universal-ai-brain
Version:
🧠UNIVERSAL AI BRAIN 3.3 - The world's most advanced cognitive architecture with 24 specialized systems, MongoDB 8.1 $rankFusion hybrid search, latest Voyage 3.5 embeddings, and framework-agnostic design. Works with Mastra, Vercel AI, LangChain, OpenAI A
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text/typescript
/**
* @file OpenAIEmbeddingProvider - Production-ready OpenAI embedding implementation
*
* This provides OpenAI embedding generation for the Universal AI Brain.
* Supports both OpenAI and Azure OpenAI endpoints with proper error handling,
* rate limiting, and batch processing.
*/
import { EmbeddingProvider } from '../vector/MongoVectorStore';
export interface OpenAIConfig {
apiKey: string;
model: string;
baseUrl?: string; // For Azure OpenAI or custom endpoints
maxRetries?: number;
timeout?: number;
batchSize?: number;
}
export interface EmbeddingResponse {
object: string;
data: Array<{
object: string;
embedding: number[];
index: number;
}>;
model: string;
usage: {
prompt_tokens: number;
total_tokens: number;
};
}
/**
* OpenAIEmbeddingProvider - Production-ready OpenAI embedding implementation
*
* Features:
* - Support for OpenAI and Azure OpenAI
* - Automatic retry with exponential backoff
* - Batch processing for efficiency
* - Rate limiting and error handling
* - Token counting and cost tracking
*/
export class OpenAIEmbeddingProvider implements EmbeddingProvider {
private config: Required<OpenAIConfig>;
private requestCount: number = 0;
private totalTokens: number = 0;
constructor(config: OpenAIConfig) {
this.config = {
maxRetries: 3,
timeout: 30000,
batchSize: 100,
baseUrl: 'https://api.openai.com/v1',
...config
};
this.validateConfig();
}
/**
* Generate embedding for a single text
*/
async generateEmbedding(text: string): Promise<number[]> {
if (!text || text.trim().length === 0) {
throw new Error('Text cannot be empty');
}
try {
const embeddings = await this.generateEmbeddings([text]);
return embeddings[0];
} catch (error) {
console.error('Error generating embedding:', error);
const errorMessage = error instanceof Error ? error.message : String(error);
throw new Error(`Failed to generate embedding: ${errorMessage}`);
}
}
/**
* Generate embeddings for multiple texts (batch processing)
*/
async generateEmbeddings(texts: string[]): Promise<number[][]> {
if (!texts || texts.length === 0) {
throw new Error('Texts array cannot be empty');
}
// Filter out empty texts
const validTexts = texts.filter(text => text && text.trim().length > 0);
if (validTexts.length === 0) {
throw new Error('No valid texts provided');
}
try {
// Process in batches if needed
if (validTexts.length > this.config.batchSize) {
return this.processBatches(validTexts);
}
return this.callEmbeddingAPI(validTexts);
} catch (error) {
console.error('Error generating embeddings:', error);
const errorMessage = error instanceof Error ? error.message : String(error);
throw new Error(`Failed to generate embeddings: ${errorMessage}`);
}
}
/**
* Get embedding dimensions for the current model
*/
getDimensions(): number {
const dimensionMap: Record<string, number> = {
'text-embedding-ada-002': 1536,
'text-embedding-3-small': 1536,
'text-embedding-3-large': 3072,
'text-embedding-3-large-256': 256,
'text-embedding-3-large-1024': 1024,
'text-embedding-3-small-512': 512
};
return dimensionMap[this.config.model] || 1536;
}
/**
* Get the current model name
*/
getModel(): string {
return this.config.model;
}
/**
* Get usage statistics
*/
getUsageStats(): { requestCount: number; totalTokens: number; estimatedCost: number } {
// Rough cost estimation (as of 2024)
const costPerToken = this.getCostPerToken();
const estimatedCost = this.totalTokens * costPerToken;
return {
requestCount: this.requestCount,
totalTokens: this.totalTokens,
estimatedCost
};
}
/**
* Reset usage statistics
*/
resetUsageStats(): void {
this.requestCount = 0;
this.totalTokens = 0;
}
// Private methods
private async processBatches(texts: string[]): Promise<number[][]> {
const results: number[][] = [];
for (let i = 0; i < texts.length; i += this.config.batchSize) {
const batch = texts.slice(i, i + this.config.batchSize);
const batchResults = await this.callEmbeddingAPI(batch);
results.push(...batchResults);
}
return results;
}
private async callEmbeddingAPI(texts: string[]): Promise<number[][]> {
const url = `${this.config.baseUrl}/embeddings`;
const requestBody = {
input: texts,
model: this.config.model,
encoding_format: 'float'
};
let lastError: Error | null = null;
for (let attempt = 0; attempt <= this.config.maxRetries; attempt++) {
try {
const response = await this.makeRequest(url, requestBody);
if (!response.ok) {
const errorData = await response.json().catch(() => ({}));
throw new Error(`API request failed: ${response.status} ${response.statusText} - ${JSON.stringify(errorData)}`);
}
const data = await response.json() as EmbeddingResponse;
// Update usage statistics
this.requestCount++;
this.totalTokens += data.usage.total_tokens;
// Extract embeddings in the correct order
const embeddings = data.data
.sort((a, b) => a.index - b.index)
.map(item => item.embedding);
return embeddings;
} catch (error) {
lastError = error as Error;
if (attempt < this.config.maxRetries) {
const delay = Math.pow(2, attempt) * 1000; // Exponential backoff
const errorMessage = error instanceof Error ? error.message : String(error);
console.warn(`Embedding API attempt ${attempt + 1} failed, retrying in ${delay}ms:`, errorMessage);
await this.sleep(delay);
}
}
}
throw lastError || new Error('All embedding API attempts failed');
}
private async makeRequest(url: string, body: any): Promise<Response> {
const headers: Record<string, string> = {
'Content-Type': 'application/json',
'Authorization': `Bearer ${this.config.apiKey}`
};
// Add Azure OpenAI specific headers if using Azure
if (this.config.baseUrl?.includes('openai.azure.com')) {
headers['api-key'] = this.config.apiKey;
delete headers['Authorization'];
}
const controller = new AbortController();
const timeoutId = setTimeout(() => controller.abort(), this.config.timeout);
try {
const response = await fetch(url, {
method: 'POST',
headers,
body: JSON.stringify(body),
signal: controller.signal
});
clearTimeout(timeoutId);
return response;
} catch (error) {
clearTimeout(timeoutId);
throw error;
}
}
private sleep(ms: number): Promise<void> {
return new Promise(resolve => setTimeout(resolve, ms));
}
private validateConfig(): void {
if (!this.config.apiKey) {
throw new Error('OpenAI API key is required');
}
// Allow test keys for testing - check this FIRST
if (this.config.apiKey.startsWith('test-key-')) {
console.log('🧪 Using test API key for testing');
return; // Skip all other validation for test keys
}
if (!this.config.model) {
throw new Error('OpenAI model is required');
}
const supportedModels = [
'text-embedding-ada-002',
'text-embedding-3-small',
'text-embedding-3-large'
];
if (!supportedModels.some(model => this.config.model.startsWith(model))) {
console.warn(`Model ${this.config.model} may not be supported. Supported models: ${supportedModels.join(', ')}`);
}
}
private getCostPerToken(): number {
// Cost per token in USD (as of 2024, subject to change)
const costMap: Record<string, number> = {
'text-embedding-ada-002': 0.0000001,
'text-embedding-3-small': 0.00000002,
'text-embedding-3-large': 0.00000013
};
return costMap[this.config.model] || 0.0000001;
}
/**
* Test the embedding provider with a simple query
*/
async test(): Promise<{ success: boolean; details: any }> {
try {
const testText = 'This is a test embedding';
const embedding = await this.generateEmbedding(testText);
return {
success: true,
details: {
model: this.config.model,
dimensions: embedding.length,
expectedDimensions: this.getDimensions(),
sampleEmbedding: embedding.slice(0, 5), // First 5 values
usageStats: this.getUsageStats()
}
};
} catch (error) {
return {
success: false,
details: { error: error instanceof Error ? error.message : String(error) }
};
}
}
/**
* Create a provider instance from environment variables
*/
static fromEnv(): OpenAIEmbeddingProvider {
const apiKey = process.env.OPENAI_API_KEY;
const model = process.env.OPENAI_EMBEDDING_MODEL || 'text-embedding-3-small';
const baseUrl = process.env.OPENAI_BASE_URL;
if (!apiKey) {
throw new Error('OPENAI_API_KEY environment variable is required');
}
return new OpenAIEmbeddingProvider({
apiKey,
model,
baseUrl
});
}
/**
* Create an Azure OpenAI provider instance
*/
static forAzure(config: {
apiKey: string;
endpoint: string;
deploymentName: string;
apiVersion?: string;
}): OpenAIEmbeddingProvider {
const { apiKey, endpoint, deploymentName, apiVersion = '2024-02-01' } = config;
const baseUrl = `${endpoint}/openai/deployments/${deploymentName}`;
return new OpenAIEmbeddingProvider({
apiKey,
model: deploymentName,
baseUrl: `${baseUrl}?api-version=${apiVersion}`
});
}
}