Ragas
Ragas is a library that helps you move from "vibe checks" to systematic evaluation loops for your AI applications. It provides tools to supercharge the evaluation of Large Language Model …
Introduction | Ragas
Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the …
Metrics - Ragas
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Get Started - Ragas
The Get Started guides will walk you through the fundamentals of working with Ragas. These tutorials assume basic knowledge of Python and building LLM application pipelines.
Evaluate a simple LLM application - Ragas
The purpose of this guide is to illustrate a simple workflow for testing and evaluating an LLM application with ragas. It assumes minimum knowledge in AI application building and evaluation.
Core Concepts - Ragas
: Ragas Metrics Use our library of available metrics or create custom metrics tailored to your use case. Metrics for evaluating RAG, Agentic workflows and more... Test Data Generation …
️ How-to Guides - Ragas
The how-to guides offer a more comprehensive overview of all the tools Ragas offers and how to use them. This will help you tackle messier real-world usecases when you’re using the …
Evaluate a simple RAG system - Ragas
The purpose of this guide is to illustrate a simple workflow for testing and evaluating a RAG system with ragas. It assumes minimum knowledge in building RAG system and evaluation.
Testset Generation for RAG - Ragas
If you are using a different LLM provider and using LangChain to interact with it, you can wrap your LLM in LangchainLLMWrapper so that it can be used with ragas.
Faithfulness - Ragas
from ragas.dataset_schema import SingleTurnSample from ragas.metrics import Faithfulness sample = SingleTurnSample( user_input="When was the first super bowl?", response="The …