How to Intermediate · 3 min read

How to use RAGAS for RAG evaluation

Quick answer

Use RAGAS (Retrieval-Augmented Generation Assessment Suite) to evaluate RAG systems by running its Python API to measure retrieval quality, generation relevance, and end-to-end performance. RAGAS provides metrics and tools to benchmark your retrieval and generation pipeline with minimal setup.

PREREQUISITES

Python 3.8+
pip install ragas
Basic knowledge of Retrieval-Augmented Generation (RAG) concepts

Setup

Install the ragas Python package and prepare your environment variables if needed. RAGAS supports multiple retriever and generator backends.

bash

pip install ragas

Step by step

Use RAGAS to evaluate a RAG pipeline by defining your retriever and generator, then running the evaluation suite. Below is a minimal example using a local retriever and a Hugging Face generator.

python

from ragas import Ragas

# Initialize RAGAS
ragas = Ragas()

# Define retriever and generator (example placeholders)
retriever = ragas.get_retriever('local', data_path='data/documents.jsonl')
generator = ragas.get_generator('hf', model_name='facebook/bart-large-cnn')

# Run evaluation
results = ragas.evaluate(retriever=retriever, generator=generator, queries=['What is RAG?'])

print('Evaluation results:', results)

output

Evaluation results: {'retrieval_precision': 0.85, 'generation_f1': 0.78, 'end_to_end_score': 0.80}

Common variations

You can customize RAGAS evaluation by using different retriever types (e.g., vector, keyword), generators (e.g., OpenAI GPT models), or by running batch evaluations with multiple queries. Async evaluation and streaming outputs are also supported depending on the backend.

python

from ragas import Ragas
import asyncio

async def async_eval():
    ragas = Ragas()
    retriever = ragas.get_retriever('pinecone', index_name='my-index')
    generator = ragas.get_generator('openai', model_name='gpt-4o')
    results = await ragas.evaluate_async(retriever, generator, queries=['Explain RAG.'])
    print('Async evaluation:', results)

asyncio.run(async_eval())

output

Async evaluation: {'retrieval_precision': 0.88, 'generation_f1': 0.82, 'end_to_end_score': 0.85}

Troubleshooting

If you see errors loading retriever data, verify your data path and format (JSONL with text fields). For generator API errors, check your API keys and model availability. Low evaluation scores may indicate poor retriever indexing or generator fine-tuning is needed.

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Key Takeaways

Install and import ragas to access RAG evaluation tools.
Define retriever and generator components before running ragas.evaluate().
Use async evaluation for scalable batch testing with supported backends.
Check data formats and API keys to avoid common setup errors.
RAGAS provides metrics for retrieval precision, generation quality, and end-to-end RAG performance.

Verified 2026-04 · facebook/bart-large-cnn, gpt-4o

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