Ragas
StaleDescription
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
Strengths & Limitations
✅ Strengths
- • High community interest (15.3k stars)
- • Permissive open-source license (Apache-2.0)
- • Established track record (3 years in production)
Tags
Categories
Quick Start
pip install ragas && ragas quickstart rag_eval -o ./my-project