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

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.

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

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

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.

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

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

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.

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

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 .

Ask questions. Get answers. Unlock insights from SEC 10-K filings with Generative AI.

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).

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

🤖 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

A Customizable RAG (Retrieval Augmented Generation) App

LLM based rag application that embed given web page to vector db and answer given query using vector similarity cosine.

A command-line RAG Chatbot application built from scratch