google vertexai sink Google Vertex AI Sink

Provided by: "Apache Software Foundation"

Support Level for this Kamelet is: "Preview"

Send data to Google Vertex AI for generating content with generative AI models.

Configuration Options

The following table summarizes the configuration options available for the google-vertexai-sink Kamelet:

Property Name Description Type Default Example

location

Google Cloud Location

Required The Google Cloud region (e.g., us-central1).

string

modelId

Model Id

Required The Model ID to use for predictions (e.g., gemini-2.5-pro).

string

projectId

Google Cloud Project Id

Required The Google Cloud Project ID.

string

maxOutputTokens

Max Output Tokens

Maximum number of tokens to generate in the response.

string

operation

Operation

The operation to perform.

Enum values:

* generateText * generateChat * generateImage * generateEmbeddings * generateCode * generateMultimodal * rawPredict

string

generateText

serviceAccountKey

Service Account Key

The service account key to use as credentials for the Vertex AI client. You must encode this value in base64.

binary

temperature

Temperature

Controls randomness in generation. Lower values make output more deterministic. Range 0.0 to 1.0.

string

topK

Top K

Only sample from the top K options for each subsequent token.

string

topP

Top P

Nucleus sampling parameter. Considers tokens with top_p probability mass. Range 0.0 to 1.0.

string

Dependencies

At runtime, the google-vertexai-sink Kamelet relies upon the presence of the following dependencies:

  • camel:core

  • camel:kamelet

  • camel:google-vertexai

Camel JBang usage

Prerequisites

  • You’ve installed JBang.

  • You have executed the following command:

jbang app install camel@apache/camel

Supposing you have a file named route.yaml with this content:

- route:
    from:
      uri: "kamelet:timer-source"
      parameters:
        period: 10000
        message: 'test'
      steps:
        - to:
            uri: "kamelet:google-vertexai-sink"

You can now run it directly through the following command

camel run route.yaml

Google Vertex AI Sink Kamelet Description

Authentication

This Kamelet uses Google Cloud service account authentication. The service account key is optional - if not provided, the Kamelet will use Application Default Credentials (ADC).

If you provide a service account key, it must be base64-encoded. Ensure that the service account has the aiplatform.endpoints.predict permission (typically granted through the Vertex AI User role).

Required Configuration

  • Project ID: The Google Cloud Project ID

  • Location: The Google Cloud region where Vertex AI models are available (e.g., us-central1)

  • Model ID: The model identifier to use for predictions (e.g., gemini-2.5-pro)

Optional Configuration

  • Service Account Key: Base64-encoded service account credentials

  • Operation: The operation to perform (default: generateText). Supported operations are generateText, generateChat, generateImage, generateEmbeddings, generateCode, generateMultimodal, and rawPredict

  • Temperature: Controls randomness in generation (0.0 to 1.0)

  • Max Output Tokens: Maximum number of tokens to generate in the response

  • Top P: Nucleus sampling parameter (0.0 to 1.0)

  • Top K: Only sample from the top K options for each subsequent token

Content Generation

The Kamelet sends the message body as a prompt to the specified Google Vertex AI model and returns the generated content. It supports Google native models (Gemini, Imagen) as well as partner models (Claude, Llama, Mistral) through the rawPredict operation.

Kamelet source file

https://github.com/apache/camel-kamelets/blob/main/kamelets/google-vertexai-sink.kamelet.yaml

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