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Generare testo utilizzando immagini da un sistema locale e da Google Cloud Storage
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Questo esempio mostra come generare testo utilizzando un'immagine locale e un'immagine in Google Cloud Storage
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Per la documentazione dettagliata che include questo esempio di codice, vedi quanto segue:
Esempio di codice
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[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],[],[],[],null,["# Generate text using images from a local and Google Cloud Storage\n\nThis example demonstrates how to generate text using a local image and an image in Google Cloud Storage\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Image understanding](/vertex-ai/generative-ai/docs/multimodal/image-understanding)\n\nCode sample\n-----------\n\n### Go\n\n\nBefore trying this sample, follow the Go setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Go API\nreference documentation](/go/docs/reference/cloud.google.com/go/aiplatform/latest/apiv1).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n import (\n \t\"context\"\n \t\"fmt\"\n \t\"io\"\n \t\"os\"\n\n \tgenai \"google.golang.org/genai\"\n )\n\n // generateWithMultiImg shows how to generate text using multiple image inputs.\n func generateWithMultiImg(w io.Writer) error {\n \tctx := context.Background()\n\n \tclient, err := genai.NewClient(ctx, &genai.ClientConfig{\n \t\tHTTPOptions: genai.HTTPOptions{APIVersion: \"v1\"},\n \t})\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to create genai client: %w\", err)\n \t}\n\n \t// TODO(Developer): Update the path to file (image source:\n \t// https://storage.googleapis.com/cloud-samples-data/generative-ai/image/latte.jpg )\n \timageBytes, err := os.ReadFile(\"./latte.jpg\")\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to read image: %w\", err)\n \t}\n\n \tcontents := []*genai.Content{\n \t\t{Parts: []*genai.Part{\n \t\t\t{Text: \"Write an advertising jingle based on the items in both images.\"},\n \t\t\t{FileData: &genai.FileData{\n \t\t\t\t// Image source: https://storage.googleapis.com/cloud-samples-data/generative-ai/image/scones.jpg\n \t\t\t\tFileURI: \"gs://cloud-samples-data/generative-ai/image/scones.jpg\",\n \t\t\t\tMIMEType: \"image/jpeg\",\n \t\t\t}},\n \t\t\t{InlineData: &genai.Blob{\n \t\t\t\tData: imageBytes,\n \t\t\t\tMIMEType: \"image/jpeg\",\n \t\t\t}},\n \t\t}},\n \t}\n \tmodelName := \"gemini-2.5-flash\"\n\n \tresp, err := client.Models.GenerateContent(ctx, modelName, contents, nil)\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to generate content: %w\", err)\n \t}\n\n \trespText := resp.Text()\n\n \tfmt.Fprintln(w, respText)\n\n \t// Example response:\n \t// Okay, here's an advertising jingle inspired by the blueberry scones, coffee, flowers, chocolate cake, and latte:\n \t//\n \t// (Upbeat, jazzy music)\n \t// ...\n\n \treturn nil\n }\n\n### Node.js\n\n\nBefore trying this sample, follow the Node.js setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Node.js API\nreference documentation](/nodejs/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n const {GoogleGenAI} = require('@google/genai');\n\n const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;\n const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION || 'global';\n\n async function generateContent(\n projectId = GOOGLE_CLOUD_PROJECT,\n location = GOOGLE_CLOUD_LOCATION\n ) {\n const ai = new GoogleGenAI({\n vertexai: true,\n project: projectId,\n location: location,\n });\n\n const image1 = {\n fileData: {\n fileUri: 'gs://cloud-samples-data/generative-ai/image/scones.jpg',\n mimeType: 'image/jpeg',\n },\n };\n\n const image2 = {\n fileData: {\n fileUri: 'gs://cloud-samples-data/generative-ai/image/fruit.png',\n mimeType: 'image/png',\n },\n };\n\n const response = await ai.models.generateContent({\n model: 'gemini-2.5-flash',\n contents: [\n image1,\n image2,\n 'Generate a list of all the objects contained in both images.',\n ],\n });\n\n console.log(response.text);\n\n return response.text;\n }\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n from google import genai\n from google.genai.types import HttpOptions, Part\n\n client = genai.Client(http_options=HttpOptions(api_version=\"v1\"))\n\n # Read content from GCS\n gcs_file_img_path = \"gs://cloud-samples-data/generative-ai/image/scones.jpg\"\n\n # Read content from a local file\n with open(\"test_data/latte.jpg\", \"rb\") as f:\n local_file_img_bytes = f.read()\n\n response = client.models.generate_content(\n model=\"gemini-2.5-flash\",\n contents=[\n \"Generate a list of all the objects contained in both images.\",\n Part.from_uri(file_uri=gcs_file_img_path, mime_type=\"image/jpeg\"),\n Part.from_bytes(data=local_file_img_bytes, mime_type=\"image/jpeg\"),\n ],\n )\n print(response.text)\n # Example response:\n # Okay, here's the list of objects present in both images:\n # ...\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=googlegenaisdk)."]]