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@memberjunction/ai-vertex
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2.59.0 • Public • Published

@memberjunction/ai-vertex

A comprehensive wrapper for Google Vertex AI services, enabling seamless integration with the MemberJunction AI framework for a wide range of AI models hosted on Google Cloud.

Features

  • Google Vertex AI Integration: Connect to Google Cloud's Vertex AI platform and access a variety of foundation models
  • Standardized Interface: Implements MemberJunction's BaseLLM and BaseEmbeddings abstract classes
  • Model Diversity: Access models like PaLM, Gemini, and third-party models through a unified interface
  • Token Usage Tracking: Automatic tracking of prompt and completion tokens
  • Response Format Control: Support for various response formats including text and structured data
  • Error Handling: Robust error handling with detailed reporting
  • Chat Completion: Full support for chat-based interactions with supported models
  • Embedding Generation: Generate text embeddings for semantic search and other applications (currently simulated, pending full Vertex AI embedding API support)
  • Streaming Support: Stream responses for real-time UI experiences

Installation

npm install @memberjunction/ai-vertex

Requirements

  • Node.js 16+
  • Google Cloud credentials with Vertex AI access
  • MemberJunction Core libraries

Usage

Basic Setup

import { VertexLLM, VertexEmbedding } from '@memberjunction/ai-vertex';

// Path to Google Cloud service account key file
const keyFilePath = '/path/to/service-account-key.json';
const projectId = 'your-google-cloud-project-id';
const location = 'us-central1'; // Optional, defaults to 'us-central1'

// Initialize with your Google Cloud credentials
const vertexLLM = new VertexLLM(keyFilePath, projectId, location);
const vertexEmbedding = new VertexEmbedding(keyFilePath, projectId, location);

Chat Completion with PaLM Models

import { ChatParams } from '@memberjunction/ai';

// Create chat parameters for PaLM on Vertex AI
const chatParams: ChatParams = {
  model: 'text-bison',  // Use the Vertex AI model name
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What are the main features of Google Vertex AI?' }
  ],
  temperature: 0.7,
  maxOutputTokens: 1000
};

// Get a response
try {
  const response = await vertexLLM.ChatCompletion(chatParams);
  if (response.success) {
    console.log('Response:', response.data.choices[0].message.content);
    console.log('Token Usage:', response.data.usage);
    console.log('Time Elapsed (ms):', response.timeElapsed);
  } else {
    console.error('Error:', response.errorMessage);
  }
} catch (error) {
  console.error('Exception:', error);
}

Chat Completion with Gemini Models

// Example with Google's Gemini model (access through Vertex)
const geminiParams: ChatParams = {
  model: 'gemini-pro',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Explain the concept of foundation models.' }
  ],
  temperature: 0.5,
  maxOutputTokens: 800
};

const geminiResponse = await vertexLLM.ChatCompletion(geminiParams);

Streaming Chat Completion

import { ChatParams, StreamingChatCallbacks } from '@memberjunction/ai';

// Define streaming callbacks
const callbacks: StreamingChatCallbacks = {
  OnContent: (chunk: string, isComplete: boolean) => {
    process.stdout.write(chunk);
  },
  OnComplete: (finalResponse) => {
    console.log('\nTotal tokens:', finalResponse.data.usage.totalTokens);
  },
  OnError: (error) => {
    console.error('Streaming error:', error);
  }
};

// Create streaming chat parameters
const streamingParams: ChatParams = {
  model: 'gemini-pro',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Write a short story about cloud computing.' }
  ],
  streaming: true,
  streamingCallbacks: callbacks
};

// Start streaming
await vertexLLM.ChatCompletion(streamingParams);

Text Embedding

Note: The embedding functionality is currently simulated as the Google Vertex AI SDK for Node.js does not yet provide direct access to embedding endpoints. This will be updated once the SDK supports native embedding generation.

import { EmbedTextParams, EmbedTextsParams } from '@memberjunction/ai';

// Embed a single text
const embedParams: EmbedTextParams = {
  model: 'textembedding-gecko',
  text: 'This is a sample text to embed.'
};

const embedResult = await vertexEmbedding.EmbedText(embedParams);
console.log('Embedding vector length:', embedResult.vector.length);
console.log('Tokens used:', embedResult.ModelUsage.promptTokens);

// Embed multiple texts
const multiEmbedParams: EmbedTextsParams = {
  model: 'textembedding-gecko',
  texts: [
    'First text to embed.',
    'Second text to embed.',
    'Third text to embed.'
  ]
};

const multiEmbedResult = await vertexEmbedding.EmbedTexts(multiEmbedParams);
console.log('Number of embeddings:', multiEmbedResult.vectors.length);

Supported Models

Google Vertex AI offers a variety of foundation models. Here are some of the key models:

Text/Chat Models

  • PaLM 2 Family: text-bison, chat-bison, text-unicorn
  • Gemini Family: gemini-pro, gemini-pro-vision, gemini-ultra
  • Code Generation: code-bison, codechat-bison
  • Third-party Models: Models from other providers may be available through Vertex AI

Embedding Models (Simulated)

  • Text Embeddings: textembedding-gecko, textembedding-gecko-multilingual
    • Note: Currently simulated pending SDK support

Multimodal Models

  • Gemini Vision: gemini-pro-vision
  • Imagen: imagegeneration@005

Check the Google Vertex AI documentation for the latest list of supported models.

API Reference

VertexLLM Class

A class that extends BaseLLM to provide Google Vertex AI-specific functionality.

Constructor

new VertexLLM(apiKey: string, projectId: string, location: string = 'us-central1')
  • apiKey: Path to the Google Cloud service account key file
  • projectId: Your Google Cloud project ID
  • location: The Google Cloud region for Vertex AI (defaults to 'us-central1')

Properties

  • Client: (read-only) Returns the underlying Vertex AI client instance
  • SupportsStreaming: (read-only) Returns true - Google Vertex AI supports streaming

Methods

  • ChatCompletion(params: ChatParams): Promise<ChatResult> - Perform a chat completion with optional streaming support
  • SummarizeText(params: SummarizeParams): Promise<SummarizeResult> - Not implemented yet
  • ClassifyText(params: ClassifyParams): Promise<ClassifyResult> - Not implemented yet

VertexEmbedding Class

A class that extends BaseEmbeddings to provide Google Vertex AI embedding functionality.

Constructor

new VertexEmbedding(apiKey: string, projectId: string, location: string = 'us-central1')
  • apiKey: Path to the Google Cloud service account key file
  • projectId: Your Google Cloud project ID
  • location: The Google Cloud region for Vertex AI (defaults to 'us-central1')

Properties

  • Client: (read-only) Returns the underlying Vertex AI client instance

Methods

  • EmbedText(params: EmbedTextParams): Promise<EmbedTextResult> - Generate embeddings for a single text (currently simulated)
  • EmbedTexts(params: EmbedTextsParams): Promise<EmbedTextsResult> - Generate embeddings for multiple texts (currently simulated)
  • GetEmbeddingModels(): Promise<any> - Get available embedding models

Loader Functions

The package exports loader functions to prevent tree-shaking of the registered classes:

import { LoadVertexLLM, LoadVertexEmbedding } from '@memberjunction/ai-vertex';

// Call these functions if you need to ensure the classes are registered
LoadVertexLLM();
LoadVertexEmbedding();

These functions are typically not needed in normal usage as importing the classes will automatically register them.

Error Handling

The wrapper provides detailed error information:

try {
  const response = await vertexLLM.ChatCompletion(params);
  if (!response.success) {
    console.error('Error:', response.errorMessage);
    console.error('Status:', response.statusText);
    console.error('Exception:', response.exception);
  }
} catch (error) {
  console.error('Exception occurred:', error);
}

Token Usage Tracking

Monitor token usage for billing and quota management:

const response = await vertexLLM.ChatCompletion(params);
if (response.success) {
  console.log('Prompt Tokens:', response.data.usage.promptTokens);
  console.log('Completion Tokens:', response.data.usage.completionTokens);
  console.log('Total Tokens:', response.data.usage.totalTokens);
}

Limitations

Currently, the wrapper implements:

  • Chat completion functionality with token usage tracking
  • Streaming response support
  • Simulated embedding functionality (pending native SDK support)

Future implementations may include:

  • Native embedding functionality (once supported by the Google Cloud Vertex AI SDK)
  • SummarizeText functionality
  • ClassifyText functionality
  • Support for image generation models
  • Support for multimodal inputs

Dependencies

  • @google-cloud/vertexai: Google Cloud SDK for Vertex AI
  • @memberjunction/ai: MemberJunction AI core framework
  • @memberjunction/global: MemberJunction global utilities

Integration with MemberJunction

This package integrates seamlessly with the MemberJunction AI framework:

  1. Standardized Interface: Implements the same interface as other AI providers in the MemberJunction ecosystem
  2. Provider Registration: Classes are automatically registered using the @RegisterClass decorator
  3. Model Management: Compatible with MemberJunction's AI model management system
  4. Token Tracking: Integrates with MemberJunction's usage tracking and billing systems

Using with MemberJunction AI Core

import { GetAIAPIKey } from '@memberjunction/ai';
import { VertexLLM } from '@memberjunction/ai-vertex';

// Get API key from MemberJunction configuration
const apiKey = GetAIAPIKey('vertex');
const projectId = 'your-project-id';

// Create provider instance
const vertex = new VertexLLM(apiKey, projectId);

// Use with MemberJunction AI services
// The provider will be automatically available through the AI factory system

Implementation Notes

Authentication

The package expects the apiKey parameter to be a path to a Google Cloud service account key file, not an API key string. This file should contain the credentials for accessing Vertex AI services.

Model Name Mapping

The package automatically handles different model types based on their name prefix:

  • Models starting with gemini- are handled as Gemini models
  • Models starting with text- are handled as text generation models
  • Models starting with code- are handled as code generation models

Message Role Mapping

The package maps MemberJunction message roles to Vertex AI format:

  • system → system
  • assistant → model
  • user → user

Building and Development

# Build the package
npm run build

# Watch mode for development
npm run watch

# Run type checking
tsc --noEmit

License

See the repository root for license information.

Readme

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npm i @memberjunction/ai-vertex

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Collaborators

  • craig.adam.bc
  • jstfelix
  • anag123
  • hiltongr
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