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The Content Understanding service is continuously updated. Bookmark this page to stay informed about the latest features and samples.
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## April 2025
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The Content Understanding **2024-12-01-preview** REST API is now available. This update for preview API introduces the following updates and enhanced capabilities:
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***General improvements**. For all modality, to request an increase from current limits, contact us at `[email protected]`.
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***Prebuilt invoice template**. The invoice template is now customizable. Once you select the invoice template, you can access a predefined list of fields that can be tailored to your specific needs by adding or removing fields.
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***Generative and classification fields**
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* Both generative and classify fields are now supported for documents modality.
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* You can now utilize the REST endpoint/Studio to define generative and classification fields with zero-shot outputs for documents. This feature enables you to generate summaries, infer results, and classify individual documents across multiple files.
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* Multiple analyzers can be invoked to process individual files.
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***Video modality**
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* Latency improvement for video processing resulting in 50% lower latency.
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* Expanded output types to add support for `Object` and `Arrays`
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* Added support for video files provided via S3 presigned URL ingestion
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* Improved video segmentation to semantically segment especially when no shot edits exist in the video
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***Audio modality**
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* API now supports the field type: `group`.
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***Text modality**
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* API support for the field type: `group`.
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***User experience improvements**
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* Added functionality to download and upload schema configurations during schema definition.
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* Enhanced file labeling and analyzer building processes.
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* Add download code samples for quick setup added.
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## November 2024
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Welcome! The Azure AI Content Understanding API version `2024-12-01-preview` is now in public preview. This version allows you to generate a structured representation of content tailored to specific tasks from various modalities or formats. Content Understanding uses a defined schema to extract content suitable for processing by large language models and subsequent applications.
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