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>
109 lines
4.5 KiB
Python
109 lines
4.5 KiB
Python
"""Detail-path guards: skip intents never pollute drafts; LLM gate routes before fact-merge."""
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from __future__ import annotations
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from types import SimpleNamespace
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from typing import Any
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from app.builder_conversation.candidate import _candidate_rewrite
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from app.builder_conversation.predicates import _is_no_information_reply
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from app.builder_conversation.rescue import llm_detail_route
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from app.chat_intents import ChatTurnClassification
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from builder_flow_helpers import create_builder_session, send_message, start_education
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from test_api import active_component
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class _StubClassifier:
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def __init__(self, result: ChatTurnClassification) -> None:
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self.result = result
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def classify(self, message: str, *, state_summary: dict[str, Any]) -> ChatTurnClassification:
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return self.result
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def _agent(classifier: Any) -> Any:
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return SimpleNamespace(
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expander=SimpleNamespace(expand=lambda entry, *, context: {}),
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_chat_intent_classifier=classifier,
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)
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def _profile_with_draft() -> dict[str, Any]:
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return {
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"job_type": "campus",
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"builder": {
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"active_section": "education",
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"identity_draft": {"school": "X 大学", "description": "完成数据库课程项目。"},
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"gap_state": {"asked": ["academic_result"], "skipped": [], "rounds": 1},
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},
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}
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def test_skip_words_count_as_no_information() -> None:
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for word in ("跳过", "先跳过", "跳过吧", "不用了", "不需要", "以后再说", "没有了"):
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assert _is_no_information_reply(word), word
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def test_skip_reply_does_not_pollute_description(client: Any) -> None:
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session_id, body = create_builder_session(client)
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start_education(client, session_id, body)
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send_message(client, session_id, "完成数据库课程项目。") # triggers the gap prompt
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reply = send_message(client, session_id, "跳过")
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card = active_component(reply)["data"]
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assert card["component"] == "experience_confirm_card"
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assert "跳过" not in str(card["value"].get("description") or "")
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def test_detail_gate_no_info_skips_gap_without_merging() -> None:
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agent = _agent(_StubClassifier(ChatTurnClassification(intent="no_info", confidence=0.9)))
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transition = llm_detail_route(agent, _profile_with_draft(), "先跳过这个")
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assert transition is not None
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state = transition.profile["builder"]
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assert "先跳过这个" not in state["identity_draft"]["description"]
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assert "academic_result" in state["gap_state"]["skipped"]
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def test_detail_gate_revise_regenerates_candidate() -> None:
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agent = _agent(_StubClassifier(ChatTurnClassification(
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intent="revise_proposal", confidence=0.9, revision_instruction="再简洁一点",
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)))
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transition = llm_detail_route(agent, _profile_with_draft(), "帮我再精简下")
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assert transition is not None
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assert transition.profile["builder"]["pending_entry"]["_proposal"] is not None
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assert "帮我再精简下" not in transition.profile["builder"]["identity_draft"]["description"]
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def test_detail_gate_chitchat_does_not_merge() -> None:
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agent = _agent(_StubClassifier(ChatTurnClassification(intent="chitchat", confidence=0.9)))
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transition = llm_detail_route(agent, _profile_with_draft(), "好的谢谢")
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assert transition is not None
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assert transition.profile["builder"]["identity_draft"]["description"] == "完成数据库课程项目。"
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def test_detail_gate_passes_facts_and_low_confidence_through() -> None:
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facts = _agent(_StubClassifier(ChatTurnClassification(intent="provide_facts", confidence=0.9)))
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assert llm_detail_route(facts, _profile_with_draft(), "GPA 4.3") is None
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shaky = _agent(_StubClassifier(ChatTurnClassification(intent="no_info", confidence=0.4)))
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assert llm_detail_route(shaky, _profile_with_draft(), "跳过") is None
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def test_candidate_rewrite_ensure_facts_appends_missing() -> None:
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class _Expander:
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def expand(self, entry: dict[str, Any], *, context: dict[str, Any]) -> dict[str, Any]:
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return {"optimized_description": "主修课程：数据结构、计算机视觉。", "source": "test"}
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agent = SimpleNamespace(expander=_Expander())
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proposal = _candidate_rewrite(
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agent,
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{"job_type": "campus"},
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{"description": "学习数据结构、计算机视觉课程。GPA: 4.3/5.0，排名前百分之10。"},
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"education",
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ensure_facts=True,
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)
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assert "GPA: 4.3/5.0" in proposal["optimized_description"]
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assert "排名前百分之10" in proposal["optimized_description"]
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assert proposal["uncovered_facts"] == []
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