| 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 |
