import { GoogleGenerativeAI } from '@google/generative-ai';

// Validate required environment variables
if (!process.env.GOOGLE_GENERATIVE_AI_API_KEY) {
  throw new Error('GOOGLE_GENERATIVE_AI_API_KEY environment variable is required');
}

if (!process.env.GOOGLE_EMBEDDING_MODEL) {
  throw new Error('GOOGLE_EMBEDDING_MODEL environment variable is required (e.g., gemini-embedding-001)');
}

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_GENERATIVE_AI_API_KEY);

const embeddingModel = genAI.getGenerativeModel({
  model: process.env.GOOGLE_EMBEDDING_MODEL,
});

/**
 * Generates a vector embedding for a given text using the configured Google embedding model.
 *
 * @param text - The text to embed
 * @returns A vector embedding (dimension depends on model)
 */
export async function generateEmbedding(text: string): Promise<number[]> {
  try {
    const result = await embeddingModel.embedContent(text);
    return result.embedding.values;
  } catch (error) {
    console.error('Error generating embedding:', error);
    throw new Error('Failed to generate AI embedding.');
  }
}