Adalo and Google Vertex AI Integration

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Enrich Adalo apps with AI: use Google Vertex AI for image analysis or sentiment detection. Latenode’s visual editor and affordable execution pricing simplify AI integration and customization without complex code.

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Adalo

Google Vertex AI

Step 1: Choose a Trigger

Step 2: Choose an Action

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How to connect Adalo and Google Vertex AI

Create a New Scenario to Connect Adalo and Google Vertex AI

In the workspace, click the “Create New Scenario” button.

Add the First Step

Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Adalo, triggered by another scenario, or executed manually (for testing purposes). In most cases, Adalo or Google Vertex AI will be your first step. To do this, click "Choose an app," find Adalo or Google Vertex AI, and select the appropriate trigger to start the scenario.

Add the Adalo Node

Select the Adalo node from the app selection panel on the right.

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Configure the Adalo

Click on the Adalo node to configure it. You can modify the Adalo URL and choose between DEV and PROD versions. You can also copy it for use in further automations.

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Run node once

Add the Google Vertex AI Node

Next, click the plus (+) icon on the Adalo node, select Google Vertex AI from the list of available apps, and choose the action you need from the list of nodes within Google Vertex AI.

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Authenticate Google Vertex AI

Now, click the Google Vertex AI node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Google Vertex AI settings. Authentication allows you to use Google Vertex AI through Latenode.

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Configure the Adalo and Google Vertex AI Nodes

Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.

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Set Up the Adalo and Google Vertex AI Integration

Use various Latenode nodes to transform data and enhance your integration:

  • Branching: Create multiple branches within the scenario to handle complex logic.
  • Merging: Combine different node branches into one, passing data through it.
  • Plug n Play Nodes: Use nodes that don’t require account credentials.
  • Ask AI: Use the GPT-powered option to add AI capabilities to any node.
  • Wait: Set waiting times, either for intervals or until specific dates.
  • Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
  • Iteration: Process arrays of data when needed.
  • Code: Write custom code or ask our AI assistant to do it for you.
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Save and Activate the Scenario

After configuring Adalo, Google Vertex AI, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.

Test the Scenario

Run the scenario by clicking “Run once” and triggering an event to check if the Adalo and Google Vertex AI integration works as expected. Depending on your setup, data should flow between Adalo and Google Vertex AI (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.

Most powerful ways to connect Adalo and Google Vertex AI

Adalo + Google Vertex AI + Slack: When a new record is created in Adalo, representing user feedback, the feedback text is sent to Google Vertex AI for sentiment analysis. Based on the analysis, a message is sent to a designated Slack channel to alert the team about the feedback's sentiment.

Adalo + Google Vertex AI + Airtable: When a new record is created or updated in Adalo, the data is sent to Google Vertex AI to categorize user data. The enriched data, along with the AI's analysis results, are then stored in Airtable for further analysis and reporting.

Adalo and Google Vertex AI integration alternatives

About Adalo

Use Adalo with Latenode to automate tasks triggered by your no-code apps. Update databases, send custom notifications, or process data from Adalo forms in real-time. Latenode adds advanced logic, data transformation, and scaling beyond Adalo's limits, with flexible JavaScript coding and cost-effective execution pricing.

About Google Vertex AI

Use Vertex AI in Latenode to build AI-powered automation. Quickly integrate machine learning models for tasks like sentiment analysis or image recognition. Automate data enrichment or content moderation workflows without complex coding. Latenode’s visual editor makes it easier to chain AI tasks and scale them reliably, paying only for the execution time of each flow.

See how Latenode works

FAQ Adalo and Google Vertex AI

How can I connect my Adalo account to Google Vertex AI using Latenode?

To connect your Adalo account to Google Vertex AI on Latenode, follow these steps:

  • Sign in to your Latenode account.
  • Navigate to the integrations section.
  • Select Adalo and click on "Connect".
  • Authenticate your Adalo and Google Vertex AI accounts by providing the necessary permissions.
  • Once connected, you can create workflows using both apps.

Can I automatically classify Adalo database entries using AI?

Yes, you can! Latenode lets you use Google Vertex AI to categorize data from Adalo automatically. This allows for efficient data analysis and custom workflows, powered by AI and enhanced by Latenode's flexibility.

What types of tasks can I perform by integrating Adalo with Google Vertex AI?

Integrating Adalo with Google Vertex AI allows you to perform various tasks, including:

  • Generate personalized content for Adalo apps using AI.
  • Analyze user sentiment from Adalo data with Vertex AI.
  • Automatically translate Adalo app text into multiple languages.
  • Enhance Adalo image recognition capabilities using Vertex AI.
  • Predict user behavior within Adalo apps with AI models.

Can I use custom JavaScript code to transform data from Adalo?

Yes, Latenode allows custom JavaScript code to transform Adalo data before sending it to Google Vertex AI. This enables advanced manipulation beyond no-code options.

Are there any limitations to the Adalo and Google Vertex AI integration on Latenode?

While the integration is powerful, there are certain limitations to be aware of:

  • Large data transfers from Adalo may impact workflow execution time.
  • Complex AI models in Google Vertex AI could increase processing costs.
  • Adalo API rate limits might affect the frequency of data updates.

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