docs: Add working batched embeddings example (Fixes #147)

Author: Aparna

examples/gemini/python/batched_embeddings.py

@@ -4,12 +4,16 @@
 Demonstrates how to efficiently process multiple texts in a single API call.
 """
 
+import os
 import google.generativeai as genai
 
 def batch_embed_example():
-    # Configure the API (in production, use environment variables)
-    genai.configure(api_key="YOUR_API_KEY_HERE")  # Replace with your actual API key
-    
+    # Configure the API
+    api_key = os.getenv("GOOGLE_API_KEY")
+    if not api_key:
+        raise ValueError("GOOGLE_API_KEY environment variable not set")
+    genai.configure(api_key=api_key)
+
     # Sample texts to embed
     texts = [
         "The quick brown fox jumps over the lazy dog.",
@@ -18,26 +22,24 @@ def batch_embed_example():
         "This example shows best practices for the Gemini Embeddings API.",
         "Batch processing reduces API calls and improves performance."
     ]
-    
+
     print(f"Embedding {len(texts)} texts in a single batch...")
-    
-    # Make a single batch request
+
     try:
         response = genai.embed_content(
             model="models/embedding-001",
             content=texts,
             task_type="RETRIEVAL_DOCUMENT"
         )
-        
-        # Verify we got embeddings for all texts
+
         assert len(response["embedding"]) == len(texts)
-        
-        print("\nSuccess! Embeddings generated:")
-        for i, (text, embedding) in enumerate(zip(texts, response["embedding"])):
+
+        print(f"\nSuccess! Generated {len(texts)} embeddings:")
+        for i, (text, emb) in enumerate(zip(texts, response["embedding"])):
             print(f"\nText {i+1}: {text[:50]}...")
-            print(f"Embedding vector (first 5 dims): {embedding[:5]}")
-            print(f"Vector length: {len(embedding)}")
-            
+            print(f"First 5 dimensions: {emb[:5]}")
+            print(f"Total dimensions: {len(emb)}")
+
     except Exception as e:
         print(f"Error during batch embedding: {e}")