TableRAG is an advanced question-answering framework that combines structured tabular data (CSV files) and unstructured text documents (PDF, DOCX, TXT, MD) using Retrieval-Augmented Generation (RAG). Ask natural language questions and get intelligent answers that leverage both your data tables and text content.
Master RAG systems from basic to advanced. Learn data preprocessing, chunking, embedding, retrieval, and optimization. Dive into specialized topics like GraphRAG, TableRAG, multimodal RAG, and agentic RAG. By the end, you'll be able to build reliable RAG-based QA systems and smart assistants for real-world enterprise applications.
An agentic AI research analyst for medical literature. Built with IBM watsonx.ai, Hybrid Search, TableRAG, and Streamlit, and containerized for IBM Cloud Code Engine.
Add a description, image, and links to the table-rag topic page so that developers can more easily learn about it.
To associate your repository with the table-rag topic, visit your repo's landing page and select "manage topics."