CohereBackend

Cohere Embedding Model

Parameters:

Name	Type	Description	Default
client		A cohere client.	required
embedding_model	str	A Cohere model. Default is "large". For an overview of models see: https://docs.cohere.ai/docs/generation-card	'large'
delay_in_seconds	float	If a batch_size is given, use this set the delay in seconds between batches.	None
batch_size	int	The size of each batch.	None

Examples:

import cohere
from bertopic.backend import CohereBackend

client = cohere.Client("APIKEY")
cohere_model = CohereBackend(client)

Source code in bertopic\backend\_cohere.py

class CohereBackend(BaseEmbedder):
    """ Cohere Embedding Model

    Arguments:
        client: A `cohere` client.
        embedding_model: A Cohere model. Default is "large".
                         For an overview of models see:
                         https://docs.cohere.ai/docs/generation-card
        delay_in_seconds: If a `batch_size` is given, use this set
                          the delay in seconds between batches.
        batch_size: The size of each batch.

    Examples:

    ```python
    import cohere
    from bertopic.backend import CohereBackend

    client = cohere.Client("APIKEY")
    cohere_model = CohereBackend(client)
    ```
    """
    def __init__(self, 
                 client,
                 embedding_model: str = "large",
                 delay_in_seconds: float = None,
                 batch_size: int = None):
        super().__init__()
        self.client = client
        self.embedding_model = embedding_model
        self.delay_in_seconds = delay_in_seconds
        self.batch_size = batch_size

    def embed(self,
              documents: List[str],
              verbose: bool = False) -> np.ndarray:
        """ Embed a list of n documents/words into an n-dimensional
        matrix of embeddings

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        """
        # Batch-wise embedding extraction
        if self.batch_size is not None:
            embeddings = []
            for batch in tqdm(self._chunks(documents), disable=not verbose):
                response = self.client.embed(batch, model=self.embedding_model)
                embeddings.extend(response.embeddings)

                # Delay subsequent calls
                if self.delay_in_seconds:
                    time.sleep(self.delay_in_seconds)

        # Extract embeddings all at once
        else:
            response = self.client.embed(documents, model=self.embedding_model)
            embeddings = response.embeddings
        return np.array(embeddings)

    def _chunks(self, documents):     
        for i in range(0, len(documents), self.batch_size):
            yield documents[i:i + self.batch_size]

embed(self, documents, verbose=False)

Embed a list of n documents/words into an n-dimensional matrix of embeddings

Parameters:

Name	Type	Description	Default
documents	List[str]	A list of documents or words to be embedded	required
verbose	bool	Controls the verbosity of the process	False

Returns:

Type	Description
ndarray	Document/words embeddings with shape (n, m) with n documents/words that each have an embeddings size of m

Source code in bertopic\backend\_cohere.py

def embed(self,
          documents: List[str],
          verbose: bool = False) -> np.ndarray:
    """ Embed a list of n documents/words into an n-dimensional
    matrix of embeddings

    Arguments:
        documents: A list of documents or words to be embedded
        verbose: Controls the verbosity of the process

    Returns:
        Document/words embeddings with shape (n, m) with `n` documents/words
        that each have an embeddings size of `m`
    """
    # Batch-wise embedding extraction
    if self.batch_size is not None:
        embeddings = []
        for batch in tqdm(self._chunks(documents), disable=not verbose):
            response = self.client.embed(batch, model=self.embedding_model)
            embeddings.extend(response.embeddings)

            # Delay subsequent calls
            if self.delay_in_seconds:
                time.sleep(self.delay_in_seconds)

    # Extract embeddings all at once
    else:
        response = self.client.embed(documents, model=self.embedding_model)
        embeddings = response.embeddings
    return np.array(embeddings)