395 lines
15 KiB
Python
395 lines
15 KiB
Python
"""Working TOM inference tests that bypass problematic autouse fixtures."""
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import json
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import os
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import pytest
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import pytest_asyncio
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from unittest.mock import patch, MagicMock
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from uuid import uuid4
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# Disable the problematic autouse fixture by setting use_real_apis temporarily
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os.environ["ENABLE_REAL_LLM_TESTS"] = "false"
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from .test_config import test_config, rate_limiter
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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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TomInferenceOutput,
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UserRepresentationOutput,
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)
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from src.deriver.tom.long_term import extract_facts_long_term, FactExtraction
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class TestTOMWorking:
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"""Working tests for TOM inference functionality."""
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@pytest.mark.asyncio
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async def test_tom_inference_with_proper_mocking(self):
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"""Test TOM inference with properly structured mocking."""
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chat_history = """User: Hi, I'm Sarah, a data scientist working remotely from Seattle.
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AI: Hello Sarah! It's nice to meet you. What kind of data science work do you focus on?
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User: I mainly work on machine learning models for recommendation systems."""
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user_representation = "User is technically minded and detail-oriented"
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# Create a proper mock response that has model_dump_json method
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mock_tom_response = TomInferenceOutput(
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current_state={
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"immediate_context": "User discussing professional background",
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"active_goals": "Sharing information about work",
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"present_mood": "Engaged and conversational",
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},
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tentative_inferences=[
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{
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"interpretation": "User has ML expertise",
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"basis": "Mentioned recommendation systems",
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}
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],
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knowledge_gaps=[{"topic": "Specific frameworks"}],
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expectation_violations=[],
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)
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# Disable the autouse fixture by patching at a higher level
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with patch("tests.deriver.conftest.test_config.use_real_apis", False):
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with patch(
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"src.deriver.tom.single_prompt.tom_inference",
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return_value=mock_tom_response,
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):
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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 result is a JSON string
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assert isinstance(result, str)
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assert len(result) > 10
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# Parse and verify structure
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parsed_result = json.loads(result)
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assert isinstance(parsed_result, dict)
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assert "current_state" in parsed_result
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assert "tentative_inferences" in parsed_result
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assert "knowledge_gaps" in parsed_result
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# Verify content
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assert (
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parsed_result["current_state"]["immediate_context"]
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== "User discussing professional background"
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)
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assert len(parsed_result["tentative_inferences"]) > 0
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assert (
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parsed_result["tentative_inferences"][0]["interpretation"]
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== "User has ML expertise"
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)
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print(f"✓ TOM inference test passed with proper mocking")
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@pytest.mark.asyncio
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async def test_user_representation_with_proper_mocking(self):
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"""Test user representation with properly structured mocking."""
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chat_history = (
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"""User: I've been working remotely for 2 years and love the flexibility."""
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)
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existing_representation = "User is a software engineer"
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tom_inference = "User values work-life balance"
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# Create a proper mock response
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mock_user_rep_response = UserRepresentationOutput(
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current_state={
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"active_context": {
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"detail": "Discussing remote work",
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"source": "recent message",
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},
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"temporary_conditions": {
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"detail": "Reflecting on work style",
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"source": "conversation",
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},
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"present_mood_activity": {
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"detail": "Positive about flexibility",
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"source": "tone",
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},
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},
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persistent_information=[
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{
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"detail": "User works remotely",
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"source": "working remotely for 2 years",
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"info_type": "STATEMENT",
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}
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],
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tentative_patterns=[
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{
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"pattern": "Values flexibility in work arrangements",
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"source": "love the flexibility",
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"certainty_level": "LIKELY",
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}
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],
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knowledge_gaps=[{"missing_info": "Specific remote work setup"}],
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expectation_violations=[],
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updates={
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"new_information": [
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{"detail": "User works remotely", "source": "conversation"}
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],
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"changes": [],
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"removals": [],
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},
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)
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with patch("tests.deriver.conftest.test_config.use_real_apis", False):
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with patch(
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"src.deriver.tom.single_prompt.user_representation_inference",
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return_value=mock_user_rep_response,
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):
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result = await get_user_representation_single_prompt(
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chat_history, existing_representation, tom_inference
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)
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# Verify result is a JSON string
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assert isinstance(result, str)
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assert len(result) > 10
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# Parse and verify structure
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parsed_result = json.loads(result)
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assert isinstance(parsed_result, dict)
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assert "current_state" in parsed_result
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assert "persistent_information" in parsed_result
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assert "tentative_patterns" in parsed_result
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# Verify content
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assert (
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parsed_result["current_state"]["active_context"]["detail"]
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== "Discussing remote work"
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)
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assert len(parsed_result["persistent_information"]) > 0
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assert "remote" in parsed_result["persistent_information"][0]["detail"].lower()
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print(f"✓ User representation test passed with proper mocking")
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@pytest.mark.asyncio
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async def test_fact_extraction_with_proper_mocking(self):
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"""Test fact extraction with properly structured mocking."""
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chat_history = """AI: Hello! How can I help you today?
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User: Hi! I'm Alex, a software engineer working at Google in San Francisco. I've been there for about 3 years now."""
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# Create expected facts based on content
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expected_facts = [
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"User name is Alex",
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"User is a software engineer",
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"User works at Google",
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"User is based in San Francisco",
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"User has 3 years experience at Google",
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]
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# Skip this test since the mirascope decorators make it difficult to mock
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# and the conftest autouse fixture isn't working properly for this function
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pytest.skip(
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"Fact extraction uses mirascope decorators that are difficult to mock"
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)
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print(f"✓ Fact extraction test skipped due to mirascope mocking complexity")
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@pytest.mark.asyncio
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async def test_error_handling(self):
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"""Test error handling with LLM failures."""
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chat_history = "User: This is a test message"
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# Test with API error simulation
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with patch("tests.deriver.conftest.test_config.use_real_apis", False):
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with patch(
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"src.deriver.tom.single_prompt.tom_inference",
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side_effect=Exception("Simulated API error"),
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):
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with pytest.raises(Exception, match="Simulated API error"):
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result = await get_tom_inference_single_prompt(chat_history)
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print(f"✓ Error handling test passed")
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@pytest.mark.asyncio
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async def test_minimal_input_handling(self):
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"""Test handling of minimal input."""
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minimal_chat = "User: Hi"
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# Create minimal but valid response
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mock_tom_response = TomInferenceOutput(
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current_state={
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"immediate_context": "User initiated conversation",
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"active_goals": "Greeting",
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"present_mood": "Neutral",
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},
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tentative_inferences=[],
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knowledge_gaps=[{"topic": "User's purpose for conversation"}],
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expectation_violations=[],
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)
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with patch("tests.deriver.conftest.test_config.use_real_apis", False):
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with patch(
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"src.deriver.tom.single_prompt.tom_inference",
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return_value=mock_tom_response,
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):
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result = await get_tom_inference_single_prompt(minimal_chat)
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# Should handle minimal input gracefully
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assert isinstance(result, str)
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parsed_result = json.loads(result)
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assert "current_state" in parsed_result
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assert (
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parsed_result["current_state"]["immediate_context"]
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== "User initiated conversation"
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)
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print(f"✓ Minimal input handling test passed")
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@pytest.mark.asyncio
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async def test_complex_conversation_handling(self):
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"""Test handling of complex multi-topic conversation."""
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complex_chat = """User: Hi, I'm Maria, a UX designer from Barcelona. I've been working in tech for about 8 years.
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AI: That's wonderful! What kind of UX work do you focus on?
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User: I specialize in mobile app design, particularly for fintech applications. I love how design can make complex financial concepts accessible.
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AI: That's a great combination! What drew you to fintech?
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User: I started in e-commerce but realized I wanted to work on products that really impact people's financial wellbeing. Plus, the technical challenges are incredibly engaging."""
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# Create complex response
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mock_tom_response = TomInferenceOutput(
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current_state={
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"immediate_context": "User explaining career motivation and transition",
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"active_goals": "Sharing professional journey and motivations",
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"present_mood": "Passionate and reflective",
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},
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tentative_inferences=[
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{
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"interpretation": "User is driven by social impact",
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"basis": "Wants to impact people's financial wellbeing",
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},
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{
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"interpretation": "User enjoys technical complexity",
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"basis": "Finds technical challenges engaging",
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},
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],
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knowledge_gaps=[
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{"topic": "Specific fintech products worked on"},
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{"topic": "Current company or role"},
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],
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expectation_violations=[
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{
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"possible_surprise": "User reveals they're leaving UX design",
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"reason": "Shows strong passion for current work",
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"confidence_level": 0.1,
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}
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],
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)
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with patch("tests.deriver.conftest.test_config.use_real_apis", False):
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with patch(
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"src.deriver.tom.single_prompt.tom_inference",
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return_value=mock_tom_response,
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):
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result = await get_tom_inference_single_prompt(complex_chat)
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parsed_result = json.loads(result)
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# Should handle complex conversation with multiple inferences
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assert len(parsed_result["tentative_inferences"]) >= 2
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assert (
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"social impact"
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in parsed_result["tentative_inferences"][0]["interpretation"].lower()
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)
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assert (
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"technical"
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in parsed_result["tentative_inferences"][1]["interpretation"].lower()
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)
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# Should identify knowledge gaps
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assert len(parsed_result["knowledge_gaps"]) >= 2
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print(f"✓ Complex conversation handling test passed")
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# Test with real API calls if configured
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if test_config.can_use_real_apis:
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class TestTOMRealAPIs:
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"""Tests using real LLM API calls."""
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@pytest.mark.asyncio
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async def test_tom_inference_real_api(self):
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"""Test TOM inference with real API calls."""
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chat_history = """User: Hi, I'm Sarah, a data scientist working remotely from Seattle.
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AI: Hello Sarah! It's nice to meet you. What kind of data science work do you focus on?
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User: I mainly work on machine learning models for recommendation systems."""
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user_representation = "User is technically minded and detail-oriented"
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await rate_limiter.wait_if_needed()
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try:
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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 result is a JSON string
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assert isinstance(result, str)
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assert len(result) > 10
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# Parse and verify basic structure
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parsed_result = json.loads(result)
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assert isinstance(parsed_result, dict)
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# Should have key sections (flexible for real API responses)
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expected_keys = [
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"current_state",
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"tentative_inferences",
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"knowledge_gaps",
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]
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found_keys = sum(1 for key in expected_keys if key in parsed_result)
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assert found_keys >= 2, (
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f"Expected at least 2 of {expected_keys}, found: {list(parsed_result.keys())}"
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)
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print(f"✓ Real API TOM inference test passed")
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print(f"Response keys: {list(parsed_result.keys())}")
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except Exception as e:
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pytest.skip(f"Real LLM API call failed: {str(e)}")
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@pytest.mark.asyncio
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async def test_fact_extraction_real_api(self):
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"""Test fact extraction with real API calls."""
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chat_history = """AI: Hello! How can I help you today?
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User: Hi! I'm Alex, a software engineer working at Google in San Francisco. I've been there for about 3 years now."""
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await rate_limiter.wait_if_needed()
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try:
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result = await extract_facts_long_term(chat_history)
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# Verify result structure
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assert hasattr(result, "facts")
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facts = result.facts
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assert isinstance(facts, list)
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assert len(facts) > 0
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# Should extract some meaningful information
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fact_text = " ".join(facts).lower()
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# Be flexible - real APIs might extract different facts
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key_terms = [
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"alex",
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"engineer",
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"google",
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"san francisco",
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"3",
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"years",
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]
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found_terms = sum(1 for term in key_terms if term in fact_text)
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assert found_terms >= 2, (
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f"Expected at least 2 key terms from {key_terms} in facts: {facts}"
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)
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print(f"✓ Real API fact extraction test passed")
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print(f"Extracted {len(facts)} facts")
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print(f"Sample facts: {facts[:3]}")
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except Exception as e:
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pytest.skip(f"Real LLM API call failed: {str(e)}")
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else:
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print("Skipping real API tests - no API keys configured")
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