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:
- Faithfulness: Measures how well the generated response aligns with the retrieved context, identifying potential hallucinations
- Answer Relevancy: Evaluates how relevant the generated response is to the question
- Context Relevancy: Assesses how relevant the retrieved documents are to the question
- 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
= evaluate(
eval_results =your_dataset,
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