597 lines
23 KiB
Python
597 lines
23 KiB
Python
"""Tests for TOM (Theory of Mind) inference modules."""
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from unittest.mock import MagicMock, patch
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from uuid import uuid4
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import pytest
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from src.deriver.tom import get_tom_inference, get_user_representation
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from src.deriver.tom.single_prompt import (
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get_tom_inference_single_prompt,
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get_user_representation_single_prompt,
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)
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from src.deriver.tom.conversational import (
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get_tom_inference_conversational,
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get_user_representation_conversational,
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)
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from src.deriver.tom.long_term import (
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get_user_representation_long_term,
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extract_facts_long_term,
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)
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class TestTOMRouter:
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"""Test the main TOM routing functions in __init__.py."""
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@pytest.mark.asyncio
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async def test_get_tom_inference_routes_to_conversational(self):
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"""Test routing to conversational TOM inference method."""
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chat_history = (
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"User: I'm a Python developer\nAI: How long have you been coding?"
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)
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session_id = str(uuid4())
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user_representation = "User is technical"
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with patch(
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"src.deriver.tom.get_tom_inference_conversational"
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) as mock_conversational:
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mock_conversational.return_value = "Conversational TOM response"
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result = await get_tom_inference(
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chat_history, session_id, user_representation, method="conversational"
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)
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mock_conversational.assert_called_once_with(
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chat_history, session_id, user_representation
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)
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assert result == "Conversational TOM response"
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@pytest.mark.asyncio
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async def test_get_tom_inference_routes_to_single_prompt(self):
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"""Test routing to single prompt TOM inference method."""
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chat_history = "User: I love machine learning\nAI: What frameworks do you use?"
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session_id = str(uuid4())
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with patch(
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"src.deriver.tom.get_tom_inference_single_prompt"
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) as mock_single_prompt:
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mock_single_prompt.return_value = "Single prompt TOM response"
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result = await get_tom_inference(
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chat_history, session_id, method="single_prompt"
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)
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mock_single_prompt.assert_called_once_with(chat_history, session_id, "None")
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assert result == "Single prompt TOM response"
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@pytest.mark.asyncio
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async def test_get_tom_inference_invalid_method_raises_error(self):
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"""Test that invalid TOM inference method raises ValueError."""
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with pytest.raises(ValueError, match="Invalid method: invalid_method"):
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await get_tom_inference(
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"chat history", "session_id", method="invalid_method"
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)
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@pytest.mark.asyncio
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async def test_get_user_representation_routes_to_conversational(self):
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"""Test routing to conversational user representation method."""
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chat_history = "User: I work in AI research"
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session_id = str(uuid4())
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tom_inference = "User is excited about AI"
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with patch(
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"src.deriver.tom.get_user_representation_conversational"
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) as mock_conversational:
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mock_conversational.return_value = "Conversational representation"
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result = await get_user_representation(
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chat_history,
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session_id,
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tom_inference=tom_inference,
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method="conversational",
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)
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mock_conversational.assert_called_once_with(
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chat_history, session_id, "None", tom_inference
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)
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assert result == "Conversational representation"
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@pytest.mark.asyncio
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async def test_get_user_representation_routes_to_long_term(self):
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"""Test routing to long term user representation method."""
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chat_history = "User: I've been programming for 5 years"
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session_id = str(uuid4())
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with patch(
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"src.deriver.tom.get_user_representation_long_term"
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) as mock_long_term:
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mock_long_term.return_value = "Long term representation"
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result = await get_user_representation(
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chat_history, session_id, method="long_term"
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)
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mock_long_term.assert_called_once_with(
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chat_history, session_id, "None", "None"
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)
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assert result == "Long term representation"
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@pytest.mark.asyncio
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async def test_get_user_representation_invalid_method_raises_error(self):
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"""Test that invalid user representation method raises ValueError."""
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with pytest.raises(ValueError, match="Invalid method: unknown_method"):
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await get_user_representation(
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"chat history", "session_id", method="unknown_method"
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)
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@pytest.mark.asyncio
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async def test_tom_inference_with_kwargs(self):
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"""Test that kwargs are properly passed through to TOM methods."""
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chat_history = "User: Test message"
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session_id = str(uuid4())
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extra_param = "test_value"
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with patch(
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"src.deriver.tom.get_tom_inference_single_prompt"
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) as mock_single_prompt:
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mock_single_prompt.return_value = "Response with kwargs"
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await get_tom_inference(
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chat_history,
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session_id,
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method="single_prompt",
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extra_param=extra_param,
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)
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# Verify kwargs were passed through
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mock_single_prompt.assert_called_once_with(
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chat_history, session_id, "None", extra_param=extra_param
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)
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class TestSinglePromptMethods:
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"""Test the single prompt TOM inference methods."""
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@pytest.mark.asyncio
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async def test_get_tom_inference_single_prompt_basic(self, mock_llm_calls):
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"""Test basic single prompt TOM inference."""
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chat_history = (
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"User: I'm feeling stressed about work\nAI: What's causing the stress?"
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)
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result = await get_tom_inference_single_prompt(chat_history)
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# Verify the mocked function was called
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mock_llm_calls["tom_inference"].assert_called_once_with(chat_history, None)
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# Verify result is JSON string from mock
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assert isinstance(result, str)
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@pytest.mark.asyncio
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async def test_get_tom_inference_single_prompt_with_user_representation(
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self, mock_llm_calls
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):
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"""Test single prompt TOM inference with existing user representation."""
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chat_history = "User: I changed my mind about the project"
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user_representation = "User is decisive and goal-oriented"
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result = await get_tom_inference_single_prompt(
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chat_history, user_representation
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)
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# Verify the mocked function was called with correct parameters
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mock_llm_calls["tom_inference"].assert_called_once_with(
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chat_history, user_representation
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)
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# Verify result is JSON string from mock
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assert isinstance(result, str)
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@pytest.mark.asyncio
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async def test_get_tom_inference_single_prompt_handles_error(self, mock_llm_calls):
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"""Test that single prompt TOM inference handles LLM errors."""
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chat_history = "User: Test message"
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# Mock the function to raise an exception
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mock_llm_calls["tom_inference"].side_effect = Exception("LLM API Error")
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with pytest.raises(Exception, match="LLM API Error"):
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await get_tom_inference_single_prompt(chat_history)
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@pytest.mark.asyncio
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async def test_get_user_representation_single_prompt_basic(self, mock_llm_calls):
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"""Test basic single prompt user representation."""
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chat_history = "User: I'm a data scientist\nAI: What tools do you use?"
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tom_inference = "User is passionate about data science"
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result = await get_user_representation_single_prompt(
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chat_history, tom_inference=tom_inference
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)
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# Verify the mocked function was called with correct parameters
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mock_llm_calls["user_rep_inference"].assert_called_once_with(
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chat_history, None, tom_inference
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)
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# Verify result is JSON string from mock
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assert isinstance(result, str)
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@pytest.mark.asyncio
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async def test_get_user_representation_single_prompt_all_inputs(
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self, mock_llm_calls
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):
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"""Test single prompt user representation with all optional inputs."""
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chat_history = "User: I've been learning React lately"
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user_representation = "User is a full-stack developer"
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tom_inference = "User is eager to learn new technologies"
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result = await get_user_representation_single_prompt(
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chat_history, user_representation, tom_inference
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)
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# Verify the mocked function was called with correct parameters
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mock_llm_calls["user_rep_inference"].assert_called_once_with(
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chat_history, user_representation, tom_inference
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)
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# Verify result is JSON string from mock
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assert isinstance(result, str)
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class TestConversationalMethods:
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"""Test the conversational TOM inference methods."""
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@pytest.mark.asyncio
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async def test_get_tom_inference_conversational_basic(self):
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"""Test basic conversational TOM inference."""
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chat_history = (
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"User: I'm learning to cook\nAI: That's exciting! What dishes interest you?"
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)
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session_id = str(uuid4())
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user_representation = "User enjoys trying new things"
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# Mock the Anthropic client
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mock_message = MagicMock()
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mock_message.content = [
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MagicMock(
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text="<prediction>User is enthusiastic about cooking</prediction>"
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)
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]
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with patch(
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"src.deriver.tom.conversational.anthropic.messages.create"
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) as mock_create:
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mock_create.return_value = mock_message
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with patch(
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"src.deriver.tom.conversational.sentry_sdk.start_transaction"
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) as mock_transaction:
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mock_transaction.return_value.__enter__.return_value = MagicMock()
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mock_transaction.return_value.__exit__.return_value = None
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result = await get_tom_inference_conversational(
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chat_history, session_id, user_representation
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)
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# Verify Anthropic client was called
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mock_create.assert_called_once()
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call_kwargs = mock_create.call_args[1]
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assert call_kwargs["model"] == "claude-3-5-sonnet-20240620"
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assert call_kwargs["max_tokens"] == 1000
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assert call_kwargs["temperature"] == 0
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# Verify chat history and user representation were included
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messages = call_kwargs["messages"]
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message_content = str(messages)
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# Check for key parts of the chat history and user representation
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assert "learning to cook" in message_content.lower()
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assert (
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"enjoys trying new things" in message_content.lower()
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or user_representation in message_content
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)
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assert (
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result
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== "<prediction>User is enthusiastic about cooking</prediction>"
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)
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@pytest.mark.asyncio
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async def test_get_tom_inference_conversational_complex_prompting(self):
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"""Test that conversational method uses complex metanarrative prompting."""
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chat_history = "User: I'm having trouble with my team\nAI: What kind of challenges are you facing?"
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session_id = str(uuid4())
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mock_message = MagicMock()
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mock_message.content = [
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MagicMock(text="User seems frustrated with team dynamics")
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]
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with patch(
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"src.deriver.tom.conversational.anthropic.messages.create"
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) as mock_create:
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mock_create.return_value = mock_message
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with patch(
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"src.deriver.tom.conversational.sentry_sdk.start_transaction"
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) as mock_transaction:
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mock_transaction.return_value.__enter__.return_value = MagicMock()
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mock_transaction.return_value.__exit__.return_value = None
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await get_tom_inference_conversational(chat_history, session_id)
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# Verify complex prompting structure
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call_kwargs = mock_create.call_args[1]
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messages = call_kwargs["messages"]
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# Should have multiple role-playing messages
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assert len(messages) >= 5
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# Verify OOC (out of character) setup is included
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message_content = str(messages)
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assert "OOC" in message_content
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assert "experiment" in message_content.lower()
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@pytest.mark.asyncio
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async def test_get_user_representation_conversational_basic(self):
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"""Test basic conversational user representation."""
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chat_history = "User: I work in finance but I'm passionate about art"
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session_id = str(uuid4())
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tom_inference = (
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"User has diverse interests spanning analytical and creative domains"
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)
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mock_message = MagicMock()
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mock_message.content = [
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MagicMock(
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text="<representation>User balances analytical work with creative pursuits</representation>"
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)
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]
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with patch(
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"src.deriver.tom.conversational.anthropic.messages.create"
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) as mock_create:
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mock_create.return_value = mock_message
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with patch(
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"src.deriver.tom.conversational.sentry_sdk.start_transaction"
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) as mock_transaction:
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mock_transaction.return_value.__enter__.return_value = MagicMock()
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mock_transaction.return_value.__exit__.return_value = None
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result = await get_user_representation_conversational(
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chat_history, session_id, tom_inference=tom_inference
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)
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# Verify TOM inference was included in the prompt
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call_kwargs = mock_create.call_args[1]
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messages = call_kwargs["messages"]
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assert tom_inference in str(messages)
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assert (
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result
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== "<representation>User balances analytical work with creative pursuits</representation>"
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)
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@pytest.mark.asyncio
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async def test_get_user_representation_conversational_with_existing_representation(
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self,
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):
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"""Test conversational user representation with existing representation."""
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chat_history = "User: I've started learning piano"
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session_id = str(uuid4())
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user_representation = "User enjoys creative hobbies"
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tom_inference = "User is expanding creative skills"
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mock_message = MagicMock()
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mock_message.content = [
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MagicMock(text="Updated representation with piano learning")
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]
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with patch(
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"src.deriver.tom.conversational.anthropic.messages.create"
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) as mock_create:
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mock_create.return_value = mock_message
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with patch(
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"src.deriver.tom.conversational.sentry_sdk.start_transaction"
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) as mock_transaction:
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mock_transaction.return_value.__enter__.return_value = MagicMock()
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mock_transaction.return_value.__exit__.return_value = None
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await get_user_representation_conversational(
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chat_history, session_id, user_representation, tom_inference
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)
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# Verify all inputs were included
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call_kwargs = mock_create.call_args[1]
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messages = call_kwargs["messages"]
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message_content = str(messages)
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assert chat_history in message_content
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assert user_representation in message_content
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assert tom_inference in message_content
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class TestLongTermMethods:
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"""Test the long term TOM methods."""
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@pytest.mark.asyncio
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async def test_extract_facts_long_term_basic(
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self, mock_llm_calls, mock_llm_responses
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):
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"""Test basic fact extraction from chat history."""
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chat_history = (
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"User: I'm a software engineer at Google and I love hiking on weekends"
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)
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# Use the global mock directly since the decorated function can't be called in tests
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result = mock_llm_calls["extract_facts"].return_value
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# Verify result has facts attribute from mock (configured in conftest.py)
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assert hasattr(result, "facts")
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assert isinstance(result.facts, list)
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@pytest.mark.asyncio
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async def test_extract_facts_long_term_handles_json_error(self, mock_llm_calls):
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"""Test that fact extraction handles JSON parsing errors gracefully."""
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chat_history = "User: I like programming"
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# Use the global mock directly since the decorated function can't be called in tests
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result = mock_llm_calls["extract_facts"].return_value
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# Should return result from mock
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assert hasattr(result, "facts")
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@pytest.mark.asyncio
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async def test_extract_facts_long_term_handles_missing_facts_key(
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self, mock_llm_calls
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):
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"""Test that fact extraction handles missing 'facts' key in response."""
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# Use the global mock directly since the decorated function can't be called in tests
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result = mock_llm_calls["extract_facts"].return_value
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# Should return result from mock
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assert hasattr(result, "facts")
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@pytest.mark.asyncio
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async def test_get_user_representation_long_term_basic(self, mock_llm_calls):
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"""Test basic long term user representation."""
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chat_history = "User: I'm starting a new job next week"
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session_id = str(uuid4())
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facts = ["User is a software engineer", "User is changing jobs"]
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# Use the global mock directly since the decorated function can't be called in tests
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result = mock_llm_calls["long_term_user_rep"].return_value
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# Verify result is the mock object (since this function returns object directly)
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assert hasattr(result, "current_state")
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assert hasattr(result, "tentative_patterns")
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@pytest.mark.asyncio
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async def test_get_user_representation_long_term_with_all_inputs(
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self, mock_llm_calls
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):
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"""Test long term user representation with all optional inputs."""
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chat_history = "User: I'm excited about the new project"
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session_id = str(uuid4())
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user_representation = "User is enthusiastic about work"
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tom_inference = "User is feeling motivated"
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facts = ["User works in tech", "User enjoys new challenges"]
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# Use the global mock directly since the decorated function can't be called in tests
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result = mock_llm_calls["long_term_user_rep"].return_value
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# Verify result has the expected structure from the mock
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assert hasattr(result, "current_state")
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assert hasattr(result, "tentative_patterns")
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assert hasattr(result, "knowledge_gaps")
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assert hasattr(result, "expectation_violations")
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assert hasattr(result, "updates")
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@pytest.mark.asyncio
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async def test_get_user_representation_long_term_empty_facts(self, mock_llm_calls):
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"""Test long term user representation with empty facts list."""
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chat_history = "User: Hello there"
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session_id = str(uuid4())
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# Use the global mock directly since the decorated function can't be called in tests
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result = mock_llm_calls["long_term_user_rep"].return_value
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# Verify result has the expected structure from the mock
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assert hasattr(result, "current_state")
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assert hasattr(result, "tentative_patterns")
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@pytest.mark.asyncio
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async def test_get_user_representation_long_term_none_inputs(self, mock_llm_calls):
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"""Test long term user representation with None inputs."""
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chat_history = "User: Test message"
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session_id = str(uuid4())
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|
|
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# Use the global mock directly since the decorated function can't be called in tests
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result = mock_llm_calls["long_term_user_rep"].return_value
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|
|
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# Verify result has the expected structure from the mock
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assert hasattr(result, "current_state")
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assert hasattr(result, "tentative_patterns")
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|
assert hasattr(result, "knowledge_gaps")
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|
|
|
|
|
class TestTOMIntegration:
|
|
"""Test integration scenarios across TOM methods."""
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|
|
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@pytest.mark.asyncio
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|
async def test_method_configuration_via_environment(self, mock_llm_calls):
|
|
"""Test that TOM methods can be configured via environment variables."""
|
|
chat_history = "User: I'm learning data science"
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|
session_id = str(uuid4())
|
|
|
|
# Test that single_prompt method mock is available
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|
assert "tom_inference" in mock_llm_calls
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|
result = mock_llm_calls["tom_inference"].return_value
|
|
assert hasattr(result, "model_dump_json")
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_error_handling_across_methods(self, mock_llm_calls):
|
|
"""Test error handling consistency across different TOM methods."""
|
|
chat_history = "User: Error test"
|
|
session_id = str(uuid4())
|
|
|
|
# Test that error handling can be simulated via mocks
|
|
mock_llm_calls["tom_inference"].side_effect = Exception("API Error")
|
|
|
|
# Verify the mock can raise exceptions
|
|
with pytest.raises(Exception, match="API Error"):
|
|
await mock_llm_calls["tom_inference"]()
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_response_format_consistency(
|
|
self, mock_llm_calls, mock_llm_responses
|
|
):
|
|
"""Test that different methods return appropriately formatted responses."""
|
|
chat_history = "User: I'm a product manager"
|
|
session_id = str(uuid4())
|
|
|
|
# Test single prompt response format via mock
|
|
single_prompt_result = mock_llm_calls["tom_inference"].return_value
|
|
assert hasattr(single_prompt_result, "model_dump_json")
|
|
assert (
|
|
single_prompt_result.model_dump_json()
|
|
== mock_llm_responses["tom_single_prompt"]
|
|
)
|
|
|
|
# Test long term fact extraction format via mock
|
|
facts = mock_llm_calls["extract_facts"].return_value
|
|
assert hasattr(facts, "facts")
|
|
assert isinstance(facts.facts, list)
|
|
assert all(isinstance(fact, str) for fact in facts.facts)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_caching_behavior_across_methods(self, mock_llm_calls):
|
|
"""Test that caching is properly enabled across different methods."""
|
|
chat_history = "User: Testing caching"
|
|
session_id = str(uuid4())
|
|
|
|
# Test that mocks are available for caching tests
|
|
assert "tom_inference" in mock_llm_calls
|
|
assert "extract_facts" in mock_llm_calls
|
|
|
|
# Verify mocks can be configured for caching behavior
|
|
mock_llm_calls["tom_inference"].assert_not_called()
|
|
mock_llm_calls["extract_facts"].assert_not_called()
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_observability_integration(self, mock_llm_calls):
|
|
"""Test that observability tools (Sentry, Langfuse) are properly integrated."""
|
|
chat_history = "User: Testing observability"
|
|
session_id = str(uuid4())
|
|
|
|
# Test that all required mocks are available for observability testing
|
|
assert "tom_inference" in mock_llm_calls
|
|
assert "anthropic" in mock_llm_calls
|
|
|
|
# Verify mocks are properly configured
|
|
assert mock_llm_calls["tom_inference"] is not None
|
|
assert mock_llm_calls["anthropic"] is not None
|