Python
Antes de testar esse exemplo, siga as instruções de configuração para Python no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente.
Para mais informações, consulte a documentação de referência da API Vertex AI para Python.
Para autenticar na Vertex AI, configure o Application Default Credentials.
Para mais informações, consulte
Configurar a autenticação para um ambiente de desenvolvimento local.
import vertexai
from vertexai.generative_models import (
FunctionDeclaration,
GenerationConfig,
GenerativeModel,
Part,
Tool,
)
# Initialize Vertex AI
# project_id = "PROJECT_ID"
# TODO(developer): Update and un-comment below lines
vertexai.init(project=project_id, location="us-central1")
# Specify a function declaration and parameters for an API request
get_product_sku = "get_product_sku"
get_product_sku_func = FunctionDeclaration(
name=get_product_sku,
description="Get the SKU for a product",
# Function parameters are specified in OpenAPI 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 OpenAPI 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,
],
)
# Initialize Gemini model
model = GenerativeModel(
model_name="gemini-1.0-pro-001",
generation_config=GenerationConfig(temperature=0),
tools=[retail_tool],
)
# Start a chat session
chat = model.start_chat()
# Send a prompt for the first conversation turn that should invoke the get_product_sku function
response = chat.send_message("Do you have the Pixel 8 Pro in stock?")
function_call = response.candidates[0].function_calls[0]
print(function_call)
# Check the function name that the model responded with, and make an API call to an external system
if function_call.name == get_product_sku:
# Extract the arguments to use in your API call
product_name = function_call.args["product_name"] # noqa: F841
# Here you can use your preferred method to make an API request to retrieve the product SKU, as in:
# api_response = requests.post(product_api_url, data={"product_name": product_name})
# In this example, we'll use synthetic data to simulate a response payload from an external API
api_response = {"sku": "GA04834-US", "in_stock": "yes"}
# Return the API response to Gemini, so it can generate a model response or request another function call
response = chat.send_message(
Part.from_function_response(
name=get_product_sku,
response={
"content": api_response,
},
),
)
# Extract the text from the model response
print(response.text)
# Send a prompt for the second conversation turn that should invoke the get_store_location function
response = chat.send_message(
"Is there a store in Mountain View, CA that I can visit to try it out?"
)
function_call = response.candidates[0].function_calls[0]
print(function_call)
# Check the function name that the model responded with, and make an API call to an external system
if function_call.name == "get_store_location":
# Extract the arguments to use in your API call
location = function_call.args["location"] # noqa: F841
# Here you can use your preferred method to make an API request to retrieve store location closest to the user, as in:
# api_response = requests.post(store_api_url, data={"location": location})
# In this example, we'll use synthetic data to simulate a response payload from an external API
api_response = {"store": "2000 N Shoreline Blvd, Mountain View, CA 94043, US"}
# Return the API response to Gemini, so it can generate a model response or request another function call
response = chat.send_message(
Part.from_function_response(
name="get_store_location",
response={
"content": api_response,
},
),
)
# Extract the text from the model response
print(response.text)
