Repository History
28 repositories tagged with RAG
LLM Wiki: Build a Self-Maintaining, Interlinked Knowledge Base with AI
LLM Wiki is a powerful cross-platform desktop application designed to transform your documents into an organized, interlinked knowledge base automatically. Unlike traditional RAG systems, it incrementally builds and maintains a persistent wiki from your sources, ensuring knowledge is compiled once and kept current. This innovative approach offers a dynamic and evolving personal knowledge management solution.
GitNexus: Zero-Server Code Intelligence Engine for AI Agents
GitNexus is a client-side knowledge graph creator that runs entirely in your browser, transforming GitHub repositories or ZIP files into interactive knowledge graphs. It features a built-in Graph RAG Agent, perfect for deep code exploration and enhancing AI agent reliability. This tool provides architectural context to AI, ensuring more accurate and efficient code analysis.
tooltrim: Drastically Reduce LLM Agent Tool Output Tokens, Improve Accuracy
tooltrim provides drop-in compression for LLM agent tool outputs, drastically cutting tokens while often improving answer accuracy. This provider-agnostic solution offers content-aware compression, faithfulness benchmarks, and seamless integration with popular frameworks or as an OpenAI-compatible proxy.
OpenViking: A Self-Evolving Context Database for AI Agents
OpenViking is an open-source context database designed for AI agents, unifying agent memory, knowledge RAG, and skills into a virtual filesystem. It allows agents to browse their context deterministically using familiar commands like `ls` and `tree`. This innovative approach aims to enhance agent performance and reduce token spend by loading content in tiered layers.
DeepTutor: Lifelong Personalized Tutoring with AI Agents
DeepTutor is an advanced AI-powered platform designed for lifelong personalized tutoring, integrating various learning modes into a single, extensible system. It leverages large language models and multi-agent systems to offer features like interactive chat, quiz generation, and skill development. This project provides a comprehensive environment for learners and educators seeking intelligent, adaptive educational tools.
Memori: Agent-Native Memory Infrastructure for LLM Production Systems
Memori provides agent-native memory infrastructure, offering an LLM-agnostic layer that transforms agent execution and conversations into structured, persistent state. Designed for enterprise use, it seamlessly integrates with existing data infrastructure and supports various deployment environments, ensuring robust memory management for AI agents.
rag-zero-to-hero-guide: Your Comprehensive Path to Mastering RAG
This repository offers a comprehensive guide to Retrieval-Augmented Generation (RAG), covering everything from fundamental concepts to advanced techniques. It includes detailed courses on RAG basics and evaluation, alongside an extensive toolkit of frameworks, libraries, and research papers. Ideal for AI engineers and LLM enthusiasts, this resource provides a structured learning path for building and optimizing RAG systems.
RAGChecker: A Fine-grained Framework for Diagnosing RAG Systems
RAGChecker is an advanced automatic evaluation framework developed by Amazon Science, specifically designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It offers a comprehensive suite of metrics and tools for in-depth analysis of RAG performance. This framework empowers developers and researchers to thoroughly evaluate and enhance their RAG systems with precision.
LazyLLM: Low-Code Development for Multi-Agent LLM Applications
LazyLLM offers a low-code development tool designed for building multi-agent LLM applications with ease. It simplifies the creation of complex AI applications, providing a streamlined workflow for rapid prototyping, data feedback, and iterative optimization. Developers can leverage its extensive features for deployment, cross-platform compatibility, and efficient model fine-tuning.
Headroom: Drastically Reduce LLM Token Usage for AI Agents
Headroom is an innovative context compression layer for AI agents, designed to significantly reduce token usage for LLMs. It achieves 60-95% fewer tokens across various inputs like tool outputs, logs, files, and RAG chunks, all while preserving answer accuracy. This powerful tool enhances efficiency and cost-effectiveness for AI interactions.
PixelRAG: Pixel-Native Search for Visual Retrieval-Augmented Generation
PixelRAG revolutionizes search by enabling pixel-native retrieval, moving beyond traditional text parsing. It renders documents as screenshots, preserving visual context like tables and charts, which is crucial for accurate answers from reader models. This allows for searching any document based on its visual appearance, not just its textual content.
GitNexus: The Zero-Server Code Intelligence Engine for Code Exploration
GitNexus is a powerful client-side knowledge graph creator that operates entirely in your browser or locally via CLI. It transforms GitHub repositories or ZIP files into interactive knowledge graphs, complete with a built-in Graph RAG Agent. This innovative engine is perfect for deep code exploration and enhancing AI agent reliability without needing any server infrastructure.