初始化提交

backend/tests/application/test_reflection.py

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