more links (#1287)

montanalow · web-flow · commit 651f2042fdf5 · 2024-01-10T21:28:53.000-08:00
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
@@ -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.
 
diff --git a/pgml-cms/blog/introducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md b/pgml-cms/blog/introducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md
@@ -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!
 
diff --git a/pgml-cms/docs/use-cases/embeddings/generating-llm-embeddings-with-open-source-models-in-postgresml.md b/pgml-cms/docs/use-cases/embeddings/generating-llm-embeddings-with-open-source-models-in-postgresml.md
@@ -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.
 
