Performing Computing Tasks Using Decoupled Models for Different Data Types

A technique executes tasks using a data store of machine-trained models. The data store specifically includes a subset of encoder-type machine-trained models for converting input data items having different input data types into respective embeddings in a vector space, and a subset of decoder-type machine-trained models for converting embeddings in the same vector space into data items having respective different output data types. When executing a particular task that involves one or more data types, the technique selects one or more machine-trained models that match those data types. In some implementations, the technique provides a clipboard store for storing embeddings produced by the encoder-type machine-trained models and consumable by the decoder-type machine-trained models. The technique includes provisions for ensuring that any decoder-type machine-model is capable of processing embeddings produced by different versions of the encoder-type machine-trained models.