A Retrieval-Augmented Generation (RAG) pipeline for help.mail.ru that features recursive web scraping, text chunking, FAISS-based semantic search, multiple embedding models (deepvk, MiniLM, LaBSE), uniformity & alignment evaluation, and TSNE visualization.
An AI-powered clinical assistant using Retrieval-Augmented Generation (RAG) on the MIMIC-IV DiReCT dataset. It retrieves relevant patient cases and generates diagnostic reasoning using LLMs. Built with Streamlit, Transformers, FAISS, and SentenceTransformers.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
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