RAGAS - RAG Assessment Framework

Overview

RAGAS is an evaluation framework specifically designed to assess the performance of Retrieval-Augmented Generation (RAG) systems. Unlike traditional metrics that might focus solely on the final output, RAGAS provides a comprehensive evaluation across multiple dimensions of RAG system performance.

Key Metrics

RAGAS includes several key metrics:

  1. Faithfulness: Measures how well the generated response aligns with the retrieved context, identifying potential hallucinations
  2. Answer Relevancy: Evaluates how relevant the generated response is to the question
  3. Context Relevancy: Assesses how relevant the retrieved documents are to the question
  4. Context Precision: Measures the proportion of retrieved context that is actually useful for answering the question

Usage

RAGAS can be installed via pip:

from ragas import evaluate
from datasets import Dataset

# Example evaluation
eval_results = evaluate(
    dataset=your_dataset,
    metrics=[
        "faithfulness",
        "answer_relevancy",
        "context_relevancy"
    ]
)

Benefits

  • Comprehensive Assessment: Evaluates multiple aspects of RAG performance
  • Standardisation: Provides consistent metrics across different RAG implementations
  • Automation: Reduces the need for manual evaluation
  • Interpretability: Offers clear insights into specific areas needing improvement