RAG / 向量检索排行

AI Agent 工程师面试资料库 - 国内大厂面经、岗位要求、高频面试题

Gemini PDF Chatbot: A Streamlit-based application powered by the Gemini conversational AI model. Upload multiple PDF files, extract text, and engage in natural language conversations to receive detailed responses based on the document context. Enhance your interaction with PDF documents using this intuitive and intelligent chatbot.

Web app for teams of 20+ members. In-built connections to major LLMs via API. Share chats, prompts, and agents in team or private folders. Modern, fully responsive stack (Next.js, Node.js). Deploy your own vibe-coded AI apps, agents, or workflows or use ready-made solutions from the library.

A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go.

Open-source RAG evaluation through users' feedback

AI chatbot for expert answers on university degrees

Local-first Memory OS for personal AI assistants with L0-L3 memory, Wiki++ knowledge, skill routing, and TokenLess context compression.

Official code for NeurIPS2025 "Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers"

AgenticX is a unified, production-ready multi-agent platform — Python SDK + CLI (agx) + Studio server + Machi desktop app. Features Meta-Agent orchestration, 15+ LLM providers, MCP Hub, hierarchical memory, avatar & group chat, skill ecosystem, safety sandbox, and IM gateway (Feishu/WeChat).

Red Teaming python-framework for testing chatbots and GenAI systems.

The implementation of Model Context Protocol (MCP) server for VictoriaMetrics

Fast, AI-agent-native code search in Rust — hybrid BM25 + semantic, Tree-sitter AST chunking, dependency & impact analysis. Drop-in replacement for grep/cat/read/ls in Claude Code, Codex, Cursor, Aider, OpenHands.

🍽️基于图RAG技术的AI美食推荐助手 - Datawhale all-in-rag教程实战案例，集成Neo4j图数据库、Milvus向量检索与智能对话系统

Unified Agentic AI and Business Intelligence Platform

MQL tool is designed to generate SQL queries directly from natural language inputs.

Self-hosted AI RAG + MCP Platform

Fully managed RAG solution implemented using Knowledge Bases for Amazon Bedrock

TreeSearch: Search your codebase like a human — not like a vector database. No embeddings. No chunking. Just millisecond search over structured documents and large codebases. 无需 embedding，无需切分文档，在结构化文档和大型代码库中实现毫秒级检索。

Visualization for a Retrieval-Augmented Generation (RAG) Assistant 🤖❤️📚

基于 AI 的个人成长轨迹分析系统，通过记录生活、回顾历史、分析模式帮助用户更好地理解自己，实现持续成长。包含智能路由、RAG 检索、自主任务智能体等功能。