OpenAI 相容性

您可以使用 OpenAI 程式庫 (Python 和 TypeScript/Javascript) 搭配 REST API 存取 Gemini 模型。Vertex AI 僅支援使用 OpenAI 程式庫的 Google Cloud Auth。如果您尚未使用 OpenAI 程式庫，建議您直接呼叫 Gemini API。

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = "PROJECT_ID"
location = "us-central1"

# # Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)

response = client.chat.completions.create(
  model="google/gemini-2.0-flash-001",
  messages=[
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain to me how AI works"}
  ]
)

print(response.choices[0].message)

異動內容

  • api_key=credentials.token：如要使用 Google Cloud 驗證，請使用範例程式碼取得Google Cloud 驗證權杖。

  • base_url：這會指示 OpenAI 程式庫將要求傳送至 Google Cloud，而非預設網址。

  • model="google/gemini-2.0-flash-001"：從 Vertex 代管的模型中選擇相容的 Gemini 模型。

思考

Gemini 2.5 模型經過訓練，能夠思考複雜問題，推理能力大幅提升。Gemini API 提供「思考預算」參數，可精細控管模型的思考量。

與 Gemini API 不同，OpenAI API 提供三種思考控制選項：「低」、「中」和「高」，這些選項會在幕後對應至 1K、8K 和 24K 思考符記預算。

如要停用思考功能，請將推理努力程度設為「none」。

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = PROJECT_ID
location = "us-central1"

# # Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)

response = client.chat.completions.create(
  model="google/gemini-2.5-flash-preview-04-17",
  reasoning_effort="low",
  messages=[
      {"role": "system", "content": "You are a helpful assistant."},
      {
          "role": "user",
          "content": "Explain to me how AI works"
      }
  ]
)
print(response.choices[0].message)

串流

Gemini API 支援串流回應。

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = PROJECT_ID
location = "us-central1"

credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)
response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
  {"role": "system", "content": "You are a helpful assistant."},
  {"role": "user", "content": "Hello!"}
],
stream=True
)

for chunk in response:
  print(chunk.choices[0].delta)

函式呼叫

呼叫函式可讓您更輕鬆地從生成式模型取得結構化資料輸出內容，且 Gemini API 支援這項功能。

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = PROJECT_ID
location = "us-central1"

credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)

tools = [
{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Get the weather in a given location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string",
          "description": "The city and state, e.g. Chicago, IL",
        },
        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
      },
      "required": ["location"],
    },
  }
}
]

messages = [{"role": "user", "content": "What's the weather like in Chicago today?"}]
response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=messages,
tools=tools,
tool_choice="auto"
)

print(response)

圖片理解

Gemini 模型原生支援多模態資料，可在許多常見的視覺任務中提供同級最佳成效。

Python

from google.auth import default
import google.auth.transport.requests

import base64
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
  return base64.b64encode(image_file.read()).decode('utf-8')

# Getting the base64 string
#base64_image = encode_image("Path/to/image.jpeg")

response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "What is in this image?",
      },
      {
        "type": "image_url",
        "image_url": {
          "url":  f"data:image/jpeg;base64,{base64_image}"
        },
      },
    ],
  }
],
)

print(response.choices[0])

生成圖片

Python

from google.auth import default
import google.auth.transport.requests

import base64
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
  return base64.b64encode(image_file.read()).decode('utf-8')

# Getting the base64 string
#base64_image = encode_image("Path/to/image.jpeg")
base64_image = encode_image("/content/wayfairsofa.jpg")

response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "What is in this image?",
      },
      {
        "type": "image_url",
        "image_url": {
          "url":  f"data:image/jpeg;base64,{base64_image}"
        },
      },
    ],
  }
],
)

print(response.choices[0])

音訊理解

分析音訊輸入內容：

Python

from google.auth import default
import google.auth.transport.requests

import base64
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

with open("/path/to/your/audio/file.wav", "rb") as audio_file:
base64_audio = base64.b64encode(audio_file.read()).decode('utf-8')

response = client.chat.completions.create(
  model="gemini-2.0-flash",
  messages=[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "Transcribe this audio",
      },
      {
            "type": "input_audio",
            "input_audio": {
              "data": base64_audio,
              "format": "wav"
        }
      }
    ],
  }
],
)

print(response.choices[0].message.content)

結構化輸出內容

Gemini 模型可輸出任何您定義的結構中的 JSON 物件。

Python

from google.auth import default
import google.auth.transport.requests

from pydantic import BaseModel
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

class CalendarEvent(BaseModel):
  name: str
  date: str
  participants: list[str]

completion = client.beta.chat.completions.parse(
  model="google/gemini-2.0-flash",
  messages=[
      {"role": "system", "content": "Extract the event information."},
      {"role": "user", "content": "John and Susan are going to an AI conference on Friday."},
  ],
  response_format=CalendarEvent,
)

print(completion.choices[0].message.parsed)

目前限制

  • 憑證的有效期限為預設的 1 小時。到期後，必須重新整理。詳情請參閱這個程式碼範例。

  • 我們正在擴大功能支援範圍，因此 OpenAI 程式庫的支援功能仍處於預覽階段。如有任何問題或疑問，請在 Google Cloud 社群中發文提問。

後續步驟