rag-chatbot

Build and deploy a full-stack RAG app on AWS with Terraform, using free tier Gemini Pro, real-time web search using Remote MCP server and Streamlit UI with token based authentication.

Multi-Agent Systems with Google's Agent Development Kit + A2A + MCP

ChatBot, show how to implement a RAG based on OceanBase or OceanBase seekdb AI capabilities escpecailly hybrid search and AI embedding.

A complete Retrieval-Augmented Generation (RAG) application that demonstrates modern AI capabilities for answering questions about Ultimate Frisbee rules and strategies. This project showcases how to build a production-ready RAG system using cutting-edge technologies.

A comprehensive collection of Generative AI projects and experiments, covering RAG, chatbots, LLM fine-tuning, and more.

Agentic AI framework built using LangGraph and Multi-Agent Control Plane (MCP) for building structured, goal-driven multi-agent systems.

x0-GPT is an advanced AI-powered tool that enables you to interact seamlessly with any website or document (including PDFs) using natural language. Whether you're looking to extract specific data, automate tasks, or gain insights, x0-GPT makes it possible with ease. Best of all, it's free and accessible to everyone.

Platypus is one app you need to organize your data. Note-taker, meeting transcriber and knowledge management, written in Rust. Easily add notes, transcribe meetings, cut and paste docs, and organize into projects with a single click. Then polish notes or query your knowledge with LLM of your choice. All data stays local, use your own LLM API.

Redis Vector Library (RedisVL) -- the AI-native Java client for Redis.

AI-powered RAG app to chat with any documentation - scrape, generate knowledge base, and query docs with source-backed answers.

A PDF Question-Answering App built with RAG (Retrieval-Augmented Generation), allowing users to upload PDFs and ask context-based questions. Powered by Streamlit, LangChain, Ollama, and Chroma for efficient and accurate answers.

GenAI 🧠 - 𝘸𝘰𝘳𝘬 𝘪𝘯 𝘱𝘳𝘰𝘨𝘳𝘦𝘴𝘴...

A Multi-Agent RAG platform using Vector DBs and AI models to accurately answer AML/CFT compliance queries.

A clean, modular implementation of an Agentic RAG (Retrieval-Augmented Generation) system built with a production-ready architecture.

本项目是一个完整的RAG系统教程，涵盖了从基础概念到高级技术的全面内容。通过Jupyter Notebook的形式，逐步讲解如何构建和优化一个高效的检索增强生成系统。本项目使用的所有模型都是本地化部署的，在3090上可以运行。

Local-first RAG platform — Ollama + Qdrant + Redis + MinIO. No cloud, no API keys, runs entirely on your machine.