自 2025 年 4 月 29 日起,Gemini 1.5 Pro 和 Gemini 1.5 Flash 模型將無法用於先前未使用這些模型的專案,包括新專案。詳情請參閱「
模型版本和生命週期」。
使用多模態提示生成文字
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
這個範例示範如何使用 Gemini 模型,從多模態提示生成文字。提示包含三張圖片和兩則文字提示。模型會生成文字回覆,說明圖片和文字提示。
程式碼範例
除非另有註明,否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權,程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],[],[],[],null,["# Generate text from multimodal prompt\n\nThis 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-----------\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 # 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\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=googlegenaisdk)."]]