AWS nova high security documentation change
Summary
Removed 'Image understanding limitations' section containing security-related content and code examples for image processing
Security assessment
The removed section contained critical security limitations including refusal to process inappropriate content (AUP violations) and healthcare application warnings. Removing this documentation could lead to misuse by omitting safeguards against sensitive content handling and medical interpretation risks.
Diff
diff --git a/nova/latest/userguide/modalities-image.md b/nova/latest/userguide/modalities-image.md index 0dba2eaa9..3f42ae365 100644 --- a//nova/latest/userguide/modalities-image.md +++ b//nova/latest/userguide/modalities-image.md @@ -5 +5 @@ -Image size informationBounding box detectionImage to tokens conversionImage understanding limitationsImage understanding examples +Image size informationBounding box detectionImage to tokens conversion @@ -29,133 +28,0 @@ So for example, consider an example image that is 800x400 in size, and you want -## Image understanding limitations - -Understand the following limitations for Amazon Nova: - - * **Multilingual Image Understanding:** The models have limited understanding of multilingual images and video frames and can struggle or hallucinate on similar tasks. - - * **People identification** : The Amazon Nova models do not support the capability to identify or name individuals in images, documents or videos. The models will refuse to perform such tasks. - - * **Spatial reasoning** : The Amazon Nova models have limited spatial reasoning capabilities. They may struggle with tasks that require precise localization or layout analysis. - - * **Small Text in Images/Videos** : If the text in the image or video is too small, consider increasing relative size of the text in the image by cropping to relevant section while preserving necessary context. - - * **Counting** : The Amazon Nova models can provide approximate counts of objects in an image, but may not always be precisely accurate, especially when dealing with large numbers of small objects. - - * **Inappropriate content** : The Amazon Nova models will not process inappropriate or explicit images that violate the Acceptable Use Policy. - - * **Healthcare applications** : Due to the sensitive nature of these artifacts, even though Amazon Nova models can give general analysis on healthcare images or videos, we do not recommend that you interpret complex diagnostic scans. Amazon Nova responses should never be considered a substitute for professional medical advice. - - - - -## Image understanding examples - -The following examples show how to send image prompts to Amazon Nova models using different input methods. - -The following example shows how to send an image prompt to Amazon Nova Model with [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html). - - - # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. - # SPDX-License-Identifier: Apache-2.0 - import base64 - import boto3 - import json - # Create a Bedrock Runtime client in the AWS Region of your choice. - client = boto3.client( - "bedrock-runtime", - region_name="us-east-1", - ) - - MODEL_ID = "us.amazon.nova-lite-v1:0" - # Open the image you'd like to use and encode it as a Base64 string. - with open("media/sunset.png", "rb") as image_file: - binary_data = image_file.read() - base_64_encoded_data = base64.b64encode(binary_data) - base64_string = base_64_encoded_data.decode("utf-8") - # Define your system prompt(s). - system_list = [ { - "text": "You are an expert artist. When the user provides you with an image, provide 3 potential art titles" - } - ] - # Define a "user" message including both the image and a text prompt. - message_list = [ - { - "role": "user", - "content": [ - { - "image": { - "format": "png", - "source": { - "bytes": image // Binary array (Converse API) or Base64-encoded string (Invoke API) - }, - } - }, - { - "text": "Provide art titles for this image." - } - ], - } - ] - # Configure the inference parameters. - inf_params = {"maxTokens": 300, "topP": 0.1, "topK": 20, "temperature": 0.3} - - native_request = { - "schemaVersion": "messages-v1", - "messages": message_list, - "system": system_list, - "inferenceConfig": inf_params, - } - # Invoke the model and extract the response body. - response = client.invoke_model(modelId=MODEL_ID, body=json.dumps(native_request)) - model_response = json.loads(response["body"].read()) - # Pretty print the response JSON. - print("[Full Response]") - print(json.dumps(model_response, indent=2)) - # Print the text content for easy readability. - content_text = model_response["output"]["message"]["content"][0]["text"] - print("\n[Response Content Text]") - print(content_text) - - -For passing large image files or multiple image files, where the overall payload is greater than 25 MB, you can use Amazon S3. The following example demonstrates how to use Amazon S3 to upload images to Amazon Nova: - - - import boto3 - import json - import base64 - # Create a Bedrock Runtime client - client = boto3.client("bedrock-runtime", - region_name="us-east-1", - ) - PRO_MODEL_ID = "us.amazon.nova-pro-v1:0" - LITE_MODEL_ID = "us.amazon.nova-lite-v1:0" - MICRO_MODEL_ID = "us.amazon.nova-micro-v1:0" - PREMIER_MODEL_ID = "us.amazon.nova-premier-v1:0" - messages = [ - { - "role": "user", - "content": [ - { - "image": { - "format": "png", - "source": { - "s3Location": { - #Replace the s3 bucket URI - "uri": "s3://demo-bucket/cat.png" - "bucketOwner" : "123456789012" - } - }, - } - }, - {"text": "Describe the following image"}, - ], - } - ] - inf_params = {"maxTokens": 300, "topP": 0.1, "temperature": 0.3} - model_response = client.converse( - modelId=LITE_MODEL_ID, messages=messages, inferenceConfig=inf_params - ) - print("\n[Full Response]") - print(json.dumps(model_response, indent=2)) - print("\n[Response Content Text]") - print(model_response["output"]["message"]["content"][0]["text"]) - @@ -170 +37 @@ Multimodal support -Video understanding +Image understanding limitations