59d4b19d7d
- Phase1/2：移除 MemoirOrchestrator.run 与 process_memoir_segments 别名；文档改为 process_memoir_phase1。 - 槽位校验集中到 stage_constants（filter_stage_slots），批处理与顺序路径及 state_service 写库一致。 - StoryRoute：no_llm/parse_error/invalid_target 保守 new_story；短篇护栏不覆盖这些 fallback。 - Phase2 低置信单路径可选延迟（StoryPipelineResult.deferred）：不写 Chapter/Story，Segment 记录 defer 元数据，冷却内不重复消费；上限后停自动重试，Phase1 同类目新段唤醒池内段。 - Alembic 0017：segments 表 narrative_defer_* 列。 - ProfileAgent：经 LlmGateway/注入 Provider 统一聊天与 JSON，新增测试。 - ImagePromptOrchestrator：LLM 初始化失败可依配置降级或硬失败；补充策略测试。 - 配套单测与 README/本地开发文档表述更新。 Co-authored-by: Cursor <cursoragent@cursor.com>
86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
"""ProfileAgent LLM gateway injection regression tests."""
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from __future__ import annotations
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import json
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from types import SimpleNamespace
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import pytest
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from app.agents.chat.profile_agent import ProfileAgent
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class _Response:
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def __init__(self, content: str) -> None:
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self.content = content
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class _BoundJsonLlm:
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async def ainvoke(self, _prompt: str) -> _Response:
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return _Response(
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json.dumps(
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{
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"birth_year": 1988,
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"birth_place": "杭州",
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"grew_up_place": "杭州",
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"occupation": "工程师",
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}
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)
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)
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class _JsonLlm:
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def bind(self, **_kwargs) -> _BoundJsonLlm: # noqa: ANN003
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return _BoundJsonLlm()
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class _Provider:
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langchain_llm = _JsonLlm()
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def __init__(self) -> None:
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self.messages: list[dict] = []
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async def complete(self, messages: list[dict], **_kwargs) -> str: # noqa: ANN003
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self.messages = messages
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return "谢谢分享！还能再说说吗？"
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async def stream(self, *_args, **_kwargs): # noqa: ANN003
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if False:
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yield ""
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@pytest.mark.asyncio
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async def test_profile_agent_llm_provider_injection_covers_chat_and_json(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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async def fake_history(*_args, **_kwargs):
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return SimpleNamespace(window=[], turn_total=0)
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monkeypatch.setattr(
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"app.agents.chat.profile_agent.get_history_with_window",
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fake_history,
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)
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provider = _Provider()
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agent = ProfileAgent(llm_provider=provider)
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extracted = await agent.extract_profile_from_message(
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"我是一名工程师，1988 年出生在杭州。",
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["birth_year", "birth_place", "occupation"],
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)
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followup = await agent.generate_profile_followup(
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conversation_id="c1",
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user_message="我在杭州长大。",
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missing_fields=["grew_up_place"],
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filled_fields={"birth_year": "1988"},
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)
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assert extracted == {
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"birth_year": 1988,
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"birth_place": "杭州",
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"grew_up_place": "杭州",
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"occupation": "工程师",
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}
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assert followup
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assert provider.messages
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assert provider.messages[0]["role"] == "system"
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