feat: initialize resume agent with OfferPai sync

2026-08-05 20:20:18 +08:00
commit 61ec750031
197 changed files with 33291 additions and 0 deletions
@@ -0,0 +1,75 @@
+"""Deterministic display categories for confirmed resume skills."""
+
+from __future__ import annotations
+
+from collections.abc import Iterable, Mapping
+
+_EXACT_CATEGORY_RULES: dict[str, str] = {
+    "sql analysis": "产品、设计与分析",
+    "data analysis": "产品、设计与分析",
+    "user research": "产品、设计与分析",
+    "数据分析": "产品、设计与分析",
+    "用户研究": "产品、设计与分析",
+}
+
+_CATEGORY_RULES: tuple[tuple[str, tuple[str, ...]], ...] = (
+    ("编程语言与框架", (
+        "python", "java", "javascript", "typescript", "go", "golang", "c++", "c#",
+        "fastapi", "django", "flask", "spring", "spring boot", "node.js", "nodejs",
+        "react native", "pytorch", "tensorflow", "编程语言", "软件工程",
+    )),
+    ("前端", (
+        "vue", "react", "angular", "html", "css", "sass", "tailwind", "webpack", "vite",
+        "前端", "小程序",
+    )),
+    ("后端与数据存储", (
+        "postgresql", "postgres", "mysql", "sqlite", "redis", "mongodb", "elasticsearch",
+        "kafka", "rabbitmq", "sql", "clickhouse", "后端", "数据库", "缓存", "消息队列",
+    )),
+    ("AI 与数据智能", (
+        "langgraph", "langchain", "llamaindex", "rag", "pgvector", "bge-m3", "bge", "tei",
+        "机器学习", "深度学习", "人工智能", "计算机视觉", "自然语言处理", "pandas", "numpy",
+    )),
+    ("云、DevOps 与工具", (
+        "docker", "kubernetes", "k8s", "git", "github actions", "gitlab ci", "jenkins",
+        "linux", "terraform", "aws", "azure", "aliyun", "云原生", "容器",
+    )),
+    ("产品、设计与分析", (
+        "figma", "axure", "tableau", "power bi", "excel", "data analysis", "sql analysis",
+        "product", "user research", "产品", "原型", "需求分析", "项目管理",
+    )),
+)
+
+
+def classify_skills(
+    skills: Iterable[object], preferred: Mapping[str, str] | None = None
+) -> list[dict[str, list[str] | str]]:
+    """Group confirmed user skills without changing their display order.
+
+    `preferred` maps skill -> category assigned by the recommender (LLM); it wins
+    over the keyword rules, which remain the fallback for manual edits.
+    """
+    preferred_normalized = {
+        " ".join(str(skill).casefold().split()): str(category).strip()
+        for skill, category in (preferred or {}).items()
+        if str(category).strip()
+    }
+    grouped: dict[str, list[str]] = {category: [] for category, _ in _CATEGORY_RULES}
+    grouped["其他技能"] = []
+    seen: set[str] = set()
+    for value in skills:
+        skill = str(value or "").strip()
+        normalized = " ".join(skill.casefold().split())
+        if not normalized or normalized in seen:
+            continue
+        seen.add(normalized)
+        category = preferred_normalized.get(normalized) or _EXACT_CATEGORY_RULES.get(normalized) or next(
+            (name for name, keywords in _CATEGORY_RULES if any(keyword in normalized for keyword in keywords)),
+            "其他技能",
+        )
+        grouped.setdefault(category, []).append(skill)
+    return [
+        {"category": category, "skills": values}
+        for category, values in grouped.items()
+        if values
+    ]