Combine Docling Parsing with LLM-Based Image Processing for Accuracy and Cost-Efficiency

Enhance retrieval augmented generation (RAG) responses with information extracted from images, such as flowcharts or diagrams, by enabling LLM-based image processing. When you select the Docling parser, which brings together multiple open-source models to understand document layout and tables, optionally turn on LLM-based image processing for accurate responses with optimal costs.

Where: This change applies to Data Cloud Developer, Enterprise, Performance, and Unlimited editions. Now available in Government Cloud.

When: This feature is available starting in March 2026.

How: In Data 360, when you configure a search index, select the Docling Parser. Next, when you select chunking strategies, turn on image processing to process images by using an LLM.

Why: Consider an airline maintenance division that manages operational processes through structured RACI-style flowcharts within its governance manuals. These process maps define critical handoffs and validations across functions such as operators, service representatives, modules, planning, and operations. Each diagram includes directional arrows, decision nodes, and annotated swim lanes that reflect step-by-step responsibilities from irregularity detection to workplan reprioritization and resource redistribution.

Now, Docling parses the text, tables, and extracts the layout. The LLM interprets the flowchart as a structured process and recognizes nodes, connectors, arrows, and step order. Instead of a flat list of labels, the LLM reconstructs the sequential flow of actions across roles and departments. As a result, answers to questions from the field related to responsibilities or the sequence of steps are more accurate.