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basic_example.py
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# 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.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from vertexai.generative_models import GenerationResponse
PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
def generate_function_call() -> GenerationResponse:
# [START generativeaionvertexai_gemini_function_calling]
import vertexai
from vertexai.generative_models import (
Content,
FunctionDeclaration,
GenerationConfig,
GenerativeModel,
Part,
Tool,
)
# TODO(developer): Update & uncomment below line
# PROJECT_ID = "your-project-id"
# Initialize Vertex AI
vertexai.init(project=PROJECT_ID, location="us-central1")
# Initialize Gemini model
model = GenerativeModel("gemini-1.5-flash-002")
# 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 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)
# Example response:
# The weather in Boston is partly cloudy with a temperature of 38 degrees Fahrenheit.
# The humidity is 65% and the wind is blowing from the northwest at 10 mph.
# [END generativeaionvertexai_gemini_function_calling]
return response
if __name__ == "__main__":
generate_function_call()