Function calling with Gemini AI Model

The code sample demonstrates how to use the Vertex AI Generative Models to do function calling.

Explore further

For detailed documentation that includes this code sample, see the following:

• Function calling reference

Code sample

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import vertexai

from vertexai.preview.generative_models import (
    FunctionDeclaration,
    GenerativeModel,
    Tool,
    ToolConfig,
)

# TODO(developer): Update & uncomment below line
# PROJECT_ID = "your-project-id"

# Initialize Vertex AI
vertexai.init(project=PROJECT_ID, location="us-central1")

# Specify a function declaration and parameters for an API request
get_product_sku_func = FunctionDeclaration(
    name="get_product_sku",
    description="Get the available inventory for a Google products, e.g: Pixel phones, Pixel Watches, Google Home etc",
    # Function parameters are specified in JSON schema format
    parameters={
        "type": "object",
        "properties": {
            "product_name": {"type": "string", "description": "Product name"}
        },
    },
)

# Specify another function declaration and parameters for an API request
get_store_location_func = FunctionDeclaration(
    name="get_store_location",
    description="Get the location of the closest store",
    # Function parameters are specified in JSON schema format
    parameters={
        "type": "object",
        "properties": {"location": {"type": "string", "description": "Location"}},
    },
)

# Define a tool that includes the above functions
retail_tool = Tool(
    function_declarations=[
        get_product_sku_func,
        get_store_location_func,
    ],
)

# Define a tool config for the above functions
retail_tool_config = ToolConfig(
    function_calling_config=ToolConfig.FunctionCallingConfig(
        # ANY mode forces the model to predict a function call
        mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
        # List of functions that can be returned when the mode is ANY.
        # If the list is empty, any declared function can be returned.
        allowed_function_names=["get_product_sku"],
    )
)

model = GenerativeModel(
    model_name="gemini-1.5-flash-002",
    tools=[retail_tool],
    tool_config=retail_tool_config,
)
response = model.generate_content(
    "Do you have the Pixel 8 Pro 128GB in stock?",
)

print(response.candidates[0].function_calls)
# Example response:
# [
# name: "get_product_sku"
# args {
#   fields { key: "product_name" value { string_value: "Pixel 8 Pro 128GB" }}
#   }
# ]

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