# To install the Python SDK, use this CLI command:# pip install google-generativeaiimportgoogle.generativeaiasgenaifromgoogle.generativeaiimportGenerativeModelAPI_KEY="API_KEY"genai.configure(api_key=API_KEY)
# To install the Python SDK, use this CLI command:# pip install google-cloud-aiplatformimportvertexaifromvertexai.generative_modelsimportGenerativeModel,ImagePROJECT_ID="PROJECT_ID"REGION="REGION"# e.g. us-central1vertexai.init(project=PROJECT_ID,location=REGION)
用于从文本提示生成文本的代码示例
Google AI
Vertex AI
model=GenerativeModel("gemini-1.5-flash")response=model.generate_content("The opposite of hot is")print(response.text)# The opposite of hot is cold.
model=GenerativeModel("gemini-1.5-flash")response=model.generate_content("The opposite of hot is")print(response.text)# The opposite of hot is cold.
用于从文本和图片生成文本的代码示例
Google AI
Vertex AI
importPIL.Imagemultimodal_model=GenerativeModel("gemini-1.5-flash")image=PIL.Image.open("image.jpg")response=multimodal_model.generate_content(["What is this picture?",image])print(response.text)# A cat is shown in this picture.
multimodal_model=GenerativeModel("gemini-1.5-flash")image=Image.load_from_file("image.jpg")response=multimodal_model.generate_content(["What is shown in this image?",image])print(response.text)# A cat is shown in this picture.
生成多轮聊天的代码示例
Google AI
Vertex AI
model=GenerativeModel("gemini-1.5-flash")chat=model.start_chat()print(chat.send_message("How are you?").text)print(chat.send_message("What can you do?").text)
model=GenerativeModel("gemini-1.5-flash")chat=model.start_chat()print(chat.send_message("How are you?").text)print(chat.send_message("What can you do?").text)
将提示迁移到 Vertex AI Studio
您的 Google AI Studio 提示数据保存在 Google 云端硬盘文件夹中。本部分介绍了如何将提示迁移到 Vertex AI Studio。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-09-08。"],[],[],null,["As your [Gemini API](https://ai.google.dev/gemini-api/docs)\napplications mature, you might find that you need a more expansive platform for\nbuilding and deploying generative AI applications and solutions end-to-end.\nVertex AI provides a comprehensive ecosystem of tools to enable\ndevelopers to harness the power of generative AI, from the initial stages of app\ndevelopment to app deployment, app hosting, and managing complex data at scale.\n\nWith Vertex AI, you get access to a suite of Machine Learning\nOperations (MLOps) tools to streamline usage, deployment, and monitoring of AI\nmodels for efficiency and reliability. Additionally, integrations with\ndatabases, Development Operations (DevOps) tools, logging, monitoring, and\nIAM offer a comprehensive approach to managing the entire\ngenerative AI lifecycle.\n\nDifferences between using the Gemini API on its own and Vertex AI\n\nThe following table summarizes the main differences between the\nGemini API and Vertex AI to help you decide which option is\nright for your use case:\n\n| **Feature** | **Gemini API** | **Vertex AI** |\n| Endpoint names | `generativelanguage.googleapis.com` | `aiplatform.googleapis.com` |\n| Sign up | Google Account | Google Cloud account (with terms agreement and billing) |\n| Authentication | API key | Google Cloud service account |\n| User interface playground | Google AI Studio | Vertex AI Studio |\n|-----------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| API \\& SDK | Server and mobile/web client SDKs - Server: Python, Node.js, Go, Dart, ABAP - Mobile/Web client: Android (Kotlin/Java), Swift, Web, Flutter | Server and mobile/web client SDKs - Server: Python, Node.js, Go, Java, ABAP - Mobile/Web client (via [Vertex AI in Firebase](https://firebase.google.com/docs/vertex-ai)): Android (Kotlin/Java), Swift, Web, Flutter |\n| No-cost usage of API \\& SDK | Yes, [where applicable](https://ai.google.dev/gemini-api/docs/billing#is-Gemini-free-in-EEA-UK-CH) | $300 Google Cloud credit for new users |\n| Quota (requests per minute) | Varies based on model and pricing plan (see [detailed information](https://ai.google.dev/pricing)) | Varies based on model and region (see [detailed information](/vertex-ai/generative-ai/docs/quotas)) |\n| Enterprise support | No | - Customer encryption key - Virtual private cloud - Data residency - Access transparency - Scalable infrastructure for application hosting - Databases and data storage |\n| MLOps | No | Full MLOps on Vertex AI (examples: model evaluation, Model Monitoring, Model Registry) |\n\nMigration steps\n\nThe following sections cover the steps required to migrate your Gemini\nAPI code to Vertex AI. These steps assume you have prompt data from\nGoogle AI Studio saved in Google Drive.\n\nWhen migrating to Vertex AI:\n\n- You can use your existing Google Cloud project (the same one you used to generate your Gemini API key) or you can create a new [Google Cloud project](/resource-manager/docs/creating-managing-projects).\n- Supported regions might differ between the Gemini API and Vertex AI. See the list of [supported regions for generative\n AI on Google Cloud](/vertex-ai/generative-ai/docs/learn/locations).\n- Any models you created in Google AI Studio need to be retrained in Vertex AI.\n\n1. Migrate your prompts to Vertex AI Studio\n\nYour Google AI Studio prompt data is saved in a Google Drive folder. This\nsection shows how to migrate your prompts to Vertex AI Studio.\n\n1. Open [Google Drive](https://drive.google.com).\n2. Navigate to the **AI_Studio** folder where the prompts are stored.\n3. Download your prompts from Google Drive to a local directory.\n\n | **Note:** Prompts downloaded from Google Drive are in the text (`txt`) format. Before you upload them to Vertex AI Studio, change the file extensions from `.txt` to `.json` to convert them to JSON files.\n4. Open [Vertex AI Studio](https://console.cloud.google.com/vertex-ai/generative) in the Google Cloud console.\n\n5. In the **Vertex AI** menu, click **Recents \\\u003e View all** to open the\n **Prompt management** menu.\n\n6. Click download**Import prompt**.\n\n7. Next to the **Prompt file** field, click **Browse** and select a prompt\n from your local directory.\n\n To upload prompts in bulk, you must manually combine your prompts into a\n single JSON file.\n8. Click **Upload**.\n\n2. Upload training data to Vertex AI Studio\n\nTo migrate your training data to Vertex AI, you need to upload your\ndata to a Cloud Storage bucket. For more information, see\n[Introduction to tuning](/vertex-ai/generative-ai/docs/models/tune-models).\n\n3. Delete unused API Keys\n\nIf you no longer need to use your Gemini API key for the\nGemini Developer API, then follow security best practices and delete\nit.\n\nTo delete an API key:\n\n1. Open the [Google Cloud API Credentials](https://console.cloud.google.com/apis/credentials)\n page.\n\n2. Find the API key that you want to delete and click the **Actions** icon.\n\n3. Select **Delete API key**.\n\n4. In the **Delete credential** modal, select **Delete**.\n\n Deleting an API key takes a few minutes to propagate. After propagation\n completes, any traffic using the deleted API key is rejected.\n\n| **Important:** If you delete a key that's still used in production and need to recover it, see [`gcloud beta services api-keys\n| undelete`](/sdk/gcloud/reference/beta/services/api-keys/undelete).\n\nWhat's next\n\n- Try a quickstart tutorial using [Vertex AI Studio](/vertex-ai/generative-ai/docs/start/quickstarts/quickstart) or the [Vertex AI API](/vertex-ai/generative-ai/docs/start/quickstarts/quickstart-multimodal)."]]
