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>
2026-08-05 11:08:30 +08:00
commit ae2d9b128d
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+"""Skill-suggestion cards and selection handling for the Builder."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import Any
+
+from ..fsm import FSMError, Transition, component
+from ..models import Stage
+from ..resume_skill_advisor import recommend_skill_candidates
+from ..skill_groups import update_skill_groups
+from .constants import u
+from .state import _set_stream_phases
+from .turns import _next_step_turn, recommended_section
+
+
+def _skill_choice_card(candidates: list[dict[str, Any]]) -> dict[str, Any]:
+    return component(
+        "ChoiceChips",
+        module="builder_skill_select",
+        title=u("确认岗位技能"),
+        description=u("请选择你愿意确认加入简历的技能；未选择的候选不会写入。没有合适的也可以暂时跳过。"),
+        multiple=True,
+        skippable=True,
+        skip_label=u("暂不添加"),
+        options=[
+            {
+                "value": str(candidate["skill"]),
+                "label": f'{candidate["skill"]}{u("（")}{candidate["category"]}{u("）")}',
+            }
+            for candidate in candidates
+        ],
+    )
+
+
+def _builder_skill_candidates(
+    profile: dict[str, Any],
+    resume_content: dict[str, Any],
+    skill_suggester: Any | None,
+) -> list[dict[str, Any]]:
+    if skill_suggester is None:
+        return []
+    existing = [
+        str(skill).strip()
+        for group in resume_content.get("skill_groups") or []
+        if isinstance(group, dict)
+        for skill in group.get("skills") or []
+        if str(skill).strip()
+    ]
+    working_profile = deepcopy(profile)
+    working_profile["tags"] = {**dict(working_profile.get("tags") or {}), "skills": existing}
+    facts: list[dict[str, Any]] = []
+    for section in resume_content.get("sections") or []:
+        if not isinstance(section, dict):
+            continue
+        for entry in section.get("items") or []:
+            if isinstance(entry, dict):
+                facts.append(deepcopy(entry))
+    working_profile["experiences"] = facts
+    return recommend_skill_candidates(
+        working_profile,
+        existing,
+        u("根据我选择的目标岗位推荐可确认技能"),
+        skill_suggester,
+    )
+
+
+def _process_skill_selection(
+    profile: dict[str, Any],
+    state: dict[str, Any],
+    action: str,
+    payload: dict[str, Any],
+    resume_content: dict[str, Any],
+) -> Transition:
+    if action == "skip":
+        state["pending_skill_candidates"] = []
+        _set_stream_phases(profile, "suggesting_next")
+        return Transition(
+            Stage.BUILDER_CONVERSATION,
+            profile,
+            _next_step_turn(recommended_section(profile, resume_content), prefix=u("好的，先不添加技能。")),
+            lifecycle="dismissed",
+        )
+    if action != "select":
+        raise FSMError("invalid_builder_skill_selection", "Confirm or skip the skill suggestions", status_code=422)
+    selected = payload.get("values")
+    if not isinstance(selected, list):
+        selected = [payload.get("value")]
+    allowed = {
+        str(item.get("skill") or "").strip()
+        for item in state.get("pending_skill_candidates") or []
+        if isinstance(item, dict) and str(item.get("skill") or "").strip()
+    }
+    chosen = [str(value).strip() for value in selected if str(value or "").strip() in allowed]
+    if not chosen:
+        raise FSMError("invalid_builder_skill_selection", "Select at least one suggested skill or skip", status_code=422)
+    existing = [
+        str(skill).strip()
+        for group in resume_content.get("skill_groups") or []
+        if isinstance(group, dict)
+        for skill in group.get("skills") or []
+        if str(skill).strip()
+    ]
+    preferred = {
+        str(item.get("skill") or "").strip(): str(item.get("category") or "").strip()
+        for item in state.get("pending_skill_candidates") or []
+        if isinstance(item, dict) and str(item.get("skill") or "").strip() and str(item.get("category") or "").strip()
+    }
+    content = update_skill_groups(resume_content, [*existing, *chosen], preferred_categories=preferred)
+    state["pending_skill_candidates"] = []
+    _set_stream_phases(profile, "saving", "suggesting_next")
+    return Transition(
+        Stage.BUILDER_CONVERSATION,
+        profile,
+        _next_step_turn(
+            recommended_section(profile, content),
+            prefix=u("已添加") + " " + u("、").join(chosen) + u("。"),
+        ),
+        lifecycle="confirmed",
+        resume_content=content,
+    )