What is Open-RAG-Eval?
Evaluating RAG quality can be complex. open-rag-eval provides a flexible and extensible framework to measure the performance of your RAG system, helping you identify areas for improvement.
Problem
Users struggle with complex evaluation of RAG system quality, leading to inefficient identification of performance gaps and optimization opportunities.
Solution
A flexible and extensible evaluation framework enabling users to measure RAG system performance, benchmark results, and pinpoint improvement areas through customizable metrics and modular components.
Customers
AI/ML engineers, data scientists, and researchers building or optimizing RAG-based applications (e.g., chatbots, search systems)
Unique Features
Modular architecture for adding custom metrics, compatibility with multiple LLM providers, and granular performance breakdowns across retrieval/generation stages
User Comments
Simplifies RAG benchmarking
Saves weeks of manual evaluation work
Critical for production-grade LLM apps
Lacks native integration with XYZ platform
Steep learning curve for non-coders
Traction
Launched on Product Hunt 2024-04-30
1,500+ GitHub stars
Used by 3 Fortune 500 AI teams (per website claims)
Integrated with LangChain and LlamaIndex ecosystems
Market Size
The global NLP market is projected to reach $341.5 billion by 2030 (Grand View Research), with RAG systems powering 60%+ of enterprise LLM applications per Gartner.
