sumudunissanka/ai.googleapis.vertex

Overview

This module offers APIs for connecting with models hosted on Google Vertex AI, including Google Gemini models and partner models from Anthropic, Mistral, Meta, DeepSeek, Qwen, Kimi, and MiniMax available through the Vertex AI Model Garden.

Prerequisites

Before using this module in your Ballerina application, you must have a Google Cloud project with Vertex AI enabled.

• Create a Google Cloud account and set up a project.
• Enable the Vertex AI API for your project.
• Obtain a valid OAuth2 access token (e.g., via gcloud auth print-access-token).

Quickstart

To use the ai.googleapis.vertex module in your Ballerina application, update the .bal file as follows:

Step 1: Import the module

Import the ai.googleapis.vertex module.

import ballerinax/ai.googleapis.vertex;

Step 2: Initialize the Model Provider

Here's how to initialize the Model Provider:

import ballerina/ai;
import ballerinax/ai.googleapis.vertex;

final ai:ModelProvider vertexModel = check new vertex:ModelProvider(
    accessToken = "your-gcp-access-token",
    projectId = "your-gcp-project-id",
    location = "us-central1",
    model = "google/gemini-2.0-flash"
);

Step 3: Invoke chat completion

ai:ChatMessage[] chatMessages = [{role: "user", content: "hi"}];
ai:ChatAssistantMessage response = check vertexModel->chat(chatMessages, tools = []);

chatMessages.push(response);

Step 4: Generate typed output

type Sentiment record {|
    string label;
    decimal score;
|};

@ai:JsonSchema {
    "type": "object",
    "required": ["label", "score"],
    "properties": {
        "label": {"type": "string", "enum": ["positive", "neutral", "negative"]},
        "score": {"type": "number"}
    }
}
type SentimentType Sentiment;

Sentiment|error result = vertexModel->generate(
    `Analyze the sentiment of: "I love this product!"`
);

Step 5: Use an embedding provider

import ballerina/ai;
import ballerinax/ai.googleapis.vertex;

final ai:EmbeddingProvider vertexEmbedding = check new vertex:EmbeddingProvider(
    accessToken = "your-gcp-access-token",
    projectId = "your-gcp-project-id",
    location = "us-central1",
    model = "text-embedding-005"
);

ai:Embedding embedding = check vertexEmbedding->embed(<ai:TextChunk>{content: "Hello, world!"});