OpenAIBackend

Bases: BaseEmbedder

OpenAI Embedding Model

Parameters:

Name	Type	Description	Default
client	OpenAI	A openai.OpenAI client.	required
embedding_model	str	An OpenAI model. Default is For an overview of models see: https://platform.openai.com/docs/models/embeddings	'text-embedding-ada-002'
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
generator_kwargs	Mapping[str, Any]	Kwargs passed to openai.Embedding.create. Can be used to define custom engines or deployment_ids.	{}

Examples:

import openai
from bertopic.backend import OpenAIBackend

client = openai.OpenAI(api_key="sk-...")
openai_embedder = OpenAIBackend(client, "text-embedding-ada-002")

Source code in bertopic\backend\_openai.py

class OpenAIBackend(BaseEmbedder):
    """ OpenAI Embedding Model

    Arguments:
        client: A `openai.OpenAI` client.
        embedding_model: An OpenAI model. Default is
                         For an overview of models see:
                         https://platform.openai.com/docs/models/embeddings
        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.
        generator_kwargs: Kwargs passed to `openai.Embedding.create`.
                          Can be used to define custom engines or
                          deployment_ids.

    Examples:

    ```python
    import openai
    from bertopic.backend import OpenAIBackend

    client = openai.OpenAI(api_key="sk-...")
    openai_embedder = OpenAIBackend(client, "text-embedding-ada-002")
    ```
    """
    def __init__(self,
                 client: openai.OpenAI,
                 embedding_model: str = "text-embedding-ada-002",
                 delay_in_seconds: float = None,
                 batch_size: int = None,
                 generator_kwargs: Mapping[str, Any] = {}):
        super().__init__()
        self.client = client
        self.embedding_model = embedding_model
        self.delay_in_seconds = delay_in_seconds
        self.batch_size = batch_size
        self.generator_kwargs = generator_kwargs

        if self.generator_kwargs.get("model"):
            self.embedding_model = generator_kwargs.get("model")
        elif not self.generator_kwargs.get("engine"):
            self.generator_kwargs["model"] = self.embedding_model

    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`
        """
        # Prepare documents, replacing empty strings with a single space
        prepared_documents = [" " if doc == "" else doc for doc in documents]

        # Batch-wise embedding extraction
        if self.batch_size is not None:
            embeddings = []
            for batch in tqdm(self._chunks(prepared_documents), disable=not verbose):
                response = self.client.embeddings.create(input=batch, **self.generator_kwargs)
                embeddings.extend([r.embedding for r in response.data])

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

        # Extract embeddings all at once
        else:
            response = self.client.embeddings.create(input=prepared_documents, **self.generator_kwargs)
            embeddings = [r.embedding for r in response.data]
        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(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
ndarray	that each have an embeddings size of m

Source code in bertopic\backend\_openai.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`
    """
    # Prepare documents, replacing empty strings with a single space
    prepared_documents = [" " if doc == "" else doc for doc in documents]

    # Batch-wise embedding extraction
    if self.batch_size is not None:
        embeddings = []
        for batch in tqdm(self._chunks(prepared_documents), disable=not verbose):
            response = self.client.embeddings.create(input=batch, **self.generator_kwargs)
            embeddings.extend([r.embedding for r in response.data])

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

    # Extract embeddings all at once
    else:
        response = self.client.embeddings.create(input=prepared_documents, **self.generator_kwargs)
        embeddings = [r.embedding for r in response.data]
    return np.array(embeddings)