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Model Benchmarking
InspectorRAGet

InspectorRAGet

Evaluate RAG systems with visual analytics

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What is InspectorRAGet ?

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.

Features

• 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.

How to use InspectorRAGet ?

  1. Install InspectorRAGet: Use pip to install the package: pip install inspectrraget.
  2. Import the Library: Add InspectorRAGet to your code:
    from inspectrraget importInspectorRAGet
    
  3. Load Your Dataset: Prepare your dataset for evaluation.
  4. Initialize InspectorRAGet: Pass your RAG model and dataset to the InspectorRAGet class.
  5. Generate Embeddings: Run embeddings generation for your dataset.
  6. Query and Analyze: Perform queries and use InspectorRAGet's analytics to visualize and compare results.

Frequently Asked Questions

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.

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