...

{
  "base_url": "https://api.openai.com/v1",
  "model": "gpt-3.5-turbo",
  "api_token": "your_token",
  "max_token": 800
} 

Send a prompt to the AI - [ai.prompt.send]

One of most generic and simplest use cases is to send a prompt (= a request) to the AI and use the response data in your pipeline. For this you can use the ai.prompt.send command. Here is an example to return some data from the AI:

pipeline:
  - ai.prompt.send:
      prompt: |
        Return the names of the 10 biggest cities in the world as JSON array.

This will result in an entry like this in the body:

[
    "Tokyo",
    "Delhi",
    "Shanghai",
    "Sao Paulo",
    "Mumbai",
    "Beijing",
    "Mexico City",
    "Osaka",
    "Cairo",
    "Dhaka"
]

Adding context data (user data)

You can also apply the prompt on a given context data which is the input data:

pipeline:
  - ai.prompt.send:
      input: |
        [
          "Tokyo",
          "Delhi",
          "Shanghai",
          "Sao Paulo",
          "Mumbai",
          "Beijing",
          "Mexico City",
          "Osaka",
          "Cairo",
          "Dhaka"
        ]
      prompt: |
        Order the given list alphabetically.

The result in the body is then:

[
    "Beijing",
    "Cairo",
    "Delhi",
    "Dhaka",
    "Mexico City",
    "Mumbai",
    "Osaka",
    "Sao Paulo",
    "Shanghai",
    "Tokyo"
]

See another example which converts a given input:

pipeline:
  - ai.prompt.send:
      input: |
        <person>
          <firstName>Max</firstName>
          <lastName>Smith</lastName>
          <age>36</age>
        </person>
      prompt: "Convert to JSON"

And the result from the AI in the body will be this:

{
    "person": {
        "firstName": "Max",
        "lastName": "Smith",
        "age": 36
    }
}

And once more you could apply data privacy filters for example:

pipeline:
  - ai.prompt.send:
      input: |
        {
          "person": {
            "firstName": "Max",
            "lastName": "Smith",
            "age": 36
          }
        }
      prompt: |
          Remove all personal data because of privacy and 
          replace by randomized names and add prefix p_

As a result, a changed JSON comes back:

{
    "person": {
        "firstName": "p_Alex",
        "lastName": "p_Johnson",
        "age": 48
    }
}

Send multiple messages

In case you need to send multiple messages, you can use the messages parameter like this:

pipeline:
  - ai.prompt.send:
      messages:
        - role: system
          content: Tell me a joke based on given user input.
        - role: user
          content: I'm a 28 year old man living in New York.

The result could be like this in the body:

Why did the New York man bring a ladder to his job interview? 
Because he wanted to climb the corporate ladder!

If messages and prompt is given, the prompt will be automatically added as message of role system at the very end.

Text-to-Command - [ai.command.detect]

This powerful feature of the AI Studio takes a non-structured text such as an email, a chat message or a PDF document for example, analyses it using AI and then automatically detects and executes the according PIPEFORCE command including its parameters which must be executed in order to take action and fulfill the user’s request.

...

• Automatically forward emails with a summary to the responsible internal team
  Scan any email sent to a given inbox such as info@mycompany.tld for example, find out the intention of the sender, then forward the email to the internal team such as support, sales, … which can handle the request. The AI can find out the type of request, whether it is a support request, an order request, a question regarding an invoice or any other type just by writing an advice to the AI and without any programming. It can also detect and extract all required data such as customerId, invoiceNumber and more from the sender’s email. Furthermore, it can also create a short summary about what the core intent of the sender is to make it easier for the internal team to process the request.

• Automatically validate and start an internal workflow by email
  Scan any email sent to a given inbox such as invoice@mycompany.tld for example and if this email matches to an existing workflow, extract all variables required for this workflow from the email, start the workflow and pass these variables along with it. For example to start an accounts payable workflow based on an given payable invoice. The AI can validate whether all required data exist and is valid in order to start the workflow.

• Automatically call endpoints of other systems by email
  Scan any email sent to a given inbox such as info@mycompany.tld for example and if this email is related to a service, offered by a third party system which provides an remote API, call this remote API (for example REST) and pass along parameters extracted from the email. For example create a new ticket on an external ticket system.

Using the command ai.command.detect

In order to integrate Text-to-Command functionality into your automation pipelines, you can use the command ai.command.detect. It will

...

See this example to construct a message out of advice parameters using a template:

...
targetCommand: "mail.send"
...
params:
  message:
    value: "The customerId is: {{advice.params.customerId}}"
  ...

...