Embeddings index method is inefficient with memory

[davidmezzetti]

The current index method makes a number of calls that force creating copies of the working embeddings index array. For larger datasources, this can cause out of memory errors. Make the following improvements:

• When streaming embeddings back from disk, create an empty NumPy array initialized to the size of the embeddings index array. Appending NumPy arrays to a list will force a copy to be created, when creating the final NumPy embeddings array.

• Modify the removePC method to operate directly on the input array vs returning a copy

• Modify the normalize method to operate directly on the input array vs returning a copy