| from dataclasses import asdict, dataclass | |
| from enum import Enum | |
| from typing import ( | |
| Any, | |
| Dict, | |
| List, | |
| Mapping, | |
| Optional, | |
| Sequence, | |
| Tuple, | |
| ) | |
| from aleph_alpha_client.prompt import Prompt | |
| @dataclass(frozen=True) | |
| class EmbeddingRequest: | |
| """ | |
| Embeds a text and returns vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers). | |
| Parameters: | |
| prompt | |
| The text and/or image(s) to be embedded. | |
| layers | |
| A list of layer indices from which to return embeddings. | |
| * Index 0 corresponds to the word embeddings used as input to the first transformer layer | |
| * Index 1 corresponds to the hidden state as output by the first transformer layer, index 2 to the output of the second layer etc. | |
| * Index -1 corresponds to the last transformer layer (not the language modelling head), index -2 to the second last layer etc. | |
| pooling | |
| Pooling operation to use. | |
| Pooling operations include: | |
| * mean: aggregate token embeddings across the sequence dimension using an average | |
| * max: aggregate token embeddings across the sequence dimension using a maximum | |
| * last_token: just use the last token | |
| * abs_max: aggregate token embeddings across the sequence dimension using a maximum of absolute values | |
| type | |
| Type of the embedding (e.g. symmetric or asymmetric) | |
| tokens | |
| Flag indicating whether the tokenized prompt is to be returned (True) or not (False) | |
| normalize | |
| Return normalized embeddings. This can be used to save on additional compute when applying a cosine similarity metric. | |
| Note that at the moment this parameter does not yet have any effect. This will change as soon as the | |
| corresponding feature is available in the backend | |
| contextual_control_threshold (float, default None) | |
| If set to None, attention control parameters only apply to those tokens that have | |
| explicitly b |