Added Examples for Batched Text Embeddings

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 os
+import google.generativeai as genai
+
+def batch_embed_example():
+    # 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.",
+        "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...")
+
+    try:
+        response = genai.embed_content(
+            model="models/embedding-001",
+            content=texts,
+            task_type="RETRIEVAL_DOCUMENT"
+        )
+
+        assert len(response["embedding"]) == len(texts)
+
+        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"First 5 dimensions: {emb[:5]}")
+            print(f"Total dimensions: {len(emb)}")
+
+    except Exception as e:
+        print(f"Error during batch embedding: {e}")
+
+if __name__ == "__main__":
+    batch_embed_example()