148 lines
5.0 KiB
Python
148 lines
5.0 KiB
Python
import json
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from unittest.mock import AsyncMock
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import pytest
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from duck_core.memory.policy import MemoryPolicy
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from duck_core.model_client import ModelClient, ModelResponse
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@pytest.fixture
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def mock_model_client():
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client = AsyncMock(spec=ModelClient)
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client.chat = AsyncMock(
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return_value=ModelResponse(
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role="critic",
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model="local-main",
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content=json.dumps({
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"should_store": True,
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"memory_type": "preference",
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"summary": "User prefers concise Russian answers.",
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"importance": 0.9,
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"scope": "global",
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"metadata": {"source": "conversation"},
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}),
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reasoning_content=None,
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raw={},
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latency_ms=42.0,
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)
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)
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return client
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@pytest.mark.asyncio
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async def test_memory_policy_stub_returns_should_store_false():
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policy = MemoryPolicy()
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decision = await policy.classify("some summary", "task_123")
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assert decision.should_store is False
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assert decision.memory_type == "event"
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assert decision.importance == 0.0
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assert decision.metadata["source"] == "stub_policy"
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@pytest.mark.asyncio
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async def test_llm_memory_policy_classifies_and_stores(mock_model_client):
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policy = MemoryPolicy(model_client=mock_model_client, role="memory_policy")
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decision = await policy.classify(
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"User said they prefer short answers in Russian.", "task_456"
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)
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assert decision.should_store is True
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assert decision.memory_type == "preference"
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assert decision.importance == 0.9
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assert decision.summary == "User prefers concise Russian answers."
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mock_model_client.chat.assert_called_once()
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call_args = mock_model_client.chat.call_args
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# ModelClient.chat(role, messages, ...) — positional args
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assert call_args.args[0] == "memory_policy"
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messages = call_args.args[1]
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assert len(messages) == 1
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assert messages[0]["role"] == "user"
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assert "User said they prefer short answers" in messages[0]["content"]
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@pytest.mark.asyncio
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async def test_llm_memory_policy_handles_non_storable(mock_model_client):
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mock_model_client.chat.return_value = ModelResponse(
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role="critic",
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model="local-main",
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content=json.dumps({
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"should_store": False,
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"memory_type": "event",
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"summary": "Routine tool call, nothing to remember.",
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"importance": 0.1,
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"scope": "workspace",
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"metadata": {},
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}),
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reasoning_content=None,
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raw={},
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latency_ms=30.0,
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)
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policy = MemoryPolicy(model_client=mock_model_client)
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decision = await policy.classify("Ran ls -la in workspace.", "task_789")
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assert decision.should_store is False
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assert decision.importance == 0.1
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@pytest.mark.asyncio
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async def test_llm_memory_policy_uses_response_format(mock_model_client):
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policy = MemoryPolicy(model_client=mock_model_client)
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await policy.classify("test summary", "task_1")
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call_args = mock_model_client.chat.call_args
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assert call_args.kwargs["response_format"]["type"] == "json_schema"
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@pytest.mark.asyncio
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async def test_llm_memory_policy_invalid_json_falls_back(mock_model_client):
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mock_model_client.chat.return_value = ModelResponse(
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role="critic",
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model="local-main",
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content="not valid json {{{",
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reasoning_content=None,
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raw={},
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latency_ms=10.0,
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)
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policy = MemoryPolicy(model_client=mock_model_client)
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decision = await policy.classify("some summary", "task_x")
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assert decision.should_store is False
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assert decision.metadata["source"] == "llm_policy_fallback"
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@pytest.mark.asyncio
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async def test_llm_memory_policy_missing_fields_falls_back(mock_model_client):
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mock_model_client.chat.return_value = ModelResponse(
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role="critic",
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model="local-main",
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content=json.dumps({"should_store": True}),
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reasoning_content=None,
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raw={},
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latency_ms=10.0,
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)
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policy = MemoryPolicy(model_client=mock_model_client)
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decision = await policy.classify("some summary", "task_y")
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assert decision.should_store is False
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assert decision.metadata["source"] == "llm_policy_fallback"
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@pytest.mark.asyncio
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async def test_llm_memory_policy_schema_violation_falls_back(mock_model_client):
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mock_model_client.chat.return_value = ModelResponse(
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role="critic",
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model="local-main",
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content=json.dumps({
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"should_store": True,
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"memory_type": "secret",
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"summary": "Store this invalid memory type.",
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"importance": 1.5,
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"scope": "everywhere",
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"metadata": {},
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}),
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reasoning_content=None,
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raw={},
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latency_ms=10.0,
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)
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policy = MemoryPolicy(model_client=mock_model_client)
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decision = await policy.classify("some summary", "task_z")
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assert decision.should_store is False
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assert decision.metadata["source"] == "llm_policy_fallback"
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assert "schema violation" in decision.metadata["error"]
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