Text Encoder Transform

Summary

This transform is using sentence encoder models to create embedding vectors of the text in each row of the input .parquet table.

The embeddings vectors generated by the transform are useful for tasks like sentence similarity, features extraction, etc which are also at the core of retrieval-augmented generation (RAG) applications.

Running

Parameters

The transform can be tuned with the following parameters.

Parameter	Default	Description
`model_name`	`BAAI/bge-small-en-v1.5`	The HF model to use for encoding the text.
`content_column_name`	`contents`	Name of the column containing the text to be encoded.
`output_embeddings_column_name`	`embeddings`	Column name to store the embeddings in the output table.
`output_path_column_name`	`doc_path`	Column name to store the document path of the chunk in the output table.

When invoking the CLI, the parameters must be set as --text_encoder_<name>, e.g. --text_encoder_column_name_key=myoutput.

Running the samples

To run the samples, use the following make targets

run-cli-sample - runs src/text_encoder_transform.py using command line args
run-local-sample - runs src/text_encoder_local.py

These targets will activate the virtual environment and set up any configuration needed. Use the -n option of make to see the detail of what is done to run the sample.

For example,

make run-cli-sample
...

Then

ls output

To see results of the transform.

Transforming data using the transform image

To use the transform image to transform your data, please refer to the running images quickstart, substituting the name of this transform image and runtime as appropriate.