Question

You are building a RAG ingestion pipeline for a library of engineering white papers that contain descriptive charts and diagrams embedded within the text. Your grounding requirement is that when an agent answers a question about a chart, the retrieved chunk must include a natural-language description of what the chart depicts (trends, axes, labeled regions) rather than just the surrounding paragraph text, so the model can reason about the visual without receiving the raw image at query time. You are using Azure AI Content Understanding to produce the grounded representation. Which configuration produces the required output for downstream reasoning?

Accepted Answer

Configure a document analyzer that emits markdown output and enable generation of natural-language descriptions for embedded figures, so each diagram is represented as descriptive text inline with the surrounding content.

Answer

Use a layout-only analyzer that returns bounding-box coordinates and reading order for each figure region, then store the coordinates alongside the text chunks.

Answer

Run an OCR skill over the pages to extract any text printed inside the charts, and rely on that extracted text as the chart's grounded representation.

Answer

Extract each figure as a separate binary image projection and attach the raw image bytes to the chunk so the retriever can pass them to the model at query time.