pyseekdb.utils.embedding_functions.MistralEmbeddingFunction
- class pyseekdb.utils.embedding_functions.MistralEmbeddingFunction(model_name: str = 'mistral-embed', api_key_env: str | None = None, api_base: str | None = None, dimensions: int | None = None, **kwargs: Any)[source]
Bases:
OpenAIBaseEmbeddingFunctionA convenient embedding function for Mistral text embedding models.
This class provides a simplified interface to Mistral text embeddings using the OpenAI-compatible API.
Note: The embeddings API only accepts the model name and input texts.
For more information about Mistral embeddings, see: https://docs.mistral.ai/capabilities/embeddings/text_embeddings
Example
pip install pyseekdb openai
- __init__(model_name: str = 'mistral-embed', api_key_env: str | None = None, api_base: str | None = None, dimensions: int | None = None, **kwargs: Any)[source]
Initialize MistralEmbeddingFunction.
- Parameters:
model_name (str, optional) – Name of the Mistral embedding model. Defaults to “mistral-embed”.
api_key_env (str, optional) – Name of the environment variable containing the Mistral API key. Defaults to “MISTRAL_API_KEY” if not provided.
api_base (str, optional) – Base URL for the Mistral API endpoint. Defaults to “https://api.mistral.ai/v1” if not provided.
dimensions (int, optional) – This parameter is not supported by the Mistral embeddings API. If provided, a warning will be issued and the parameter will be ignored.
**kwargs – Additional arguments to pass to the OpenAI client. Common options include: - timeout: Request timeout in seconds - max_retries: Maximum number of retries - See https://github.com/openai/openai-python for more options
Methods
__init__([model_name, api_key_env, ...])Initialize MistralEmbeddingFunction.
build_from_config(config)Get the configuration dictionary for the OpenAIBaseEmbeddingFunction.
name()support_persistence(embedding_function)Check if the embedding function supports persistence.
Attributes
dimensionGet the dimension of embeddings produced by this function.