Build and automate RAG pipeline evaluation using RAGAS metrics like faithfulness and context relevance. The guide walks through setting up a RAG system with LangChain and FAISS, loading the Dolly-15k dataset, implementing RAGAS evaluation metrics, and integrating continuous quality checks into CircleCI workflows. Learn to measure retrieval quality and generation accuracy programmatically, configure pipeline parameters, and establish automated benchmarking that runs with every code change to catch performance regressions early.
Table of contents
This tutorial covers:PrerequisitesInstalling required packages and setting up codebaseSetting up the RAG evaluation datasetSetting up your RAG pipelineEvaluating RAG pipeline with RAGASOrchestrating and testing RAG evaluation locallyAutomating evaluation with CircleCISetting up your project on CircleCIConclusionSort: