Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window.
Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window.
Generate content with function calls
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Generate content with function calls. This example demonstrates a text modality scenario with one function and one prompt.
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For detailed documentation that includes this code sample, see the following:
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.
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Set up authentication for a local development environment.
import vertexai
from vertexai.generative_models import (
Content,
FunctionDeclaration,
GenerationConfig,
GenerativeModel,
Part,
Tool,
)
# Initialize Vertex AI
# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
vertexai.init(project=project_id, location="us-central1")
# Initialize Gemini model
model = GenerativeModel(model_name="gemini-1.0-pro-001")
# 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("What is the weather like in Boston?"),
],
)
# Specify a function declaration and parameters for an API request
function_name = "get_current_weather"
get_current_weather_func = FunctionDeclaration(
name=function_name,
description="Get the current weather in a given location",
# 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 get_current_weather_func
weather_tool = Tool(
function_declarations=[get_current_weather_func],
)
# Send the prompt and instruct the model to generate content using the Tool that you just created
response = model.generate_content(
user_prompt_content,
generation_config=GenerationConfig(temperature=0),
tools=[weather_tool],
)
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 == function_name:
# 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 fetch the current weather, for example:
# api_response = requests.post(weather_api_url, data={"location": location})
# 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.candidates[0].content, # Function call response
Content(
parts=[
Part.from_function_response(
name=function_name,
response={
"content": api_response, # Return the API response to Gemini
},
),
],
),
],
tools=[weather_tool],
)
# Get the model response
print(response.text)
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"type": "thumb-down",
"id": "hardToUnderstand",
"label":"Hard to understand"
},{
"type": "thumb-down",
"id": "incorrectInformationOrSampleCode",
"label":"Incorrect information or sample code"
},{
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"label":"Missing the information/samples I need"
},{
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"type": "thumb-up",
"id": "easyToUnderstand",
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},{
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"label":"Solved my problem"
},{
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}]