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Multimodal support for Amazon Nova

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Multimodal support for Amazon Nova - Amazon Nova

Amazon Nova Understanding Models are multimodal understanding models, that means they support multimodal inputs such as images, videos, and documents to infer and answer question based on the content provided. The Amazon Nova model family is equipped with novel vision capabilities that enable the model to comprehend and analyze images, documents, and videos thereby realizing multimodal understanding use cases.

The following section outline guidelines for working with images, documents, and videos in Amazon Nova. These include preprocessing strategies employed, code examples, and relevant limitations to consider.

Supported content type by modality

The following information details the file formats supported by media file and the accepted input method.

Media File Type

File Formats supported

Input Method

Parsing Strategy

Image

PNG, JPG, JPEG, GIF, WebP

Base64

(Overall Payload should be less than 25MB)

Image Vision Understanding

Text Document

(Converse API Only)

csv, xls, xlsx, html, txt, md, doc

Bytes

(Size limit per document to be less than 4.5MB)

Textual Understanding from the document ONLY.

Media Document

(Converse API Only)

pdf, docx

Bytes

(18MB Size limit across all documents)

Text with interleaved Image Understanding

Video

(Optimized for videos less than 30 minutes long)

MP4, MOV, MKV, WebM, FLV, MPEG, MPG, WMV, 3GP

Base64 - (Recommended for payload size less than 25MB)

Amazon S3 URI - (Recommended for payloads greater than 25MB, up to 1GB)

Video Vision Understanding

Note

Because 25MB is the overall payload limit, ensure that you account for the base64 overhead. While working, remember that libraries and frameworks maintain memory, and passed media content can quickly add up. When using video, specifying an s3Location should alleviate many storage issues.

Note

Large videos and documents take time to process, regardless of input method. If boto3 SDK times-out while waiting for a response from Amazon Bedrock, ensure that you have an appropriate read_timeout value set.

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