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Technical Reference

Google Vertex AI

Generate content and images using Gemini and Imagen models on Google Vertex AI.

CategoryArtificial Intelligence
Authenticationcustom
Version0.1.6
Published scopes0 published scopes
Last updatedSeptember 30, 2026

Overview

Connect Google Vertex AI with Flows360

Generate content and images using Gemini and Imagen models on Google Vertex AI.

Use the Google Vertex AI connector to include its declared actions and events in Flows360 workflows without duplicating the integration logic inside each consuming system. This pinned connector version currently exposes 3 actions and no declared triggers.

Available actions

  • Generate Content
  • Generate Image
  • Custom API Call

Available triggers

  • No triggers are declared in this pinned package.

The capability list above is generated from the exact reviewed connector package so the public description stays aligned with the version Flows360 operates.

Technical guide

Technical reference

Package identity

  • Connector: Google Vertex AI
  • Package: @activepieces/piece-google-vertexai@0.1.6
  • Exact artifact digest: sha256:74e0ee7650240aa2067cd65066aec7f0b270c3930f2d0fcde8990a1daf8467f1
  • Runtime: Activepieces-compatible deterministic worker
  • Source: the exact reviewed Activepieces source

Authentication

CUSTOM AUTH. Connection values are tenant-specific and remain in the consuming Studio instance; CDK stores definitions and field metadata, not customer credential values.

Action contracts

  • Generate Content (generate_content), Call a Gemini model on Vertex AI to generate a text response. Classification: read.
  • Generate Image (generate_image), Generate an image from a text prompt using Google Imagen models on Vertex AI. Classification: read.
  • Custom API Call (custom_api_call), Make a custom API call to a specific endpoint Classification: read.

Trigger contracts

  • No trigger delivery contracts are declared.

Verification

Runtime execution evidence is managed separately from package metadata.

Capability summary

Authenticationcustom
Supported objects3
Supported actions3
TriggersNone published

Authentication

Authentication method: custom

Authentication profiles

connection · custom
  • Service Account JSON Required

Supported objects

ObjectReadWriteNotes
Generate Content Yes No Call a Gemini model on Vertex AI to generate a text response.
Generate Image Yes No Generate an image from a text prompt using Google Imagen models on Vertex AI.
Custom API Call Yes No Make a custom API call to a specific endpoint

Supported actions

Generate Content · Read

Call a Gemini model on Vertex AI to generate a text response.

generate_content · Risk: low · Retry: safe

Inputs

  • location · string · Required, Google Cloud region where your Vertex AI resources are hosted.
  • model · string · Required, Gemini model to use for content generation.
  • systemMessage · string · Optional, Instructions that guide the model's behavior throughout the conversation.
  • userMessage · string · Required, The prompt to send to the model.
  • files · array · Optional, Optional files to include in the prompt (images, PDFs, text, audio, video)
  • youtubeUrl · string · Optional, Optional public YouTube video URL for the AI to use as a reference.
  • imageUrls · array · Optional, Public image URLs to include alongside the user message. The model will analyze these images.
  • temperature · number · Optional, Controls randomness. Lower values are more deterministic (0u20132). Leave empty to use the model default.
  • maxOutputTokens · number · Optional, Maximum number of tokens to generate. Leave empty to use the model default.
  • thinkingLevel · string · Optional, Controls how much the model thinks before responding. Supported on Gemini 2.5 and later models.
  • thinkingBudget · number · Optional, Maximum number of tokens the model can use for internal reasoning. Set to 0 to disable thinking. Supported on Gemini 2.5 and later models.
  • includeThoughts · boolean · Optional, When enabled, the model's reasoning is returned alongside the final response. Supported on Gemini 2.5 and later models.
Generate Image · Read

Generate an image from a text prompt using Google Imagen models on Vertex AI.

generate_image · Risk: low · Retry: safe

Inputs

  • location · string · Required, Google Cloud region where your Vertex AI resources are hosted.
  • model · string · Required, Model to use for image generation.
  • prompt · string · Required, A text description of the image you want to generate.
  • modelOptions · object · Required
Custom API Call · Read

Make a custom API call to a specific endpoint

custom_api_call · Risk: low · Retry: safe

Inputs

  • url · object · Required
  • method · string · Required
  • headers · object · Required, Authorization headers are injected automatically from your connection.
  • queryParams · object · Required
  • body_type · string · Optional
  • body · object · Optional
  • response_is_binary · boolean · Optional, Enable for files like PDFs, images, etc.
  • failsafe · boolean · Optional
  • timeout · number · Optional
  • followRedirects · boolean · Optional

Setup

Setup

  1. Add the Google Vertex AI connector to the workflow in the target Flows360 Studio instance.
  2. Create or select the tenant-specific connection required by the connector. Connection secrets remain in the Studio connection boundary and are not copied into CDK documentation.
  3. Provide the connection fields declared by the pinned package:
  • Service Account JSON (serviceAccountJson), required.
  1. Select the required action or trigger and complete its declared input fields. Required and optional inputs are defined by the exact package schema.
  2. Test the workflow in the appropriate environment before enabling production scheduling or event delivery.

Limitations

Limitations and operational notes

  • This content describes @activepieces/piece-google-vertexai@0.1.6; provider behaviour can change independently and should be revalidated when the provider or connector version changes.
  • Only the 3 actions and 0 triggers declared by this pinned package are represented here.
  • Tenant credentials and connection values are not stored in the public connector record.
  • Provider-side permissions, account entitlements, quotas and rate limits remain subject to the connected provider account and are not inferred when the package does not declare them.
  • Runtime execution evidence is managed separately from package metadata.

Troubleshooting

Why canu2019t the connector authenticate?

Check the tenant connection in Studio and confirm every required Google Vertex AI connection field is present. Re-authorise OAuth-based connections if the provider token or consent has expired. Do not place credential values in CDK content or logs.

Why is an action or trigger unavailable?

Confirm that the workflow is using @activepieces/piece-google-vertexai@0.1.6 and compare the requested capability with the declared action and trigger list for this version. A capability that is not declared by the pinned package should not be presented as supported.

What should I check after a provider-side change?

Revalidate authentication, required fields, action/trigger behaviour and provider documentation before publishing refreshed connector content or moving a new package version into production.

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