docs: add GenAI client examples to readme

sararob · copybara-github · commit f1e17a6b35fb · 2025-06-16T07:30:48.000-07:00
PiperOrigin-RevId: 772016883
diff --git a/README.rst b/README.rst
@@ -10,6 +10,65 @@ Gemini API and Generative AI on Vertex AI
    For Gemini API and Generative AI on Vertex AI, please reference `Vertex Generative AI SDK for Python`_
 .. _Vertex Generative AI SDK for Python: https://cloud.google.com/vertex-ai/generative-ai/docs/reference/python/latest
 
+Using the Google Gen AI SDK client from the Vertex AI SDK (Experimental)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To use features from the Google Gen AI SDK from the Vertex AI SDK, you can instantiate the client with the following:
+
+.. code-block:: Python
+
+    import vertexai
+    from vertexai import types
+
+    # Instantiate GenAI client from Vertex SDK
+    # Replace with your project ID and location
+    client = vertexai.Client(project='my-project', location='us-central1')
+
+See the examples below for guidance on how to use specific features supported by the Gen AI SDK client.
+
+Gen AI Evaluation
+^^^^^^^^^^^^^^^^^
+
+To run evaluation, first generate model responses from a set of prompts.
+
+.. code-block:: Python
+
+    import pandas as pd
+
+    prompts_df = pd.DataFrame({
+        "prompt": [
+            "What is the capital of France?",
+            "Write a haiku about a cat.",
+            "Write a Python function to calculate the factorial of a number.",
+            "Translate 'How are you?' to French.",
+        ],
+
+        "reference": [
+            "Paris",
+            "Sunbeam on the floor,\nA furry puddle sleeping,\nTwitching tail tells tales.",
+            "def factorial(n):\n    if n < 0:\n        return 'Factorial does not exist for negative numbers'\n    elif n == 0:\n        return 1\n    else:\n        fact = 1\n        i = 1\n        while i <= n:\n            fact *= i\n            i += 1\n        return fact",
+            "Comment ça va ?",
+        ]
+    })
+
+    inference_results = client.evals.run_inference(
+        model="gemini-2.5-flash-preview-05-20",
+        src=prompts_df
+    )
+
+Then run evaluation by providing the inference results and specifying the metric types.
+
+.. code-block:: Python
+
+    eval_result = client.evals.evaluate(
+        dataset=inference_results,
+        metrics=[
+            types.Metric(name='exact_match'),
+            types.Metric(name='rouge_l_sum'),
+            types.PrebuiltMetric.TEXT_QUALITY,
+        ]
+    )
+
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