Vertex AI 中的 Gemini API 快速入門

本快速入門說明如何安裝 Google Gen AI SDK 的所選語言版本，然後提出第一個 API 要求。根據您使用API 金鑰或應用程式預設憑證 (ADC) 進行驗證，範例會略有不同。

選擇驗證方法：

事前準備

如果您尚未設定應用程式預設憑證，請參閱「設定應用程式預設憑證」。

設定環境

Python

  1. 使用 Python 3.9 以上版本安裝 Google Gen AI SDK：

    pip install -q -U google-genai

  2. 設定環境變數：

    export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID

REST

設定環境變數：

export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID

發出第一項要求

使用 generateContent 方法，將要求傳送至 Vertex AI 中的 Gemini API：

Python

from google import genai

client = genai.Client(vertexai=True, project="${GOOGLE_CLOUD_PROJECT}", location="global")

response = client.models.generate_content(
    model="gemini-2.0-flash", contents="Explain how AI works in a few words"
)
print(response.text)

REST

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://aiplatform.googleapis.com/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/global/publishers/google/models/gemini-2.0-flash-001:generateContent -d \
$'{
  "contents": {
    "role": "user",
    "parts": {
      "text": "Explain how AI works in a few words"
    }
  }
}'

生成圖像

Gemini 可透過對話生成及處理圖片。您可以使用文字、圖片或兩者的組合來提示 Gemini，執行各種圖片相關工作，例如圖片生成和編輯。以下程式碼示範如何根據說明性提示產生圖片：

您必須在設定中加入 responseModalities: ["TEXT", "IMAGE"]。這些模型不支援僅輸出圖片。

Python

from google import genai
from google.genai import types
from PIL import Image
from io import BytesIO
import base64

client = genai.Client(vertexai=True, project="${GOOGLE_CLOUD_PROJECT}", location="global")

contents = ('Hi, can you create an image of the Eiffel tower with fireworks in the background?')

response = client.models.generate_content(
    model="gemini-2.0-flash-preview-image-generation",
    contents=contents,
    config=types.GenerateContentConfig(
      response_modalities=['TEXT', 'IMAGE']
    )
)


for part in response.candidates[0].content.parts:
  if part.text is not None:
    print(part.text)
  elif part.inline_data is not None:
    image = Image.open(BytesIO((part.inline_data.data)))
    image.save('gemini-native-image.png')
    image.show()

REST

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://aiplatform.googleapis.com/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/global/publishers/google/models/gemini-2.0-flash-preview-image-generation:generateContent -d \
$'{
  "contents": {
    "role": "user",
    "parts": {
      "text": "Can you create an image of the Eiffel tower with fireworks in the background?"
    }
  },
  "generationConfig": {
    "responseModalities":["TEXT","IMAGE"]
  }
}'

圖像解讀

Gemini 也能理解圖片。以下程式碼會使用上一節產生的圖片，並使用其他模型推斷圖片相關資訊：

Python

from google import genai
from google.genai import types


with open("gemini-native-image.png", "rb") as f:
    image = f.read()


client = genai.Client(vertexai=True, project="${GOOGLE_CLOUD_PROJECT}", location="global")


response = client.models.generate_content(
    model="gemini-flash-2.0",
    contents=[
        Part.from_bytes(data=image, mime_type="image/png"),
        "Write a short and engaging blog post based on this picture.",
    ],
)

print(response.text)
'''
Okay, here's a short and engaging blog post inspired by the image:

**Paris Aglow: A Night to Remember!**

WOW! Just look at this image. Fireworks exploding over Paris, the Eiffel Tower brilliantly lit – can you imagine a more magical sight? This is Paris at its most dazzling, a city transformed into a canvas of light and color.

The Eiffel Tower, the iconic symbol of Paris, stands proud amidst the spectacle.  It's illuminated in blue, adding to the magical atmosphere of the celebrations.

Whether it's a special occasion, a national holiday, or just a spontaneous celebration of life, Paris knows how to put on a show. This image is a reminder of the city's enduring beauty and its power to inspire awe. Have you ever witnessed the magic of Paris? Let me know in the comments!
'''

REST

  1. 為您使用的映像檔設定 shell 變數：

    export B64_BASE_IMAGE=YOUR_B64_ENCODED_IMAGE

  2. 執行下列指令：

    curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://aiplatform.googleapis.com/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/global/publishers/google/models/gemini-2.0-flash:generateContent -d \
$'{
  "contents": [
    {
      "role": "user",
      "parts": [
        {"text": "Write a short and engaging blog post based on this picture."},
        {"inlineData": {
            "data": "${B64_BASE_IMAGE}",
            "mimeType": "image/png",
          }
        }
      ]
    }
  ]
}'

程式碼執行

Vertex AI 程式碼執行功能中的 Gemini API 可讓模型生成及執行 Python 程式碼，並根據結果反覆學習，直到生成最終輸出內容。Vertex AI 提供程式碼執行工具，與函式呼叫類似。您可以使用這個程式碼執行功能，建構應用程式來生成文字輸出內容，並在其中運用以程式碼為基礎的推理技術。例如：

Python

from google import genai
from google.genai import types

client = genai.Client(vertexai=True, project="${GOOGLE_CLOUD_PROJECT}", location="global")

response = client.models.generate_content(
  model="gemini-2.0-flash",
  contents="What is the sum of the first 50 prime numbers? "
  "Generate and run code for the calculation, and make sure you get all 50.",
  config=types.GenerateContentConfig(
      tools=[types.Tool(code_execution=types.ToolCodeExecution)]
  ),
)

for part in response.candidates[0].content.parts:
  if part.text is not None:
      print(part.text)
  if part.executable_code is not None:
      print(part.executable_code.code)
  if part.code_execution_result is not None:
      print(part.code_execution_result.output)

REST

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://us-central1-aiplatform.googleapis.com/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/us-central1/publishers/google/models/gemini-2.0-flash-001:generateContent -d \
$'{
  "tools": [{'codeExecution': {}}],
  "contents": {
    "role": "user",
    "parts": {
      "text": "What is the sum of the first 50 prime numbers? Generate and run code for the calculation, and make sure you get all 50."
    }
  },
}'

如需更多程式碼執行範例，請參閱程式碼執行說明文件。

後續步驟

您已完成第一個 API 要求，建議您參閱下列指南，瞭解如何為實際工作環境程式碼設定更進階的 Vertex AI 功能：