To store embeddings generated by OpenAI for later use, you first need to obtain the embeddings from the API or the model you are using. For example, if you are using the OpenAI API, you typically send text data to the embedding endpoint, and the model returns a numeric vector representing that text. Once you have these embeddings, you can store them in a variety of ways depending on your requirements regarding performance, scalability, and retrieval ease.
One common approach is to save the embeddings in a database. You could use a relational database such as PostgreSQL or MySQL, where you can create a table that includes columns for the text, the corresponding embedding, and any additional metadata you may need, like creation date or user ID. Storing embeddings as arrays in the database is straightforward, especially for smaller datasets. For instance, you can convert the embedding vector into a string format (like JSON or CSV) for storage, then convert it back to an array when retrieving.
Another option is to store the embeddings in a file system. You could save each embedding to a file (such as a binary .npy file using NumPy) or even as a JSON object in a text file. This approach is particularly practical when dealing with large datasets. You can also use specialized data structures like FAISS or Annoy, which are designed for high-performance similarity searches. These tools can index embeddings and allow you to retrieve them efficiently based on their similarity to new data points. Regardless of the method you choose, be sure to consider factors such as data security and access speed when deciding how to store your embeddings.