Générez du contenu avec des appels de fonction.

Générez du contenu avec des appels de fonction. Cet exemple illustre un scénario de modalité textuelle avec une fonction et une requête.

En savoir plus

Pour obtenir une documentation détaillée incluant cet exemple de code, consultez les articles suivants :

• Appel de fonction
• API d'appel de fonction
• API de modèle pour Gemini dans Vertex AI

Exemple de code

C#

Avant d'essayer cet exemple, suivez les instructions de configuration pour C# décrites dans le guide de démarrage rapide de Vertex AI à l'aide des bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI C#.

Pour vous authentifier auprès de Vertex AI, configurez le service Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

using Google.Cloud.AIPlatform.V1;
using System;
using System.Threading.Tasks;
using Type = Google.Cloud.AIPlatform.V1.Type;
using Value = Google.Protobuf.WellKnownTypes.Value;

public class FunctionCalling
{
    public async Task<string> GenerateFunctionCall(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.0-pro-001")
    {
        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

        // Define the user's prompt in a Content object that we can reuse in
        // model calls
        var userPromptContent = new Content
        {
            Role = "USER",
            Parts =
            {
                new Part { Text = "What is the weather like in Boston?" }
            }
        };

        // Specify a function declaration and parameters for an API request
        var functionName = "get_current_weather";
        var getCurrentWeatherFunc = new FunctionDeclaration
        {
            Name = functionName,
            Description = "Get the current weather in a given location",
            Parameters = new OpenApiSchema
            {
                Type = Type.Object,
                Properties =
                {
                    ["location"] = new()
                    {
                        Type = Type.String,
                        Description = "Get the current weather in a given location"
                    },
                    ["unit"] = new()
                    {
                        Type = Type.String,
                        Description = "The unit of measurement for the temperature",
                        Enum = {"celsius", "fahrenheit"}
                    }
                },
                Required = { "location" }
            }
        };

        // Send the prompt and instruct the model to generate content using the tool that you just created
        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            GenerationConfig = new GenerationConfig
            {
                Temperature = 0f
            },
            Contents =
            {
                userPromptContent
            },
            Tools =
            {
                new Tool
                {
                    FunctionDeclarations = { getCurrentWeatherFunc }
                }
            }
        };

        GenerateContentResponse response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

        var functionCall = response.Candidates[0].Content.Parts[0].FunctionCall;
        Console.WriteLine(functionCall);

        string apiResponse = "";

        // Check the function name that the model responded with, and make an API call to an external system
        if (functionCall.Name == functionName)
        {
            // Extract the arguments to use in your API call
            string locationCity = functionCall.Args.Fields["location"].StringValue;

            // Here you can use your preferred method to make an API request to
            // fetch the current weather

            // In this example, we'll use synthetic data to simulate a response
            // payload from an external API
            apiResponse = @"{ ""location"": ""Boston, MA"",
                    ""temperature"": 38, ""description"": ""Partly Cloudy""}";
        }

        // Return the API response to Gemini so it can generate a model response or request another function call
        generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            Contents =
            {
                userPromptContent, // User prompt
                response.Candidates[0].Content, // Function call response,
                new Content
                {
                    Parts =
                    {
                        new Part
                        {
                            FunctionResponse = new()
                            {
                                Name = functionName,
                                Response = new()
                                {
                                    Fields =
                                    {
                                        { "content", new Value { StringValue = apiResponse } }
                                    }
                                }
                            }
                        }
                    }
                }
            },
            Tools =
            {
                new Tool
                {
                    FunctionDeclarations = { getCurrentWeatherFunc }
                }
            }
        };

        response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

        string responseText = response.Candidates[0].Content.Parts[0].Text;
        Console.WriteLine(responseText);

        return responseText;
    }
}

Python

Avant d'essayer cet exemple, suivez les instructions de configuration pour Python décrites dans le guide de démarrage rapide de Vertex AI à l'aide des bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Python.

Pour vous authentifier auprès de Vertex AI, configurez le service Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

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)

Étapes suivantes

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