pyseekdb.utils.embedding_functions.SiliconflowEmbeddingFunction

class pyseekdb.utils.embedding_functions.SiliconflowEmbeddingFunction(model_name: str = 'BAAI/bge-large-zh-v1.5', api_key_env: str | None = None, api_base: str | None = None, dimensions: int | None = None, **kwargs: Any)

Bases: OpenAIBaseEmbeddingFunction

A convenient embedding function for SiliconFlow embedding models.

This class provides a simplified interface to SiliconFlow embedding models using the OpenAI-compatible API. SiliconFlow provides OpenAI-compatible API endpoints for embedding generation.

For more information about SiliconFlow models, see https://docs.siliconflow.cn/en/api-reference/embeddings/create-embeddings

Example

pip install pyseekdb openai

__init__(model_name: str = 'BAAI/bge-large-zh-v1.5', api_key_env: str | None = None, api_base: str | None = None, dimensions: int | None = None, **kwargs: Any)

Initialize SiliconflowEmbeddingFunction.

Parameters:

• model_name (str, optional) – Name of the SiliconFlow embedding model. Defaults to “BAAI/bge-large-zh-v1.5”. See SiliconFlow documentation for available models: https://docs.siliconflow.cn/en/api-reference/embeddings/create-embeddings

• api_key_env (str, optional) – Name of the environment variable containing the SiliconFlow API key. Defaults to “SILICONFLOW_API_KEY” if not provided.

• api_base (str, optional) – Base URL for the SiliconFlow API endpoint. Defaults to “https://api.siliconflow.cn/v1” if not provided.

• dimensions (int, optional) – The number of dimensions the resulting embeddings should have. See SiliconFlow documentation for available models: https://docs.siliconflow.cn/en/api-reference/embeddings/create-embeddings

• **kwargs – Additional arguments to pass to the OpenAI client. See https://github.com/openai/openai-python for more information.

Methods

__init__([model_name, api_key_env, ...])	Initialize SiliconflowEmbeddingFunction.
build_from_config(config)
get_config()	Get the configuration dictionary for the OpenAIBaseEmbeddingFunction.
name()
support_persistence(embedding_function)	Check if the embedding function supports persistence.

Attributes

dimension

Get the dimension of embeddings produced by this function.

get_config() → dict[str, Any]

Get the configuration dictionary for the OpenAIBaseEmbeddingFunction.

Subclasses should override the name() method to provide the correct name for routing.

Returns:

Dictionary containing configuration needed to restore this embedding function