Sharath TS comments

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                                            Sharath TS

sklearn column transformer with Tfidfvectorizer requires column to be defined with its positional reference as integer

I am not sure if this is bug or a currently requires us to specify column positional integer as column name (string) currently not supported?

sklearn column transformer with Tfidfvectorizer requires column to be defined with its positional reference as integer

I realize the issue in the above code in defining the "initial_types" which is obviously changed after converting to dataframe. with `initial_types=[("subject", StringTensorType([None, 1])), ("body", StringTensorType([None, 1]))],` the code works...

sklearn column transformer with Tfidfvectorizer requires column to be defined with its positional reference as integer

Ah, I see the 'note' section in https://onnx.ai/sklearn-onnx/api_summary.html which exactly mentions the same

[Bug]: FieldSchema.construct_from_dict() not support params max_length and dim

+1 I can confirm the same issue for "dim" parameter here is the dict: ![image](https://github.com/user-attachments/assets/cb1569e3-3236-4e28-a41d-0828b687d02e) and here is the converted FieldSchema using the FieldSchema.convert_from_dict(): ![image](https://github.com/user-attachments/assets/927676c1-8ddb-4cc1-bc26-db15abcf7ec9) this is with `pymilvus==2.4.3`