RAGAS
A framework for performing Retrieval-Augmented Generation (RAG) evaluation, supporting multiple ways of validating results.
About this tool
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
- Retrieval Augmented Generation Metrics:
- Core Components: Utilizes key components such as Prompts, Evaluation Samples, and Evaluation Datasets to facilitate thorough assessments.
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