retry_decorator = _create_retry_decorator(embeddings)
    @retry_decorator
    def _embed_with_retry(**kwargs: Any) -> Any:
        response = embeddings.client.create(**kwargs)
        return _check_response(response)
    return _embed_with_retry(**kwargs)
[docs]async def async_embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) -> Any:
    """Use tenacity to retry the embedding call."""
    @_async_retry_decorator(embeddings)
    async def _async_embed_with_retry(**kwargs: Any) -> Any:
        response = await embeddings.client.acreate(**kwargs)
        return _check_response(response)
    return await _async_embed_with_retry(**kwargs)
[docs]class LocalAIEmbeddings(BaseModel, Embeddings):
    """LocalAI embedding models.
    Since LocalAI and OpenAI have 1:1 compatibility between APIs, this class
    uses the ``openai`` Python package's ``openai.Embedding`` as its client.
    Thus, you should have the ``openai`` python package installed, and defeat
    the environment variable ``OPENAI_API_KEY`` by setting to a random string.
    You also need to specify ``OPENAI_API_BASE`` to point to your LocalAI
    service endpoint.
    Example:
        .. code-block:: python
            from langchain.embeddings import LocalAIEmbeddings
            openai = LocalAIEmbeddings(
                openai_api_key="random-string",
                openai_api_base="http://localhost:8080"
            )
    """
    client: Any  #: :meta private:
    model: str = "text-embedding-ada-002"
    deployment: str = model
    openai_api_version: Optional[str] = None
    openai_api_base: Optional[str] = None