RAGAs
Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines.
Overview
RAGAs is a specialized open-source framework designed for the evaluation of Retrieval Augmented Generation (RAG) pipelines. It provides a set of metrics to assess the performance of RAG systems from different perspectives, including the effectiveness of the retrieval component and the quality of the generated text. RAGAs helps developers to identify weaknesses in their RAG pipelines and to iterate on them to improve their performance.
✨ Key Features
- Evaluation of RAG pipelines
- Metrics for faithfulness, answer relevance, and context precision
- Reference-free evaluation
- Integration with LangChain and LlamaIndex
🎯 Key Differentiators
- Specialized for RAG pipeline evaluation
- Reference-free metrics
- Lightweight and easy to integrate
Unique Value: RAGAs provides a simple yet powerful way to evaluate and improve the performance of RAG pipelines, which are becoming increasingly popular for building knowledge-intensive LLM applications.
🎯 Use Cases (3)
✅ Best For
- Component-wise evaluation of RAG pipelines to identify and address bottlenecks.
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Not suitable for evaluating LLMs in non-RAG contexts.
🏆 Alternatives
While other evaluation frameworks can be used to evaluate RAG pipelines, RAGAs offers a set of metrics that are specifically designed for this purpose. This makes it a more effective and efficient tool for teams that are working with RAG.
💻 Platforms
✅ Offline Mode Available
🔌 Integrations
💰 Pricing
Free tier: Free and open-source.
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