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Create and upload a Hugging Face model card
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Push a ML model to Hugging Face Hub
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InspectorRAGet is a tool designed to evaluate and benchmark RAG (Retrieval-Augmented Generation) systems. It provides visual analytics and insights to help users understand the performance and behavior of their RAG models, enabling data-driven optimizations and improvements.
• Visual Analytics: Gain insights into RAG system performance through interactive visualizations.
• Benchmarking Capabilities: Compare multiple RAG models side-by-side to identify strengths and weaknesses.
• Efficient Evaluation: Streamline the evaluation process with automated workflows and reporting.
• Customizable Metrics: Define and track key performance indicators tailored to your needs.
• Integration Support: Easily integrate with popular RAG frameworks and tools.
pip install inspectrraget.from inspectrraget importInspectorRAGet
What is a RAG system?
A Retrieval-Augmented Generation (RAG) system combines retrieval mechanisms (e.g., databases or search engines) with generative models (e.g., large language models) to produce more accurate and contextually relevant responses.
Can I customize the evaluation metrics?
Yes, InspectorRAGet allows you to define and use custom metrics to align with your specific evaluation goals.
How do I visualize the results?
InspectorRAGet provides built-in visualization tools that generate interactive charts and graphs. You can access these by calling the visualize() method after running your queries.