@memberjunction/ai-azure

MemberJunction AI provider for Azure OpenAI Service. This package provides both LLM and embedding capabilities through Azure's enterprise-grade deployment of OpenAI models, implementing BaseLLM and BaseEmbeddings from @memberjunction/ai.

Architecture

graph TD
    A["AzureLLM
(Provider)"] -->|extends| B["BaseLLM
(@memberjunction/ai)"]
    C["AzureEmbedding
(Provider)"] -->|extends| D["BaseEmbeddings
(@memberjunction/ai)"]
    A -->|wraps| E["Azure AI Inference
REST Client"]
    C -->|wraps| E
    A -->|authenticates via| F["API Key or
Azure AD (Entra ID)"]
    B -->|registered via| G["@RegisterClass"]
    D -->|registered via| G

    style A fill:#7c5295,stroke:#563a6b,color:#fff
    style C fill:#7c5295,stroke:#563a6b,color:#fff
    style B fill:#2d6a9f,stroke:#1a4971,color:#fff
    style D fill:#2d6a9f,stroke:#1a4971,color:#fff
    style E fill:#2d8659,stroke:#1a5c3a,color:#fff
    style F fill:#b8762f,stroke:#8a5722,color:#fff
    style G fill:#b8762f,stroke:#8a5722,color:#fff
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Features

• Chat Completions: Full support for Azure-hosted language models including GPT-4, GPT-3.5-Turbo, and Phi-4
• Streaming Support: Real-time streaming responses for enhanced user experience
• Text Embeddings: Generate vector embeddings for semantic search and similarity matching
• Dual Authentication: Support for both API key and Azure Active Directory (Entra ID) authentication
• Text Processing: Built-in text summarization and classification capabilities
• Type Safety: Full TypeScript support with comprehensive type definitions
• Factory Pattern: Seamless integration with MemberJunction's AI factory system

Installation

npm install @memberjunction/ai-azure
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Configuration

Prerequisites

1. An Azure subscription with Azure AI or Azure OpenAI service deployed
2. Either an API key for your Azure AI resource or Azure AD credentials
3. The endpoint URL for your Azure AI resource

Authentication Methods

API Key Authentication

import { AzureLLM } from '@memberjunction/ai-azure';

const azureLLM = new AzureLLM('your-api-key');

azureLLM.SetAdditionalSettings({
    endpoint: 'https://your-resource.openai.azure.com/'
});
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Azure AD Authentication

const azureLLM = new AzureLLM('');

azureLLM.SetAdditionalSettings({
    endpoint: 'https://your-resource.openai.azure.com/',
    useAzureAD: true
});
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Usage

Chat Completion

const result = await azureLLM.ChatCompletion({
    model: 'gpt-4',
    messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: 'Explain the theory of relativity.' }
    ],
    maxOutputTokens: 500,
    temperature: 0.7
});

if (result.success) {
    console.log(result.data.choices[0].message.content);
}
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Streaming

const result = await azureLLM.ChatCompletion({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Write a detailed story.' }],
    streaming: true,
    streamingCallbacks: {
        OnContent: (content) => process.stdout.write(content),
        OnComplete: (result) => console.log('\nDone!')
    }
});
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Embeddings

import { AzureEmbedding } from '@memberjunction/ai-azure';

const embedder = new AzureEmbedding('your-api-key');
embedder.SetAdditionalSettings({
    endpoint: 'https://your-resource.openai.azure.com/'
});

const result = await embedder.EmbedText({
    model: 'text-embedding-ada-002',
    text: 'Sample text for embedding'
});

console.log(`Dimensions: ${result.vector.length}`);
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Configuration Options

Supported Parameters

Class Registration

• AzureLLM -- Registered via @RegisterClass(BaseLLM, 'AzureLLM')
• AzureEmbedding -- Registered via @RegisterClass(BaseEmbeddings, 'AzureEmbedding')

Dependencies

• @memberjunction/ai - Core AI abstractions
• @memberjunction/global - Class registration
• @azure-rest/ai-inference - Azure AI inference REST client
• @azure/core-auth - Azure authentication core
• @azure/identity - Azure identity and credential management