How to connect Google Vertex AI and Google Cloud Firestore
Create a New Scenario to Connect Google Vertex AI and Google Cloud Firestore
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 Google Vertex AI, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Vertex AI or Google Cloud Firestore will be your first step. To do this, click "Choose an app," find Google Vertex AI or Google Cloud Firestore, and select the appropriate trigger to start the scenario.

Add the Google Vertex AI Node
Select the Google Vertex AI node from the app selection panel on the right.

Google Vertex AI
Configure the Google Vertex AI
Click on the Google Vertex AI node to configure it. You can modify the Google Vertex AI URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Google Cloud Firestore Node
Next, click the plus (+) icon on the Google Vertex AI node, select Google Cloud Firestore from the list of available apps, and choose the action you need from the list of nodes within Google Cloud Firestore.

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Authenticate Google Cloud Firestore
Now, click the Google Cloud Firestore node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Google Cloud Firestore settings. Authentication allows you to use Google Cloud Firestore through Latenode.
Configure the Google Vertex AI and Google Cloud Firestore Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Google Vertex AI and Google Cloud Firestore 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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AI Anthropic Claude 3
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Trigger on Webhook
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Save and Activate the Scenario
After configuring Google Vertex AI, Google Cloud Firestore, 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 Google Vertex AI and Google Cloud Firestore integration works as expected. Depending on your setup, data should flow between Google Vertex AI and Google Cloud Firestore (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Google Vertex AI and Google Cloud Firestore
Google Cloud Firestore + Google Vertex AI + Slack: This automation monitors Google Cloud Firestore for new customer feedback documents. It then uses Google Vertex AI (Gemini) to analyze and summarize the feedback. Finally, it posts the summary to a designated Slack channel for the team to review.
Google Cloud Firestore + Google Vertex AI + Google Sheets: This flow tracks AI model training data stored in Google Cloud Firestore. Google Vertex AI (Gemini) analyzes the training data. The analyzed results, such as performance metrics, are then logged into a Google Sheet for easy tracking and analysis.
Google Vertex AI and Google Cloud Firestore integration alternatives
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.
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About Google Cloud Firestore
Use Google Cloud Firestore in Latenode to build real-time data workflows. Automate database tasks like data synchronization, backups, or event-driven updates without coding. Combine Firestore with AI tools and webhooks for powerful apps. Create complex workflows with simple visual tools and scale affordably with Latenode's pay-as-you-go pricing.
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FAQ Google Vertex AI and Google Cloud Firestore
How can I connect my Google Vertex AI account to Google Cloud Firestore using Latenode?
To connect your Google Vertex AI account to Google Cloud Firestore on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Vertex AI and click on "Connect".
- Authenticate your Google Vertex AI and Google Cloud Firestore accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze user sentiment data stored in Firestore using Vertex AI?
Yes, you can! Latenode lets you automate sentiment analysis, triggering actions in other apps based on the results—enhancing customer support workflows.
What types of tasks can I perform by integrating Google Vertex AI with Google Cloud Firestore?
Integrating Google Vertex AI with Google Cloud Firestore allows you to perform various tasks, including:
- Automating AI model training data storage.
- Generating content using AI and saving it directly.
- Creating personalized user experiences based on AI insights.
- Analyzing customer feedback and storing sentiment scores.
- Building AI-powered chatbots with persistent conversation logs.
How does Latenode handle data transformations between Vertex AI and Firestore?
Latenode provides flexible data mapping and transformation tools, including JavaScript blocks, ensuring seamless data flow between apps.
Are there any limitations to the Google Vertex AI and Google Cloud Firestore integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large data transfers may incur additional processing time.
- Complex AI models might require optimization for real-time performance.
- API rate limits of Google Vertex AI and Google Cloud Firestore apply.