import { OpenAICompatibleEmbedder } from "./openai-compatible"
import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces/embedder"
import { MAX_ITEM_TOKENS } from "../constants"
/**
* Vercel AI Gateway embedder implementation that wraps the OpenAI Compatible embedder
* with configuration for Vercel AI Gateway's embedding API.
*
* Supported models:
* - openai/text-embedding-3-small (dimension: 1536)
* - openai/text-embedding-3-large (dimension: 3072)
* - openai/text-embedding-ada-002 (dimension: 1536)
* - cohere/embed-v4.0 (dimension: 1024)
* - google/gemini-embedding-001 (dimension: 768)
* - google/text-embedding-005 (dimension: 768)
* - google/text-multilingual-embedding-002 (dimension: 768)
* - amazon/titan-embed-text-v2 (dimension: 1024)
* - mistral/codestral-embed (dimension: 1536)
* - mistral/mistral-embed (dimension: 1024)
*/
export class VercelAiGatewayEmbedder implements IEmbedder {
private readonly openAICompatibleEmbedder: OpenAICompatibleEmbedder
private static readonly VERCEL_AI_GATEWAY_BASE_URL = "https://ai-gateway.vercel.sh/v1"
private static readonly DEFAULT_MODEL = "openai/text-embedding-3-large"
private readonly modelId: string
/**
* Creates a new Vercel AI Gateway embedder
* @param apiKey The Vercel AI Gateway API key for authentication
* @param modelId The model ID to use (defaults to openai/text-embedding-3-large)
*/
constructor(apiKey: string, modelId?: string) {
if (!apiKey) {
throw new Error("API key is required for Vercel AI Gateway embedder")
}
// Use provided model or default
this.modelId = modelId || VercelAiGatewayEmbedder.DEFAULT_MODEL
// Create an OpenAI Compatible embedder with Vercel AI Gateway's configuration
this.openAICompatibleEmbedder = new OpenAICompatibleEmbedder(
VercelAiGatewayEmbedder.VERCEL_AI_GATEWAY_BASE_URL,
apiKey,
this.modelId,
MAX_ITEM_TOKENS,
)
}
/**
* Creates embeddings for the given texts using Vercel AI Gateway's embedding API
* @param texts Array of text strings to embed
* @param model Optional model identifier (uses constructor model if not provided)
* @returns Promise resolving to embedding response
*/
async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
try {
// Use the provided model or fall back to the instance's model
const modelToUse = model || this.modelId
return await this.openAICompatibleEmbedder.createEmbeddings(texts, modelToUse)
} catch (error) {
// TelemetryService calls removed as per requirements
throw error
}
}
/**
* Validates the Vercel AI Gateway embedder configuration by delegating to the underlying OpenAI-compatible embedder
* @returns Promise resolving to validation result with success status and optional error message
*/
async validateConfiguration(): Promise<{ valid: boolean; error?: string }> {
try {
// Delegate validation to the OpenAI-compatible embedder
// The error messages will be specific to Vercel AI Gateway since we're using Vercel's base URL
return await this.openAICompatibleEmbedder.validateConfiguration()
} catch (error) {
// TelemetryService calls removed as per requirements
throw error
}
}
/**
* Returns information about this embedder
*/
get embedderInfo(): EmbedderInfo {
return {
name: "vercel-ai-gateway",
}
}
/**
* Gets the optimal batch size for this Vercel AI Gateway embedder
*/
get optimalBatchSize(): number {
// Delegate to the underlying OpenAI-compatible embedder's optimal batch size
return this.openAICompatibleEmbedder.optimalBatchSize
}
}
