Hi all.

I'm trying to create an indexed table with an vector field so I can search by the vector value.
I've been investigating and found that to get the vector value based on the text (token), use a Python method like the following:

ClassMethod TokenizeData(desc As %String) As %String [ Language = python ]
{
    import iris
    # Step 2: Generate Document Embeddings
    from sentence_transformers import SentenceTransformer

    model = SentenceTransformer('/opt/irisbuild/all-MiniLM-L6-v2')

    # Generate embeddings for each document
    document_embeddings = model.encode(desc)

    return document_embeddings.tolist()
}

The model all-MiniLM-L6-v2 is downloaded from https://ollama.com/library/all-minilm and installed into my Docker instance.

When I've tryed to test this métod (from Visual Studio), it throws the following error:

<THROW>DebugStub+40^%Debugger.System.1 *%Exception.PythonException <PYTHON EXCEPTION> 246 <class 'OSError'>: It looks like the config file at '/opt/irisbuild/all-MiniLM-L6-v2/config.json' is not a valid JSON file.  

Then I changed the config.json file to create a valid JSon file (I only wrote the curly braces) and repeated the test, but there is a new error.

<THROW>DebugStub+40^%Debugger.System.1 *%Exception.PythonException <PYTHON EXCEPTION> 246 <class 'safetensors_rust.SafetensorError'>: Error while deserializing header: HeaderTooSmall  

Does anyone know how to fix this problem?
Is there any other way to create the vector value so I can index it?

Best regards.