Add LlamaIndex integration (JUD-455) #461
Conversation
- Add llama-index>=0.12.49 as dev dependency - Implement LlamaIndex OpenAI client detection in _get_client_config() - Add _create_llamaindex_wrapper() for Pydantic model compatibility - Add import guards for optional LlamaIndex dependency in core.py - Include comprehensive test coverage (7/7 unit tests + 4 e2e tests) - Add multi-step agent workflow and performance benchmarks - Maintain backward compatibility with existing client types Resolves JudgmentLabs#455
…o feature/llamaindex-integration
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Summary of Changes
Hello @suysoftware, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly expands the judgeval library's tracing capabilities by integrating with LlamaIndex OpenAI clients. The primary goal is to allow users to seamlessly trace LLM interactions within LlamaIndex applications, addressing a previous limitation and providing a more complete observability solution for complex agent workflows.
Highlights
- LlamaIndex Integration: Added support for LlamaIndex OpenAI clients to the
judgeval.wrap()function, allowing tracing of LLM calls made by LlamaIndex agents. - Pydantic Model Compatibility: Implemented a new
LlamaIndexWrapperclass to handle LlamaIndex's Pydantic model restrictions, ensuring that the original client interface is preserved while enabling tracing capabilities forcomplete,acomplete,chat, andachatmethods. - Optional Dependency & Error Handling: Incorporated import guards for the LlamaIndex dependency, making it optional, and enhanced error handling for unsupported client types.
- Comprehensive Testing: Introduced a new dedicated test suite for LlamaIndex integration, including E2E tests for basic functionality, multi-step agent workflows, performance benchmarks, and concurrent processing, ensuring robust and reliable integration.
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Code Review
This pull request introduces LlamaIndex integration, which is a valuable addition. The core implementation is solid. My review focuses on improving the tests, particularly fixing a critical issue in the concurrency test and other correctness problems. I've also included suggestions to enhance code maintainability by refactoring some logic.
| """Make a single request and return results""" | ||
| try: | ||
| start_time = time.time() | ||
| response = wrapped_llm.complete(prompt) |
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The make_request async function is using the synchronous wrapped_llm.complete(prompt) method. This will block the event loop, causing the requests to run sequentially rather than concurrently. This defeats the purpose of a concurrency test.
To correctly test concurrent processing, you should use the asynchronous acomplete method and await its result.
| response = wrapped_llm.complete(prompt) | |
| response = await wrapped_llm.acomplete(prompt) |
| # Test acomplete method (if available) | ||
| try: | ||
| async_response = await wrapped_llm.acomplete("What is 2+2?") | ||
| print(f"✓ LlamaIndex acomplete() response: {async_response.text[:100]}...") | ||
| except (AttributeError, TypeError) as e: | ||
| print(f"⚠ acomplete() method not available or not async: {e}") | ||
| # Try sync version if async not available | ||
| try: | ||
| async_response = wrapped_llm.acomplete("What is 2+2?") | ||
| print(f"✓ LlamaIndex acomplete() (sync) response: {async_response.text[:100]}...") | ||
| except Exception as sync_e: | ||
| print(f"⚠ acomplete() method not working: {sync_e}") |
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The logic for testing the acomplete method is flawed. The except block attempts to call acomplete without await, which returns a coroutine, not a response. Accessing .text on this coroutine will then fail. Since the wrapper ensures acomplete is an async method, this complex fallback logic is unnecessary and incorrect. It should be simplified to a single try/except block that awaits the acomplete call.
| # Test acomplete method (if available) | |
| try: | |
| async_response = await wrapped_llm.acomplete("What is 2+2?") | |
| print(f"✓ LlamaIndex acomplete() response: {async_response.text[:100]}...") | |
| except (AttributeError, TypeError) as e: | |
| print(f"⚠ acomplete() method not available or not async: {e}") | |
| # Try sync version if async not available | |
| try: | |
| async_response = wrapped_llm.acomplete("What is 2+2?") | |
| print(f"✓ LlamaIndex acomplete() (sync) response: {async_response.text[:100]}...") | |
| except Exception as sync_e: | |
| print(f"⚠ acomplete() method not working: {sync_e}") | |
| # Test acomplete method (if available) | |
| try: | |
| async_response = await wrapped_llm.acomplete("What is 2+2?") | |
| print(f"✓ LlamaIndex acomplete() response: {async_response.text[:100]}...") | |
| except Exception as e: | |
| pytest.fail(f"acomplete() method failed: {e}") |
| def __getattr__(self, name): | ||
| # For traced methods, return the traced version | ||
| if name == "complete": | ||
| return self._wrapped_sync | ||
| elif name == "acomplete": | ||
| return self._wrapped_async | ||
| elif name == "chat": | ||
| return self._wrapped_sync | ||
| elif name == "achat": | ||
| return self._wrapped_async | ||
| else: | ||
| # For all other attributes, delegate to the original client | ||
| return getattr(self._original_client, name) |
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The __getattr__ method contains a series of if/elif statements that can be simplified for better readability and maintainability by grouping the conditions for sync and async methods.
def __getattr__(self, name):
# For traced methods, return the traced version
if name in ("complete", "chat"):
return self._wrapped_sync
if name in ("acomplete", "achat"):
return self._wrapped_async
# For all other attributes, delegate to the original client
return getattr(self._original_client, name)| def test_llamaindex_not_available_fallback(self): | ||
| """Test behavior when LlamaIndex is not available""" | ||
| # Mock LlamaIndex as not available | ||
| with patch('judgeval.common.tracer.core.LLAMAINDEX_AVAILABLE', False): | ||
| # Should still handle the error gracefully | ||
| # but would raise error for actual LlamaIndex objects | ||
| # This tests the import guard logic | ||
| assert True # Import guard logic is tested during import |
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This test is not very effective as it only asserts True. To properly test the fallback behavior when LlamaIndex is not installed, you should verify that wrap() raises a ValueError for an unsupported client type when LLAMAINDEX_AVAILABLE is patched to False.
| def test_llamaindex_not_available_fallback(self): | |
| """Test behavior when LlamaIndex is not available""" | |
| # Mock LlamaIndex as not available | |
| with patch('judgeval.common.tracer.core.LLAMAINDEX_AVAILABLE', False): | |
| # Should still handle the error gracefully | |
| # but would raise error for actual LlamaIndex objects | |
| # This tests the import guard logic | |
| assert True # Import guard logic is tested during import | |
| def test_llamaindex_not_available_fallback(self): | |
| """Test behavior when LlamaIndex is not available""" | |
| class MockLlamaIndexClient: | |
| pass | |
| client = MockLlamaIndexClient() | |
| # Mock LlamaIndex as not available | |
| with patch('judgeval.common.tracer.core.LLAMAINDEX_AVAILABLE', False): | |
| # When LlamaIndex is not available, wrap() should raise a ValueError | |
| # for a client that would otherwise be identified as a LlamaIndex client. | |
| with pytest.raises(ValueError, match="Unsupported client type"): | |
| wrap(client) |
📝 Summary
judgeval.wrap()function🎥 Demo of Changes
The integration allows users to wrap LlamaIndex OpenAI clients seamlessly:
✅ Checklist
🔧 Technical Implementation
Problem Solved
This PR addresses GitHub Issue #455 where users couldn't trace LLM calls made by LlamaIndex ReActAgent because
judgeval.wrap()didn't supportllama_index.llms.openai.OpenAIclient type.Solution
llama-index>=0.12.49as dev dependencyLlamaIndexWrapperto handle Pydantic model restrictionsKey Features
complete,chat) and async (acomplete,achat) methodsFiles Changed
pyproject.toml: Added llama-index>=0.12.49 dev dependencysrc/judgeval/common/tracer/core.py: Added LlamaIndex supportsrc/tests/common/test_llamaindex_integration.py: Comprehensive test suite##Testing
All tests pass successfully:
📊 Impact
This enhancement enables complete tracing of LlamaIndex-based agents, including:
Users can now achieve comprehensive tracing without any API changes to their existing code.