Introduction to DeepEval

DeepEval is an innovative open-source evaluation framework developed by Confident AI. It aims to provide a robust platform for evaluating AI models, ensuring that developers can assess their models effectively and efficiently. With a substantial codebase of 103,961 lines across 781 files, DeepEval is designed to be both comprehensive and user-friendly.

Key Features of DeepEval

• Modular Architecture: DeepEval is built with a modular design, allowing users to easily add new benchmarks and metrics.
• Live Development: The framework supports live updates during development, enhancing productivity.
• Comprehensive Documentation: Detailed documentation is available to guide users through installation, usage, and contribution.
• Community Support: Engage with a vibrant community through Discord for discussions and support.

Technical Architecture and Implementation

DeepEval is constructed using Docusaurus 2, a modern static website generator that facilitates the creation of documentation websites. The architecture is designed to be scalable and maintainable, ensuring that developers can easily navigate and contribute to the codebase.

const express = require('express');
const app = express();

app.get('/', (req, res) => {
  res.send('Welcome to DeepEval!');
});

app.listen(3000, () => {
  console.log('Server is running on port 3000');
});

Setup and Installation Process

To get started with DeepEval, follow these simple steps:

1. Clone the repository:

   git clone https://github.com/confident-ai/deepeval.git

2. Navigate to the project directory:

   cd deepeval

3. Install dependencies using Yarn:

   yarn

4. Start the local development server:

   yarn start

For production builds, use:

yarn build

Usage Examples and API Overview

DeepEval provides a variety of APIs for evaluating different AI models. Here’s a simple example of how to evaluate a model:

const evaluation = new DeepEval.ModelEvaluation(model);
const results = evaluation.run();
console.log(results);

For more detailed usage, refer to the official documentation.

Community and Contribution Aspects

DeepEval thrives on community contributions. Whether you want to fix bugs, add features, or improve documentation, your input is valuable. To contribute:

• Follow existing patterns in the codebase.
• Run black for code formatting.
• Join discussions on our Discord server.

We appreciate your contributions and look forward to your involvement!

License and Legal Considerations

DeepEval is licensed under the Apache License 2.0. This allows you to use, modify, and distribute the software under certain conditions. For more details, refer to the Apache License.

Conclusion

DeepEval is a powerful tool for evaluating AI models, offering a modular architecture, comprehensive documentation, and a supportive community. Whether you are a developer looking to enhance your AI projects or a contributor eager to improve the framework, DeepEval provides the resources you need.

For more information, visit the DeepEval GitHub repository.

FAQ Section