The data for ML & AI systems is inherently larger and more dynamic than the models. It's more efficient, manageable and reliable to move the models to the database, rather than continuously moving the data to themodels_._ PostgresML allows you to take advantage of the fundamental relationship between data and models, by extending the database with the following capabilities and goals:

The data for ML & AI systems is inherently larger and more dynamic than the models. It's more efficient, manageable and reliable to move the models to the database, rather than continuously moving the data to themodels. PostgresML allows you to take advantage of the fundamental relationship between data and models, by extending the database with the following capabilities and goals:

***Model Serving** -_**GPU accelerated**_ inference engine for interactive applications, with no additional networking latency or reliability costs.

***Model Store** - Download_**open-source**_ models including state of the art LLMs from HuggingFace, and track changes in performance between versions.

PostgresML provides a simple interface to generate embeddings from text in your database. You can use the[`pgml.embed`](https://postgresml.org/docs/transformers/embeddings) function to generate embeddings for a column of text. The function takes a transformer name and a text value. The transformer will automatically be downloaded and cached on your connection process for reuse. You can see a list of potential good candidate models to generate embeddings on the[Massive Text Embedding Benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard).

PostgresML provides a simple interface to generate embeddings from text in your database. You can use the[`pgml.embed`](/docs/introduction/apis/sql-extensions/pgml.embed) function to generate embeddings for a column of text. The function takes a transformer name and a text value. The transformer will automatically be downloaded and cached on your connection process for reuse. You can see a list of potential good candidate models to generate embeddings on the[Massive Text Embedding Benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard).

Since our corpus of documents (movie reviews) are all relatively short and similar in style, we don't need a large model.[`intfloat/e5-small`](https://huggingface.co/intfloat/e5-small) will be a good first attempt. The great thing about PostgresML is you can always regenerate your embeddings later to experiment with different embedding models.