RAG Evaluation & Retrieval Toolkit

Overview

A comprehensive evaluation framework for RAG (Retrieval-Augmented Generation) systems that measures retrieval quality, response accuracy, latency, and groundedness. The toolkit provides standardized metrics, dataset harnesses, regression tests, and deployment tooling for production RAG systems.

Key Features

• •Retrieval Quality Metrics: Precision, recall, and relevance scoring for retrieved documents
• •Response Evaluation: Groundedness checks, factuality, and coherence scoring
• •Latency Measurement: End-to-end response time tracking and optimization insights
• •Dataset Harness: Standardized test datasets for consistent evaluation
• •Regression Testing: Automated tests to prevent performance degradation
• •Deployment Tooling: CI/CD integration and monitoring dashboards

Architecture

Use Cases

• Evaluating retrieval pipeline improvements before deployment
• Comparing different embedding models and vector search strategies
• Monitoring RAG system performance over time
• Running regression tests in CI/CD pipelines
• Benchmarking against industry standards

Status

This project is currently in development. The framework is being built to support production RAG evaluation needs with a focus on reliability, reproducibility, and actionable insights.