Jan 11, 2024 · Jan 11, 2024 · Jan 11, 2024
diff --git a/README.md b/README.md
 ```

 ## Tabular data
 - [47+ classification and regression algorithms](https://postgresml.org/docs/training/algorithm_selection)
 - [47+ classification and regression algorithms](https://postgresml.org/docs/introduction/apis/sql-extensions/pgml.train/)
 - [8 - 40X faster inference than HTTP based model serving](https://postgresml.org/blog/postgresml-is-8x-faster-than-python-http-microservices)
 - [Millions of transactions per second](https://postgresml.org/blog/scaling-postgresml-to-one-million-requests-per-second)
 - [Horizontal scalability](https://github.com/postgresml/pgcat)
diff --git a/pgml-cms/blog/generating-llm-embeddings-with-open-source-models-in-postgresml.md b/pgml-cms/blog/generating-llm-embeddings-with-open-source-models-in-postgresml.md

 ## 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`](/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.

diff --git a/...roducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md b/...roducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md

 !!!

 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/introduction/machine-learning/sdks/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](/docs/introduction/machine-learning/sdks/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 pgml, and get running!

diff --git a/...s/embeddings/generating-llm-embeddings-with-open-source-models-in-postgresml.md b/...s/embeddings/generating-llm-embeddings-with-open-source-models-in-postgresml.md

 ## 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/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.

diff --git a/pgml-extension/README.md b/pgml-extension/README.md
 Please see the [quick start instructions](https://postgresml.org/docs/developer-docs/quick-start-with-docker) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/docs/developer-docs/contributing) is also available for those who would like to contribute.
 Please see the [quick start instructions](https://postgresml.org/docs/resources/developer-docs/quick-start-with-docker) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/docs/resources/developer-docs/contributing) is also available for those who would like to contribute.
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Expand Up		@@ -108,7 +108,7 @@ SELECT pgml.transform(
		```

		## Tabular data
		- [47+ classification and regression algorithms](https://postgresml.org/docs/training/algorithm_selection)
		- [47+ classification and regression algorithms](https://postgresml.org/docs/introduction/apis/sql-extensions/pgml.train/)
		- [8 - 40X faster inference than HTTP based model serving](https://postgresml.org/blog/postgresml-is-8x-faster-than-python-http-microservices)
		- [Millions of transactions per second](https://postgresml.org/blog/scaling-postgresml-to-one-million-requests-per-second)
		- [Horizontal scalability](https://github.com/postgresml/pgcat)
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Expand Up		@@ -118,7 +118,7 @@ LIMIT 5;

		## 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`](/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.

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Expand Up		@@ -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/introduction/machine-learning/sdks/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](/docs/introduction/machine-learning/sdks/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 pgml, and get running!

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Original file line number	Diff line number	Diff line change
Expand Up		@@ -106,7 +106,7 @@ LIMIT 5;

		## 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/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.

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Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		Please see the [quick start instructions](https://postgresml.org/docs/developer-docs/quick-start-with-docker) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/docs/developer-docs/contributing) is also available for those who would like to contribute.
		Please see the [quick start instructions](https://postgresml.org/docs/resources/developer-docs/quick-start-with-docker) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/docs/resources/developer-docs/contributing) is also available for those who would like to contribute.