Embedding

Overview

Embedding is an abstract base class that lays the groundwork for different embedding objects in the Automata codebase. This class manages the embedding vector and provides methods to convert the instance to a string, and vice versa.

The Embedding class is typically used as a base class that specific types of embeddings inherit from. An embedding takes an input object and transforms it into a vector form that can be easily manipulated by machine learning models. The class holds a key, an input object, and its corresponding vector representation.

Example

As an abstract base class, Embedding is not directly instantiated in most cases. Instead, it is extended by other classes, which implement the specific type of embedding. Here is an example of a hypothetical class ExampleEmbedding that extends Embedding:

class ExampleEmbedding(Embedding):
    def __init__(self, key: Any, input_object: str, vector: np.ndarray):
        super().__init__(key, input_object, vector)

    def __str__(self):
        description = f'Example Embedding for the object {self.input_object} with key {self.key}'
        return description

Limitations

As an abstract base class, Embedding doesn’t provide any implementations. The __str__ method is expected to be overridden in child classes since it’s decorated with @abc.abstractmethod. It’s also assumed the embedding vector will be of the type numpy.ndarray, though this isn’t enforced in the Embedding class itself.

Follow-up Questions

What are the requirements for the key and input_object during the initialization of the Embedding object?
What practical implementations are used in the Automata codebase, and what are specific use-cases?
What error handling is used if the vector object passed during initialization is not a numpy.ndarray?
Are there size or dimension requirements for the array, or can it be of any shape?