101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
"""小周天每轮记忆候选 + 反思抽取测试。
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高置信度（≥0.7）低敏感度记忆候选直接落地到 memories 表而非 MemoryCandidate。
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"""
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import pytest
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from sqlalchemy import select
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from app.application.reflection import (
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drain_pending_reflections,
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start_reflection_after_turn,
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)
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from app.db.session import AsyncSessionLocal
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from app.models.memory import Memory
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from app.repositories import candidates as candidates_repo
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from app.services.characters import get_active_character_model
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_REFLECTION_RESPONSE = (
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'{"memories":[{"type":"preference","title":"喜欢简洁","content":"用户偏好简洁",'
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'"importance":0.8,"sensitivity":"low","confidence":0.7}],'
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'"reflection":"本轮应更主动确认用户意图。"}'
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)
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@pytest.fixture
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def mock_reflection_chat(monkeypatch: pytest.MonkeyPatch) -> None:
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async def fake_chat(*_args, **_kwargs):
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return _REFLECTION_RESPONSE
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monkeypatch.setattr("app.application.reflection._call_model", fake_chat)
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@pytest.mark.asyncio
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async def test_reflection_extracts_memory_and_writes_reflection(
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mock_reflection_chat, # noqa: ARG001
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) -> None:
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async with AsyncSessionLocal() as session:
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character = await get_active_character_model(session)
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start_reflection_after_turn(character.id, "session_test", "你好", "你好，我是魂镜。")
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await drain_pending_reflections()
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async with AsyncSessionLocal() as session:
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# 高置信度记忆直接落地到 memories 表（非 MemoryCandidate）
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auto_mem = (
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await session.execute(
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select(Memory).where(
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Memory.character_id == character.id,
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Memory.type == "preference",
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Memory.source == "reflection",
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)
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)
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).scalar_one_or_none()
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# 反思文本也落地为 memories 表
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ref_mem = (
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await session.execute(
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select(Memory).where(
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Memory.character_id == character.id,
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Memory.type == "reflection",
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)
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)
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).scalar_one_or_none()
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assert auto_mem is not None
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assert auto_mem.title == "喜欢简洁"
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assert ref_mem is not None
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assert ref_mem.source == "reflection"
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assert ref_mem.status == "active" # Memory 无 status 字段，用 default
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assert "确认用户意图" in ref_mem.content
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@pytest.mark.asyncio
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async def test_reflection_without_memories_only_writes_reflection(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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async def fake_chat(*_args, **_kwargs):
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return '{"memories":[],"reflection":"只反思不抽记忆。"}'
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monkeypatch.setattr("app.application.reflection._call_model", fake_chat)
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async with AsyncSessionLocal() as session:
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character = await get_active_character_model(session)
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start_reflection_after_turn(character.id, "session_test", "hi", "你好。")
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await drain_pending_reflections()
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async with AsyncSessionLocal() as session:
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cands = await candidates_repo.list_memory_candidates(session, character.id, source="cycle")
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ref_mem = (
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await session.execute(
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select(Memory).where(
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Memory.character_id == character.id,
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Memory.type == "reflection",
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)
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)
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).scalar_one_or_none()
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# 无记忆候选（空 memories 数组），只有反思
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assert len(cands) == 0
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assert ref_mem is not None
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assert "只反思" in ref_mem.content
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