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.
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Updated
Jun 4, 2025 - Python
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.
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.
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.
A Streamlit-based app for asking questions directly from uploaded documents using Gemini embeddings and a language model. Supports PDF, TXT, and DOCX files. Fast, simple, and powerful document-based QA.
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.
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.
Turn your documents into instant answers with FAISS + Streamlit.
A lightweight, modular Retrieval-Augmented Generation (RAG) system built with Streamlit, FAISS, and LLMs like OpenAI and Ollama. Upload documents, embed them, and ask intelligent questions with real-time context-aware responses.
📄 Create a local, free Retrieval-Augmented Q&A system to easily extract answers from your personal documents in minutes.
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