How do I use eland to upload a CSV containing dense vectors to Elasticsearch?

Question

I want to use approximate KNN search in Elasticsearch. This requires some fields to have a dense vector mapping type with additional parameters. I would like to use eland to upload CSVs of my data, including the embeddings.

For example, I have the following data frame.

f = DataFrame({"text":["blue square", "red triangle"], "embedding":[[1.0, 2.0], [3.0, 4.0]]})
           text   embedding
0   blue square  [1.0, 2.0]
1  red triangle  [3.0, 4.0]

I want to run pandas_to_eland(f, ...) to create a new index containing the data in the CSV. I want the "embedding" field to be a dense vector that can be used by KNN search, which requires that its mapping look like this:

"embedding": {
        "type": "dense_vector",
        "dims": 2,
        "index": true,
        "similarity": "cosine" 
      }

Obviously I could just manually create the mappings for the entire CSV before indexing the data, but that will get tedious for larger data frames. I'd like to have eland/Elasticsearch figure out all the mappings and customize my "embedding" column in code at the time the index is created.

pandas_to_eland(f, ...) does have an es_type_overrides parameter that allows you to specify a mapping data type, but it does not enable any further mapping customization.

Is there some way to get eland to do this without manually creating the entire mapping beforehand?