| An example would be: | |
| py | |
| agent.run("Draw me the picture of a capybara swimming in the sea") | |
| Here, the model could interpret in two ways: | |
| - Have the text-to-image generate a capybara swimming in the sea | |
| - Or, have the text-to-image generate capybara, then use the image-transformation tool to have it swim in the sea | |
| In case you would like to force the first scenario, you could do so by passing it the prompt as an argument: | |
| py | |
| agent.run("Draw me a picture of the `prompt`", prompt="a capybara swimming in the sea") | |
| Chat-based execution (chat) | |
| The agent also has a chat-based approach, using the [~Agent.chat] method: | |
| py | |
| agent.chat("Generate a picture of rivers and lakes") | |
| py | |
| agent.chat("Transform the picture so that there is a rock in there") | |
| This is an interesting approach when you want to keep the state across instructions. |