The article provides a detailed guide on how to use `continuous-eval`, including running a single metric, defining custom metrics, and running evaluation on pipeline modules. It also lists off-the-shelf metrics available for different modules and categories. The article concludes by providing resources for further learning and information about the project's license and usage-tracking policy.
Key takeaways:
- 'continuous-eval' is an open-source package designed for detailed and comprehensive evaluation of GenAI application pipelines.
- It offers features like Modularized Evaluation, Comprehensive Metric Library, User Feedback in Evaluation, and Synthetic Dataset Generation.
- The code is provided as a PyPi package and requires at least one of the LLM API keys in '.env' to run LLM-based metrics.
- It allows defining your own metrics by extending the Metric class and implementing the '__call__' method.