feat: initialize resume agent with OfferPai sync

backend/tests/test_builder_detail_gate.py

@@ -0,0 +1,108 @@
+"""Detail-path guards: skip intents never pollute drafts; LLM gate routes before fact-merge."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any
+
+from app.builder_conversation.candidate import _candidate_rewrite
+from app.builder_conversation.predicates import _is_no_information_reply
+from app.builder_conversation.rescue import llm_detail_route
+from app.chat_intents import ChatTurnClassification
+from builder_flow_helpers import create_builder_session, send_message, start_education
+from test_api import active_component
+
+
+class _StubClassifier:
+    def __init__(self, result: ChatTurnClassification) -> None:
+        self.result = result
+
+    def classify(self, message: str, *, state_summary: dict[str, Any]) -> ChatTurnClassification:
+        return self.result
+
+
+def _agent(classifier: Any) -> Any:
+    return SimpleNamespace(
+        expander=SimpleNamespace(expand=lambda entry, *, context: {}),
+        _chat_intent_classifier=classifier,
+    )
+
+
+def _profile_with_draft() -> dict[str, Any]:
+    return {
+        "job_type": "campus",
+        "builder": {
+            "active_section": "education",
+            "identity_draft": {"school": "X 大学", "description": "完成数据库课程项目。"},
+            "gap_state": {"asked": ["academic_result"], "skipped": [], "rounds": 1},
+        },
+    }
+
+
+def test_skip_words_count_as_no_information() -> None:
+    for word in ("跳过", "先跳过", "跳过吧", "不用了", "不需要", "以后再说", "没有了"):
+        assert _is_no_information_reply(word), word
+
+
+def test_skip_reply_does_not_pollute_description(client: Any) -> None:
+    session_id, body = create_builder_session(client)
+    start_education(client, session_id, body)
+    send_message(client, session_id, "完成数据库课程项目。")  # triggers the gap prompt
+    reply = send_message(client, session_id, "跳过")
+    card = active_component(reply)["data"]
+    assert card["component"] == "experience_confirm_card"
+    assert "跳过" not in str(card["value"].get("description") or "")
+
+
+def test_detail_gate_no_info_skips_gap_without_merging() -> None:
+    agent = _agent(_StubClassifier(ChatTurnClassification(intent="no_info", confidence=0.9)))
+    transition = llm_detail_route(agent, _profile_with_draft(), "先跳过这个")
+
+    assert transition is not None
+    state = transition.profile["builder"]
+    assert "先跳过这个" not in state["identity_draft"]["description"]
+    assert "academic_result" in state["gap_state"]["skipped"]
+
+
+def test_detail_gate_revise_regenerates_candidate() -> None:
+    agent = _agent(_StubClassifier(ChatTurnClassification(
+        intent="revise_proposal", confidence=0.9, revision_instruction="再简洁一点",
+    )))
+    transition = llm_detail_route(agent, _profile_with_draft(), "帮我再精简下")
+
+    assert transition is not None
+    assert transition.profile["builder"]["pending_entry"]["_proposal"] is not None
+    assert "帮我再精简下" not in transition.profile["builder"]["identity_draft"]["description"]
+
+
+def test_detail_gate_chitchat_does_not_merge() -> None:
+    agent = _agent(_StubClassifier(ChatTurnClassification(intent="chitchat", confidence=0.9)))
+    transition = llm_detail_route(agent, _profile_with_draft(), "好的谢谢")
+
+    assert transition is not None
+    assert transition.profile["builder"]["identity_draft"]["description"] == "完成数据库课程项目。"
+
+
+def test_detail_gate_passes_facts_and_low_confidence_through() -> None:
+    facts = _agent(_StubClassifier(ChatTurnClassification(intent="provide_facts", confidence=0.9)))
+    assert llm_detail_route(facts, _profile_with_draft(), "GPA 4.3") is None
+    shaky = _agent(_StubClassifier(ChatTurnClassification(intent="no_info", confidence=0.4)))
+    assert llm_detail_route(shaky, _profile_with_draft(), "跳过") is None
+
+
+def test_candidate_rewrite_ensure_facts_appends_missing() -> None:
+    class _Expander:
+        def expand(self, entry: dict[str, Any], *, context: dict[str, Any]) -> dict[str, Any]:
+            return {"optimized_description": "主修课程：数据结构、计算机视觉。", "source": "test"}
+
+    agent = SimpleNamespace(expander=_Expander())
+    proposal = _candidate_rewrite(
+        agent,
+        {"job_type": "campus"},
+        {"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。"},
+        "education",
+        ensure_facts=True,
+    )
+    assert "GPA: 4.3/5.0" in proposal["optimized_description"]
+    assert "排名前百分之10" in proposal["optimized_description"]
+    assert proposal["uncovered_facts"] == []