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/entry_expander.py

@@ -0,0 +1,86 @@
+"""Injectable entry expansion protocol and deterministic P0 implementation."""
+
+from __future__ import annotations
+
+import re
+from typing import Any, Protocol
+
+
+class EntryExpander(Protocol):
+    """Produce an optimization proposal without mutating the source entry."""
+
+    def expand(self, entry: dict[str, Any], *, context: dict[str, Any]) -> dict[str, Any]: ...
+
+
+class RuleBasedEntryExpander:
+    """Conservative local fallback used when no model is configured or available."""
+
+    def expand(self, entry: dict[str, Any], *, context: dict[str, Any]) -> dict[str, Any]:
+        description = str(entry.get("description") or "").strip()
+        highlights = [
+            str(value).strip()
+            for value in entry.get("highlights") or []
+            if str(value).strip()
+        ]
+        material = description or "；".join(highlights)
+        if material:
+            optimized = _polish_text(material)
+        else:
+            optimized = _description_from_structured_facts(entry, str(context.get("entry_type") or ""))
+        if not optimized:
+            return {"optimized_description": "", "changes": [], "source": "rule_polish"}
+        changes = ["统一为简洁、正式的简历表达"]
+        if not description and not highlights:
+            changes = ["根据已填写的结构化事实补充经历描述"]
+        return {
+            "optimized_description": optimized,
+            "changes": changes,
+            "source": "rule_polish",
+        }
+
+
+def _polish_text(text: str) -> str:
+    replacements = (
+        (r"^做过", "完成"),
+        (r"^做了", "完成"),
+        (r"^拿了奖(?:项)?", "获得奖项"),
+        (r"^参与了", "参与"),
+        (r"^使用了?\s*(?=[A-Za-z0-9])", "基于 "),
+        (r"^帮忙", "协助"),
+        (r"^负责", "承担"),
+        (r"^参加", "参与"),
+        (r"，将", "，推动"),
+        (r"，获得", "，并获得"),
+        (r"降低了", "降低"),
+        (r"提升了", "提升"),
+        (r"优化了", "优化"),
+    )
+    parts: list[str] = []
+    for raw in re.split(r"[。；;\n]+", text):
+        part = raw.strip(" ，,。；;")
+        if not part:
+            continue
+        for pattern, replacement in replacements:
+            part = re.sub(pattern, replacement, part)
+        parts.append(part)
+    return "；".join(parts[:5]) + ("。" if parts else "")
+
+
+def _description_from_structured_facts(entry: dict[str, Any], entry_type: str) -> str:
+    if entry_type in {"work_experience", "internship_experience"}:
+        company = str(entry.get("company") or "").strip()
+        position = str(entry.get("position") or "").strip()
+        return f"在{company}担任{position}。" if company and position else ""
+    if entry_type == "project_experience":
+        name = str(entry.get("project_name") or "").strip()
+        role = str(entry.get("project_role") or "").strip()
+        return f"参与{name}，担任{role}。" if name and role else ""
+    if entry_type == "competition":
+        name = str(entry.get("name") or "").strip()
+        award = str(entry.get("award") or "").strip()
+        return f"参加{name}并获得{award}。" if name and award else ""
+    if entry_type == "campus_experience":
+        organization = str(entry.get("organization") or "").strip()
+        role = str(entry.get("role") or "").strip()
+        return f"在{organization}担任{role}。" if organization and role else ""
+    return ""