...

• base_url: The base url of the API (requried).

• model: The AI model to be used (required).

• api_token: The security token to be used.

• max_token: The max token to be send (defaults to 800)

• custom_headers: Key-value pairs to be passed along as HTTP headers on any request. This is handy for example in case basic authentication or any other additional header setting is required.

Connect to OpenAI (ChatGPT)

...

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

Send a question or advice (prompt) to

...

AI

...

One of most generic and simplest use cases is to send a prompt (= a requestquestion / advice) 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:

...

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

Adding context data (

...

input) to the prompt

You can also apply the prompt on a given context data which is the input datawith context data. This context data can be set as input to the command:

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 of this example 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:

...

...

The input of the command will become the context data. It can be plain text, a file or an URI. In case it is a file (for example a PDF or Word document) or any other supported format, it will be automatically converted into an AI compatible format.

Here is an example which uses a PDF file as file context, stored in PIPEFORCE’s Drive cloud storage:

pipeline:
  - ai.prompt.send:
     <person>    input: $uri:drive:invoice-3662.pdf
      <firstName>Max</firstName>prompt: |
        Check <lastName>Smith</lastName>the invoice to ensure it is correct both in terms 
<age>36</age>        of </person>content and calculations. If everything is fine, prompt:return "Convert to JSON"

...

"OK". 
        If not, provide the reason for the error in one sentence.

See another example which converts a given input:

{pipeline:
    "person"- ai.prompt.send:
{      input: |
 "firstName": "Max",      <person>
  "lastName": "Smith",       <firstName>Max</firstName>
 "age": 36     } }

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

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

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

{
    "person": {
        "firstName": "Max",

           "lastName": "Smith",
   
        "age": 36
    }
}

And one more example: Apply a data privacy filter:

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

As a result, a changed JSON comes back:

{   "lastName":  "person": {Smith",
            "firstNameage": "p_Alex", 36
          }
     "lastName": "p_Johnson",  }
      "age": 48prompt: |
          }
}

Send multiple messages

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

pipeline:
  - ai.prompt.send:
 Remove all personal data because of privacy and 
   messages:       replace by -randomized role:names systemand add          content: Tell me a joke based on given user input.prefix p_

As a result, a changed JSON comes back:

{
    "person": {
        "firstName": "p_Alex",
 - role: user     "lastName": "p_Johnson",
    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!

...

"age": 48
    }
}

Avanced prompting: Send multiple messages

In case you need to send multiple messages in one prompt, you can use the parameter messages 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 both parameters, messages and prompt are given, prompt will be automatically added to messages of role system at the very end.

Possible values for role are:

• system = A system message from the caller (typically the context data with basic advice).

• user = A message from the user (typically the question or advice based on the context data).

• ai = A message from the AI (typically used to enrich the context or trace the conversation).

The parameter content can be a plain text or any AI convertable object (like a PDF file for example). The conversion and preparation to an AI compatible format is done by PIPEFORCE automatically.

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

...