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

MongoDB Content Library
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3

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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