@memberjunction/ai-openai NPM

@memberjunction/ai-openai

A comprehensive wrapper for OpenAI's API and models that seamlessly integrates with the MemberJunction AI framework, providing a standardized interface for GPT, embedding, and text-to-speech models.

Features

OpenAI Integration: Full integration with OpenAI's chat completion, embedding, and TTS models
Standardized Interface: Follows MemberJunction's BaseLLM, BaseEmbeddings, and BaseAudioGenerator abstract classes
Streaming Support: Full support for streaming chat completions
Message Formatting: Handles conversion between MemberJunction and OpenAI message formats
Multi-modal Support: Supports text and image content in messages
Response Format Support: Support for different response formats (Text, JSON, Markdown, ModelSpecific)
Reasoning Models: Support for reasoning effort levels (o1 models)
Error Handling: Comprehensive error handling with detailed reporting
Token Usage Tracking: Automatic tracking of prompt and completion tokens
Embeddings: Text embedding generation with multiple models
Text-to-Speech: Generate speech from text using OpenAI's TTS models

Installation

npm install @memberjunction/ai-openai

Requirements

Node.js 16+
An OpenAI API key
MemberJunction Core libraries

Usage

Basic Setup

import { OpenAILLM } from '@memberjunction/ai-openai';

// Initialize with your API key
const openAI = new OpenAILLM('your-openai-api-key');

Chat Completion

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

// Create chat parameters
const chatParams: ChatParams = {
  model: 'gpt-4',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is machine learning?' }
  ],
  temperature: 0.7,
  maxOutputTokens: 500,
  responseFormat: 'Text',
  includeLogProbs: false
};

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

Streaming Chat Completion

const streamingParams: ChatParams = {
  model: 'gpt-4',
  messages: [
    { role: 'user', content: 'Tell me a story' }
  ],
  temperature: 0.8,
  maxOutputTokens: 1000
};

// Stream the response
await openAI.StreamingChatCompletion(streamingParams, {
  onStart: () => console.log('Streaming started...'),
  onContent: (content) => process.stdout.write(content),
  onComplete: (fullContent) => console.log('\n\nComplete:', fullContent),
  onError: (error) => console.error('Error:', error),
  onUsage: (usage) => console.log('Token usage:', usage)
});

Multi-modal Messages (Text + Images)

const multiModalParams: ChatParams = {
  model: 'gpt-4-vision-preview',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', content: 'What do you see in this image?' },
        { type: 'image_url', content: 'https://example.com/image.jpg' }
      ]
    }
  ],
  maxOutputTokens: 500
};

const response = await openAI.ChatCompletion(multiModalParams);

JSON Response Format

const jsonParams: ChatParams = {
  model: 'gpt-4',
  messages: [
    { role: 'system', content: 'You are a helpful assistant that outputs JSON.' },
    { role: 'user', content: 'Generate a JSON object with name, age, and city for 3 fictional people.' }
  ],
  temperature: 0.3,
  maxOutputTokens: 500,
  responseFormat: 'JSON'
};

const jsonResponse = await openAI.ChatCompletion(jsonParams);
const jsonData = JSON.parse(jsonResponse.data.choices[0].message.content);
console.log('Structured Data:', jsonData);

Reasoning Models (o1 series)

const reasoningParams: ChatParams = {
  model: 'o1-preview',
  messages: [
    { role: 'user', content: 'Solve this complex math problem...' }
  ],
  effortLevel: 'high', // 'low', 'medium', or 'high'
  maxOutputTokens: 2000
};

const response = await openAI.ChatCompletion(reasoningParams);

Text Summarization

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

const text = `Long text that you want to summarize...`;

const summarizeParams: SummarizeParams = {
  text: text,
  model: 'gpt-3.5-turbo',
  temperature: 0.3,
  maxWords: 100
};

const summary = await openAI.SummarizeText(summarizeParams);
console.log('Summary:', summary.summary);

Text Embeddings

import { OpenAIEmbedding } from '@memberjunction/ai-openai';

const embedding = new OpenAIEmbedding('your-openai-api-key');

// Embed a single text
const singleResult = await embedding.EmbedText({
  text: 'The quick brown fox jumps over the lazy dog',
  model: 'text-embedding-3-small' // or 'text-embedding-3-large', 'text-embedding-ada-002'
});
console.log('Embedding vector:', singleResult.vector);

// Embed multiple texts
const multiResult = await embedding.EmbedTexts({
  texts: ['First text', 'Second text', 'Third text'],
  model: 'text-embedding-3-large'
});
console.log('Embedding vectors:', multiResult.vectors);

// Get available models
const models = await embedding.GetEmbeddingModels();
console.log('Available models:', models);

Text-to-Speech

import { OpenAIAudioGenerator } from '@memberjunction/ai-openai';

const tts = new OpenAIAudioGenerator('your-openai-api-key');

// Generate speech
const speechResult = await tts.CreateSpeech({
  text: 'Hello, this is a test of OpenAI text-to-speech.',
  model_id: 'gpt-4o-mini-tts',
  voice: 'nova', // 'alloy', 'echo', 'fable', 'onyx', 'nova', or 'shimmer'
  instructions: 'Speak in a cheerful and positive tone'
});

if (speechResult.success) {
  // speechResult.data contains the audio buffer
  // speechResult.content contains base64-encoded audio
  fs.writeFileSync('output.mp3', speechResult.data);
}

// Get available voices and models
const voices = await tts.GetVoices();
const models = await tts.GetModels();

Direct Access to OpenAI Client

// Access the underlying OpenAI client for advanced usage
const openAIClient = openAI.OpenAI;

// Use the client directly for features not wrapped
const completion = await openAIClient.completions.create({
  model: 'gpt-3.5-turbo-instruct',
  prompt: 'Say this is a test',
  max_tokens: 7
});

API Reference

OpenAILLM Class

Extends BaseLLM to provide OpenAI-specific chat and completion functionality.

Constructor

new OpenAILLM(apiKey: string)

Properties

OpenAI: (read-only) Returns the underlying OpenAI client instance
SupportsStreaming: (read-only) Returns true - OpenAI supports streaming

Methods

ChatCompletion(params: ChatParams): Promise<ChatResult> - Perform a chat completion
StreamingChatCompletion(params: ChatParams, callbacks: StreamingChatCallbacks): Promise<void> - Stream a chat completion
SummarizeText(params: SummarizeParams): Promise<SummarizeResult> - Summarize text
ClassifyText(params: ClassifyParams): Promise<ClassifyResult> - Classify text (not implemented)
ConvertMJToOpenAIChatMessages(messages: ChatMessage[]): ChatCompletionMessageParam[] - Convert MJ to OpenAI format
ConvertMJToOpenAIRole(role: string): 'system' | 'user' | 'assistant' - Convert MJ roles to OpenAI roles

OpenAIEmbedding Class

Extends BaseEmbeddings to provide OpenAI embedding functionality.

Constructor

new OpenAIEmbedding(apiKey: string)

Methods

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

OpenAIAudioGenerator Class

Extends BaseAudioGenerator to provide OpenAI text-to-speech functionality.

Constructor

new OpenAIAudioGenerator(apiKey: string)

Methods

CreateSpeech(params: TextToSpeechParams): Promise<SpeechResult> - Generate speech from text
SpeechToText(params: SpeechToTextParams): Promise<SpeechResult> - Convert speech to text (not implemented)
GetVoices(): Promise<VoiceInfo[]> - Get available voices
GetModels(): Promise<AudioModel[]> - Get available TTS models
GetPronounciationDictionaries(): Promise<PronounciationDictionary[]> - Get pronunciation dictionaries (empty)
GetSupportedMethods(): Promise<string[]> - Get supported methods

Embedding Models

text-embedding-3-large: Most capable model (3,072 dimensions)
text-embedding-3-small: Balanced performance (1,536 dimensions)
text-embedding-ada-002: Legacy 2nd generation model (1,536 dimensions)

TTS Voices

alloy: Neutral and balanced
echo: Warm and conversational
fable: Expressive and animated
onyx: Deep and authoritative
nova: Friendly and upbeat
shimmer: Soft and gentle

Response Formats

The OpenAILLM class supports various response formats:

Text: Regular text responses (default)
JSON: Structured JSON responses (requires compatible model)
Markdown: Markdown-formatted responses
Any: Model decides the format
ModelSpecific: Custom formats with modelSpecificResponseFormat parameter

Error Handling

The wrapper provides comprehensive error information:

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

Integration with MemberJunction

This package seamlessly integrates with the MemberJunction AI framework:

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

// The OpenAI classes are automatically registered
const engine = new AIEngine();
const llm = engine.GetLLM('OpenAILLM', 'your-api-key');
const embeddings = engine.GetEmbedding('OpenAIEmbedding', 'your-api-key');
const tts = engine.GetAudioGenerator('OpenAIAudioGenerator', 'your-api-key');