Chat with PDF using LangChain, Streamlit, Ollama (for LLM inference) and PDFPlumber. Overall which is an example of a Retrieval-Augmented Generation (RAG) system with Deepseek r1 model.

Vortex is a self-hosted RAG (Retrieval-Augmented Generation) application that lets you chat with your documents using any LLM provider. Upload PDFs, ingest URLs, and get accurate answers grounded in your own knowledge bases — all with a clean, modern interface.

A full-stack AI-powered application that replicates Google's NotebookLM functionality. Upload PDF documents, chat with them using advanced RAG (Retrieval-Augmented Generation), and automatically generate engaging podcast-style audio conversations with AI hosts discussing the document content.

Whisperer is a privacy-first, browser-based AI chat application that stores all data locally using the pglite library. It supports advanced AI interactions with Retrieval-Augmented Generation (RAG) using pgvector for enhanced, context-aware conversations, including seamless interactions with PDF documents.

This POC demonstrates how to deploy the DeepSeek model on AWS EC2 and build a RAG (Retrieval-Augmented Generation) application using LangChain & ChromaDB. You'll learn to set up an EC2 instance, configure dependencies, run the DeepSeek Ollama API, and integrate it with a Streamlit-based chat app to process and analyze PDF documents with AI-powered

PDFChatRAG is an AI-powered application that enables users to interact and chat with PDF documents using advanced Retrieval-Augmented Generation (RAG) technology, powered by Google Gemini. This tool allows for seamless question answering and information retrieval from PDF files, making document interaction intuitive and efficient.

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.

This repository implements a Conversational Retrieval-Augmented Generation (RAG) model with PDF document uploads and chat history tracking using Streamlit for the web interface. It enables users to interact with uploaded PDFs, retrieve context-aware responses, and maintain session-based chat history.

A Conversational AI application that supports both general chat and PDF document question-answering using Retrieval-Augmented Generation (RAG) with conversation history.

A chat with research paper application used to explain comprehensive evaluation system for RAG (Retrieval-Augmented Generation) systems using synthetic question-answer pairs generated from PDF documents. [Made for PyCon India 2025]

A lightweight local web chatbot for chatting with PDF files (focused on scientific papers), to get context-powered answers. Uses RAG (Retrieval-Augmented Generation) to combine vector search and generate LLM-based answers that cite sources.

🤖 NotebookLM Mini - AI-Powered Document Chat Application A modern RAG (Retrieval-Augmented Generation) application built with Next.js that lets you upload documents (PDF, CSV, TXT) or URLs and chat with your content using OpenAI GPT-4. Features dark theme UI, vector search with Qdrant, and intelligent document processing.

A Retrieval-Augmented Generation (RAG) web application that lets users upload PDFs and chat with the document using Gemini models.

A RAG (Retrieval-Augmented Generation) application that allows you to chat with your PDF documents using a local LLM (Ollama) and Vector Database (Qdrant).

A Retrieval-Augmented Generation (RAG) application that enables intelligent conversations with PDF documents using Groq's Gemma2-9b-It model and maintains chat history across sessions.

A Streamlit-based conversational RAG (Retrieval-Augmented Generation) chatbot that allows users to upload PDFs and interact with their content using LangChain, Groq, and Hugging Face embeddings, while preserving chat history per session.

🚀 Conversational RAG Chatbot is an interactive Retrieval-Augmented Generation (RAG) application that allows users to upload PDFs and chat with them using Streamlit. It leverages Gemini Vision for image analysis, ChromaDB for vector storage, and Groq's DeepSeek model for response generation.

Chat with PDF is a simple LLM based pdf chatbot where users can upload their pdfs and ask questions about it. It used LangChain. FAISS and HuggingFace Embeddings to build a Retrieval Augmented Generation(RAG) pipeline. It also provides a user-friendly interface using Streamlit.

This repository contains a Retrieval-Augmented Generation (RAG) Chatbot built using LangChain, OpenAI, and Streamlit. The chatbot allows users to upload a PDF via a URL, process it into vector embeddings using ChromaDB, and interact with the document using a chat interface.

TalkToPDF is an intelligent chatbot that allows you to upload and chat with PDF documents using the power of Retrieval-Augmented Generation (RAG). It uses Gemini 2.5 Pro as the LLM and ChromaDB as the vector store, making it blazing fast and highly accurate.

A powerful local RAG (Retrieval Augmented Generation) application that lets you chat with your PDF documents using Ollama and LangChain. This project includes both a Jupyter notebook for experimentation and a Streamlit web interface for easy interaction.

A Retrieval Augmented Generation (RAG) engine that allows users to chat with PDF documents using natural language.

Chat with your PDF, Word, and PowerPoint documents using Retrieval-Augmented Generation (RAG) with LangChain, Ollama (Mistral), and Streamlit

Chat with Your PDFs using Gemini AI + RAG • Upload PDFs, ask questions, and get smart, contextual answers — powered by Retrieval-Augmented Generation (RAG) and Gemini Pro.

AI chatbot with RAG (Retrieval-Augmented Generation) and conversational memory. Upload PDFs and chat with your documents using LangChain, FastAPI, and Groq.

Retrieval-Augmented Generation (RAG) application that allows users to upload PDF documents and chat with them using a large language model

The "Chat with PDF using AWS Bedrock" application is a Retrieval-Augmented Generation (RAG) system that allows users to interact with PDF documents through a chat interface.

An AI-powered document assistant built with Flask and Tailwind CSS that lets you upload PDFs and chat with them using Retrieval-Augmented Generation (RAG).

A professional, production-ready Retrieval-Augmented Generation (RAG) system for chatting with PDF documents using local LLMs, vector search, and streaming responses.

A modern web application that allows you to upload PDF documents and chat with them using advanced RAG (Retrieval-Augmented Generation) technology.