_summary,
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"registry_version": CHAT_INTENT_REGISTRY_VERSION,
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},
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)
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class FallbackChatIntentClassifier:
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def __init__(self, primary: ChatIntentClassifier, fallback: ChatIntentClassifier) -> None:
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self.primary = primary
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self.fallback = fallback
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def classify(self, message: str, *, state_summary: dict[str, Any]) -> ChatTurnClassification:
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try:
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return self.primary.classify(message, state_summary=state_summary)
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except Exception as exc:
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log_ai_event(
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"chat_intent_classification_failed",
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level=logging.ERROR,
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reason_code=getattr(exc, "reason_code", type(exc).__name__.lower()[:48]),
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trace_id=getattr(exc, "trace_id", None),
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exception=type(exc).__name__,
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)
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return self.fallback.classify(message, state_summary=state_summary)
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def build_chat_intent_classifier(settings: Settings, client: Any | None = None) -> ChatIntentClassifier:
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rules = RuleBasedChatIntentClassifier()
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if not settings.use_openai:
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return rules
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completion = OpenAICompatibleStructuredClient(settings, client)
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return FallbackChatIntentClassifier(LLMChatIntentClassifier(completion), rules)
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