Desde el 29 de abril del 2025, los modelos Gemini 1.5 Pro y Gemini 1.5 Flash no están disponibles en proyectos donde no se hayan utilizado previamente, incluidos los proyectos nuevos. Para obtener más información, consulta Versiones y ciclo de vida de los modelos.

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Generar contenido con la llamada de función

En este ejemplo se muestra cómo usar las declaraciones de funciones para influir en el contenido generado por Gemini Multimodal.

Investigar más

Para obtener documentación detallada que incluya este código de muestra, consulta lo siguiente:

Código de ejemplo

Go

Antes de probar este ejemplo, sigue las Go instrucciones de configuración de la guía de inicio rápido de Vertex AI con bibliotecas de cliente. Para obtener más información, consulta la documentación de referencia de la API Go de Vertex AI.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Para obtener más información, consulta el artículo Configurar la autenticación en un entorno de desarrollo local.

import (
	"context"
	"fmt"
	"io"

	genai "google.golang.org/genai"
)

// generateWithFuncCall shows how to submit a prompt and a function declaration to the model,
// allowing it to suggest a call to the function to fetch external data. Returning this data
// enables the model to generate a text response that incorporates the data.
func generateWithFuncCall(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	weatherFunc := &genai.FunctionDeclaration{
		Description: "Returns the current weather in a location.",
		Name:        "getCurrentWeather",
		Parameters: &genai.Schema{
			Type: "object",
			Properties: map[string]*genai.Schema{
				"location": {Type: "string"},
			},
			Required: []string{"location"},
		},
	}
	config := &genai.GenerateContentConfig{
		Tools: []*genai.Tool{
			{FunctionDeclarations: []*genai.FunctionDeclaration{weatherFunc}},
		},
		Temperature: genai.Ptr(float32(0.0)),
	}

	modelName := "gemini-2.5-flash"
	contents := []*genai.Content{
		{Parts: []*genai.Part{
			{Text: "What is the weather like in Boston?"},
		},
			Role: "user"},
	}

	resp, err := client.Models.GenerateContent(ctx, modelName, contents, config)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	var funcCall *genai.FunctionCall
	for _, p := range resp.Candidates[0].Content.Parts {
		if p.FunctionCall != nil {
			funcCall = p.FunctionCall
			fmt.Fprint(w, "The model suggests to call the function ")
			fmt.Fprintf(w, "%q with args: %v\n", funcCall.Name, funcCall.Args)
			// Example response:
			// The model suggests to call the function "getCurrentWeather" with args: map[location:Boston]
		}
	}
	if funcCall == nil {
		return fmt.Errorf("model did not suggest a function call")
	}

	// Use synthetic data to simulate a response from the external API.
	// In a real application, this would come from an actual weather API.
	funcResp := &genai.FunctionResponse{
		Name: "getCurrentWeather",
		Response: map[string]any{
			"location":         "Boston",
			"temperature":      "38",
			"temperature_unit": "F",
			"description":      "Cold and cloudy",
			"humidity":         "65",
			"wind":             `{"speed": "10", "direction": "NW"}`,
		},
	}

	// Return conversation turns and API response to complete the model's response.
	contents = []*genai.Content{
		{Parts: []*genai.Part{
			{Text: "What is the weather like in Boston?"},
		},
			Role: "user"},
		{Parts: []*genai.Part{
			{FunctionCall: funcCall},
		}},
		{Parts: []*genai.Part{
			{FunctionResponse: funcResp},
		}},
	}

	resp, err = client.Models.GenerateContent(ctx, modelName, contents, config)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	respText := resp.Text()

	fmt.Fprintln(w, respText)

	// Example response:
	// The weather in Boston is cold and cloudy with a temperature of 38 degrees Fahrenheit. The humidity is ...

	return nil
}

Java

Antes de probar este ejemplo, sigue las Java instrucciones de configuración de la guía de inicio rápido de Vertex AI con bibliotecas de cliente. Para obtener más información, consulta la documentación de referencia de la API Java de Vertex AI.


import com.google.genai.Client;
import com.google.genai.types.FunctionDeclaration;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.HttpOptions;
import com.google.genai.types.Schema;
import com.google.genai.types.Tool;
import com.google.genai.types.Type;
import java.util.Map;

public class ToolFunctionDescriptionWithText {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "gemini-2.5-flash";
    String contents =
        "At Stellar Sounds, a music label, 2024 was a rollercoaster. \"Echoes of the Night,\""
            + " a debut synth-pop album, \n surprisingly sold 350,000 copies, while veteran"
            + " rock band \"Crimson Tide's\" latest, \"Reckless Hearts,\" \n lagged at"
            + " 120,000. Their up-and-coming indie artist, \"Luna Bloom's\" EP, \"Whispers "
            + "of Dawn,\" \n secured 75,000 sales. The biggest disappointment was the "
            + "highly-anticipated rap album \"Street Symphony\" \n only reaching 100,000"
            + " units. Overall, Stellar Sounds moved over 645,000 units this year, revealing"
            + " unexpected \n trends in music consumption.";

    generateContent(modelId, contents);
  }

  // Generates content with text input and function declaration that
  // the model may use to retrieve external data for the response
  public static String generateContent(String modelId, String contents) {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (Client client =
        Client.builder()
            .location("global")
            .vertexAI(true)
            .httpOptions(HttpOptions.builder().apiVersion("v1").build())
            .build()) {

      FunctionDeclaration getAlbumSales =
          FunctionDeclaration.builder()
              .name("get_album_sales")
              .description("Gets the number of albums sold")
              // Function parameters are specified in schema format
              .parameters(
                  Schema.builder()
                      .type(Type.Known.OBJECT)
                      .properties(
                          Map.of(
                              "albums",
                              Schema.builder()
                                  .type(Type.Known.ARRAY)
                                  .description("List of albums")
                                  .items(
                                      Schema.builder()
                                          .description("Album and its sales")
                                          .type(Type.Known.OBJECT)
                                          .properties(
                                              Map.of(
                                                  "album_name",
                                                      Schema.builder()
                                                          .type(Type.Known.STRING)
                                                          .description("Name of the music album")
                                                          .build(),
                                                  "copies_sold",
                                                      Schema.builder()
                                                          .type(Type.Known.INTEGER)
                                                          .description("Number of copies sold")
                                                          .build()))
                                          .build()) // End items schema for albums
                                  .build() // End "albums" property schema
                              ))
                      .build()) // End parameters schema
              .build(); // End function declaration

      Tool salesTool = Tool.builder().functionDeclarations(getAlbumSales).build();

      GenerateContentConfig config =
          GenerateContentConfig.builder().tools(salesTool).temperature(0.0f).build();

      GenerateContentResponse response = client.models.generateContent(modelId, contents, config);

      // response.functionCalls() returns an Immutable<FunctionCall>.
      System.out.println(response.functionCalls().get(0));

      return response.functionCalls().toString();
      // Example response:
      // FunctionCall{id=Optional.empty, args=Optional[{albums=[{copies_sold=350000,
      // album_name=Echoes of the Night},
      // {copies_sold=120000, album_name=Reckless Hearts}, {copies_sold=75000, album_name=Whispers
      // of Dawn},
      // {album_name=Street Symphony, copies_sold=100000}]}], name=Optional[get_album_sales]}
    }
  }
}

Node.js

Antes de probar este ejemplo, sigue las Node.js instrucciones de configuración de la guía de inicio rápido de Vertex AI con bibliotecas de cliente. Para obtener más información, consulta la documentación de referencia de la API Node.js de Vertex AI.

const {GoogleGenAI, Type} = require('@google/genai');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION || 'global';

async function generateContent(
  projectId = GOOGLE_CLOUD_PROJECT,
  location = GOOGLE_CLOUD_LOCATION
) {
  const ai = new GoogleGenAI({
    vertexai: true,
    project: projectId,
    location: location,
  });

  const get_album_sales = {
    name: 'get_album_sales',
    description: 'Gets the number of albums sold',
    parameters: {
      type: Type.OBJECT,
      properties: {
        albums: {
          type: Type.ARRAY,
          description: 'List of albums',
          items: {
            description: 'Album and its sales',
            type: Type.OBJECT,
            properties: {
              album_name: {
                type: Type.STRING,
                description: 'Name of the music album',
              },
              copies_sold: {
                type: Type.INTEGER,
                description: 'Number of copies sold',
              },
            },
          },
        },
      },
    },
  };

  const sales_tool = {
    functionDeclarations: [get_album_sales],
  };

  const prompt = `
    At Stellar Sounds, a music label, 2024 was a rollercoaster. "Echoes of the Night", a debut synth-pop album, 
    surprisingly sold 350,000 copies, while veteran rock band "Crimson Tide's" latest, "Reckless Hearts",
    lagged at 120,000. Their up-and-coming indie artist, "Luna Bloom's" EP, "Whispers of Dawn",
    secured 75,000 sales. The biggest disappointment was the highly-anticipated rap album "Street Symphony" 
    only reaching 100,000 units. Overall, Stellar Sounds moved over 645,000 units this year, revealing unexpected
    trends in music consumption.
  `;

  const MODEL_NAME = 'gemini-2.5-flash';

  const response = await ai.models.generateContent({
    model: MODEL_NAME,
    contents: prompt,
    config: {
      tools: [sales_tool],
      temperature: 0,
    },
  });

  console.log(response.functionCalls);

  return response.functionCalls;
}

Python

Antes de probar este ejemplo, sigue las Python instrucciones de configuración de la guía de inicio rápido de Vertex AI con bibliotecas de cliente. Para obtener más información, consulta la documentación de referencia de la API Python de Vertex AI.

from google import genai
from google.genai.types import (
    FunctionDeclaration,
    GenerateContentConfig,
    HttpOptions,
    Tool,
)

client = genai.Client(http_options=HttpOptions(api_version="v1"))
model_id = "gemini-2.5-flash"

get_album_sales = FunctionDeclaration(
    name="get_album_sales",
    description="Gets the number of albums sold",
    # Function parameters are specified in JSON schema format
    parameters={
        "type": "OBJECT",
        "properties": {
            "albums": {
                "type": "ARRAY",
                "description": "List of albums",
                "items": {
                    "description": "Album and its sales",
                    "type": "OBJECT",
                    "properties": {
                        "album_name": {
                            "type": "STRING",
                            "description": "Name of the music album",
                        },
                        "copies_sold": {
                            "type": "INTEGER",
                            "description": "Number of copies sold",
                        },
                    },
                },
            },
        },
    },
)

sales_tool = Tool(
    function_declarations=[get_album_sales],
)

response = client.models.generate_content(
    model=model_id,
    contents='At Stellar Sounds, a music label, 2024 was a rollercoaster. "Echoes of the Night," a debut synth-pop album, '
    'surprisingly sold 350,000 copies, while veteran rock band "Crimson Tide\'s" latest, "Reckless Hearts," '
    'lagged at 120,000. Their up-and-coming indie artist, "Luna Bloom\'s" EP, "Whispers of Dawn," '
    'secured 75,000 sales. The biggest disappointment was the highly-anticipated rap album "Street Symphony" '
    "only reaching 100,000 units. Overall, Stellar Sounds moved over 645,000 units this year, revealing unexpected "
    "trends in music consumption.",
    config=GenerateContentConfig(
        tools=[sales_tool],
        temperature=0,
    ),
)

print(response.function_calls)
# Example response:
# [FunctionCall(
#     id=None,
#     name="get_album_sales",
#     args={
#         "albums": [
#             {"album_name": "Echoes of the Night", "copies_sold": 350000},
#             {"copies_sold": 120000, "album_name": "Reckless Hearts"},
#             {"copies_sold": 75000, "album_name": "Whispers of Dawn"},
#             {"copies_sold": 100000, "album_name": "Street Symphony"},
#         ]
#     },
# )]

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