Mulai 29 April 2025, model Gemini 1.5 Pro dan Gemini 1.5 Flash tidak tersedia di project yang belum pernah menggunakan model ini, termasuk project baru. Untuk mengetahui detailnya, lihat
Versi dan siklus proses model.
Membuat teks menggunakan gambar dari lokal dan Google Cloud Storage
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Contoh ini menunjukkan cara membuat teks menggunakan gambar lokal dan gambar di Google Cloud Storage
Mempelajari lebih lanjut
Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:
Contoh kode
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","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)."]]