🐢 Open-Source Evaluation & Testing for AI & LLM systems

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

Open source RAG evaluation package

Framework for testing vulnerabilities of large language models (LLM).

This project aims to compare different Retrieval-Augmented Generation (RAG) frameworks in terms of speed and performance.

A framework for systematic evaluation of retrieval strategies and prompt engineering in RAG systems, featuring an interactive chat interface for document analysis.

RAG Chatbot for Financial Analysis

A comprehensive evaluation toolkit for assessing Retrieval-Augmented Generation (RAG) outputs using linguistic, semantic, and fairness metrics

Using MLflow to deploy your RAG pipeline, using LLamaIndex, Langchain and Ollama/HuggingfaceLLMs/Groq

BetterRAG: Powerful RAG evaluation toolkit for LLMs. Measure, analyze, and optimize how your AI processes text chunks with precision metrics. Perfect for RAG systems, document processing, and embedding quality assessment.

Proposal for industry RAG evaluation: Generative Universal Evaluation of LLMs and Information retrieval

RAG Chatbot over pre-defined set of articles about LangChain

Home assignment featuring two AI projects: a Medical Q&A Bot for Israeli HMOs and a National Insurance Form Extractor. Built with Azure OpenAI to demonstrate practical GenAI implementation skills.

PandaChat-RAG benchmark for evaluation of RAG systems on a non-synthetic Slovenian test dataset.