Ragas
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About Ragas
Ragas is an open-source toolkit for evaluating and optimizing Large Language Model (LLM) applications. It provides objective metrics, intelligent test generation, and data-driven insights, moving beyond subjective assessments. Ragas supports pre-built metrics for common evaluation tasks, such as Aspect Critique using DiscreteMetric, and offers production-aligned test set generation. It integrates seamlessly with frameworks like LangChain and major observability tools, and enables building feedback loops from production data to continuously improve LLM apps.
Key Features
Objective Metrics: Evaluate LLM applications with precision using both LLM-based and traditional metrics.
Test Data Generation: Automatically create comprehensive test datasets covering a wide range of scenarios.
Seamless Integrations: Works flawlessly with popular LLM frameworks like LangChain and major observability tools.
Build Feedback Loops: Leverage production data to continually improve LLM applications.
Pre-built metrics for common evaluation tasks, including Aspect Critique via DiscreteMetric.
Quickstart templates for RAG evaluation, agent evals, benchmark LLMs, prompt evals, and workflow eval.
Pros & Cons
Pros
- Provides objective, data-driven evaluation metrics for LLM apps
- Automates test dataset generation, saving time and effort
- Integrates with popular frameworks like LangChain and observability tools
- Supports building feedback loops using production data for continuous improvement
- Open-source and free to use
Cons
- Evaluation metrics depend on access to external LLMs (e.g., OpenAI), which may incur costs
- Primarily focused on RAG and LLM evaluation; may require customization for other application types
Best For
Evaluating Retrieval-Augmented Generation (RAG) systemsBenchmarking and comparing LLMsEvaluating AI agentsEvaluating prompt variationsEvaluating complex LLM workflowsGenerating production-aligned test datasets for LLM applications
FAQ
How do I install Ragas?
Ragas can be installed via pip: 'pip install ragas'. Alternatively, from source: 'pip install git+https://github.com/vibrantlabsai/ragas'.
What metrics does Ragas provide?
Ragas provides objective metrics using both LLM-based and traditional metrics. For example, Aspect Critique uses DiscreteMetric to evaluate aspects of output with allowed values like 'accurate' or 'inaccurate'.
Does Ragas generate test data?
Yes, Ragas offers production-aligned test set generation, automatically creating comprehensive test datasets covering a wide range of scenarios.
What integrations does Ragas support?
Ragas integrates seamlessly with popular LLM frameworks like LangChain and major observability tools.