DocChat is an AI-powered Multi-Agent RAG system using Docling for structured document parsing and BM25 + vector search retrievers to retrieve fact-checked answers from PDFs, DOCX, and text files, preventing hallucinations. 🚀

A Multimodal RAG system using Nomic AI's text-image retrieval model and Qwen2.5 VL to search PDFs and webpages on Zotero.

Develop an end-to-end Retrieval-Augmented Generation (RAG) system to create a 'Document Search' application, enabling users to chat with their own data from PDFs and text files. This project emphasizes modular coding for production-ready AI applications, utilizing UV for environment setup and comprehensive data lifecycle management.

This project implements a Agentic Retrieval-Augmented Generation (RAG) system along with tools and voice integration capabilities. It's designed to work with NCERT textbook PDF's. Built it as a part of internship assignment @ Sarvam.ai

The RAG Application (Gemini) is a Streamlit web application designed as a question-answering system. It retrieves information from uploaded PDFs using Google Generative AI and LangChain, allowing users to ask questions about the document's content and receive detailed, context-aware answers.

A lightweight AI tool to chat with your PDF study materials. Privacy-focused RAG system using local LLMs for instant document insights.

This repository contains a Retrieval Augmented Generation (RAG) system that leverages Vision Language Models (VLMs) to query visually rich documents. Built using FastAPI and PyTorch, the system processes PDFs, images, and text, and utilizes Google’s Generative AI for context-aware responses.

🤖 Advanced RAG Document Chat System - Chat with your documents using AI! Upload PDF, DOCX, or TXT files and ask questions to get intelligent answers with source citations. Powered by LangChain, FLAN-T5, and FAISS. 100% free, no API keys required, runs locally. Perfect for research, education, business analysis, and legal review.

An intelligent multi-agent AI system built with FastAPI that dynamically routes user queries to the best information source, featuring RAG for PDF analysis, web search, and ArXiv integration.

A production-grade Retrieval Augmented Generation (RAG) system built with FastAPI, Inngest, Qdrant, and Google Gemini. This system allows you to ingest PDF documents, store their embeddings in a vector database, and query them using natural language with AI-powered responses.

AskDocs is an basic Retrieval-Augmented Generation (RAG) system designed to make document search and question-answering effortless. Simply upload PDFs, ask questions, and get AI-powered answers with relevant document excerpts displayed side by side.

PatentHub is a self-hosted platform for analyzing patents and scientific documents. It combines global search with a local RAG system to index and chat with PDFs, generate AI-powered technical summaries, and manage documents with OCR and local storage.

An intelligent question-answering system built with Retrieval-Augmented Generation (RAG) architecture, powered by IBM Watsonx AI foundation models. This application enables users to upload PDF documents and ask natural language questions, receiving accurate answers extracted from the document content.

AI-powered system that summarizes research papers and extracts key insights using NLP and transformer models. It parses PDFs, generates concise summaries, identifies important concepts, and supports semantic search with RAG. Built to help researchers and students understand papers faster and save time.

An AI-powered HR policy assistant built using Retrieval-Augmented Generation (RAG) to deliver accurate, context-aware answers strictly grounded in official company HR documents. This system ingests HR policy PDFs, converts them into vector embeddings, and retrieves only relevant policy sections before generating responses using a local LLM.

AI-powered PDF Question Answering system using Retrieval-Augmented Generation (RAG) to provide accurate, document-based answers.

Production-grade RAG-based AI system to chat with PDFs using semantic search, vector databases, and LLaMA3 (Groq API)

A RAG (Retrieval-Augmented Generation) based system that allows users to upload PDFs and ask intelligent questions, generating context-aware answers using AI

AI-powered document retrieval system that uses RAG architecture, vector search (FAISS), and hybrid retrieval to answer natural language queries from uploaded PDFs.

An AI-powered RAG system that functions as a 'Department ChatGPT,' allowing students to query notes, PDFs, and exam papers for instant, sourced answers.

AI Incident Intelligence Platform is a production-ready AI system that uses Gemini LLM and Retrieval-Augmented Generation (RAG) to analyze PDFs, logs, and technical documents through a chat-based interface.

NeuroSearch – AI-Powered Research Assistant (RAG System) Built a production-style Retrieval-Augmented Generation (RAG) system using FastAPI, FAISS, and SentenceTransformers to enable semantic search across PDF documents. Implemented vector embeddings, persistent indexing, and similarity-based retrieval for accurate, context-grounded responses.

Document Chat System – A Streamlit app to upload documents (PDF, TXT, DOCX), ingest them, and interact with their content through an AI-powered chat interface. Supports Agent-based RAG, Streaming RAG, multi-language responses, and optional voice input.

AI-powered Retrieval-Augmented Generation (RAG) chatbot that allows users to upload PDF documents and ask questions. The system retrieves relevant document sections using embeddings and generates accurate answers using OpenAI models.

A Hybrid Retrieval-Augmented Generation (RAG) System powered by Google Gemini This project enables chatting with multiple PDF documents using AI. The system uses a combination of semantic search and keyword search to retrieve relevant context and generate responses — grounded strictly in the content of uploaded PDFs.

This project implements a Retrieval Augmented Generation (RAG) system using Streamlit, Google's Gemini AI, and FAISS for efficient similarity search. It allows users to upload PDF documents, ask questions about the content, and receive AI-generated answers.

Prompt2Support is a multi-agent AI customer support system that answers queries using uploaded business documents (PDF, DOCX, TXT). It uses an agentic RAG pipeline to deliver accurate, document-grounded responses for MSMEs.

ecently developed a Retrieval-Augmented Generation (RAG) based AI Assistant that can intelligently answer questions from a PDF using semantic search and LLM reasoning. This project demonstrates how to combine Vector Databases + LLMs + Persistent Memory to build a real-world conversational AI system.

PDF_CHAT_AI is a learning-first RAG implementation built to understand how LLMs can be grounded in external documents. The project intentionally avoids embeddings in its initial versions to expose the limitations of lexical retrieval and highlight why modern RAG systems rely on semantic search.

AI-powered question-answering system using Haystack for efficient retrieval-augmented generation (RAG) with PDF documents as the data source. The project demonstrates how to build an AI agent using Haystack for robust natural language understanding and document-based knowledge retrieval.