> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aurelio.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# semantic_router.encoders.clip

## CLIPEncoder Objects

```python theme={null}
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.
* `str`0 (`str`1): The tokenizer for the model.
* `str`2 (`str`1): The processor for the model.
* `str`4 (`str`1): The model.
* `str`6 (`str`1): The torch library.
* `str`8 (`str`1): The PIL library.

#### \_\_init\_\_

```python theme={null}
def __init__(**data)
```

Initialize the CLIPEncoder.

**Arguments**:

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

#### \_\_call\_\_

```python theme={null}
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.