# Introduction to Aya Vision

> In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses.

<CookbookHeader href="https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/aya_vision_intro.ipynb" />

Introducing Aya Vision - a state-of-the-art open-weights multimodal multilingual model.

<img src="https://github.com/cohere-ai/cohere-developer-experience/raw/main/notebooks/images/aya-vision/Aya-Vision.jpg" />

In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses.

The following links provide further details about the Aya Vision model:

* [The launch blog](https://cohere.com/blog/aya-vision)
* [Documentation](https://docs.cohere.com/docs/aya-multimodal)
* HuggingFace model page for the [32B](https://huggingface.co/CohereForAI/aya-vision-32b) and [8B](https://huggingface.co/CohereForAI/aya-vision-8b) models.

This tutorial will provide a walkthrough of the various use cases that you can build with Aya Vision. By the end of this notebook, you will have a solid understanding of how to use Aya Vision for a wide range of applications.

The list of possible use cases with multimodal models is endless, but this notebook will cover the following:

* Setup
* Question answering
* Multilingual multimodal understanding
* Captioning
* Recognizing text
* Classification
* Comparing multiple images
* Conclusion

## Setup

First, install the Cohere Python SDK and create a client.

```python PYTHON
%pip install cohere -q
```

```python PYTHON
import cohere
import base64

co = cohere.ClientV2(
    "COHERE_API_KEY"
)  # Get your free API key here: https://dashboard.cohere.com/api-keys
```

Next, let's set up a function to generate text responses, given an image and a message. It uses the Cohere API via the Chat endpoint to call the Aya Vision model.

To pass an image to the API, pass a Base64-encoded image as the `image_url` argument in the `messages` parameter. To convert and image into a Base64-encoded version, we can use the `base64` library as in this example.

```python PYTHON