Vertex AI: Chat AI action

The Vertex AI: Chat AI action uses Google's Vertex AI completion API for interacting with the models. It generates text in a conversational format.

Prerequisites

  • You must have the Bot creator role to use the Vertex Chat AI action in a bot.
  • Ensure that you have the necessary credentials to send a request and have included Connect action before calling any Google Cloud actions.

This example shows how to send a natural language message using the Vertex AI: Chat AI action and get an appropriate response.

Procedure

  1. In the Automation Anywhere Control Room, navigate to the Actions pane, select Generative AI > Google, drag Vertex AI: Chat AI, and place it in the canvas.
  2. Enter or select the following fields:

    Vertex Chat AI

    1. Enter the Project Number/Name. This is the unique Project ID from the GCP. For information on Project ID, see Google Cloud Project's Project ID.
    2. Enter the Location. For more information on Vertex AI location, see Vertex AI locations.
    3. Click Publisher drop-down and select Google; or select 3rd Party to enter a third-party publisher.
    4. Select a large language model (LLM) to use for your prompt from the Model dropdown. You can select the following models:
      • chat-bison (latest)
      • chat-bison-32k (latest)
      • chat-bison-32k@002
      • chat-bison@001
      • chat-bison@002
      • codechat-bison
      • codechat-bison-32k
      • codechat-bison-32k@002
      • codechat-bison@001
      • codechat-bison@002
      • Other supported version to input other supported models.
    5. Enter a chat Message to use by the model to generate a response.
      Note: Chat actions retain the result of the previous chat action within the same session. If you call chat actions consecutively, the model can understand subsequent messages and relate them to the previous message. However, all chat history is deleted after the session ends.
    6. Enter the maximum number of tokens (Max tokens) to generate. By default, if you do not enter a value, then the maximum number of tokens generated is automatically set to keep it within the maximum context length of the selected model by considering the length of the generated response.
    7. Enter a Temperature. This value refers to the randomness of the response. As the temperature approaches zero, the response becomes more focused and deterministic. The higher the value, the more random is the response.
    8. Enter Default as the session name to limit the session to the current session.
    9. To manage the optional parameters, click the Show more options and select Yes. If you select Yes, you can add other parameters such as: Context, Examples, Top K, and Top P. For information about these optional parameters, see Learn Models.
    10. Save the response to a variable. In this example, the response is saved to VertexChatResponse.
  3. Click Run to start the bot. You can read the value of the field by printing the response in a Message box action. In this example, VertexChatResponse prints the response. You can add additional chat requests to get additional responses.
    Tip: To maintain multiple chats in the same bot, you will need to create multiple sessions with different names or variables.