Exemplos

Chamar o Gemini com a API Chat Completions

O exemplo a seguir mostra como enviar solicitações sem streaming:

REST

  curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
  https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/openapi/chat/completions \
  -d '{
    "model": "google/${MODEL_ID}",
    "messages": [{
      "role": "user",
      "content": "Write a story about a magic backpack."
    }]
  }'

Python

Antes de testar esta amostra, siga as instruções de configuração do Python no Guia de início rápido da plataforma de agentes: como usar bibliotecas de cliente.

Para autenticar na plataforma de agentes, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

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

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,
)

response = client.chat.completions.create(
    model="google/gemini-2.0-flash-001",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)

print(response)

O exemplo a seguir mostra como enviar solicitações de streaming para um Modelo do Gemini usando a API Chat Completions:

REST

  curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
  https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/openapi/chat/completions \
  -d '{
    "model": "google/${MODEL_ID}",
    "stream": true,
    "messages": [{
      "role": "user",
      "content": "Write a story about a magic backpack."
    }]
  }'

Python

Antes de testar esta amostra, siga as instruções de configuração do Python no Guia de início rápido da plataforma de agentes: como usar bibliotecas de cliente.

Para autenticar na plataforma de agentes, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

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

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,
)

response = client.chat.completions.create(
    model="google/gemini-2.0-flash-001",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
    stream=True,
)
for chunk in response:
    print(chunk)

Enviar um comando e uma imagem para a API Gemini na plataforma de agentes do Gemini Enterprise

Python

Antes de testar esta amostra, siga as instruções de configuração do Python no Guia de início rápido da plataforma de agentes: como usar bibliotecas de cliente.

Para autenticar na plataforma de agentes, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 


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

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,
)

response = client.chat.completions.create(
    model="google/gemini-2.0-flash-001",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe the following image:"},
                {
                    "type": "image_url",
                    "image_url": "gs://cloud-samples-data/generative-ai/image/scones.jpg",
                },
            ],
        }
    ],
)

print(response)

Chamar um modelo autoimplantado com a API Chat Completions

O exemplo a seguir mostra como enviar solicitações sem streaming:

REST

  curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/endpoints/${ENDPOINT}/chat/completions \
  -d '{
    "messages": [{
      "role": "user",
      "content": "Write a story about a magic backpack."
    }]
  }'

Python

Antes de testar esta amostra, siga as instruções de configuração do Python no Guia de início rápido da plataforma de agentes: como usar bibliotecas de cliente.

Para autenticar na plataforma de agentes, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

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

import openai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"
# model_id = "gemma-2-9b-it"
# endpoint_id = "YOUR_ENDPOINT_ID"

# 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/{endpoint_id}",
    api_key=credentials.token,
)

response = client.chat.completions.create(
    model=model_id,
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)
print(response)

O exemplo a seguir mostra como enviar solicitações de streaming para um modelo autoimplantado usando a API Chat Completions:

REST

    curl -X POST \
      -H "Authorization: Bearer $(gcloud auth print-access-token)" \
      -H "Content-Type: application/json" \
    https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/endpoints/${ENDPOINT}/chat/completions \
    -d '{
      "stream": true,
      "messages": [{
        "role": "user",
        "content": "Write a story about a magic backpack."
      }]
    }'

Python

Antes de testar esta amostra, siga as instruções de configuração do Python no Guia de início rápido da plataforma de agentes: como usar bibliotecas de cliente.

Para autenticar na plataforma de agentes, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

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

import openai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"
# model_id = "gemma-2-9b-it"
# endpoint_id = "YOUR_ENDPOINT_ID"

# 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/{endpoint_id}",
    api_key=credentials.token,
)

response = client.chat.completions.create(
    model=model_id,
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
    stream=True,
)
for chunk in response:
    print(chunk)

Exemplos de extra_body

É possível usar o SDK ou a API REST para transmitir extra_body.

Adicionar thought_tag_marker

{
  ...,
  "extra_body": {
     "google": {
       ...,
       "thought_tag_marker": "..."
     }
   }
}

Adicionar o extra_body usando o SDK

client.chat.completions.create(
  ...,
  extra_body = {
    'extra_body': { 'google': { ... } }
  },
)

Exemplos de extra_content

É possível preencher esse campo usando a API REST diretamente.

extra_content com a string content

{
  "messages": [
    { "role": "...", "content": "...", "extra_content": { "google": { ... } } }
  ]
}

extra_content por mensagem

{
  "messages": [
    {
      "role": "...",
      "content": [
        { "type": "...", ..., "extra_content": { "google": { ... } } }
      ]
    }
}

Chamada de ferramenta por extra_content

{
  "messages": [
    {
      "role": "...",
      "tool_calls": [
        {
          ...,
          "extra_content": { "google": { ... } }
        }
      ]
    }
  ]
}

Exemplos de solicitações de curl

É possível usar essas solicitações curl diretamente, sem passar pelo SDK.

Use o thinking_config com extra_body

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-2.5-flash-preview-04-17", \
    "messages": [ \
      { "role": "user", \
      "content": [ \
        { "type": "text", \
          "text": "Are there any primes number of the form n*ceil(log(n))" \
        }] }], \
    "extra_body": { \
      "google": { \
          "thinking_config": { \
          "include_thoughts": true, "thinking_budget": 10000 \
        }, \
        "thought_tag_marker": "think" } }, \
    "stream": true }'

Usar stream_function_call_arguments

Exemplo de solicitação:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions \
  -d '{
  "model": "google/gemini-3.1-pro-preview", \
  "messages": [ \
    { "role": "user", "content": "What is the weather like in Boston and New Delhi today?" } ], \
  "tools": [ \
    { \
      "type": "function", \
      "function": { \
        "name": "get_current_weather", \
        "description": "Get the current weather in a given location", \
        "parameters": { \
          "type": "object", \
          "properties": { \
            "location": { \
              "type": "string", \
              "description": "The city and state, e.g. San Francisco, CA" \
            }, \
            "unit": { \
              "type": "string", \
              "enum": [ \
                "celsius", \
                "fahrenheit" \
              ] \
            } \
          }, \
          "required": [ \
            "location", \
            "unit" \
          ] \
        } \
      } \
    } \
  ], \
  "extra_body": { \
    "google": { \
      "stream_function_call_arguments": true \
    } \
  }, \
  "stream": true \
}'

Exemplos de respostas:

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"extra_content":{"google":{"thought_signature":"..."}},"function":{"arguments":"","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"{\"location\":\"Boston, MA","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":",\"unit\":\"celsius","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"}","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"{\"location\":\"New Delhi, India","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":",\"unit\":\"celsius","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"}","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"finish_reason":"tool_calls","index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":"","usage":{"completion_tokens":45,"completion_tokens_details":{"reasoning_tokens":504},"extra_properties":{"google":{"traffic_type":"PROVISIONED_THROUGHPUT"}},"prompt_tokens":27,"total_tokens":576}}

data: [DONE]

Geração de imagens

Para manter a compatibilidade com o formato de resposta da OpenAI, o campo audio da resposta é preenchido explicitamente com um extra_content.google.mime_type que indica o tipo MIME do resultado.

Exemplo de solicitação:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions \
  -d '{"model":"google/gemini-3-pro-image-preview", "messages":[{ "role": "user", "content": "Generate an image of a cat." }], "modalities": ["image"] }'

Exemplo de resposta:

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "audio": {
          "data": "<BASE64_BYTES>",
          "extra_content": {
            "google": {
              "mime_type": "image/png"
            }
          }
        },
        "content": null,
        "extra_content": {
          "google": {
            "thought_signature": "..."
          }
        },
        "role": "assistant"
      }
    }
  ],
  "created": 1770850692,
  "id": "hAmNaZb8BZOX4_UPlNXoEA",
  "model": "google/gemini-3-pro-image-preview",
  "object": "chat.completion",
  "system_fingerprint": "",
  "usage": {
    "completion_tokens": 1120,
    "completion_tokens_details": {
      "reasoning_tokens": 251
    },
    "extra_properties": {
      "google": {
        "traffic_type": "PROVISIONED_THROUGHPUT"
      }
    },
    "prompt_tokens": 7,
    "total_tokens": 1378
  }
}

Solicitações multimodais

A API Chat Completions é compatível com vários tipos de entrada multimodal, incluindo áudio e vídeo.

Usar image_url para transmitir dados de imagem

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-2.0-flash-001", \
    "messages": [{ "role": "user", "content": [ \
      { "type": "text", "text": "Describe this image" }, \
      { "type": "image_url", "image_url": "gs://cloud-samples-data/generative-ai/image/scones.jpg" }] }] }'

Usar input_audio para transmitir dados de áudio

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-2.0-flash-001", \
    "messages": [ \
      { "role": "user", \
        "content": [ \
          { "type": "text", "text": "Describe this: " }, \
          { "type": "input_audio", "input_audio": { \
            "format": "audio/mp3", \
            "data": "gs://cloud-samples-data/generative-ai/audio/pixel.mp3" } }] }] }'

Respostas de função multimodal

Exemplo de solicitação:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-3.1-pro-preview", \
    "messages": [ \
      { "role": "user", "content": "Show me the green shirt I ordered last month." }, \
      { \
        "role": "assistant", \
        "tool_calls": [ \
          { \
            "extra_content": { \
              "google": { \
                "thought_signature": "<THOUGHT_SIGNATURE>" \
              } \
            }, \
            "function": { \
              "arguments": "{\"item_name\":\"green shirt\"}", \
              "name": "get_image" \
            }, \
            "id": "function-call-a350228d-0283-4792-8bfa-40da064fb959", \
            "type": "function" \
          } \
        ] \
      }, \
      { \
        "role": "tool", \
        "tool_call_id": "function-call-a350228d-0283-4792-8bfa-40da064fb959", \
        "content": "{\"image_ref\":{\"$ref\":\"dress.jpg\"}}", \
        "extra_content": { \
          "google": { \
            "parts": [ \
              { \
                "file_data": { \
                  "mime_type": "image/jpg", \
                  "display_name": "dress.jpg", \
                  "file_uri": "gs://cloud-samples-data/generative-ai/image/dress.jpg" \
                } \
              } \
            ] \
          } \
        } \
      } \
    ], \
    "tools": [ \
      { \
        "type": "function", \
        "function": { \
          "name": "get_image", \
          "description": "Retrieves the image file reference for a specific order item.", \
          "parameters": { \
            "type": "object", \
            "properties": { \
              "item_name": { \
                "type": "string", \
                "description": "The name or description of the item ordered (e.g., 'green shirt')." \
              } \
            }, \
            "required": [ \
              "item_name" \
            ] \
          } \
        } \
      } \
    ] \
  }'

Exemplo de resposta:

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "Here is the image of the green shirt you ordered.",
        "role": "assistant"
      }
    }
  ],
  "created": 1770852204,
  "id": "bA-NacCPKoae_9MPsNCn6Qc",
  "model": "google/gemini-3.1-pro-preview",
  "object": "chat.completion",
  "system_fingerprint": "",
  "usage": {
    "completion_tokens": 16,
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "prompt_tokens": 1139,
    "total_tokens": 1155
  }
}

Resposta estruturada

Use o parâmetro response_format para receber uma saída estruturada.

Exemplo usando o SDK

from pydantic import BaseModel
from openai import OpenAI

client = OpenAI()

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

completion = client.beta.chat.completions.parse(
    model="google/gemini-2.5-flash-preview-04-17",
    messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ],
    response_format=CalendarEvent,
)

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

Como usar o endpoint global no modo compatível com a OpenAI

O exemplo a seguir mostra como usar o endpoint global no modo compatível com a OpenAI:

REST

  curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions\
  -d '{ \
    "model": "google/gemini-2.0-flash-001", \
    "messages": [ \
    {"role": "user", \
      "content": "Hello World" \
      }] \
      }'

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