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Google VertexAI Integration

Overview​

Google VertexAI integration allows you to leverage Google's powerful language models within AnswerAI. This feature enables you to use ChatGoogleVertexAI for various natural language processing tasks, enhancing your AI-powered applications.

Key Benefits​

  • Access to state-of-the-art language models from Google
  • Seamless integration with AnswerAI's workflow
  • Customizable model parameters for fine-tuned outputs

How to Use​

Prerequisites​

  1. Set up a Google Cloud Platform (GCP) account
  2. Install the Google Cloud CLI

Step 1: Enable Vertex AI API​

  1. Navigate to Vertex AI on your GCP console
  2. Click "ENABLE ALL RECOMMENDED API"

Step 2: Create Credential File (Optional)​

Choose one of the following methods:

Method 1: Use GCP CLI​

  1. Open your terminal and run:

    gcloud auth application-default login

  2. Log in to your GCP account

  3. Locate your credential file at ~/.config/gcloud/application_default_credentials.json

Method 2: Use GCP Console​

  1. Go to GCP console and click "CREATE CREDENTIALS"
  2. Create a service account
  3. Fill in the service account details and click "CREATE AND CONTINUE"
  4. Select an appropriate role (e.g., Vertex AI User) and click "DONE"
  5. Click on the created service account, then "ADD KEY" -> "Create new key"
  6. Select JSON format and click "CREATE" to download your credential file

Step 3: Configure AnswerAI​

  1. In AnswerAI, go to the Credential page and click "Add credential"
  2. Select "Google Vertex Auth"
  3. Register your credential file using one of these options:
    • Enter the path to your credential file in "Google Application Credential File Path"
    • Copy and paste the content of your credential file into "Google Credential JSON Object"
  4. Click "Add" to save the credential

Step 4: Use ChatGoogleVertexAI in Your Workflow​

  1. In your AnswerAI workflow, add a ChatGoogleVertexAI node
  2. Configure the node parameters:
    • Select the credential you created
    • Choose a model name (e.g., "chat-bison")
    • Set temperature and other optional parameters
  3. Connect the ChatGoogleVertexAI node to other nodes in your workflow

Tips and Best Practices​

  • Experiment with different temperature settings to balance creativity and coherence in outputs
  • Use the cache option to improve response times for repeated queries
  • Adjust maxOutputTokens to control the length of generated responses
  • Fine-tune topP and topK parameters for more diverse outputs

Troubleshooting​

  • If you encounter authentication errors, double-check your credential file and ensure it's correctly configured in AnswerAI
  • For "Model not found" errors, verify that you've selected a valid model name from the available options
  • If you experience rate limiting, consider upgrading your GCP account or optimizing your usage

Remember to comply with Google's usage policies and monitor your API usage to manage costs effectively.