How to connect Google Vertex AI and LlamaCloud
Create a New Scenario to Connect Google Vertex AI and LlamaCloud
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 LlamaCloud will be your first step. To do this, click "Choose an app," find Google Vertex AI or LlamaCloud, 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 LlamaCloud Node
Next, click the plus (+) icon on the Google Vertex AI node, select LlamaCloud from the list of available apps, and choose the action you need from the list of nodes within LlamaCloud.

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Authenticate LlamaCloud
Now, click the LlamaCloud node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your LlamaCloud settings. Authentication allows you to use LlamaCloud through Latenode.
Configure the Google Vertex AI and LlamaCloud 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 LlamaCloud 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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LlamaCloud
Trigger on Webhook
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Webhook response
Save and Activate the Scenario
After configuring Google Vertex AI, LlamaCloud, 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 LlamaCloud integration works as expected. Depending on your setup, data should flow between Google Vertex AI and LlamaCloud (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 LlamaCloud
Google Sheets + Google Vertex AI + Google Sheets: When a new row is added to a Google Sheet, the content from a specified column is sent to Google Vertex AI's Gemini model for sentiment analysis. The sentiment score is then added to a new column in the same Google Sheet.
LlamaCloud + Google Vertex AI + Slack: When a document is parsed by LlamaCloud, the extracted data is sent to Google Vertex AI's Gemini model for analysis. If the analysis meets a predefined criteria (e.g., a significant change in sentiment or a keyword is detected), a message is sent to a designated Slack channel.
Google Vertex AI and LlamaCloud 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 LlamaCloud
Use LlamaCloud inside Latenode to streamline AI model deployment. Build workflows that automate prompt engineering, A/B testing, and model evaluation. Connect data sources, trigger LlamaCloud jobs, and manage results via webhooks or REST. Scale AI tasks and track performance visually without complex code.
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FAQ Google Vertex AI and LlamaCloud
How can I connect my Google Vertex AI account to LlamaCloud using Latenode?
To connect your Google Vertex AI account to LlamaCloud 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 LlamaCloud accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate document summarization using Vertex AI and LlamaCloud?
Yes, you can! Latenode's visual editor makes combining Vertex AI's text capabilities with LlamaCloud's data processing easy, automating summarization workflows.
What types of tasks can I perform by integrating Google Vertex AI with LlamaCloud?
Integrating Google Vertex AI with LlamaCloud allows you to perform various tasks, including:
- Automating sentiment analysis of customer feedback in real time.
- Building AI-powered chatbots with enhanced knowledge retrieval.
- Creating personalized marketing content at scale.
- Automatically tagging images and videos for content management.
- Generating summaries of long documents.
How do I handle errors when using Google Vertex AI on Latenode?
Latenode offers built-in error handling and logging features to help you identify and resolve issues efficiently.
Are there any limitations to the Google Vertex AI and LlamaCloud integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Rate limits imposed by Google Vertex AI and LlamaCloud still apply.
- Complex data transformations may require custom JavaScript code.
- Large file processing may experience performance bottlenecks.