Update code sample for generative-ai/function-calling to include AP…

…I response and model summary response from Gemini (#11254)

* Update function calling example and tests

* Remove unused params dict

* Update copyright

* Minor edit to comment

* Extract function name and parameters

* Update API calling example

* 🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

* Simplify and reuse content blocks in followup calls to generate model content

* 🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

---------

Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>

generative_ai/function_calling.py

@@ -1,4 +1,4 @@
-# Copyright 2023 Google LLC
+# Copyright 2024 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,15 +13,21 @@
 # limitations under the License.
 
 # [START aiplatform_gemini_function_calling]
+import vertexai
 from vertexai.generative_models import (
+    Content,
     FunctionDeclaration,
     GenerativeModel,
+    Part,
     Tool,
 )
 
 
-def generate_function_call(prompt: str) -> str:
-    # Load the Vertex AI Gemini API to use function calling
+def generate_function_call(prompt: str, project_id: str, location: str) -> str:
+    # Initialize Vertex AI
+    vertexai.init(project=project_id, location=location)
+
+    # Initialize Gemini model
     model = GenerativeModel("gemini-1.0-pro")
 
     # Specify a function declaration and parameters for an API request
@@ -40,20 +46,62 @@ def generate_function_call(prompt: str) -> str:
         function_declarations=[get_current_weather_func],
     )
 
-    # Prompt to ask the model about weather, which will invoke the Tool
-    prompt = prompt
+    # Define the user's prompt in a Content object that we can reuse in model calls
+    user_prompt_content = Content(
+        role="user",
+        parts=[
+            Part.from_text(prompt),
+        ],
+    )
 
-    # Instruct the model to generate content using the Tool that you just created:
+    # Send the prompt and instruct the model to generate content using the Tool that you just created
     response = model.generate_content(
-        prompt,
+        user_prompt_content,
         generation_config={"temperature": 0},
         tools=[weather_tool],
     )
+    response_function_call_content = response.candidates[0].content
 
-    return str(response)
+    # Check the function name that the model responded with, and make an API call to an external system
+    if (
+        response.candidates[0].content.parts[0].function_call.name
+        == "get_current_weather"
+    ):
+        # Extract the arguments to use in your API call
+        location = (
+            response.candidates[0].content.parts[0].function_call.args["location"]
+        )
 
+        # Here you can use your preferred method to make an API request to fetch the current weather, for example:
+        # api_response = requests.post(weather_api_url, data={"location": location})
 
-# [END aiplatform_gemini_function_calling]
+        # In this example, we'll use synthetic data to simulate a response payload from an external API
+        api_response = """{ "location": "Boston, MA", "temperature": 38, "description": "Partly Cloudy",
+                        "icon": "partly-cloudy", "humidity": 65, "wind": { "speed": 10, "direction": "NW" } }"""
+
+    # Return the API response to Gemini so it can generate a model response or request another function call
+    response = model.generate_content(
+        [
+            user_prompt_content,  # User prompt
+            response_function_call_content,  # Function call response
+            Content(
+                role="function",
+                parts=[
+                    Part.from_function_response(
+                        name="get_current_weather",
+                        response={
+                            "content": api_response,  # Return the API response to Gemini
+                        },
+                    )
+                ],
+            ),
+        ],
+        tools=[weather_tool],
+    )
+    # Get the model summary response
+    summary = response.candidates[0].content.parts[0].text
+
+    return summary, response
 
-if __name__ == "__main__":
-    print(generate_function_call("What is the weather like in Boston?"))
+
+# [END aiplatform_gemini_function_calling]

generative_ai/function_calling_test.py

@@ -1,4 +1,4 @@
-# Copyright 2023 Google LLC
+# Copyright 2024 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -24,19 +24,26 @@
 _LOCATION = "us-central1"
 
 
-function_expected_responses = [
-    "function_call",
-    "get_current_weather",
-    "args",
-    "fields",
-    "location",
+summary_expected = [
+    "Boston",
+]
+
+response_expected = [
+    "candidates",
+    "content",
+    "role",
+    "model",
+    "parts",
     "Boston",
 ]
 
 
 @backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
-def test_interview() -> None:
-    content = function_calling.generate_function_call(
-        prompt="What is the weather like in Boston?"
+def test_function_calling() -> None:
+    summary, response = function_calling.generate_function_call(
+        prompt="What is the weather like in Boston?",
+        project_id=_PROJECT_ID,
+        location=_LOCATION,
     )
-    assert all(x in content for x in function_expected_responses)
+    assert all(x in str(summary) for x in summary_expected)
+    assert all(x in str(response) for x in response_expected)