From 9ade936d1ae8f6ce4c81dc20a4d02f32c4ebf571 Mon Sep 17 00:00:00 2001
From: aparna534 <aparnasivakumar135@gmail.com>
Date: Wed, 16 Apr 2025 22:41:39 +0530
Subject: [PATCH] Add batched embeddings example
---
examples/gemini/python/README.md | 3 ++
examples/gemini/python/batched_embeddings.py | 47 ++++++++++++++++++++
2 files changed, 50 insertions(+)
create mode 100644 examples/gemini/python/README.md
create mode 100644 examples/gemini/python/batched_embeddings.py
diff --git a/examples/gemini/python/README.md b/examples/gemini/python/README.md
new file mode 100644
index 000000000..b6dbe12d9
--- /dev/null
+++ b/examples/gemini/python/README.md
@@ -0,0 +1,3 @@
+## Embedding Examples
+
+- [batched_embeddings.py](./batched_embeddings.py) - Demonstrates efficient batch processing of multiple texts in a single API call
diff --git a/examples/gemini/python/batched_embeddings.py b/examples/gemini/python/batched_embeddings.py
new file mode 100644
index 000000000..14aa1dbf4
--- /dev/null
+++ b/examples/gemini/python/batched_embeddings.py
@@ -0,0 +1,47 @@
+"""
+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()