(aws-rag-appsync-stepfn-opensearch): image ingestion failing

Describe the bug

I am trying to ingest an image using the aws-rag-appsync-stepfn-opensearch construct by way of the Document Explorer sample, but the embedding lambda function is failing.
Expected Behavior

The image will be ingested and embedded.
Current Behavior

The ingestion step function is failing here:
with the following error:
{"errorMessage":"('1 document(s) failed to index.', [{'index': {'_index': 'image-rag-search', '_id': '1%3A0%3AIvgRL5EBPXTi45xc0i3W', 'status': 400, 'error': {'type': 'mapper_parsing_exception', 'reason': \"failed to parse field [vector_field] of type [knn_vector] in document with id 'IvgRL5EBPXTi45xc0i3W'. Preview of field's value: 'null'\", 'caused_by': {'type': 'illegal_argument_exception', 'reason': 'Vector dimension mismatch. Expected: 1536, Given: 1024'}}, 'data': {'vector_field': [0.010487701, -0.004896831, -0.010025009, -0.014189242, 0.014729051, 0.059533127, 0.13695703, -0.026990406, 0.015885781, -0.001320602, 0.007094621, 0.0011229938, 0.02575656, -0.050896198, 0.012646933, 0.047503117, -0.023751559, 0.026373483, 0.006709044, -0.008482699, 0.033930797, 0.031154642, 0.117215484, 0.04164234, 0.027915793, -0.060766973, -0.007094621, -0.018353477, 0.025448099, -0.023134634, -0.002660483, 0.008097122, 0.04997081, 0.028378485, 0.007557314, -0.0069403904, 0.013186742, -0.06724467, 0.021283863, 0.019895786, 0.02066694, -0.03470195, -0.03470195, 0.024522712, -0.031771563, 0.035010412, 0.029303871, -0.028532716, 0.042259265, 0.034085028, 0.0026797617, 0.025139635, 0.04133388, -0.016656937, -0.027761562, 0.039791573, 0.003759378, -0.028378485, -0.04997081, -0.019278862, -0.003759378, -0.045652345, 0.06909544, -0.08020007, 0.0002494203, 0.025139635, 0.0444185, 0.058916204, -0.04133388, -0.014960397, -0.0035858683, 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...
Reproduction Steps

Deploy the Document Explorer sample.
Create a user.
Start streamlit client app.
Login and navigate to Select Document page.
Upload an image. I used this cat image, but needed to reduce the resolution to 400 W x 267 H.
Possible Solution

No response
Additional Information/Context

No response
CDK CLI Version

2.150.0
Framework Version

No response
Node.js Version

v18.17.0
OS

Windows 10 Enterprise
Language

Typescript, Python
Language Version

No response
Region experiencing the issue

us-east-1
Code modification

The embeddings_job lambda has not been modified. I am working with Custom Lambda code passed in to the aws-rag-appsync-stepfn-opensearch/s3_file_transformer and aws-qa-appsync-opensearch/question_answering constructs.
Other information

No response
Service quota

[X] I have reviewed the service quotas for this construct
awslabs / generative-ai-cdk-constructs
