Appel de fonction avec le modèle d'IA Gemini

L'exemple de code montre comment utiliser les modèles génératifs Vertex AI pour appeler des fonctions.

En savoir plus

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

Exemple de code

Go

Avant d'essayer cet exemple, suivez les instructions de configuration pour Go 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 Go.

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 (
	"context"
	"encoding/json"
	"errors"
	"fmt"
	"io"

	"cloud.google.com/go/vertexai/genai"
)

// functionCallsChat opens a chat session and sends 4 messages to the model:
// - convert a first text question into a structured function call request
// - convert the first structured function call response into natural language
// - convert a second text question into a structured function call request
// - convert the second structured function call response into natural language
func functionCallsChat(w io.Writer, projectID, location, modelName string) error {
	// location := "us-central1"
	// modelName := "gemini-1.5-flash-001"
	ctx := context.Background()
	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("unable to create client: %w", err)
	}
	defer client.Close()

	model := client.GenerativeModel(modelName)

	// Build an OpenAPI schema, in memory
	paramsProduct := &genai.Schema{
		Type: genai.TypeObject,
		Properties: map[string]*genai.Schema{
			"productName": {
				Type:        genai.TypeString,
				Description: "Product name",
			},
		},
	}
	fundeclProductInfo := &genai.FunctionDeclaration{
		Name:        "getProductSku",
		Description: "Get the SKU for a product",
		Parameters:  paramsProduct,
	}
	paramsStore := &genai.Schema{
		Type: genai.TypeObject,
		Properties: map[string]*genai.Schema{
			"location": {
				Type:        genai.TypeString,
				Description: "Location",
			},
		},
	}
	fundeclStoreLocation := &genai.FunctionDeclaration{
		Name:        "getStoreLocation",
		Description: "Get the location of the closest store",
		Parameters:  paramsStore,
	}
	model.Tools = []*genai.Tool{
		{FunctionDeclarations: []*genai.FunctionDeclaration{
			fundeclProductInfo,
			fundeclStoreLocation,
		}},
	}
	model.SetTemperature(0.0)

	chat := model.StartChat()

	// Send a prompt for the first conversation turn that should invoke the getProductSku function
	prompt := "Do you have the Pixel 8 Pro in stock?"
	fmt.Fprintf(w, "Question: %s\n", prompt)
	resp, err := chat.SendMessage(ctx, genai.Text(prompt))
	if err != nil {
		return err
	}
	if len(resp.Candidates) == 0 ||
		len(resp.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	// The model has returned a function call to the declared function `getProductSku`
	// with a value for the argument `productName`.
	jsondata, err := json.MarshalIndent(resp.Candidates[0].Content.Parts[0], "\t", "  ")
	if err != nil {
		return fmt.Errorf("json.MarshalIndent: %w", err)
	}
	fmt.Fprintf(w, "function call generated by the model:\n\t%s\n", string(jsondata))

	// Create a function call response, to simulate the result of a call to a
	// real service
	funresp := &genai.FunctionResponse{
		Name: "getProductSku",
		Response: map[string]any{
			"sku":      "GA04834-US",
			"in_stock": "yes",
		},
	}
	jsondata, err = json.MarshalIndent(funresp, "\t", "  ")
	if err != nil {
		return fmt.Errorf("json.MarshalIndent: %w", err)
	}
	fmt.Fprintf(w, "function call response sent to the model:\n\t%s\n\n", string(jsondata))

	// And provide the function call response to the model
	resp, err = chat.SendMessage(ctx, funresp)
	if err != nil {
		return err
	}
	if len(resp.Candidates) == 0 ||
		len(resp.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	// The model has taken the function call response as input, and has
	// reformulated the response to the user.
	jsondata, err = json.MarshalIndent(resp.Candidates[0].Content.Parts[0], "\t", "  ")
	if err != nil {
		return fmt.Errorf("json.MarshalIndent: %w", err)
	}
	fmt.Fprintf(w, "Answer generated by the model:\n\t%s\n\n", string(jsondata))

	// Send a prompt for the second conversation turn that should invoke the getStoreLocation function
	prompt2 := "Is there a store in Mountain View, CA that I can visit to try it out?"
	fmt.Fprintf(w, "Question: %s\n", prompt)

	resp, err = chat.SendMessage(ctx, genai.Text(prompt2))
	if err != nil {
		return err
	}
	if len(resp.Candidates) == 0 ||
		len(resp.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	// The model has returned a function call to the declared function `getStoreLocation`
	// with a value for the argument `store`.
	jsondata, err = json.MarshalIndent(resp.Candidates[0].Content.Parts[0], "\t", "  ")
	if err != nil {
		return fmt.Errorf("json.MarshalIndent: %w", err)
	}
	fmt.Fprintf(w, "function call generated by the model:\n\t%s\n", string(jsondata))

	// Create a function call response, to simulate the result of a call to a
	// real service
	funresp = &genai.FunctionResponse{
		Name: "getStoreLocation",
		Response: map[string]any{
			"store": "2000 N Shoreline Blvd, Mountain View, CA 94043, US",
		},
	}
	jsondata, err = json.MarshalIndent(funresp, "\t", "  ")
	if err != nil {
		return fmt.Errorf("json.MarshalIndent: %w", err)
	}
	fmt.Fprintf(w, "function call response sent to the model:\n\t%s\n\n", string(jsondata))

	// And provide the function call response to the model
	resp, err = chat.SendMessage(ctx, funresp)
	if err != nil {
		return err
	}
	if len(resp.Candidates) == 0 ||
		len(resp.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	// The model has taken the function call response as input, and has
	// reformulated the response to the user.
	jsondata, err = json.MarshalIndent(resp.Candidates[0].Content.Parts[0], "\t", "  ")
	if err != nil {
		return fmt.Errorf("json.MarshalIndent: %w", err)
	}
	fmt.Fprintf(w, "Answer generated by the model:\n\t%s\n\n", string(jsondata))
	return nil
}

Node.js

Avant d'essayer cet exemple, suivez les instructions de configuration pour Node.js 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 Node.js.

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.

const {
  VertexAI,
  FunctionDeclarationSchemaType,
} = require('@google-cloud/vertexai');

const functionDeclarations = [
  {
    function_declarations: [
      {
        name: 'get_product_sku',
        description:
          'Get the available inventory for a Google products, e.g: Pixel phones, Pixel Watches, Google Home etc',
        parameters: {
          type: FunctionDeclarationSchemaType.OBJECT,
          properties: {
            productName: {type: FunctionDeclarationSchemaType.STRING},
          },
        },
      },
      {
        name: 'get_store_location',
        description: 'Get the location of the closest store',
        parameters: {
          type: FunctionDeclarationSchemaType.OBJECT,
          properties: {
            location: {type: FunctionDeclarationSchemaType.STRING},
          },
        },
      },
    ],
  },
];

const toolConfig = {
  function_calling_config: {
    mode: 'ANY',
    allowed_function_names: ['get_product_sku'],
  },
};

const generationConfig = {
  temperature: 0.95,
  topP: 1.0,
  maxOutputTokens: 8192,
};

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function functionCallingAdvanced(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-1.5-flash-001'
) {
  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: projectId, location: location});

  // Instantiate the model
  const generativeModel = vertexAI.preview.getGenerativeModel({
    model: model,
  });

  const request = {
    contents: [
      {
        role: 'user',
        parts: [
          {text: 'Do you have the White Pixel 8 Pro 128GB in stock in the US?'},
        ],
      },
    ],
    tools: functionDeclarations,
    tool_config: toolConfig,
    generation_config: generationConfig,
  };
  const result = await generativeModel.generateContent(request);
  console.log(JSON.stringify(result.response.candidates[0].content));
}

Étapes suivantes

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