RAG implemented from scratch without using LangChain and LangGraph - designed specifically for processing and querying PDF documents with advanced support for visual content like tables, charts, and mathematical formulas.

An advanced, fully local, and GPU-accelerated RAG pipeline. Features a sophisticated LLM-based preprocessing engine, state-of-the-art Parent Document Retriever with RAG Fusion, and a modular, Hydra-configurable architecture. Built with LangChain, Ollama, and ChromaDB for 100% private, high-performance document Q&A.

An AI-powered chatbot that answers student questions using university PDFs with the help of Google's Gemini API and RAG (Retrieval-Augmented Generation) architecture.

Built for HackRx 6.0 – Bajaj Finserv’s Annual Hackathon, this backend system enables intelligent query–retrieval over large documents using LLMs, semantic search, and explainable decision logic.

A fully local document intelligence system that allows users to build a persistent private knowledge base from documents and query it using retrieval augmented generation. The system runs entirely offline with local embeddings, vector search, and LLM based answer generation.

PolicyBot AI Agent is an enterprise-grade intelligent document question-answering system built as an AI agent that can interpret and answer questions about company policies using Retrieval-Augmented Generation (RAG) techniques and general queries.

Streamlit-based chatbot to interact with PDFs using Retrieval-Augmented Generation (RAG), FAISS, Sentence Transformers, and Mistral LLM

Genr-Kit: The ultimate open-source playground for multi-modal AI. One toolkit to build it all: from text and image generation to speech synthesis and analysis, powered by Gradio and Transformers.

AI-Powered Document Q&A System for Confluence

🚀 Prototype and deploy generative AI applications with ease using Python, Gradio, and Transformers for text, image, and speech tasks.

End-to-end Retrieval-Augmented Generation (RAG) system for semantic document search and question answering using LangChain, FAISS, and OpenAI. Supports PDF ingestion, MMR-based retrieval, Streamlit UI, and API deployment with LangServe.

AskMyDocs helps you chat with your PDFs: upload, ask, and get cited, factual answers. Built with Streamlit and LangChain, featuring swap-in components for chunking, embeddings, and vector stores.

AI Quiz Creator 2026 🧠 | Free Generator & Templates for Educators

A Streamlit-based multi-PDF document Q&A system using Retrieval-Augmented Generation (RAG) with Llama-3 via Groq and ChromaDB.

AI-powered document chat system using LLaMA 3 and RAG architecture. Upload documents and ask questions with fully local inference (no API required). Built with FastAPI, Streamlit, and FAISS.

End-to-end RAG pipeline with semantic search, FAISS vector database, Groq LLM integration, and Streamlit UI for document question answering.

Chat With My PDFs is a web-based AI application that allows users to upload PDF documents and ask questions about their content using AI-powered document understanding.

AI-Powered Research Assistant using RAG is a Streamlit web application that uses Retrieval-Augmented Generation (RAG) with Large Language Models to analyze documents and answer user queries with context-aware responses. It performs semantic search over embedded document chunks and generates accurate answers based on retrieved information.

Gen AI App Builder 2026 🚀 - Python & Gradio Templates

Turn your documents into instant answers with FAISS + Streamlit.