RAGAS

RAGAS (Retrieval Augmented Generation Assessment) is a framework specifically designed to evaluate RAG pipeline quality with metrics that assess each component of the retrieval-generation pipeline independently.

Key use cases include:

• Faithfulness — does the generated answer accurately reflect the retrieved context?
• Answer relevancy — is the answer actually relevant to the user's question?
• Context precision — are the retrieved documents actually relevant?
• Context recall — did the retriever find all the relevant information?
• Synthetic test generation — automatically create evaluation datasets from your documents
• Pipeline benchmarking — compare different retrieval strategies, chunk sizes, and models

RAGAS is used by teams building RAG applications who need to quantify pipeline quality and identify which component (retriever, generator, or both) needs improvement. Its LLM-as-judge approach requires no human annotations, making evaluation fast and scalable.

It integrates with LangChain, LlamaIndex, and Haystack through built-in adapters.

1. Install RAGAS:

   pip install ragas

2. Set your evaluator LLM key:

   export OPENAI_API_KEY='sk-...'

3. Run an evaluation:

   from ragas import evaluate
   from ragas.metrics import faithfulness, answer_relevancy, context_precision
   from datasets import Dataset
   
   data = Dataset.from_dict({
       'question': ['What is the capital of France?'],
       'answer': ['Paris is the capital of France.'],
       'contexts': [['France is a country. Its capital is Paris.']],
       'ground_truth': ['Paris']
   })
   
   result = evaluate(
       dataset=data,
       metrics=[faithfulness, answer_relevancy, context_precision]
   )
   print(result)

Pricing: RAGAS is free and open source (Apache 2.0). Evaluation uses LLM API calls for metric computation (~$0.01-0.05 per evaluation row depending on metrics).