Ragas

Stale
GitHub Python Apache-2.0

Description

Ragas is a framework for evaluating RAG (Retrieval Augmented Generation) systems. It provides various evaluation metrics including faithfulness, answer relevance, context precision, helping developers optimize RAG application performance.

Key Features

  • Objective evaluation metrics using both LLM-based and traditional approaches for precise LLM app assessment
  • Automatic test data generation covering a wide range of scenarios for RAG systems
  • Seamless integrations with LangChain, major observability tools, and popular LLM frameworks
  • Production-aligned feedback loops leveraging real data to continually improve LLM applications
  • Pre-built quickstart templates for RAG evaluation, agent evaluation, and LLM benchmarking
  • DiscreteMetric support for custom aspect evaluation with fine-grained scoring and reasoning

Use Cases

💡 Evaluating RAG pipeline quality with faithfulness, relevance, and context precision metrics
💡 Benchmarking different LLM prompts and configurations to find optimal settings
💡 Generating synthetic test datasets for stress-testing retrieval and generation components
💡 Building CI/CD evaluation gates for LLM-powered applications in production

Tags

rag evaluation llm testing

Categories

📚 RAG Tools 📊 Observability

Quick Start

pip install ragas && ragas quickstart rag_eval -o ./my-project
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RAG System Evaluation in Practice: Building High-Quality RAG Apps with Ragas and DeepEval

Learn how to evaluate RAG systems using Ragas and DeepEval, including measuring key metrics like faithfulness, answer relevance, and context precision.

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