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

# AutoEmbeddings

> Install extra dependencies to generate embedding vectors

<img className="inline m-0" src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/9e9834e4-image.jpeg" /> [View Source on Github](https://github.com/Arize-ai/client_python/blob/main/src/arize/pandas/embeddings/auto_generator.py#L11)

<Warning>
  <img className="inline m-0" src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/7539e0ab-python-3_8-red.svg" /> minimum required for Auto Embeddings
</Warning>

Install extra dependencies in the SDK:

```bash theme={null}
%pip install -q "arize[AutoEmbeddings]<8.0.0"
```

### **The **`EmbeddingGenerator`** Class**

Arize class to generate embeddings data.

Import and initialize `EmbeddingGenerator` from `arize.pandas.embeddings`:

```python theme={null}
from arize.pandas.embeddings import EmbeddingGenerator
```

#### Methods

`from_use_case`

[View Source](https://github.com/Arize-ai/client_python/blob/main/src/arize/pandas/embeddings/auto_generator.py#L19)

Pass in use\_case and more options depending on the use case.

| Argument    | Description                                                                                                |
| ----------- | ---------------------------------------------------------------------------------------------------------- |
| use\_case   | `UseCases.NLP.SEQUENCE_CLASSIFICATION` or`UseCases.NLP.SUMMARIZATION` or`UseCases.CV.IMAGE_CLASSIFICATION` |
| model\_name | Refer to Supported Models                                                                                  |

`list_pretrained_models`

[View Source](https://github.com/Arize-ai/client_python/blob/main/src/arize/pandas/embeddings/auto_generator.py#L28)

Returns updated table listing of supported models.

```
EmbeddingGenerator.list_pretrained_models()
```

### Code Example

```bash theme={null}
from arize.pandas.embeddings import EmbeddingGenerator, UseCases

# example CV
generator = EmbeddingGenerator.from_use_case(
    use_case=UseCases.CV.IMAGE_CLASSIFICATION,
    model_name="google/vit-base-patch16-224-in21k",
    batch_size=100
)
df["image_vector"] = generator.generate_embeddings(
    local_image_path_col=df["local_path"]
)

# example NLP
generator = EmbeddingGenerator.from_use_case(
    use_case=UseCases.NLP.SEQUENCE_CLASSIFICATION,
    model_name="distilbert-base-uncased",
    tokenizer_max_length=512,
    batch_size=100
)
df["text_vector"] = generator.generate_embeddings(text_col=df["text"])
```