Micronaire

RAG pipelines enable developers to augment their chat experiences with informational documents that their agents can leverage to provide better answers. Evaluating these pipelines is a new area of study, with solutions like RAGChecker and RAGAS being state of the art frameworks that are implemented in Python. This project aims to take these ideas and bring them to DotNet through Semantic Kernel.

Micronaire brings actionable metrics to RAG pipeline evaluation by taking a set of ground truth questions and answers as well as a RAG pipeline, evaluating the pipeline against the ground truth using our metrics (see below), and then producing an evaluation report.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.