ChatPDF leverages Retrieval Augmented Generation (RAG) to let users chat with their PDF documents using natural language. Simply upload a PDF, and interactively query its content with ease. Perfect for extracting information, summarizing text, and enhancing document accessibility.

Multimodal Document Processing RAG with LangChain

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In this end to end project I have built a RAG app using ObjectBox Vector Databse and LangChain. With Objectbox you can do OnDevice AI, without the data ever needing to leave the device.

Chat With Documents is a Streamlit application designed to facilitate interactive, context-aware conversations with large language models (LLMs) by leveraging Retrieval-Augmented Generation (RAG). Users can upload documents or provide URLs, and the app indexes the content using a vector store called Chroma to supply relevant context during chats.

Implement RAG using LangChain and HuggingFace embedding models

SDLC AI Agent is an AI-powered tool that streamlines the entire Software Development Lifecycle from requirements gathering to code generation and testing.

In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.

A knowledge base constructed based on Langchain+RAG+LLM

基于LangGraph的智能保险合同 PDF 分析与问答助手，支持要点提取、检索、风险高亮、公式解析与可视化。AI-powered insurance contract PDF assistant: summarization, semantic/keyword search, risk highlighting, formula extraction, and visualization.

Repo for DermAssist: Your AI Assitant for Skin Problems. Powered by a vision model and a reliable RAG system.

Memomind is a sleek note-taking app built with React 18, Next.js 14, and TypeScript. It features a chat-based RAG workflow, AI-powered insights with Langchain and Llama3, and secure authentication via Clerk. It uses Tailwind CSS for styling and Shadcn-UI for components.

A ChatBot designed to assist WhatsAgenda customers in configuring their calendar. This tool streamlines the setup of scheduling, managing appointments, and customizing service hours, ensuring an efficient and user-friendly experience.

Agentic Chatbot: for Navigating Red Hat Internal resources from THE SOURCE

This project demonstrates a routing agent setup using LlamaIndex, Groq's LLaMA3-70B model, and HuggingFace Embeddings for answering queries from multiple domain-specific documents.

This project implements a classic Retrieval-Augmented Generation (RAG) system using HuggingFace models with quantization techniques. The system processes PDF documents, extracts their content, and enables interactive question-answering through a Streamlit web application.

A Fast API server that provides local text and multi-modal embedding using LlamaIndex Hugging Face Embedding

Conversational RAG with PDF and chat history

Analysis Agent on Llamaindex Typescript with a simple caching mechanism

Retrieval-Augmented Generation on YouTube transcripts and PDFs to deliver accurate and contextual answers.