RAG / 向量检索排行

Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/.

Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

Vane is an AI-powered answering engine.

A modular graph-based Retrieval-Augmented Generation (RAG) system

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

📚 从零开始构建大模型

Open Source AI Platform - AI Chat with advanced features that works with every LLM

Build Real-Time Knowledge Graphs for AI Agents

Python scraper based on AI

Sim is the collaborative workspace to build, deploy, and monitor AI agents and workflows. Used by 100,000+ builders.

FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

An open-source RAG-based tool for chatting with your documents.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?