FastEmbedEncoder Objects
pip install 'semantic-router[fastembed]'
Arguments:
name: The name of the embedding model to use.max_length: The maximum length of the input text.cache_dir: The directory to cache the embedding model.threads: The number of threads to use for the embedding.
__init__
score_threshold(float): The threshold for the score of the embedding.
__call__
docs(List[str]): The documents to embed.
ValueError: If the embedding fails.
List[List[float]]: The vector embeddings of the documents.
