semantic_router.encoders.clip - Aurelio AI

CLIPEncoder Objects

class CLIPEncoder(DenseEncoder)

Multi-modal dense encoder for text and images using CLIP-type models via HuggingFace. Arguments:

name (str): The name of the model to use.
tokenizer_kwargs (Dict): Keyword arguments for the tokenizer.
processor_kwargs (Dict): Keyword arguments for the processor.
model_kwargs (Dict): Keyword arguments for the model.
device (Optional[str]): The device to use for the model.
str0 (str1): The tokenizer for the model.
str2 (str1): The processor for the model.
str4 (str1): The model.
str6 (str1): The torch library.
str8 (str1): The PIL library.

init

def __init__(**data)

Initialize the CLIPEncoder. Arguments:

**data (Dict): Keyword arguments for the encoder.

call

def __call__(docs: List[Any],
             batch_size: int = 32,
             normalize_embeddings: bool = True) -> List[List[float]]

Encode a list of documents. Can handle both text and images. Arguments:

docs (List[Any]): The documents to encode.
batch_size (int): The batch size for the encoding.
normalize_embeddings (bool): Whether to normalize the embeddings.

Returns: List[List[float]]: A list of embeddings.

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