ther in use cases such as search where you want to compare shorter queries against larger documents.

        `Document` embeddings are optimized for larger pieces of text to compare queries against.
    Query:
        `Document` and `Query` are used together in use cases such as search where you want to compare shorter queries against larger documents.

        `Query` embeddings are optimized for shorter texts, such as questions or keywords.
    """

    Symmetric = "symmetric"
    Document = "document"
    Query = "query"


@dataclass(frozen=True)
class SemanticEmbeddingRequest:
    """
    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.
        representation
            Semantic representation to embed the prompt with.
        compress_to_size
            Options available: 128

            The default behavior is to return the full embedding, but you can optionally request an embedding compressed to a smaller set of dimensions.

            Full embedding sizes for supported models:
              - luminous-base: 5120

            The 128 size is expected to have a small drop in accuracy performance (4-6%), with the benefit of being much smaller, which makes comparing these embeddings much faster for use cases where speed is critical.

            The 128 size can also perform better if you are embedding really short texts or documents.

        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 been set in the request.
            If set to a non-None value, we apply the control parameters to similar tokens as well.
            Controls that have been applied to one token will then be applied to all other tokens
            that have at least the similarity score defined by this parameter.
            The similarity score is the cosine similarity of token embeddings.

        control_log_additive (bool, default True)
            True: apply control by adding the log(control_factor) to attention scores.
            False: apply control by (attention_scores - - attention_scores.min(-1)) * control_factor

    Examples
        >>> texts = [
                "deep learning",
                "artificial intelligence",
                "deep diving",
                "artificial snow",
            ]
        >>> # Texts to compare
        >>> embeddings = []
        >>> for text in texts:
                request = SemanticEmbeddingRequest(prompt=Prompt.from_text(text), representation=SemanticRepresentation.Symmetric)
                result = model.semantic_embed(request)
                embeddings.append(result.embedding)
    """

    prompt: Prompt
    representation: SemanticRepresentation
    compress_to_size: Optional[int] = None
    normalize: bool = False
    contextual_control_threshold: Optional[float] = None
    control_log_additive: Optional[bool] = True

    def to_json(self) -> Mapping[str, Any]:
        return {
            **self._asdict(),
            "representation": self.representation.value,
            "prompt": self.prompt.to_json(),
        }

    def _asdict(self) -> Mapping[str, Any]:
        return asdict(self)


@dataclass(frozen=True)
class BatchSemanticEmbeddingRequest:
    """
    Embeds multiple multi-modal prompts and returns their embeddings in the same order as they were supplied.

    Parameters:
        prompts
            A list of texts and/or images to be embedded.
        representation
            Semantic representation to embed the prompt with.
        compress_to_size
            Options available: 128

            The default behavior is to return the full embedding, but you can optionally request an embedding compressed to a smaller set of dimensions.

            Full embedding sizes for supported models:
              - luminous-base: 5120

            The 128 size is expected to have a small drop in accuracy performance (4-6%), with the benefit of being much smaller, which makes comparing these embeddings much faster for use cases where spe