feature: Introducing VoyageAI

[fzowl]

Introducing VoyageAI's text embedding models.

[joaomdmoura]

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

Code Review Comment for PR #1871: Introducing VoyageAI's embedding models

Overview

This pull request successfully integrates VoyageAI's embedding models into the crewAI framework, providing enhancements to the embedding configurator and updating relevant documentation. Below are observations and suggestions for improvement.

Modifications Overview

1. src/crewai/utilities/embedding_configurator.py: Introduced the _configure_voyageai method for configuring VoyageAI embeddings.
2. Documentation: Updates made across various .mdx files to support proper configuration and usage instructions for the new embedding provider.

Positive Aspects

• The implementation demonstrates a clean and efficient integration approach, staying consistent with existing methods for other providers such as Google and Cohere.
• Proper handling of imports within the embedding configurator contributes positively to modular code structure.
• Documentation has been thoroughly updated, providing clear examples that enhance usability and guide developers effectively in using the new functionality.

Suggestions for Improvement

1. Type Hints: Integrate type hints across the methods for better code readability and maintainability. For example:

   @staticmethod
   def _configure_voyageai(config: dict, model_name: str) -> VoyageAIEmbeddingFunction:

2. Error Handling: Implement error handling for scenarios where the API key is not provided to avoid runtime exceptions:

   api_key = config.get("api_key")
   if not api_key:
       raise ValueError("VoyageAI API key is required but not provided in config")

3. Documentation Enhancements:

   • Add specific examples of model configuration in VoyageAI documentation, along with links to VoyageAI's API documentation for obtaining keys and understanding model options:

   embedder={
       "provider": "voyageai",
       "config": {
           "api_key": "YOUR_API_KEY",
           "model_name": "voyage-01",
       }
   }

4. Code Style: Address inconsistent whitespace issues in documentation files to promote clarity and professionalism in formatting. For example, ensure consistent use of line endings and remove trailing spaces.

Testing Recommendations

1. Unit Tests: It's crucial to implement unit tests for the _configure_voyageai function to ensure functionality and prevent future regressions:

   def test_voyageai_embedding_configuration():
       config = {
           "api_key": "test_key",
           "model_name": "voyage-01"
       }
       embedder = EmbeddingConfigurator()._configure_voyageai(config, "voyage-01")
       assert isinstance(embedder, VoyageAIEmbeddingFunction)
       assert embedder.model_name == "voyage-01"

2. Integration Testing: Include integration tests with VoyageAI’s services to validate overall functionality and compatibility.

3. Error Handling Tests: Develop test cases that validate handling of missing or incorrect configurations to ensure robustness of the implementation.

Historical Context and Related Patterns

The implementation aligns with recent PRs that introduced new embedding providers, thus maintaining coding consistency. As observed in previous integrations, incorporating comprehensive documentation has been a significant aspect of successful feature rollouts in the crewAI framework.

Conclusion

The effort to integrate VoyageAI's embedding models is commendable and effectively enhances the crewAI framework's capabilities. It is recommended to address the outlined improvements, specifically in terms of error handling, type hints, and comprehensive testing, to complete this integration. After these adjustments, the pull request will be ready for merging, aligning well with user needs and enhancing the overall developer experience.

[joaomdmoura]

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

Code Review Comment for PR #1871: Introducing VoyageAI's Embedding Models

Overview

This PR introduces support for VoyageAI's embedding models to the CrewAI framework, enhancing its capabilities and bringing comprehensive documentation updates along with the necessary configuration code.

Code Quality Findings

src/crewai/utilities/embedding_configurator.py

Positives:

• The integration of VoyageAI embedding configuration is executed cleanly.
• Maintains established patterns seen with other embedding providers, ensuring consistency.
• Method names are clear and descriptive, which improves readability.

Suggestions for Improvement:

1. Input Validation: The _configure_voyageai method can be improved by adding input validation for API keys and model names. Here’s a refined implementation:

   @staticmethod
   def _configure_voyageai(config, model_name):
       from chromadb.utils.embedding_functions.voyageai_embedding_function import VoyageAIEmbeddingFunction
       
       api_key = config.get("api_key")
       if not api_key:
           raise ValueError("VoyageAI API key is required")
       
       if not model_name:
           raise ValueError("VoyageAI model name is required")
           
       return VoyageAIEmbeddingFunction(
           model_name=model_name,
           api_key=api_key,
       )

2. Default Model Name: Implement a default model name to enhance user experience:

   VOYAGEAI_DEFAULT_MODEL = "voyage-01"  # Declare at the top
   
   # Update configuration method accordingly:
   model_name = model_name or VOYAGEAI_DEFAULT_MODEL

Documentation Files

Positives:

• The documentation updates are thorough and clearly explain the VoyageAI integration process.
• Examples provided are helpful and maintain consistency with the existing documentation structure.

Suggestions for Improvement:

1. In docs/concepts/memory.mdx, consider adding more context around the capabilities of VoyageAI:

   ### Using VoyageAI Embeddings
   
   VoyageAI provides state-of-the-art embedding models optimized for various use cases, including:
   - High-quality semantic representations.
   - Fast processing speeds.
   - Multilingual support.
   
   Example Usage:
   ```python
   from crewai import Crew, Agent, Task, Process
   
   my_crew = Crew(
       agents=[...],
       tasks=[...],
       process=Process.sequential,
       memory=True,
       verbose=True,
       embedder={
           "provider": "voyageai",
           "config": {
               "api_key": "YOUR_API_KEY",
               "model_name": "voyage-01"  # Recommended model
           }
       }
   )

2. In docs/how-to/llm-connections.mdx, add a specific example for VoyageAI configuration:

   ### VoyageAI Configuration Example
   
   ```python
   llm = LLM(
       provider="voyageai",
       config={
           "api_key": "YOUR_VOYAGEAI_API_KEY",
           "model": "voyage-01",
           "max_tokens": 2048
       }
   )

   For further details, please refer to VoyageAI's documentation.

General Recommendations:

1. Documentation Enhancements:

   • Introduce a troubleshooting section for potential integration issues with VoyageAI.
   • Include information about VoyageAI's rate limits and pricing policies.
   • Consider adding performance comparison metrics with other embedding providers to help users make informed decisions.

2. Code Structure:

   • Please include unit tests for the VoyageAI embedding configuration to ensure reliability and performance.
   • Implement error handling for API connection failures to prevent runtime exceptions.
   • Adding logging during embedding operations would help with debugging.

3. Environment Variables:

   • Add support for the VOYAGEAI_API_KEY environment variable.
   • Ensure the documentation in the README is clear on how to set up these environment variables.

Security Considerations:

1. API Key Handling: Ensure API key management follows security best practices.
2. Documentation Warnings: Include a warning about the importance of API key protection in the documentation.
3. API Key Rotation Guidance: Consider providing best practices for API key rotation.

Performance Impact:

• No significant performance impact has been identified from the changes, and the integration follows established patterns used across other providers.

Conclusion:

The implementation is solid and well-documented. The above suggestions focus on enhancing usability, error handling, and security best practices, which will ultimately aid in the successful adoption of VoyageAI features. The PR is ready to merge pending these improvements.

Thank you for your contributions! Looking forward to the enhancements.

[bhancockio]

Hey @fzowl! Can you please allow changes from maintains on this PR so that I can update it with our base branch?

Steps for the Contributor:

• Go to the Pull Request page in the GitHub repository.
• On the right-hand sidebar (near the bottom), they should see a checkbox labeled "Allow edits by maintainers" under the "Reviewer" and "Assignees" sections.
• Check the box to allow maintainers to push changes to their PR branch.

[fzowl]

@bhancockio I don't see that option. I think this is because the forked repo belongs to an Org.

We gave you write permission to our repo; please try making changes now.