Initial commit

templates/basic-embeddings.ts

@@ -0,0 +1,99 @@
+/**
+ * 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();