docs: Add batched embeddings example with current model names (Fixes #147)

Author: Aparna

examples/gemini/python/README.md

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+## Embedding Examples
+
+- [batched_embeddings.py](./batched_embeddings.py) - Demonstrates efficient batch processing of multiple texts in a single API call

examples/gemini/python/batched_embeddings.py

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+"""
+Batched Embeddings Example for Gemini API
+
+Demonstrates how to efficiently process multiple texts in a single API call.
+"""
+
+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
+    
+    # Sample texts to embed
+    texts = [
+        "The quick brown fox jumps over the lazy dog.",
+        "Gemini's batch embeddings are more efficient than individual calls.",
+        "Always prefer batch processing when possible.",
+        "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"\nText {i+1}: {text[:50]}...")
+            print(f"Embedding vector (first 5 dims): {embedding[:5]}")
+            print(f"Vector length: {len(embedding)}")
+            
+    except Exception as e:
+        print(f"Error during batch embedding: {e}")
+
+if __name__ == "__main__":
+    batch_embed_example()