Services or capabilities described in AWS documentation might vary by Region. To see the differences applicable to the AWS European Sovereign Cloud Region, see the AWS European Sovereign Cloud User Guide.

Pegasus v1.5

TwelveLabs — Pegasus v1.5

Model Details

Pegasus v1.5 is TwelveLabs' video-to-text generation model that produces summaries, detailed descriptions, and structured responses about video content. Building on Pegasus v1.2, it adds video segmentation to transform raw video into structured, timestamped metadata across an entire video. For more information about model development and performance, see the model/service card.

Capabilities and Features

Bedrock Features

Features supported using bedrock-runtime endpoint

Pricing

This model is a third-party model offered and billed through AWS Marketplace. Charges appear on your AWS bill and in AWS Cost Explorer under the model provider (not under Amazon Bedrock). For pricing, see the Amazon Bedrock Pricing page.

Programmatic Access

Use the following model IDs and endpoint URLs to access this model programmatically. Pegasus v1.5 is available through cross-Region inference only, so you must use the geo or global inference ID rather than the base model ID. For more information about the available APIs and endpoints, see APIs supported and Endpoints supported.

For the full request parameters, response fields, and request and response examples, see TwelveLabs Pegasus 1.5.

Service Tiers

Amazon Bedrock offers multiple service tiers to match your workload requirements. Standard provides pay-per-token access with no commitment (set "service_tier": "default" or omit the field). Priority delivers the fastest response times for a price premium (set "service_tier": "priority"). Flex provides lower-cost access for flexible, non-time-sensitive workloads (set "service_tier": "flex"). Reserved provides dedicated throughput with a term commitment for predictable workloads; it is set at the account level rather than per request (contact your AWS account team to enable). For more information, see service tiers.

Regional Availability

Regional availability at a glance

Amazon Bedrock offers three inference options: In-Region keeps requests within a single Region for strict compliance, Geo Cross-Region routes across Regions within a geography (such as US, EU, and APAC) while respecting data residency, and Global Cross-Region routes anywhere worldwide when there are no residency constraints. Refer to the Regional availability by models page for more details.

Pegasus v1.5 supports Geo Cross-Region inference from the US Regions and Global Cross-Region inference from the Regions listed in the following table. In-Region inference is not supported.

Quotas and Limits

Your AWS account has default quotas to maintain the performance of the service and to ensure appropriate usage of Amazon Bedrock. The default quotas assigned to an account might be updated depending on regional factors, payment history, fraudulent usage, and/or approval of a quota increase request. For more information, see Quotas for Amazon Bedrock documentation and see the limits for the model.

Sample Code

Step 1 - AWS Account: If you have an AWS account already, skip this step. If you are new to AWS, sign up for an AWS account.

Step 2 - API key: Go to the Amazon Bedrock console and generate a long-term API key.

Step 3 - Get the SDK: To use this getting started guide, you must have Python already installed. Then install the relevant software depending on the APIs you are using.

pip install boto3

Step 4 - Set environment variables: Configure your environment to use the API key for authentication.

AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"

Step 5 - Run your first inference request: Save the file as bedrock-first-request.py

Invoke API

import json
import boto3

client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.invoke_model(
    modelId='us.twelvelabs.pegasus-1-5-v1:0',
    body=json.dumps({
            'inputPrompt': 'Tell me about this video',
            'mediaSource': {
                's3Location': {
                    'uri': 's3://your-bucket/your-video.mp4',
                    'bucketOwner': '123456789012'
                }
            },
            'maxOutputTokens': 4096
    })
)
print(json.loads(response['body'].read()))