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Part #2: Create Your Model Endpoint With Amazon SageMaker, AWS Lambda, and AWS API Gateway

Originally Published Feb 02, 2024

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In this series, we look at how to use Amazon SageMaker and MongoDB Atlas Vector Search to semantically search your data.

Read more on Developer Center

Author: Dominic Frei

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I am getting this error : curl -X GET ‘https://lpeg644c9b.execute-api.us-east-1.amazonaws.com/proddidi/sageMakerResource?query=foo’
{“error”: “An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (422) from primary with message "Failed to deserialize the JSON body into the target type: missing field inputs at line 1 column 22". See https://eu-central-1.console.aws.amazon.com/cloudwatch/home?region=eu-central-1#logEventViewer:group=/aws/sagemaker/Endpoints/jumpstart-dft-hf-textembedding-all-20250417-123155 in account 102570980430 for more information.”}% (base) didibitini@Didis-Air atlas_starter_python-master %

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Hello, it working with the below code :

import json
import boto3

sagemaker_runtime_client = boto3.client(“sagemaker-runtime”)

def lambda_handler(event, context):
try:
# Extract the query parameter ‘query’ from the event
query_param = event.get(‘queryStringParameters’, {}).get(‘query’, ‘’)

    if query_param:
        embedding = get_embedding(query_param)
        return {
            'statusCode': 200,
            'body': json.dumps({'embedding': embedding})
        }
    else:
        return {
            'statusCode': 400,
            'body': json.dumps({'error': 'No query parameter provided didi'})
        }

except Exception as e:
    return {
        'statusCode': 500,
        'body': json.dumps({'error': str(e)})
    }

def get_embedding(synopsis):
input_data = {“inputs”: synopsis}
response = sagemaker_runtime_client.invoke_endpoint(
EndpointName=“jumpstart-dft-hf-textembedding-all-20250417-123155”,
Body=json.dumps(input_data),
ContentType=“application/json”
)
result = json.loads(response[“Body”].read().decode())
embedding = result[0]
return embedding

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