Fix NumPy 2.x compatibility issue (#2431)

[devin-ai-integration]

Fix NumPy 2.x compatibility issue (#2431)

This PR addresses issue #2431 by implementing robust error handling for chromadb imports when NumPy 2.x is installed.

Changes

• Added conditional imports for chromadb in embedding_configurator.py
• Added fallback mechanisms when chromadb is unavailable
• Updated RAGStorage and KnowledgeStorage to handle cases when chromadb cannot be imported
• Added tests to verify NumPy 2.x compatibility

Testing

• Added test_numpy_compatibility.py with tests for all affected components
• All tests pass successfully with NumPy 2.x

Link to Devin run: https://app.devin.ai/sessions/8a66948028824d14963cd06affcb5763
Requested by: Joe Moura (joao@crewai.com)

[devin-ai-integration]

🤖 Devin AI Engineer

I'll be helping with this pull request! Here's what you should know:

✅ I will automatically:

• Address comments on this PR. Add "(aside)" to your comment to have me ignore it.
• Look at CI failures and help fix them

Note: I can only respond to comments from users who have write access to this repository.

⚙️ Control Options:

• Disable automatic comment and CI monitoring

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment: NumPy 2.x Compatibility Fixes

Summary

The recent changes to the crewAI codebase implement critical compatibility fixes for NumPy 2.x across various core components. The focus on graceful degradation when chromadb dependencies are unavailable significantly enhances the application's robustness. Below are my insights based on your modifications.

Key Improvements

1. Knowledge Storage Module

• Error Handling: The introduction of the _initialize_imports method includes error handling for the chromadb import, which previously could lead to application crashes. This change provides feedback through the _log_import_error method, enhancing maintainability.
• Type Hints: Adding type hints improves code clarity and aids in future maintenance.

Example Improvements:

def _initialize_imports(self) -> None:
    """Centralized import handling with proper error tracking"""

2. RAG Storage Module

• Dependency Validation: The _check_dependencies method validates required dependencies, reducing the risk of runtime errors. Also, proper logging for unavailable features enhances transparency for developers regarding the application's state.
• Enhanced Error Management: The save method now gracefully handles errors, returning a warning message without crashing the application.

Example Improvements:

if not self._features_available['storage']:
    logging.warning("Storage features unavailable")

3. Embedding Configurator

• Provider Initialization: The registry pattern utilized in _initialize_providers allows for easy addition of new embedding providers while maintaining existing functionality.
• Context Management for Errors: The error_boundary context manager significantly centralizes error handling around provider initialization, making the code cleaner and reducing redundancy.

Example Improvements:

@contextmanager
def error_boundary(self, provider: str):
    """Context manager for provider operations"""

Testing Strategy

The implemented tests demonstrate a proactive step towards ensuring the application works as intended under varying conditions, especially concerning missing dependencies. The new mock_numpy_environment fixture effectively simulates compatibility testing scenarios.

Recommended Tests:

• Comprehensive tests guarantee that the application degrades gracefully when NumPy is absent, ensuring that features are validated accurately.

General Recommendations

1. Centralized Error Logging: Create a dedicated error logging utility to streamline error handling across modules.
2. Documentation Updates: The documentation should reflect the recent changes including troubleshooting guides and NumPy compatibility notes.
3. Feature Flags: Consider implementing feature flags for controlling the behavior under different dependency scenarios, which could provide even smoother user experiences.
4. Performance Benchmarks: Adding performance benchmarks for different embedding configurations can help in assessing performance impacts before and after modifications.

These enhancements collectively improve both the robustness and maintainability of the codebase while simplifying future development tasks. Great work addressing these compatibility issues!

Let’s continue to validate that all features perform as expected under the specified conditions and iterate where necessary.

[devin-ai-integration]

Closing due to inactivity for more than 7 days.