generated from kgod/ai-review-template
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>
77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
"""Import parsing speed-ups: slim schema without model evidence (A) + sha256 parse cache (B)."""
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from __future__ import annotations
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import io
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from typing import Any
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from docx import Document
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from app.import_parser import ImportParseOutput, OpenAIResumeImportParser
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from app.resume_import_service import ResumeImportService
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class FakeCompletion:
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def __init__(self) -> None:
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self.calls: list[dict[str, Any]] = []
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def complete(self, **kwargs: Any) -> Any:
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self.calls.append(kwargs)
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return kwargs["schema"].model_validate(
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{
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"basics": {"name": "张三"},
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"sections": [
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{
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"kind": "education",
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"heading": "教育经历",
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"items": [{"fields": {"school": "示例大学", "major": "软件工程"}}],
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}
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],
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"skill_groups": [],
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}
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)
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def _docx(text: str) -> bytes:
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document = Document()
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document.add_paragraph(text)
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buffer = io.BytesIO()
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document.save(buffer)
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return buffer.getvalue()
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def _service(tmp_path, completion: FakeCompletion) -> ResumeImportService:
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return ResumeImportService(
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storage_root=tmp_path / "imports",
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parser=OpenAIResumeImportParser(completion=completion),
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)
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def test_service_uses_slim_schema_without_model_evidence(tmp_path) -> None:
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"""模型不再逐条输出 evidence 引用（本地匹配已覆盖），输出 token 与时间同降。"""
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from app.import_parser_fast import SlimImportParseOutput
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completion = FakeCompletion()
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prepared = _service(tmp_path, completion).prepare(
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file_name="r.docx", declared_mime=None, content=_docx("张三 示例大学 软件工程")
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)
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assert completion.calls[0]["schema"] is SlimImportParseOutput
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assert "evidence" not in completion.calls[0]["system_prompt"].casefold()
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assert prepared["document"]["sections"][0]["items"][0]["school"] == "示例大学"
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assert all(item["evidence"] for item in prepared["field_reviews"]) # 本地匹配仍然提供证据
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def test_repeated_upload_of_same_file_skips_llm_parse(tmp_path) -> None:
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"""同一文件跨会话重复上传命中 sha256 缓存，不再调 LLM 解析。"""
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completion = FakeCompletion()
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service = _service(tmp_path, completion)
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content = _docx("张三 示例大学 软件工程")
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first = service.prepare(file_name="a.docx", declared_mime=None, content=content)
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second = service.prepare(file_name="b.docx", declared_mime=None, content=content)
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assert len(completion.calls) == 1
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assert second["document"] == first["document"]
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assert second["sha256"] == first["sha256"]
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