Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration. Used by OpenAI and Anthropic.

A highly efficient, secure, and stable application development platform with excellent performance, easy scalability, and deep integration of AI capabilities such as LLM, AI Chat, RAG, and Agents.高效、安全、稳定的服务研发平台，具备良好性能，同时易扩展，深度集成LLM、AIChat、RAG、Agent等AI能力

This repository contains a Retrieval-Augmented Generation (RAG) framework developed in C++ for high performance and scalability, with CUDA support for computational acceleration.

An agent based LLM assistant that extends RAG with batch entity extraction and SQL querying to improve performance on multi-step and analytical questions.

Maximizing the Performance of a Simple RAG using RL

RocketRAG is a high-performance Retrieval-Augmented Generation (RAG) system designed with a focus on speed, simplicity, and extensibility. Built on top of state-of-the-art libraries, it provides both CLI and web server capabilities for seamless integration into any workflow.

MaxKB4j is an open-source LLMOps platform for LLM workflow applications and RAG developed based on the Java language. The project mainly draws on MaxKB, Dify and FastGPT, and combines the advantages of the two into one project. It is redesigned and developed using the high-performance, high-stability and secure reliable JAVA language.

Agentic-RAG Framework: A high-performance knowledge retrieval system featuring Hybrid Search (FAISS + BM25), RRF Re-ranking, and MySQL/Redis persistent storage.

The GitHub Action for Promptfoo. Test your prompts, agents, and RAGs. AI Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

Korean Sentence Embedding Model Performance Benchmark for RAG

A simple, robust, RAG engine that focuses on speed and performance

I have explained how to create superior RAG pipeline for complex pdfs using LlamaParse. We can extract text and tables from pdf and QA on it with high performance.

Diagnose the performance of your RAG🩺

MIRAGE is a light benchmark to evaluate RAG performance.

Is a high-performance Augmented Recovery-Generation (RAG) solution based on Redis, Qdrant or PostgreSQL. It offers a high-level interface using FastAPI REST APIs

🦀 High-performance local RAG server in Rust that integrates with Claude Desktop via MCP. Search PDF documents privately using Ollama embeddings - no external API calls.

A high-performance RAG indexing pipeline implemented in Rust using LanceDB and Candle

Measuring RAG solutions throughput and latency

Graph RAG with pure vector search, achieving SOTA performance in multi-hop reasoning scenarios.

a project aim to improve the RAG performance on BelR/nfcorpus dataset

💸 A comprehensive AI-powered data explorer that combines FRED economic data & insights with vector search, regression analysis, and interactive RAG chatbot via Pinecone Vector DB, OpenAI, Claude, and Gemini. Built with TypeScript, React, and Express for seamless full-stack performance.

A high-performance RAG system built to manage and retrieve knowledge from 4,000+ HR documents.

This Guidance demonstrates how to develop a high-performance Retrieval-Augmented Generation (RAG) chatbot by combining Amazon Aurora PostgreSQL-Compatible Edition with the open-source pgvector extension.

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

Vectrain is a high-performance, modular, plug-and-play RAG pipeline that ingests data, generates vector embeddings, and stores them in vector databases for semantic search, recommendations, and analytics.

Falcon Evaluate is an open-source Python library aims to revolutionise the LLM - RAG evaluation process by offering a low-code solution. Our goal is to make the evaluation process as seamless and efficient as possible, allowing you to focus on what truly matters.This library aims to provide an easy-to-use toolkit for assessing the performance, bias

RagCheck is a proactive corpus quality assessment tool that analyses RAG application document collections before deployment, identifying content gaps and providing specific recommendations to improve query performance. The platform transforms reactive corpus fixes into proactive quality assurance, helping organisations achieve as high as 85% score.

🐍📦 High-performance cosine similarity ranking for Retrieval-Augmented Generation (RAG) pipelines.

An advanced, fully local, and GPU-accelerated RAG pipeline. Features a sophisticated LLM-based preprocessing engine, state-of-the-art Parent Document Retriever with RAG Fusion, and a modular, Hydra-configurable architecture. Built with LangChain, Ollama, and ChromaDB for 100% private, high-performance document Q&A.

A high-performance memory system for AI applications with advanced RAG capabilities, vector-graph hybrid storage, and adaptive learning. Built with Rust for scalability and efficiency.