RAGAS

framework active open-source

Retrieval Augmented Generation Assessment - a framework for evaluating RAG pipelines. RAGAS provides reference-free metrics to evaluate retrieval quality and generation faithfulness without requiring ground truth labels. It measures context relevance, answer faithfulness, answer relevance, and context recall.

Implements

Concepts this tool claims to implement:

  • Benchmark primary

    Evaluation metrics suite for RAG: faithfulness (is answer grounded in context), answer relevancy (is answer relevant to question), context precision and recall (retrieval quality). Supports synthetic test data generation.

  • Faithfulness primary

    Faithfulness metric measures whether claims in the generated answer can be inferred from the retrieved context. Uses LLM-as-judge approach.

  • Grounding secondary

    Context relevance and precision metrics measure how well retrieved documents support answering the question.

Integration Surfaces

  • Python SDK
  • LangChain integration
  • LlamaIndex integration
  • Hugging Face datasets integration

Details

Vendor
Explodinggradients
License
Apache-2.0
Runs On
local, cloud
Used By
human, system

Links

Notes

RAGAS is widely used for RAG evaluation due to its reference-free approach. Metrics use LLMs internally for evaluation, which adds cost but enables evaluation without human-labeled datasets. Works well for rapid iteration on RAG pipelines.

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