Rag Evaluation With Deepeval Framework

via “rag-evaluation-with-deepeval-framework”

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

Unique: Provides an integrated evaluation framework (DeepEval) with pre-built metrics for retrieval quality, answer quality, and end-to-end performance, enabling systematic RAG evaluation without building custom evaluation pipelines — a comprehensive approach to RAG quality assurance

vs others: More comprehensive than ad-hoc evaluation because it provides standardized metrics and automated evaluation pipelines, and more practical than building custom evaluators because it includes pre-built metrics for common RAG quality dimensions

via “cli and configuration management for evaluation workflows”

The LLM Evaluation Framework

Unique: Implements a CLI interface for running evaluations and managing projects without Python code. Supports configuration files and environment variables for flexible deployment.

vs others: More accessible than Python-only APIs and more flexible than fixed configuration because it provides both CLI and programmatic interfaces with support for configuration files and environment variables.