Amazon Q Business now supports answers from tables embedded in documents

Amazon Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. A large portion of that information is found in text narratives stored in various document formats such as PDFs, Word files, and HTML pages. Some information is also stored in tables (e.g. price or product specification tables) embedded in those same document types, CSVs, or spreadsheets. Although Amazon Q Business can provide accurate answers from narrative text, getting answers from these tables requires special handling of more structured information.

Today, we are happy to announce support for tabular search in Amazon Q Business, enabling end-users to extract answers from tables embedded in documents ingested in Amazon Q Business. With tabular search in Amazon Q Business, users can ask questions like “what’s the credit card with the lowest APR and no annual fees?” or “which credit cards offer travel insurance?” where the answers may be found in a product-comparison table, inside a marketing PDF stored in an internal repository, or on a website. Answers are returned as tables, lists or text narratives depending on the context. Tabular search is an out-of-the-box feature in Amazon Q Business that works seamlessly across many domains, with no setup required from admin or end-users. The feature supports tables embedded in HTML, PDF, Word, Excel, CSV, and SmartSheet (via SmartSheet connector) formats.

Amazon Q Business tabular search is available in all AWS Regions where Amazon Q Business is available. To explore Amazon Q Business, visit the website.