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hypandClaude
ae2d9b128d
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>
30 lines
921 B
Python
30 lines
921 B
Python
"""Characterization tests for optimization context anchoring."""
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from app.optimization_flow import OptimizationFlowMixin
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def test_context_carries_target_position_and_major():
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session = {
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"profile": {
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"job_type": "校招",
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"target_position": "数据分析师",
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"anchor": {"major": "统计学"},
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}
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}
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section = {"kind": "internship_experience"}
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context = OptimizationFlowMixin._context(session, section, None)
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assert context["target_position"] == "数据分析师"
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assert context["major"] == "统计学"
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assert context["entry_type"] == "internship_experience"
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def test_context_tolerates_missing_target_position():
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session = {"profile": {"job_type": "社招", "anchor": {}}}
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section = {"kind": "work_experience"}
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context = OptimizationFlowMixin._context(session, section, None)
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assert context["target_position"] is None
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