How can I create a ChatGPT plugin and which output can I display in the chat?

edit

Creating a ChatGPT plugin involves several steps:

1. Define the Plugin Interface: This is the contract between the plugin and the model. It includes the operations that the plugin can perform and the data types it uses. The interface is defined in a schema language that is similar to GraphQL.

2. Implement the Plugin: This involves writing the code that performs the operations defined in the interface. The implementation can be in any language and can use any technology stack. It can run on any infrastructure, as long as it can receive HTTP requests from the model and send HTTP responses back.

3. Register the Plugin: Once the plugin is implemented, it needs to be registered with OpenAI. This involves providing the URL where the plugin is hosted, and the interface definition. OpenAI will then validate the interface and test the plugin to ensure it works correctly.

4. Use the Plugin in a Model: Once the plugin is registered, it can be used in a model. This involves adding the plugin to the model's configuration and then using the plugin's operations in the model's prompt.

As for the output that can be displayed in the chat, it depends on the specific plugin. However, generally, a plugin can return any data that can be represented in JSON format. This includes:

• Text: This is the most common type of output. The plugin can return a string of text that the model can include in its response.

• Images: The plugin can return a URL of an image. The model can then include this URL in its response, and the image will be displayed in the chat.

• Links: The plugin can return a URL of a webpage. The model can then include this URL in its response, and the link will be clickable in the chat.

• Structured Data: The plugin can return structured data, such as a list or a dictionary. The model can then use this data to generate a more complex response.

• Markdown: The plugin can return text formatted in Markdown, which allows for more complex formatting options, such as bold, italic, bullet points, etc.

Remember, the model is responsible for deciding how to incorporate the plugin's output into its response. The plugin just provides the data.