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.

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

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

A RAG based smart pdf querying application.

we will develop a streamlit application capable of understanding the contents of a PDF and responding to user queries based on that content using the Retrieval-Augmented Generation (RAG).

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.

Welcome to the **PDF Knowledge Base Query App**! This application allows you to upload a PDF document, extract its content, and ask questions based on the extracted knowledge using a Retrieval-Augmented Generation (RAG) pipeline.

A Python-based application that extracts and processes PDF content using a Retrieval-Augmented Generation (RAG) approach. Leverage vector embeddings to enable efficient querying of both text-based and scanned PDFs, and interact with your documents using a large language model.

Know Your PDF is a Retrieval-Augmented Generation (RAG) based application that allows users to interact with any PDF using natural language queries. Upload your PDF and ask questions—the app returns accurate, context-aware answers using LLaMA 3B and LangChain.

Exam Preparation Buddy is a Retrieval Augmentation Generation (RAG) based python application which uses PDFs given by the user for grounded generation of answers of the user's queries.

A simple Streamlit application which leverages the Gemini natural language processing capabilities, specifically the RAG pipeline, to extract information from uploaded PDF documents and provide answers to user queries based on the content of these documents.

A Streamlit-based Retrieval-Augmented Generation (RAG) application that enables users to upload PDF or image files, extract text using Tesseract OCR for images and embedded images in PDFs, and query the content using the Kimi-K2-Instruct model from Moonshot AI via HuggingFace's InferenceClient.

This project demonstrates a Retrieval-Augmented Generation (RAG) system that generates questions from PDFs using HuggingFace models and FAISS for efficient document retrieval. The application takes 5-10 PDFs as input, extracts text, retrieves relevant sections based on user queries, and generates questions using a pre-trained HuggingFace model.

talk2pdf is an AI-powered application that enables seamless, multilingual voice and text interaction with your PDFs. It combines advanced retrieval-augmented generation (RAG), Gemini AI, and speech APIs to support natural, conversational, and voice-based queries in multiple languages, making document exploration simple and interactive.

A multi-provider RAG application with a Tkinter GUI. It builds a local vector knowledge base from PDFs using FAISS and sentence-transformers. The app queries this data and sends prompts to various LLM APIs like Google, OpenAI, and Anthropic. A build script bundles the app and embedding model into a single executable for distribution.

Rag-based pdf querying python application

A RAG Based application to query a PDF document.

This project is an end-to-end Retrieval-Augmented Generation (RAG) LLM App that leverages LlamaIndex and OpenAI to index and query multiple PDF documents. The application allows users to retrieve information from large document collections using AI-powered natural language queries.

An basic RAG application that retrieve queries based on the pdf provided

RAG Based application, which helps you to query on your uploaded pdf

RAG Application which reads PDF and provides response to user query based on pdf context

RAG based application to query and extract insights from PDF documents using LLM

RAG-based Resume Query Application This project implements a Retrieval-Augmented Generation (RAG) system to query IT resumes from a PDF document.

A RAG-based application for querying multiple PDF documents using FastAPI, Next.js, LangChain, and ChromaDB.

Created a Multimodal Rag Application to parse pdf input and generate output based on user query

This is a Streamlit-based RAG web application for answering queries on the uploaded PDF document.

Multimodal pdf based RAG application where it scans the pdf for text and image then generated answer for the query

RAG (Retrieval-Augmented Generation) + LangChain + Nvidia NIM based application that loads Golang PDFs and answers queries based on Context

This is RAG based generative AI application where user can upload any pdf and query llm specific to the document.

This is a rag application in which user can use chatbot and query based on the pdf uploaded by him/her.