Use Content Understanding in Azure AI Foundry

Azure AI Foundry is a comprehensive platform for developing and deploying generative AI applications and APIs responsibly. This guide shows you how to use Content Understanding and build an analyzer, either by creating your own schema from scratch or by using a suggested analyzer template.

Steps to create a Content Understanding analyzer

Azure AI Foundry enables you to build a Content Understanding analyzer tailored to your specific needs. An analyzer can extract data from your content based on your scenario.

Follow these steps to create your own analyzer:

1. Upload a sample data file.

2. Select an analyzer template or build your own schema from scratch.

3. Customize the schema to fit your specific scenario.

4. Test the analyzer on your data and validate its accuracy.

5. Build the analyzer to integrate it into your applications.

Build a schema

To follow is an example of building an analyzer to extract key data from an invoice document:

1. Upload a sample file of an invoice document or any other data relevant to your scenario.

   

2. Content Understanding suggests analyzer templates based on your content type. For this example, select Document analysis and build your own schema tailored to the invoice scenario. When using your own data, select the analyzer template that best fits your needs, or create your own. See Analyzer templates for a full list of available templates.

3. Select Create.

   

4. Add fields to your schema:

   • Specify clear and simple field names. Example fields: vendorName, items, price.

   • Indicate the value type for each field (strings, dates, numbers, lists, groups). To learn more, see supported field types.

   • [Optional] Provide field descriptions to explain the desired behavior, including any exceptions or rules.

   • [Optional] Specify the method to generate the value for each field.

5. Select Save.

   

6. Content Understanding generates the output based on your schema. Test the analyzer's accuracy on added data or make changes to the schema if needed.

   

7. Once you're satisfied with the quality, select Build analyzer. This action creates an analyzer that you can integrate into your applications. You also receive an analyzer ID, which you can use to call the analyzer from your code.

   

Analyzer templates

Content Understanding analyzer templates give you a head start by allowing you to build your analyzer without creating schemas from scratch. They're fully customizable, allowing you to adjust any fields in the schemas to better fit your needs.

The following analyzer templates are available for use in the Azure AI Foundry Content Understanding experience.

Document

Template	Description
Document analysis	Analyze documents to extract text, layout, structured fields, and more.
Text analysis	Analyze texts and extract structured fields.

Image

Template	Description
Image analysis	Analyze images and extract structured fields.
Retail inventory management	Retail inventory management for monitoring of products on shelves.
Defect detection	Identify potential defects in provided images of metal plates.

Audio

Template	Description
Audio transcription	Transcribe audio recordings.
Conversation summarization	Transcribe conversations and extract summaries.
Post call analytics	Analyze call center conversations to extract transcripts, summaries, sentiment, and more.

Video

Template	Description
Video shot analysis	Analyze videos to extract transcript and structured fields for each shot.
Media asset management	Extract structured information from marketing videos, news content, broadcast media, television episodes, and film archives.
Advertising	Advertising analysis and moderation.

Next steps

• In this quickstart, you learned how to create an analyzer in Azure AI Foundry. To use REST API, see the REST API quickstart.