17b97751ab
- Remove over-engineered allow_all_models fixture (6 operations → 1 line API key setting) - Replace 10 lines of monkey patching boilerplate with 1-line inject_transport helper - Remove cargo-cult error handling that allowed test to pass with API failures - Create reusable transport_helpers.py for HTTP transport injection patterns - Fix provider registration state pollution between batch test runs - Test now works reliably in both individual and batch execution modes The test is significantly cleaner and addresses root cause (provider registration timing) rather than symptoms (cache clearing). 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
"""
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Tests for o3-pro output_text parsing fix using HTTP transport recording.
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This test validates the fix that uses `response.output_text` convenience field
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instead of manually parsing `response.output.content[].text`.
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Uses HTTP transport recorder to record real o3-pro API responses at the HTTP level while allowing
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the OpenAI SDK to create real response objects that we can test.
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RECORDING: To record new responses, delete the cassette file and run with real API keys.
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"""
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import unittest
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from pathlib import Path
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import pytest
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from dotenv import load_dotenv
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from providers import ModelProviderRegistry
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from providers.base import ProviderType
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from providers.openai_provider import OpenAIModelProvider
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from tests.transport_helpers import inject_transport
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from tools.chat import ChatTool
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# Load environment variables from .env file
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load_dotenv()
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# Use absolute path for cassette directory
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cassette_dir = Path(__file__).parent / "openai_cassettes"
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cassette_dir.mkdir(exist_ok=True)
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@pytest.mark.asyncio
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class TestO3ProOutputTextFix:
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"""Test o3-pro response parsing fix using respx for HTTP recording/replay."""
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def setup_method(self):
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"""Set up the test by ensuring OpenAI provider is registered."""
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# Clear any cached providers to ensure clean state
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ModelProviderRegistry.clear_cache()
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# Reset the entire registry to ensure clean state
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ModelProviderRegistry._instance = None
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# Clear both class and instance level attributes
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if hasattr(ModelProviderRegistry, "_providers"):
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ModelProviderRegistry._providers = {}
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# Get the instance and clear its providers
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instance = ModelProviderRegistry()
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instance._providers = {}
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instance._initialized_providers = {}
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# Manually register the OpenAI provider to ensure it's available
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ModelProviderRegistry.register_provider(ProviderType.OPENAI, OpenAIModelProvider)
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def teardown_method(self):
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"""Clean up after test to ensure no state pollution."""
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# Clear registry to prevent affecting other tests
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ModelProviderRegistry.clear_cache()
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ModelProviderRegistry._instance = None
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ModelProviderRegistry._providers = {}
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@pytest.mark.no_mock_provider # Disable provider mocking for this test
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async def test_o3_pro_uses_output_text_field(self, monkeypatch):
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"""Test that o3-pro parsing uses the output_text convenience field via ChatTool."""
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# Set API key inline - helper will handle provider registration
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monkeypatch.setenv("OPENAI_API_KEY", "dummy-key-for-replay")
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cassette_path = cassette_dir / "o3_pro_basic_math.json"
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# Require cassette for test - no cargo culting
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if not cassette_path.exists():
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pytest.skip("Cassette file required - record with real OPENAI_API_KEY")
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# Simplified transport injection - just one line!
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inject_transport(monkeypatch, cassette_path)
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# Execute ChatTool test with custom transport
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result = await self._execute_chat_tool_test()
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# Verify the response works correctly
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self._verify_chat_tool_response(result)
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# Verify cassette exists
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assert cassette_path.exists()
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async def _execute_chat_tool_test(self):
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"""Execute the ChatTool with o3-pro and return the result."""
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chat_tool = ChatTool()
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arguments = {"prompt": "What is 2 + 2?", "model": "o3-pro", "temperature": 1.0}
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return await chat_tool.execute(arguments)
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def _verify_chat_tool_response(self, result):
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"""Verify the ChatTool response contains expected data."""
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# Basic response validation
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assert result is not None
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assert isinstance(result, list)
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assert len(result) > 0
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assert result[0].type == "text"
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# Parse JSON response
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import json
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response_data = json.loads(result[0].text)
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# Verify response structure - no cargo culting
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assert response_data["status"] in ["success", "continuation_available"]
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assert "4" in response_data["content"]
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# Verify o3-pro was actually used
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metadata = response_data["metadata"]
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assert metadata["model_used"] == "o3-pro"
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assert metadata["provider_used"] == "openai"
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if __name__ == "__main__":
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print("🎥 OpenAI Response Recording Tests for O3-Pro Output Text Fix")
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print("=" * 50)
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print("RECORD MODE: Requires OPENAI_API_KEY - makes real API calls through ChatTool")
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print("REPLAY MODE: Uses recorded HTTP responses - free and fast")
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print("RECORDING: Delete .json files in tests/openai_cassettes/ to re-record")
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print()
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unittest.main()
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