The API docs provides a brief overview of the available functions exposed by pgml.

Function Description
pgml.embed() Generate embeddings using the latest sentence transformers from Hugging Face.
pgml.transform() Text generation using LLMs like Llama, Mixtral, and many more, with models downloaded from Hugging Face.
pgml.transform_stream() Streaming version of pgml.transform(), which fetches partial responses as they are being generated by the model, substantially decreasing time to first token.
pgml.tune() Perform fine tuning tasks on Hugging Face models, using data stored in the database.
pgml.train() Train a model on PostgreSQL tables or views using any algorithm from Scikit-learn, with the additional support for XGBoost, LightGBM and Catboost.
pgml.predict() Run inference on live application data using a model trained with pgml.train().
pgml.deploy() Deploy a specific version of a model trained with pgml.train(), using your own accuracy metrics.
pgml.load_dataset() Load any of the toy datasets from Scikit-learn or any dataset from Hugging Face.
pgml.decompose() Reduces the number of dimensions in a vector via matrix decomposition.
pgml.chunk() Break large bodies of text into smaller pieces via commonly used splitters.
pgml.generate() Perform inference with custom models.