update

Author: mrbullwinkle

articles/ai-services/openai/includes/embeddings-python.md

@@ -340,7 +340,7 @@ len(decode)
 
 Now that we understand more about how tokenization works we can move on to embedding. It is important to note, that we haven't actually tokenized the documents yet. The `n_tokens` column is simply a way of making sure none of the data we pass to the model for tokenization and embedding exceeds the input token limit of 8,192. When we pass the documents to the embeddings model, it will break the documents into tokens similar (though not necessarily identical) to the examples above and then convert the tokens to a series of floating point numbers that will be accessible via vector search. These embeddings can be stored locally or in an [Azure Database to support Vector Search](../../../cosmos-db/mongodb/vcore/vector-search.md). As a result, each bill will have its own corresponding embedding vector in the new `ada_v2` column on the right side of the DataFrame.
 
-In the example below we are calling the embedding model once per every item that we want to embed. When working with large embedding projects you can alternatively pass the model an array of inputs to embed rather than one input at a time. When you pass the model an array of inputs the max number input items per call to embedding API is 2048.
+In the example below we are calling the embedding model once per every item that we want to embed. When working with large embedding projects you can alternatively pass the model an array of inputs to embed rather than one input at a time. When you pass the model an array of inputs the max number of input items per call to the embedding endpoint is 2048.
 
 # [OpenAI Python 0.28.1](#tab/python)