Ragas
Ragas is an open-source framework designed for evaluating the performance and robustness of Large Language Model (LLM) applications, particularly those leveraging Retr…
Our verdict
Who it's for
Evaluate and monitor the quality of your LLM applications with automatic metrics and synthetic data.
Who should look elsewhere
Requires technical expertise to implement and interpret evaluation results.
Pros
- +Provides comprehensive evaluation for RAG applications component-wise and end-to-end.
- +Simplifies the creation of evaluation datasets with synthetic data generation.
- +Offers continuous quality assurance for LLM applications in production.
- +Integrates with popular LLM frameworks like LlamaIndex and LangChain.
- +Recommended by industry leaders and integrated into key AI development tools.
Cons
- −Requires technical expertise to implement and interpret evaluation results.
- −Focuses primarily on RAG systems, potentially less comprehensive for other LLM use cases.
- −Relies on LLM-based evaluation metrics, which can have their own biases or limitations.
Key features
- ✓Automatic evaluation metrics for LLM applications
- ✓Synthetic evaluation data generation
- ✓Online monitoring of LLM application quality
- ✓Context relevance metric
- ✓Context recall metric
- ✓Context precision metric
- ✓Faithfulness metric
- ✓Answer relevancy metric
Pricing
| Plan | Price | Billing | Notes |
|---|---|---|---|
| Open-source | — | month | Automatic metrics for LLM application performance and robustness; Synthetically generate high quality and diverse evaluation data; Online Monitoring |
| Enterprise | — | month | Enterprise features and collaborations |