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 dari prompt multimodal
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Contoh ini menunjukkan cara membuat teks dari perintah multimodal menggunakan model Gemini. Perintah terdiri dari tiga gambar dan dua perintah teks. Model menghasilkan respons teks yang mendeskripsikan gambar dan perintah teks.
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,["This sample demonstrates how to generate text from a multimodal prompt using the Gemini model. The prompt consists of three images and two text prompts. The model generates a text response that describes the images and the text prompts.\n\nCode sample \n\nJava\n\n\nBefore trying this sample, follow the Java 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 Java API\nreference documentation](/java/docs/reference/google-cloud-aiplatform/latest/com.google.cloud.aiplatform.v1).\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\n import com.google.genai.Client;\n import com.google.genai.types.Content;\n import com.google.genai.types.GenerateContentResponse;\n import com.google.genai.types.HttpOptions;\n import com.google.genai.types.Part;\n import java.io.IOException;\n import java.nio.file.Files;\n import java.nio.file.Paths;\n\n public class TextGenerationWithMultiLocalImage {\n\n public static void main(String[] args) throws IOException {\n // TODO(developer): Replace these variables before running the sample.\n String modelId = \"gemini-2.5-flash\";\n String localImageFilePath1 = \"your/local/img1.jpg\";\n String localImageFilePath2 = \"your/local/img2.jpg\";\n generateContent(modelId, localImageFilePath1, localImageFilePath2);\n }\n\n // Generates text using multiple local images\n public static String generateContent(\n String modelId, String localImageFilePath1, String localImageFilePath2) throws IOException {\n // Initialize client that will be used to send requests. This client only needs to be created\n // once, and can be reused for multiple requests.\n try (Client client =\n Client.builder()\n .location(\"global\")\n .vertexAI(true)\n .httpOptions(HttpOptions.builder().apiVersion(\"v1\").build())\n .build()) {\n\n // Read content from local files.\n byte[] localFileImg1Bytes = Files.readAllBytes(Paths.get(localImageFilePath1));\n byte[] localFileImg2Bytes = Files.readAllBytes(Paths.get(localImageFilePath2));\n\n GenerateContentResponse response =\n client.models.generateContent(\n modelId,\n Content.fromParts(\n Part.fromBytes(localFileImg1Bytes, \"image/jpeg\"),\n Part.fromBytes(localFileImg2Bytes, \"image/jpeg\"),\n Part.fromText(\"Generate a list of all the objects contained in both images\")),\n null);\n\n System.out.print(response.text());\n // Example response:\n // Based on both images, here are the objects contained in both:\n //\n // 1. **Coffee cups (or mugs)**: Both images feature one or more cups containing a beverage.\n // 2. **Coffee (or a similar beverage)**: Both images contain a liquid beverage in the cups,\n // appearing to be coffee or a coffee-like drink.\n // 3. **Table (or a flat surface)**: Both compositions are set on a flat surface, likely a\n // table or countertop.\n return response.text();\n }\n }\n }\n\nNode.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 const fs = require('fs');\n\n const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;\n const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION || 'global';\n\n function loadImageAsBase64(path) {\n const bytes = fs.readFileSync(path);\n return bytes.toString('base64');\n }\n\n async function generateContent(\n projectId = GOOGLE_CLOUD_PROJECT,\n location = GOOGLE_CLOUD_LOCATION,\n imagePath1,\n imagePath2\n ) {\n const ai = new GoogleGenAI({\n vertexai: true,\n project: projectId,\n location: location,\n });\n\n // TODO(Developer): Update the below file paths to your images\n const image1 = loadImageAsBase64(imagePath1);\n const image2 = loadImageAsBase64(imagePath2);\n\n const response = await ai.models.generateContent({\n model: 'gemini-2.5-flash',\n contents: [\n {\n role: 'user',\n parts: [\n {\n text: 'Generate a list of all the objects contained in both images.',\n },\n {\n inlineData: {\n data: image1,\n mimeType: 'image/jpeg',\n },\n },\n {\n inlineData: {\n data: image2,\n mimeType: 'image/jpeg',\n },\n },\n ],\n },\n ],\n });\n\n console.log(response.text);\n\n return response.text;\n }\n // Example response:\n // Okay, here's a jingle combining the elements of both sets of images, focusing on ...\n // ...\n\nPython\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 # TODO(Developer): Update the below file paths to your images\n # image_path_1 = \"path/to/your/image1.jpg\"\n # image_path_2 = \"path/to/your/image2.jpg\"\n with open(image_path_1, \"rb\") as f:\n image_1_bytes = f.read()\n with open(image_path_2, \"rb\") as f:\n image_2_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_bytes(data=image_1_bytes, mime_type=\"image/jpeg\"),\n Part.from_bytes(data=image_2_bytes, mime_type=\"image/jpeg\"),\n ],\n )\n print(response.text)\n # Example response:\n # Okay, here's a jingle combining the elements of both sets of images, focusing on ...\n # ...\n\nWhat's next\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=googlegenaisdk)."]]
