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

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

Save and Activate the Scenario
After configuring Google Vertex AI, Bitbucket, 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 Bitbucket integration works as expected. Depending on your setup, data should flow between Google Vertex AI and Bitbucket (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 Bitbucket
Bitbucket + Google Vertex AI + Slack: When a new commit is made to a Bitbucket repository, the file content is retrieved and analyzed by Google Vertex AI for potential vulnerabilities. If vulnerabilities are found, a notification is sent to a Slack channel.
Bitbucket + Google Vertex AI + Jira: When a new pull request is created in Bitbucket, and the code review fails, Google Vertex AI generates a bug report based on the code and creates a Jira ticket with the generated report.
Google Vertex AI and Bitbucket 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 Bitbucket
Automate code deployments and issue tracking by connecting Bitbucket to Latenode. Trigger workflows on commit events, automatically update project management tools, and send notifications. Latenode provides a visual editor, affordable scaling, and custom JS nodes for complex branching and data transformation. Streamline your DevOps pipeline with flexible automation.
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See how Latenode works
FAQ Google Vertex AI and Bitbucket
How can I connect my Google Vertex AI account to Bitbucket using Latenode?
To connect your Google Vertex AI account to Bitbucket 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 Bitbucket accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate code review summaries using Google Vertex AI and Bitbucket integration?
Yes, you can! Latenode lets you trigger AI-powered summaries upon each commit, enhancing code quality. Benefit from immediate, actionable feedback, streamlining your development cycles.
What types of tasks can I perform by integrating Google Vertex AI with Bitbucket?
Integrating Google Vertex AI with Bitbucket allows you to perform various tasks, including:
- Generating code documentation automatically based on commit messages.
- Performing sentiment analysis on issue comments to prioritize urgent issues.
- Automating code vulnerability scanning with AI-driven security analysis.
- Creating AI-powered chatbots to answer common developer questions.
- Generating release notes drafts based on commit history analysis.
How can I use VertexAI to improve code quality metrics in Bitbucket?
Latenode enables AI-driven code analysis, feeding insights back into Bitbucket for proactive improvements and better code maintainability.
Are there any limitations to the Google Vertex AI and Bitbucket integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex prompt engineering for Vertex AI may require advanced skills.
- Bitbucket API rate limits may affect high-volume automation workflows.
- Custom JavaScript code requires technical expertise for advanced logic.