Kompatibilität mit OpenAI

Auf Gemini-Modelle kann über die OpenAI-Bibliotheken (Python und TypeScript/Javascript) und die REST API zugegriffen werden. In Vertex AI wird nur Google Cloud Auth mit der OpenAI-Bibliothek unterstützt. Wenn Sie noch keine OpenAI-Bibliotheken nutzen, sollten Sie die Gemini API direkt aufrufen.

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)

Was hat sich geändert?

  • api_key=credentials.token: Wenn Sie die Google Cloud -Authentifizierung verwenden möchten, rufen Sie mit dem Beispielcode einGoogle Cloud -Authentifizierungstoken ab.

  • base_url: Hiermit wird die OpenAI-Bibliothek angewiesen, Anfragen an Google Cloudstatt an die Standard-URL zu senden.

  • model="google/gemini-2.0-flash-001": Wählen Sie ein kompatibles Gemini-Modell aus den von Vertex gehosteten Modellen aus.

Denken

Gemini 2.5-Modelle sind darauf trainiert, komplexe Probleme zu durchdenken, was zu einer deutlich verbesserten Argumentation führt. Die Gemini API bietet den Parameter „Denken-Budget“, mit dem Sie genau festlegen können, wie viel das Modell nachdenken soll.

Im Gegensatz zur Gemini API bietet die OpenAI API drei Stufen der Denksteuerung: „niedrig“, „mittel“ und „hoch“. Diese werden im Hintergrund den Budgets für Denktokens von 1.000, 8.000 und 24.000 zugeordnet.

Wenn Sie das Denken deaktivieren möchten, legen Sie den Wert für die Denkleistung auf „Kein“ fest.

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)

Streaming

Die Gemini API unterstützt Streamingantworten.

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)

Funktionsaufrufe

Funktionsaufrufe erleichtern den Abruf strukturierter Datenausgaben aus generativen Modellen und werden in der Gemini API unterstützt.

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)

Bilder verstehen

Gemini-Modelle sind nativ multimodal und bieten bei vielen gängigen Aufgaben im Bereich Computer Vision eine erstklassige Leistung.

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])

Image generieren

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])

Audioinhalte verstehen

Audioeingabe analysieren:

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)

Strukturierte Ausgabe

Gemini-Modelle können JSON-Objekte in jeder von Ihnen definierten Struktur ausgeben.

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)

Aktuelle Beschränkungen

  • Anmeldedaten sind standardmäßig eine Stunde lang gültig. Nach Ablauf müssen sie aktualisiert werden. Weitere Informationen finden Sie in diesem Codebeispiel.

  • Die Unterstützung der OpenAI-Bibliotheken befindet sich noch in der Vorabversion, während wir den Funktionssupport erweitern. Wenn Sie Fragen oder Probleme haben, können Sie sie in der Google Cloud Community stellen.

Nächste Schritte