Multi AI agents for customer support email automation built with Langchain & Langgraph

Multi Generative AI agents for customer support email automation built with Golang, Google-GenAi and Customgraph solution

A curated collection of LLM-powered Flutter apps built using RAG, AI Agents, Multi-Agent Systems, MCP, and Voice Agents.

Chat with your Obsidian notes; entirely locally

Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python

🛡️ Web3 Guardian is a comprehensive security suite for Web3 that combines browser extension and backend services to provide real-time transaction analysis, smart contract auditing, and risk assessment for decentralized applications (dApps).

RAG-API: A production-ready Retrieval Augmented Generation API leveraging LLMs, vector databases, and hybrid search for accurate, context-aware responses with citation support.

🤖 NoCapGenAI is a Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, Ollama, MongoDB, and ChromaDB. It features a clean, modern UI and persistent vector memory for context-aware conversations. Easily integrates with Ollama-supported models like phi3:mini, llama3, mistral, and more. Designed to support customizable assistant modes

This python powered AI based RAG Scraper allows you to ask question based on PDF/URL provided to the software using local Ollama powered LLMs

RAG-powered PDF QA system with self-reflection and multiple retrieval strategies (Stuff/Map Reduce/Refine). Includes monitoring via Langfuse & LangSmith and containerization with Docker

📧 Send personalized mass emails securely with Mailflow, a CLI tool built in Python using only standard libraries and no external dependencies.

This workflow assistant is a fast and easy way to convert natural-language user requests into valid workflow configuration snippets by using retrieval (from existing real configs) + an LLM prompt.

This project implements a Retrieval-Augmented Generation (RAG) based chatbot designed to handle university-related queries using natural language understanding. It combines semantic search with generative AI to provide precise, context-aware answers to students, faculty, and visitors.

BetterRAG: Powerful RAG evaluation toolkit for LLMs. Measure, analyze, and optimize how your AI processes text chunks with precision metrics. Perfect for RAG systems, document processing, and embedding quality assessment.

🧠 The Enterprise "Neural Architecture" for Agents. Build self-evolving, privacy-first reasoning engines with Gemini 2.5 & LangGraph. ⚡ Zero-Latency.

pdfKotha.AI - Interact with PDFs using AI! Upload, ask questions, and get instant answers from Google's Gemini model. Streamline your research and information retrieval tasks effortlessly

AI-powered platform that turns study notes into podcast episodes with two hosts and lets you chat with documents.

A supportive server to handle telegram messages using telegram bot API, return back the response to the user with RAG application techniques

A Customizable RAG (Retrieval Augmented Generation) App

A basic RAG application for inventory management. Provides real-time stock updates, checks availability, suggests similar products, and generates responses to both customer and manager queries .

ML Bot is a RAG Application built using google/gemma-2b-it local LLM

Local rag app example

A comprehensive Retrieval Augmented Generation (RAG) application built with Next.js, featuring document processing, website scraping, and AI-powered chat functionality.

📄 Transform your PDF documents into actionable insights with this RAG-based Question-Answering App for efficient and accurate responses.

Structure‑aware RAG platform with semantic search and citations.

The Coursera QA Assistant is a browser extension that helps learners get answers to their questions about Coursera course content directly from the course page they're viewing. The extension uses AI to analyze the course content and provide relevant answers.

🚀 VIDGENIUS AI — An open-source RAG-powered app that transforms YouTube videos into interactive chat experiences and smart, structured notes. Paste any video link with language code, translate instantly, extract insights, and chat with the content — all in one click.

This project processes and retrieves information from PDF file or PDF collection. It leverages Qdrant as a vector database for similarity searches and employs a Retrieval-Augmented Generation (RAG).

🤖 AI-powered drug intelligence system using RAG, Spring AI, OpenAI GPT-4, ChromaDB vector search, and multi-tier Redis caching. Production-ready microservice with comprehensive documentation.

A custom Agentic Retrieval-Augmented Generation (RAG) model that is an expert in cell culture techniques and knowledge.