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@@ -88,7 +88,7 @@ What are embeddings?
 - __Technical__: An embedding is the latent-space position of a document at a layer of a deep neural network. For models trained specifically to embed data, this is the last layer.
 - __A small example__: If you search your photos for "famous bridge in San Francisco". By embedding this query and comparing it to the embeddings of your photos and their metadata - it should return photos of the Golden Gate Bridge.
 
-Embeddings databases (also known as **vector databases**) store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database. By default, Chroma uses [Sentence Transformers](https://docs.trychroma.com/embeddings#default-sentence-transformers) to embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings, or your own.
+Embeddings databases (also known as **vector databases**) store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database. By default, Chroma uses [Sentence Transformers](https://docs.trychroma.com/embeddings#sentence-transformers) to embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings, or your own.
 
 ## Get involved