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## Generating embeddings from natural language text
PostgresML provides a simple interface to generate embeddings from text in your database. You can use the [`pgml.embed`](https://postgresml.org/docs/guides/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`](https://postgresml.org/docs/open-source/pgml/guides/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).
Since our corpus of documents (movie reviews) are all relatively short and similar in style, we don't need a large model. [`Alibaba-NLP/gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) 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.
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@@ -210,7 +210,7 @@ We have truncated the output to two items
!!!
We also have asynchronous versions of the create and `create_stream` functions relatively named `create_async` and `create_stream_async`. Checkout [our documentation](https://postgresml.org/docs/guides/opensourceai) for a complete guide of the open-source AI SDK including guides on how to specify custom models.
We also have asynchronous versions of the create and `create_stream` functions relatively named `create_async` and `create_stream_async`. Checkout [our documentation](https://postgresml.org/docs/open-source/pgml/guides/opensourceai) for a complete guide of the open-source AI SDK including guides on how to specify custom models.
PostgresML is free and open source. To run the above examples yourself [create an account](https://postgresml.org/signup), install korvus, and get running!
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Other distance functions have similar formulas and provide convenient operators to use as well. It may be worth testing other operators and to see which performs better for your use case. For more information on the other distance functions, take a look at our [Embeddings guide](https://postgresml.org/docs/guides/embeddings/vector-similarity).
Other distance functions have similar formulas and provide convenient operators to use as well. It may be worth testing other operators and to see which performs better for your use case. For more information on the other distance functions, take a look at our [Embeddings guide](https://postgresml.org/docs/open-source/pgml/guides/embeddings/vector-similarity).
Going back to our search example, we can compute the cosine distance between our query embedding and our documents:
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* [LLM based pipelines with PostgresML and dbt (data build tool)](introduction/import-your-data/storage-and-retrieval/llm-based-pipelines-with-postgresml-and-dbt-data-build-tool.md)
* [LLM based pipelines with PostgresML and dbt (data build tool)](resources/data-storage-and-retrieval/llm-based-pipelines-with-postgresml-and-dbt-data-build-tool.md)
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