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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.
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.