React app that highlights relevant segments in a PDF document based on user questions using natural language processing and AI context segmentation. A useful tool for quickly extracting information from large PDF documents.
React app that highlights relevant segments in a PDF document based on user questions using natural language processing and AI context segmentation. A useful tool for quickly extracting information from large PDF documents.
langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.
A Python tool for extracting highlighted text from PDF files while preserving formatting attributes (headers, bold, italic) and removing unwanted line breaks and page breaks. Perfect for integrating with content management systems.
Duke OpenAI Hackathon
"AI-powered RAG-based candidate screening system — FastAPI + React + FAISS"
Autonomous Multi-Agent AI Operating System — Orchestrating specialized LangGraph agents, 5-stage FAISS vector RAG, live web intelligence, and verified execution via FastAPI, PostgreSQL & React.
Duke OpenAI Hackathon
CNN news rag assistant based on CNN articles dataset
On-demand Faiss vector search from S3. Lazy-loads clusters without fetching entire index.
This repository contains code and resources for a question-answering bot using Langchain, FAISS, and Streamlit. The bot is designed to answer related to the questions based on a given dataset.
A conversational Retrieval-Augmented Generation (RAG) chatbot that answers questions using United Nations human rights documents.
DocMind AI is a RAG-based PDF assistant powered by FAISS, open-source embeddings, Groq GPT-OSS, and Streamlit.
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