Defining an embedding is quite similar to defining a feature. It’s associated with an entity, and the data is stored in a data set. An embedding must be a vector of floats, so all that needs specification is the dimensions of the embedding and the vector DB in which it’s stored.
Here’s an example:
comment_embeddings = ff.Embedding( vectorize_comments[["PK", "Vector"]], dims=384, vector_db=pinecone, description="Embeddings created from speakers' comments in episodes", variant="v1" )
Additionally, we provide a more detailed end-to-end example of building with embeddings.