RAGAS

Description Ragas is a framework designed for evaluating Retrieval-Augmented Generation (RAG) systems, supporting multiple ways of validating results.

Features

• LLM Application Evaluation: Enables evaluation of your first LLM application.
• RAG System Evaluation: Provides capabilities to evaluate simple RAG setups.
• Synthetic Testset Generation: Allows generation of synthetic test datasets specifically for RAG systems.
• Comprehensive Evaluation Metrics: Offers a wide array of metrics for assessing RAG and LLM performance, categorized as:
  • Retrieval Augmented Generation Metrics:
    • Context Precision
    • Context Recall
    • Context Entities Recall
    • Noise Sensitivity
    • Response Relevancy
    • Faithfulness
  • Nvidia Metrics:
    • Answer Accuracy
    • Context Relevance
    • Response Groundedness
  • Agents or Tool Use Cases
• Core Components: Utilizes key components such as Prompts, Evaluation Samples, and Evaluation Datasets to facilitate thorough assessments.