Add CambAI Text-to-Speech Tool Integration

[Saransh-Shankar]

Summary

• Add CambAITTSTool for text-to-speech conversion using CambAI API
• Implement tool with support for custom voice selection and language options
• Include comprehensive documentation and example usage
• Add test file for verifying tool functionality

Features

• Text-to-speech conversion using CambAI service
• Automatic voice selection or custom voice_id parameter
• Language support with configurable options
• Returns audio file URL in JSON format
• Proper error handling for missing API keys and dependencies

Dependencies

• Requires cambai-sdk package
• Uses CAMB_API_KEY environment variable for authentication

[joaomdmoura]

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

Code Review Comment for PR #336 - CambAI Integration

Overview

This PR introduces the CambAI Text-to-Speech tool into the crewAI-tools package, integrating a new feature that enables conversion from text to speech. Overall, the implementation shows promise, but there are several areas that could benefit from improvement to enhance code quality, maintainability, and user experience.

1. Code Quality Improvements

a) Error Handling

The implementation currently lacks robust error handling mechanisms. It’s critical to ensure that potential errors are caught and reported effectively. Consider implementing granular exception handling to manage common issues such as missing parameters and API failures. For instance:

def _run(self, **kwargs) -> str:
    try:
        text = kwargs.get("text")
        if not text:
            raise ValueError("Text parameter is required.")

        # additional processing...
        
    except ValueError as ve:
        raise RuntimeError(f"Input error: {str(ve)}")
    except Exception as e:
        raise RuntimeError(f"Unexpected error: {str(e)}")

b) Type Hints

Adding type hints across the codebase would significantly improve readability. Consider annotating method parameters and return types, which clarifies expected usage and reduces ambiguity throughout the codebase.

Example:

def _run(self, **kwargs: Dict[str, Any]) -> str:

2. Design Recommendations

a) Configuration Management

It might be beneficial to redesign your configuration management by using a dataclass for API keys and default parameters. This will create a more organized structure for configuration handling:

@dataclass
class CambAIConfig:
    api_key: str
    voice_id: Optional[int] = None
    language: Optional[int] = 1

b) Voice Management

Implement a VoiceManager class to encapsulate logic related to voice handling, such as retrieving available voices and selecting them randomly. This design promotes separation of concerns and can help reduce redundancy.

class VoiceManager:
    def __init__(self, client):
        self.client = client

    def get_random_voice(self) -> int:
        voices = self.client.list_voices()
        return random.choice([voice.id for voice in voices])

3. Documentation Enhancements

Ensure that docstrings are comprehensive and informative. Include purpose, parameters, and return types for functions and classes, enhancing usability for others, as well as for future you.

Example:

class CambAITTSTool(BaseTool):
    """
    This tool converts text into speech using the CambAI API.
    
    Attributes:
        api_key (str): API key for authentication.
        
    Usage:
        >>> tool = CambAITTSTool(api_key="your-api-key")
        >>> result = tool.run(text="Hello!")
    """

4. Testing Recommendations

Adding unit tests is crucial for maintaining code quality. Develop tests that cover the main functionalities, ensuring that changes do not inadvertently break existing features. Consider using mocking for external API interactions to isolate unit tests.

Example Test:

def test_cambai_tts_tool():
    # Define mock behavior and assertions here

5. Additional Suggestions

1. Package Dependencies: Ensure all dependencies like cambai-sdk are explicitly listed in the requirements.txt or setup.py.
2. Constant Definitions: Extract magic numbers and default values into a constants file to improve readability and maintainability.

Conclusion

This PR is a step forward in expanding the crewAI-tools with valuable functionality. By addressing the highlighted areas for improvement, including error handling and code clarity, we can ensure a robust and maintainable addition to the codebase. Please consider these suggestions to enhance overall quality and maintainability.

Feel free to reach out if you need further clarification on any of the points raised above!

[Saransh-Shankar]

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

Code Review Comment for PR #336 - CambAI Integration

Overview

This PR introduces the CambAI Text-to-Speech tool into the crewAI-tools package, integrating a new feature that enables conversion from text to speech. Overall, the implementation shows promise, but there are several areas that could benefit from improvement to enhance code quality, maintainability, and user experience.

1. Code Quality Improvements

a) Error Handling

The implementation currently lacks robust error handling mechanisms. It’s critical to ensure that potential errors are caught and reported effectively. Consider implementing granular exception handling to manage common issues such as missing parameters and API failures. For instance:

def _run(self, **kwargs) -> str:
    try:
        text = kwargs.get("text")
        if not text:
            raise ValueError("Text parameter is required.")

        # additional processing...
        
    except ValueError as ve:
        raise RuntimeError(f"Input error: {str(ve)}")
    except Exception as e:
        raise RuntimeError(f"Unexpected error: {str(e)}")

b) Type Hints

Adding type hints across the codebase would significantly improve readability. Consider annotating method parameters and return types, which clarifies expected usage and reduces ambiguity throughout the codebase.

Example:

def _run(self, **kwargs: Dict[str, Any]) -> str:

2. Design Recommendations

a) Configuration Management

It might be beneficial to redesign your configuration management by using a dataclass for API keys and default parameters. This will create a more organized structure for configuration handling:

@dataclass
class CambAIConfig:
    api_key: str
    voice_id: Optional[int] = None
    language: Optional[int] = 1

b) Voice Management

Implement a VoiceManager class to encapsulate logic related to voice handling, such as retrieving available voices and selecting them randomly. This design promotes separation of concerns and can help reduce redundancy.

class VoiceManager:
    def __init__(self, client):
        self.client = client

    def get_random_voice(self) -> int:
        voices = self.client.list_voices()
        return random.choice([voice.id for voice in voices])

3. Documentation Enhancements

Ensure that docstrings are comprehensive and informative. Include purpose, parameters, and return types for functions and classes, enhancing usability for others, as well as for future you.

Example:

class CambAITTSTool(BaseTool):
    """
    This tool converts text into speech using the CambAI API.
    
    Attributes:
        api_key (str): API key for authentication.
        
    Usage:
        >>> tool = CambAITTSTool(api_key="your-api-key")
        >>> result = tool.run(text="Hello!")
    """

4. Testing Recommendations

Adding unit tests is crucial for maintaining code quality. Develop tests that cover the main functionalities, ensuring that changes do not inadvertently break existing features. Consider using mocking for external API interactions to isolate unit tests.

Example Test:

def test_cambai_tts_tool():
    # Define mock behavior and assertions here

5. Additional Suggestions

1. Package Dependencies: Ensure all dependencies like cambai-sdk are explicitly listed in the requirements.txt or setup.py.
2. Constant Definitions: Extract magic numbers and default values into a constants file to improve readability and maintainability.

Conclusion

This PR is a step forward in expanding the crewAI-tools with valuable functionality. By addressing the highlighted areas for improvement, including error handling and code clarity, we can ensure a robust and maintainable addition to the codebase. Please consider these suggestions to enhance overall quality and maintainability.

Feel free to reach out if you need further clarification on any of the points raised above!

Hi @joaomdmoura, thank you for the review. I have made the changes as you mentioned. Please let me know if there is anything else that needs to be done.

[Saransh-Shankar]

Hi @joaomdmoura,

Just a friendly bump on this PR—I've been sitting on the updates we discussed about a week ago and wanted to check:

• Do you need anything else from my side before merging?
• Is there any remaining feedback or edge cases I should address?

Whenever you have a moment, please let me know if it’s good to merge or if any other tweaks are needed. Thanks! 😊