What is the purpose of the batch_size variable in the code for generating embeddings, and why is batching necessary?

The batch_size variable sets the maximum number of text items sent to the embedding model in each API call during embedding generation. Batching is necessary because the total number of items a user may want to embed can be very large, whereas the model API can only process a limited quantity of text at one time. Setting batch_size to 100 in the example allows the code to be adapted to different needs and API limits.

In the code, batch_size is used by the get_embeddings_in_batches() function, which generates embeddings for a list of texts by making API calls in batches. The variable limits how many text items are included in each batch sent to the embedding model. The authors explain that users may need to embed arbitrarily many items, but the model has a limit on the amount it can process in a single API call. Therefore, batching is required to avoid exceeding that limit. The example sets batch_size to 100, and because the value is a variable, users can adjust it for their own API constraints or dataset sizes.