Parte #2: criar um modelo de endpoint com o Amazon SageMaker, o AWS Lambda e o AWS API Gateway

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import json
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import boto3
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sagemaker_runtime_client = boto3.client("sagemaker-runtime")
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def lambda_handler(event, context):
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    try:
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        # Extract the query parameter 'query' from the event
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        query_param = event.get('queryStringParameters', {}).get('query', '')
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        if query_param:
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            embedding = get_embedding(query_param)
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            return {
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                'statusCode': 200,
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                'body': json.dumps({'embedding': embedding})
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            }
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        else:
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            return {
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                'statusCode': 400,
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                'body': json.dumps({'error': 'No query parameter provided'})
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            }
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    except Exception as e:
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        return {
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            'statusCode': 500,
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            'body': json.dumps({'error': str(e)})
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        }
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def get_embedding(synopsis):
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    input_data = {"text_inputs": synopsis}
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    response = sagemaker_runtime_client.invoke_endpoint(
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        EndpointName="<YOUR_ENDPOINT_NAME>",
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        Body=json.dumps(input_data),
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        ContentType="application/json"
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    )
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    result = json.loads(response["Body"].read().decode())
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    embedding = result["embedding"][0]
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    return embedding

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curl -X GET 'https://4ug2td0e44.execute-api.ap-northeast-2.amazonaws.com/TEST/sageMakerResource?query=foo'

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{"embedding": [0.01623343490064144, -0.007662375457584858, 0.01860642433166504, 0.031969036906957626,................... -0.031003709882497787, 0.008777940645813942]}