RAGAS

Overview

RAGAS (Retrieval Augmented Generation Assessment) is a framework for evaluating RAG systems without requiring reference answers, using LLM-based judges for quality assessment.

Key Metrics

Retrieval Metrics:

• Context Precision: Relevance of retrieved chunks
• Context Recall: Coverage of relevant information

Generation Metrics:

• Faithfulness: LLM answers based on context
• Answer Relevance: Response addresses query

Reference-Free Evaluation

Unlike traditional metrics requiring ground truth:

• Uses LLMs as judges
• No need for labeled test data
• Evaluates both retrieval and generation

Use Cases

• Automated RAG quality monitoring
• A/B testing retrieval strategies
• Embedding model selection
• Chunking strategy optimization

Integration

Supports major frameworks:

• LangChain
• LlamaIndex
• Haystack
• Custom RAG pipelines

Availability

Open-source Python package

Paper: arXiv:2309.15217