An open‑source desktop RAG application that enables semantic search across your Zotero library. Easily discover conceptually related papers and ideas within your PDF collection using local or cloud‑based LLMs. The app provides source attribution, metadata filtering, and seamless integration with Zotero. macOS and Linux. Windows support limited.

An RAG (retrieval augmented generation) app which iterates through a PDF document and can answer user's questions based on the document uploaded. This application needs a Google API Key.

This application allows users to upload PDF files, process them, and ask questions about the content using a locally hosted language model. The system uses Retrieval-Augmented Generation (RAG) to provide accurate answers based on the uploaded PDFs.

A Python-based RAG application that uses Google's Gemini-2.0-flash-exp API to analyze PDF documents and answer questions about their content. Turn any PDF into an interactive knowledge base with advanced vision-language capabilities.

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 project is a Document Retrieval application that utilizes Retrieval-Augmented Generation (RAG) techniques to enable users to interact with uploaded PDF documents. By leveraging a Large Language Model (LLM), users can ask questions about the content of the documents and receive accurate answers based on the information retrieved.

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

A Streamlit-based Retrieval-Augmented Generation (RAG) application that enables searching within PDF files using Ollama and DeepSeek R1. It leverages FAISS for vector search, HuggingFace embeddings, and LangChain for document retrieval and response generation in Arabic.

LLM-based application leveraging LangChain for Retrieval-Augmented Generation (RAG) on imported PDF documents. Enables users to interactively query and converse with PDF content using vector-based retrieval.

This is Q&A application that allows users to upload and interact with multiple types of documents (PDF, DOCX, TXT, etc.) or provide links to webpages. The application uses Retrieval-Augmented Generation (RAG) to process the documents, index them, and answer questions based on their content.

A Q & A based PDF - Retrieval Augmented Generation (RAG) application developed with Next js & OpenAi API

A Streamlit-based Retrieval-Augmented Generation (RAG) application using DeepSeek, FAISS, and LangChain to answer natural language questions from uploaded PDF documents.

Conversational RAG: Chat with PDF & Chat History — A Streamlit-based Retrieval-Augmented Generation (RAG) application that lets you upload PDFs, process them into embeddings with HuggingFace, store them in ChromaDB, and have context-aware conversations powered by Groq’s Gemma2-9b-It, with full session-based chat history.

A Streamlit-based application that enables users to have interactive conversations with their PDF documents using DeepSeek LLM and RAG (Retrieval-Augmented Generation) technology, featuring step-by-step reasoning.

a streamlit web application running RAG model in background. user can upload a PDF document and ask query related to that.

bookacle is a RAPTOR-based RAG application to aid in understanding complex PDF documents.

A RAG based streamlit application that allows users to index multiple PDFs and query them independently.

RAG based generative AI application that allows to chat with PDF documents. Developed using Streamlit and Groq AI Inference technology.

A Java RAG application using Spring Boot, Vertex AI embeddings, BigQuery vector search, and a web UI for interactive PDF-based question answering.

A Retrieval-Augmented Generation (RAG) based PDF question-answering application that allows users to upload documents and ask context-aware questions using vector search and LLMs.

A full-stack AI-powered web application that allows users to upload PDF documents and ask questions based on their content using RAG (Retrieval-Augmented Generation) with LangChain and LLM integration.

An intelligent Retrieval-Augmented Generation (RAG) based application that allows users to upload multiple PDF documents and ask questions to get accurate, context-aware answers — all powered by a locally hosted language model.

This project is a Q&A Retrieval-Augmented Generation (RAG) application that utilizes Nvidia Nim API-Key for enhanced performance. The application is designed to handle PDF documents, allowing users to upload a PDF and retrieve answers based on their questions. The application uses advanced machine learning and natural language processing techniques

A microservices-based stateless RAG (Retrieval-Augmented Generation) application that allows users to upload PDF files and ask questions about them. The application uses AWS Bedrock for embeddings and text generation, deployed on Amazon EKS with Infrastructure as Code.

A native iOS application for document-based question answering using Retrieval Augmented Generation (RAG). The app combines Couchbase Lite vector search with AI-powered language models to enable intelligent conversations about your PDF documents.

AskIt is a Q&A application that allows users to upload and interact with multiple types of documents (PDF, DOCX, TXT, etc.) or provide links to webpages. The application uses Retrieval-Augmented Generation (RAG) to process the documents, index them, and answer questions based on their content.

An intelligent, stateful chatbot system that utilizes Retrieval-Augmented Generation (RAG) to provide precise answers based on user-provided documents. Built with a FastAPI backend and a Streamlit frontend, the application allows users to upload PDF files which are then processed, segmented into chunks, and indexed in a local FAISS vector store.

Une application chatbot fonctionne selon le principe RAG (Retrieval-Augmented Generation), per- mettant aux utilisateurs de fournir des PDFs et des liens de sites web. L’application extrait les données, les stocke dans une base vectorielle Chromadb, et génère des réponses avec un LLM (Gemini Flash 1.5). L’interface est développée en JavaFX et Java,

A Python-based Retrieval-Augmented Generation (RAG) application for querying PDF documents. This project demonstrates advanced RAG techniques, including local LLM integration, vector database updates, and response quality testing.

A RAG based smart pdf querying application.