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>

backend/app/chat_intent_shadow.py

@@ -0,0 +1,60 @@
+"""P0 shadow rollout: run the LLM intent classifier beside rule routing, log only.
+
+Observation-only — the LLM result never influences routing. Disagreements are
+logged as `chat_intent_shadow` events to build the P1 golden dataset.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+import logging
+from typing import Any
+
+from .chat_intent_classifier import (
+    ChatIntentClassifier,
+    LLMChatIntentClassifier,
+    RuleBasedChatIntentClassifier,
+)
+from .chat_intents import CHAT_INTENT_REGISTRY_VERSION
+from .llm_services import OpenAICompatibleStructuredClient, log_ai_event
+from .settings import Settings
+
+
+class ChatIntentShadowLogger:
+    """Compares LLM vs rule classification per message. Never used for routing."""
+
+    def __init__(self, primary: ChatIntentClassifier, rules: ChatIntentClassifier) -> None:
+        self.primary = primary
+        self.rules = rules
+
+    def observe(self, message: str, *, state_summary: dict[str, Any]) -> None:
+        rule_result = self.rules.classify(message, state_summary=state_summary)
+        try:
+            llm_result = self.primary.classify(message, state_summary=state_summary)
+        except Exception as exc:
+            log_ai_event(
+                "chat_intent_shadow_error",
+                level=logging.WARNING,
+                exception=type(exc).__name__,
+                trace_id=getattr(exc, "trace_id", None),
+            )
+            return
+        log_ai_event(
+            "chat_intent_shadow",
+            registry_version=CHAT_INTENT_REGISTRY_VERSION,
+            rule_intent=rule_result.intent.value,
+            llm_intent=llm_result.intent.value,
+            llm_confidence=llm_result.confidence,
+            disagreement=llm_result.intent != rule_result.intent,
+        )
+
+
+def build_chat_intent_shadow(settings: Settings, client: Any | None = None) -> ChatIntentShadowLogger | None:
+    """Shadow only when explicitly enabled *and* an LLM provider is configured."""
+    if settings.intent_router_mode != "shadow" or not settings.use_openai:
+        return None
+    llm_settings = settings
+    if settings.intent_model:
+        llm_settings = dataclasses.replace(settings, openai_model=settings.intent_model)
+    completion = OpenAICompatibleStructuredClient(llm_settings, client)
+    return ChatIntentShadowLogger(LLMChatIntentClassifier(completion), RuleBasedChatIntentClassifier())