Expressions and Variables

Key Concepts In This Chapter: - Variables to store and retain data - Expressions to perform conditions inside variables

Variables: Concepts and Basic Use Cases

In this chapter, we will demonstrate how variables are defined, updated, and passed. As mentioned before, variables can be created within inputs of a state. For example:

{
  "type": "automata",
  "id": "hello_demo",
  "initial": "home_page_state",
  "inputs": {},
  "outputs": {},
  "transitions": {},
  "states": {
    "home_page_state": {
      "inputs": {
        "intro_message": {
          "type": "text",
          "user_input": true,
          "default_value": "Hi, this is your Pro Config Tutorial Bot"
        }
      },
      "render": {
        "text": "{{intro_message}}"
      }
    }
  }
}

The above simple example creates a variable called intro_message of type text with a default value and render it in the message. If user_input is false, user will not be prompted to input via a form, and a new variable with the value default_value will be automatically generated.

Expressions

We can use expressions to take value of previously defined variables and perform basic calculations from it. In Pro Config, expressions are represented in a string wrapped by double curly braces like {{expression}}. Expressions uses the grammar of JavaScript, and supports most of the basic syntaxes.

Unlike defining a variable through inputs, the result type of an expression can be dynamically deduced during the execution of that expression. A more common use case is to use expressions inside a string (such as the prompts of LLM):

     "chat_page_state": {
      "inputs": {
        "user_message": {
          "type": "IM",
          "user_input": true
        }
      },
      "tasks": [
        {
          "name": "generate_reply",
          "module_type": "AnyWidgetModule",
          "module_config": {
            "widget_id": "1744214024104448000", // GPT-3.5
            "system_prompt": "You are a teacher teaching Pro Config.",
            "user_prompt": "{{user_message}}",
            "memory": "{{context.memory}}",
            "output_name": "reply"
          }
        },
        ...
      ],
      "render": {
        "text": "{{reply}}",
        ...
      },
      ...
    }

In this case, Pro Config will replace the expression with the evaluated result of user_message (which is converted to string) and use that as the user_prompt.

Scope of Variables

It is also important to understand the scope of variables. Currently, there are two ways to retrieve a variable:

1. Referring to a variable that's defined within an AutomicState: we can directly use the variable name to refer to that variable (such as the {{user_message}} in the user_prompt). Note that the execution of the AutomicState follows the order of inputs->tasks->outputs->render

2. Passing a variable across different AutomicStates: we can use the context of the Automata to pass the variable. For example:

{
...
 "type": "automata",
 "context": {
    "var1": "",
  },
  "states": {
    "state1": {
      ...
      "outputs": {
        "context.var1": "{{some_variable}}",
      },
    },
    "state2": {
      "render": {
        "text": "{{context.var1}}",
      },
    }
  }
}

In the outputs of state1, we set context.var1 as some_variable (which should be defined previously in state1), and we can display that variable in state2 by {{context.var1}}. Note that the variable to be passed across states need to be declared in the context of the Automata

Practice Example

In the following example, we will improve the chatbot built in previous chapters in two aspects:

• Support customized intro_message and tts_widget_id

• Implement memory in LLM Widget to enable multiple rounds of chat.

Here is the config:

{
  "type": "automata",
  "id": "variable_expression_demo",
  "initial": "home_page_state",
  "context": {
    "intro_message": "",
    "tts_widget_id": "",
    "memory": ""
  },
  "transitions": {
    "go_home": "home_page_state"
  },
  "states": {
    "home_page_state": {
      "inputs": {
        "intro_message": {
          "type": "text",
          "user_input": true,
          "default_value": "Hi, this is your Pro Config Tutorial Bot, how can I assist you today"
        },
        "tts_widget_id": {
          "type": "text",
          "user_input": true,
          "default_value": "1743159010695057408"
        }
      },
      "outputs": {
        "context.intro_message": "{{intro_message}}",
        "context.tts_widget_id": "{{tts_widget_id}}"
      },
      "render": {
        "text": "Welcome to this demo. Click 'Start' to chat!",
        "buttons": [
          {
            "content": "Start Chat",
            "description": "Click to Start Chatting.",
            "on_click": "start_chat"
          }
        ]
      },
      "transitions": {
        "start_chat": "intro_message_state"
      }
    },
    "intro_message_state": {
      "render": {
        "text": "{{context.intro_message}}",
        "buttons": [
          {
            "content": "Home",
            "description": "Click to Go Back to Home.",
            "on_click": "go_home"
          }
        ]
      },
      "outputs": {
        "context.memory": "{{[]}}"
      },
      "transitions": {
        "CHAT": "chat_page_state"
      }
    },
    "chat_page_state": {
      "inputs": {
        "user_message": {
          "type": "IM",
          "user_input": true
        }
      },
      "tasks": [
        {
          "name": "generate_reply",
          "module_type": "AnyWidgetModule",
          "module_config": {
            "widget_id": "1744214024104448000", // GPT-3.5
            "system_prompt": "You are a teacher teaching Pro Config.",
            "user_prompt": "{{user_message}}",
            "memory": "{{context.memory}}",
            "output_name": "reply"
          }
        },
        {
          "name": "generate_voice",
          "module_type": "AnyWidgetModule",
          "module_config": {
            "widget_id": "{{context.tts_widget_id}}",
            "content": "{{reply}}",
            "output_name": "reply_voice"
          }
        }
      ],
      "outputs": {
        "context.memory": "{{[...context.memory, {'user': user_message}, {'assistant': reply}]}}"
      },
      "render": {
        "text": "{{reply}}",
        "audio": "{{reply_voice}}",
        "buttons": [
          {
            "content": "Home",
            "description": "Click to Go Back to Home.",
            "on_click": "go_home"
          }
        ]
      },
      "transitions": {
        "CHAT": "chat_page_state"
      }
    }
  }
}

In the above example config, we prompt the user to input intro_message and tts_widget_id, which are written to context in the outputs. These two variables are reused later in intro_message_state and chat_page_state respectively. Besides, we leverage an array called memory to store the chat history and update the memory through an expression (for the grammar of this, follow the Javascript syntax guide) :

  "outputs": {
    "context.memory": "{{[...memory, {'user': user_message}, {'assistant': reply}]}}"
  },

which will append the latest chat messages to the memory. Then we pass the memory to the memory parameter of LLMModule so that the LLM can retain understand how to react based on previous interactions.