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RAG

EndGame RAGHub is a scalable multimodal Retrieval-Augmented Generation platform supporting document, image, audio, and video understanding using FastAPI, Qdrant, LangGraph, and modern open-source AI infrastructure.

Memento AI – A CPU-first, offline AI assistant that transforms documents, images, audio, video, and text into structured knowledge using local LLMs, SQLite, and Retrieval-Augmented Generation (RAG), with

StudentAI - AI-powered RAG chatbot for document analysis and question answering using FastAPI, React, Ollama and Retrieval-Augmented Generation (RAG).

AI-powered PDF RAG Chatbot built with Streamlit, LangChain, FAISS, and Google Gemini for intelligent document question answering.

Local-first RAG platform with ChromaDB, Ollama, Groq BYOK, semantic retrieval, document intelligence, and business insights.

Streamlit PDF RAG chatbot

A Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, LangChain, FAISS, and sentence-transformers. Upload PDFs and ask questions using Gemini or local Llama.cpp LLMs.

A production-ready Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, LangChain, Google Gemini, and ChromaDB. Upload PDFs and ask questions — the system retrieves relevant document chunks and answers using Gemini's LLM.

Fully offline document Q&A system using PyMuPDF4LLM, Ollama, and llama.cpp. Dual LLM backend with pre-commit quality gates.

RAG Chatbot built using LangChain, FAISS, Groq LLM, and Flask. Supports PDF document ingestion and intelligent question answering through Retrieval-Augmented Generation.

AI-powered PDF Chatbot using Retrieval Augmented Generation (RAG), FAISS, and OpenAI APIs.

DocSage — Streamlit RAG chatbot for document Q&A with PDF structured-qa and Gemini RAG for other sources.

Production-ready RAG chatbot built with Streamlit, LangChain, FAISS, and local LLM support. Features document Q&A, PDF processing, and AGPLv3 compliance.

Local-First Document Q&A with RAG using Ollama — Find-Retrieve-Answer pattern

A Streamlit-based research paper assistant that uses AI (RAG + PyMuPDF4LLM) for document summarization, Q&A, and multimedia analysis with local-first inference via Ollama.

An enterprise-grade offline Medical RAG Assistant running local quantized LLM inference engines. The system features advanced document layout ingestion processing, strict private hardware memory isolation frameworks, and automated software validation pipelines.

CineRAG is a Retrieval-Augmented Generation (RAG) application designed to answer cinema-related queries. The platform currently focuses on the Telugu film industry, providing responses based on information stored in a local knowledge repository.

A local-first Retrieval-Augmented Generation chatbot for PDF Q&A built with Streamlit, LangChain, ChromaDB, and llama.cpp

CampusGPT is an AI-powered academic assistant that enables intelligent document question answering using Retrieval-Augmented Generation (RAG). Built with Streamlit, LangChain, FAISS, and Gemini/Llama.cpp, it supports semantic search, document summarization, source traceability, and fully offline operation with local GGUF models.

A complete, production-ready local-first Retrieval-Augmented Generation (RAG) assistant for researchers, professors, and students.