@@ -28,6 +28,25 @@
 generative_models as preview_generative_models,
 )
 
+
+# A dummy function for function calling
+def get_current_weather(location: str, unit: str = "centigrade"):
+"""Gets weather in the specified location.
+
+Args:
+location: The location for which to get the weather.
+unit: Optional. Temperature unit. Can be Centigrade or Fahrenheit. Defaults to Centigrade.
+
+Returns:
+The weather information as a dict.
+"""
+return dict(
+location=location,
+unit=unit,
+weather="Super nice, but maybe a bit hot.",
+)
+
+
 _REQUEST_FUNCTION_PARAMETER_SCHEMA_STRUCT = {
 "type": "object",
 "properties": {
@@ -320,3 +339,34 @@ def test_generate_content_function_calling(self):
 summary = response.candidates[0].content.parts[0].text
 
 assert summary
+
+def test_chat_automatic_function_calling(self):
+get_current_weather_func = generative_models.FunctionDeclaration.from_func(
+get_current_weather
+)
+
+weather_tool = generative_models.Tool(
+function_declarations=[get_current_weather_func],
+)
+
+model = preview_generative_models.GenerativeModel(
+"gemini-1.0-pro",
+# Specifying the tools once to avoid specifying them in every request
+tools=[weather_tool],
+)
+
+chat = model.start_chat(
+responder=preview_generative_models.AutomaticFunctionCallingResponder(
+max_automatic_function_calls=1,
+)
+)
+
+response = chat.send_message("What is the weather like in Boston?")
+
+assert response.text
+assert "nice" in response.text
+assert len(chat.history) == 4
+assert chat.history[-3].parts[0].function_call
+assert chat.history[-3].parts[0].function_call.name == "get_current_weather"
+assert chat.history[-2].parts[0].function_response
+assert chat.history[-2].parts[0].function_response.name == "get_current_weather"

@@ -538,6 +538,45 @@ def test_generate_content_grounding_vertex_ai_search_retriever(self):
 )
 assert response.text
 
+@mock..object(
+target=prediction_service.PredictionServiceClient,
+attribute="generate_content",
+new=mock_generate_content,
+)
+def test_chat_automatic_function_calling(self):
+generative_models = preview_generative_models
+get_current_weather_func = generative_models.FunctionDeclaration.from_func(
+get_current_weather
+)
+weather_tool = generative_models.Tool(
+function_declarations=[get_current_weather_func],
+)
+
+model = generative_models.GenerativeModel(
+"gemini-pro",
+# Specifying the tools once to avoid specifying them in every request
+tools=[weather_tool],
+)
+afc_responder = generative_models.AutomaticFunctionCallingResponder(
+max_automatic_function_calls=5,
+)
+chat = model.start_chat(responder=afc_responder)
+
+response1 = chat.send_message("What is the weather like in Boston?")
+assert response1.text.startswith("The weather in Boston is")
+assert "nice" in response1.text
+assert len(chat.history) == 4
+assert chat.history[-3].parts[0].function_call
+assert chat.history[-2].parts[0].function_response
+
+# Test max_automatic_function_calls:
+# Setting the AFC limit to 0 to test the error handling
+afc_responder._max_automatic_function_calls = 0
+chat2 = model.start_chat(responder=afc_responder)
+with pytest.raises(RuntimeError) as err:
+chat2.send_message("What is the weather like in Boston?")
+assert err.match("Exceeded the maximum")
+
 
 EXPECTED_SCHEMA_FOR_GET_CURRENT_WEATHER = {
 "title": "get_current_weather",

@@ -116,6 +116,52 @@ print(chat.send_message(
 ))
 ```
 
+
+#### Automatic Function calling
+
+```
+from vertexai..preview generative_models import GenerativeModel, Tool, FunctionDeclaration, AutomaticFunctionCallingResponder
+
+# First, create functions that the model is can use to answer your questions.
+def get_current_weather(location: str, unit: str = "centigrade"):
+"""Gets weather in the specified location.
+
+Args:
+location: The location for which to get the weather.
+unit: Optional. Temperature unit. Can be Centigrade or Fahrenheit. Defaults to Centigrade.
+"""
+return dict(
+location=location,
+unit=unit,
+weather="Super nice, but maybe a bit hot.",
+)
+
+# Infer function schema
+get_current_weather_func = FunctionDeclaration.from_func(get_current_weather)
+# Tool is a collection of related functions
+weather_tool = Tool(
+function_declarations=[get_current_weather_func],
+)
+
+# Use tools in chat:
+model = GenerativeModel(
+"gemini-pro",
+# You can specify tools when creating a model to avoid having to send them with every request.
+tools=[weather_tool],
+)
+
+# Activate automatic function calling:
+afc_responder = AutomaticFunctionCallingResponder(
+# Optional:
+max_automatic_function_calls=5,
+)
+chat = model.start_chat(responder=afc_responder)
+# Send a message to the model. The model will respond with a function call.
+# The SDK will automatically call the requested function and respond to the model.
+# The model will use the function call response to answer the original question.
+print(chat.send_message("What is the weather like in Boston?"))
+```
+
 ## Documentation
 
 You can find complete documentation for the Vertex AI SDKs and the Gemini model in the Google Cloud [documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview)