/**
 * Basic Gemini Embeddings Example (SDK)
 *
 * Demonstrates single text embedding generation using the @google/genai SDK.
 *
 * Setup:
 * 1. npm install @google/genai@^1.27.0
 * 2. export GEMINI_API_KEY="your-api-key"
 * 3. Get API key from: https://aistudio.google.com/apikey
 *
 * Usage:
 * npx tsx basic-embeddings.ts
 */

import { GoogleGenAI } from "@google/genai";

async function generateEmbedding(text: string) {
  // Initialize client with API key
  const ai = new GoogleGenAI({
    apiKey: process.env.GEMINI_API_KEY
  });

  if (!process.env.GEMINI_API_KEY) {
    throw new Error('GEMINI_API_KEY environment variable not set');
  }

  console.log(`\nGenerating embedding for: "${text}"\n`);

  // Generate embedding
  const response = await ai.models.embedContent({
    model: 'gemini-embedding-001', // Stable production model
    content: text,
    config: {
      taskType: 'SEMANTIC_SIMILARITY', // Optimize for similarity comparison
      outputDimensionality: 768 // Recommended for most use cases
    }
  });

  const embedding = response.embedding.values;

  console.log(`✅ Embedding generated successfully!`);
  console.log(`Dimensions: ${embedding.length}`);
  console.log(`First 10 values: [${embedding.slice(0, 10).map(v => v.toFixed(4)).join(', ')}...]`);
  console.log(`\nVector magnitude: ${Math.sqrt(embedding.reduce((sum, v) => sum + v * v, 0)).toFixed(4)}`);

  return embedding;
}

// Example usage
async function main() {
  try {
    const text = "What is the meaning of life?";
    const embedding = await generateEmbedding(text);

    // Compare with another text
    const text2 = "What is the purpose of existence?";
    console.log(`\nGenerating embedding for: "${text2}"\n`);

    const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
    const response2 = await ai.models.embedContent({
      model: 'gemini-embedding-001',
      content: text2,
      config: {
        taskType: 'SEMANTIC_SIMILARITY',
        outputDimensionality: 768
      }
    });

    const embedding2 = response2.embedding.values;

    // Calculate cosine similarity
    const cosineSimilarity = (a: number[], b: number[]): number => {
      let dotProduct = 0, magA = 0, magB = 0;
      for (let i = 0; i < a.length; i++) {
        dotProduct += a[i] * b[i];
        magA += a[i] * a[i];
        magB += b[i] * b[i];
      }
      return dotProduct / (Math.sqrt(magA) * Math.sqrt(magB));
    };

    const similarity = cosineSimilarity(embedding, embedding2);
    console.log(`\n🔗 Similarity between texts: ${(similarity * 100).toFixed(2)}%`);
    console.log('(1.0 = identical, 0.0 = completely different)\n');

  } catch (error: any) {
    console.error('❌ Error:', error.message);

    if (error.status === 401) {
      console.error('\nCheck that GEMINI_API_KEY is set correctly');
    } else if (error.status === 429) {
      console.error('\nRate limit exceeded. Free tier: 100 requests/minute');
    }

    process.exit(1);
  }
}

main();