Google Vertex AI - Portkey Docs

Portkey provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs), and embedding models into your apps, including Google Vertex AI. With Portkey, you can take advantage of features like fast AI gateway access, observability, prompt management, and more, all while ensuring the secure management of your Vertex auth through a Portkey’s Model Catalog

Provider Slug. vertex-ai

Portkey SDK Integration with Google Vertex AI

Portkey provides a consistent API to interact with models from various providers. To integrate Google Vertex AI with Portkey:

1. Install the Portkey SDK

Add the Portkey SDK to your application to interact with Google Vertex AI API through Portkey’s gateway.

npm install --save portkey-ai

2. Initialize Portkey Client

To integrate Vertex AI with Portkey, you’ll need your Vertex Project Id Or Service Account JSON & Vertex Region, with which you can set up the Portkey’s AI Provider. Here’s a guide on how to find your Vertex Project details If you are integrating through Service Account File, refer to this guide.

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]

})

If you do not want to add your Vertex AI details to Portkey vault, you can directly pass them while instantiating the Portkey client. More on that here.

3. Invoke Chat Completions with Vertex AI

Use the Portkey instance to send requests to any models hosted on Vertex AI. You can also override the Portkey’s AI Provider directly in the API call if needed.

Vertex AI uses OAuth2 to authenticate its requests, so you need to send the access token additionally along with the request.

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: '@VERTEX_PROVIDER/gemini-1.5-pro-latest', // your model slug from Portkey's Model Catalog
}, {Authorization: "Bearer $YOUR_VERTEX_ACCESS_TOKEN"});

console.log(chatCompletion.choices);

To use Anthopic models on Vertex AI, prepend anthropic. to the model name.
Example: @VERTEX_PROVIDER/anthropic.claude-3-5-sonnet@20240620Similarly, for Meta models, prepend meta. to the model name.
Example: @VERTEX_PROVIDER/meta.llama-3-8b-8192

Using the /messages Route with Vertex AI Models

Access Claude models on Vertex AI through Anthropic’s native/messages endpoint using Portkey’s SDK or Anthropic’s SDK.

This route only works with Claude models. For other models, use the standard OpenAI compliant endpoint.

curl --location 'https://api.portkey.ai/v1/messages' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: YOUR_PORTKEY_API_KEY' \
--data '{
    "model": "@YOUR_VERTEX_PROVIDER/MODEL_NAME",
    "max_tokens": 250,
    "messages": [
        {
            "role": "user",
            "content": "Hello, Claude"
        }
    ]
}'

Counting Tokens

Portkey also supports the token counting endpoint for vertex. Checkout the example in this link for more details.

Using Self-Deployed Models on Vertex AI (Hugging Face, Custom Models)

Portkey supports connecting to self-deployed models on Vertex AI, including models from Hugging Face or any custom models you’ve deployed to a Vertex AI endpoint. Requirements for Self-Deployed Models To use self-deployed models on Vertex AI through Portkey:

Model Naming Convention: When making requests to your self-deployed model, you must prefix the model name with endpoints.
```
endpoints.my_endpoint_name
```
Required Permissions: The Google Cloud service account used in your Portkey Model Catalog must have the aiplatform.endpoints.predict permission.

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'endpoints.my_custom_llm', // Notice the 'endpoints.' prefix
}, {Authorization: "Bearer $YOUR_VERTEX_ACCESS_TOKEN"});

console.log(chatCompletion.choices);

Why the prefix? Vertex AI’s product offering for self-deployed models is called “Endpoints.” This naming convention indicates to Portkey that it should route requests to your custom endpoint rather than a standard Vertex AI model.This approach works for all models you can self-deploy on Vertex AI Model Garden, including Hugging Face models and your own custom models.

Document, Video, Audio Processing

Vertex AI supports attaching webm, mp4, pdf, jpg, mp3, wav, etc. file types to your Gemini messages.

Gemini Docs:

Using Portkey, here’s how you can send these media files:

const chatCompletion = await portkey.chat.completions.create({
    messages: [
        { role: 'system', content: 'You are a helpful assistant' },
        { role: 'user', content: [
            {
                type: 'image_url',
                image_url: {
                    url: 'gs://cloud-samples-data/generative-ai/image/scones.jpg'
                }
            },
            {
                type: 'text',
                text: 'Describe the image'
            }
        ]}
    ],
    model: 'gemini-1.5-pro-001',
    max_tokens: 200
});

Document Processing (PDF)

Gemini’s vision capabilities excel at understanding the content of PDF documents, including text, tables, and images.

Gemini Documents Understanding Docs

Method 1: Sending a Document via Google Files URL Upload your PDF using the Files API to get a Google Files URL.

const chatCompletion = await portkey.chat.completions.create({
    model: 'gemini-1.5-pro',
    messages: [{
        role: 'user',
        content: [
            {
                type: 'image_url',
                image_url: {
                    url: 'https://generativelanguage.googleapis.com/v1beta/files/your-pdf-file-id'
                }
            },
            { type: 'text', text: 'Summarize the key findings of this research paper.' }
        ]
    }],
});
console.log(chatCompletion.choices[0].message.content);

Method 2: Sending a Local Document as Base64 Data This is suitable for smaller, local PDF files.

import fs from 'fs';

const pdfBytes = fs.readFileSync('whitepaper.pdf');
const base64Pdf = pdfBytes.toString('base64');
const pdfUri = `data:application/pdf;base64,${base64Pdf}`;

const chatCompletion = await portkey.chat.completions.create({
    model: '@VERTEX_PROVIDER/MODEL_NAME',
    messages: [{
        role: 'user',
        content: [
            { type: 'image_url', image_url: { url: pdfUri }},
            { type: 'text', text: 'What is the main conclusion of this document?' }
        ]
    }],
});
console.log(chatCompletion.choices[0].message.content);

While you can send other document types like .txt or .html, they will be treated as plain text. Gemini’s native document vision capabilities are optimized for the application/pdf MIME type.

Extended Thinking (Reasoning Models) (Beta)

The assistants thinking response is returned in the response_chunk.choices[0].delta.content_blocks array, not the response.choices[0].message.content string.Gemini models do not support plugging back the reasoning into multi turn conversations, so you don’t need to send the thinking message back to the model.

Models like google.gemini-2.5-flash-preview-04-17 anthropic.claude-3-7-sonnet@20250219 support extended thinking. This is similar to openai thinking, but you get the model’s reasoning as it processes the request as well. Note that you will have to set strict_open_ai_compliance=False in the headers to use this feature.

Single turn conversation

from portkey_ai import Portkey

# Initialize the Portkey clien
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="@VERTEX_PROVIDER/anthropic.claude-3-7-sonnet@20250219", # your model slug from Portkey's Model Catalog
  max_tokens=3000,
  thinking={
      "type": "enabled",
      "budget_tokens": 2030
  },
  stream=True,
  messages=[
      {
          "role": "user",
          "content": [
              {
                  "type": "text",
                  "text": "when does the flight from new york to bengaluru land tomorrow, what time, what is its flight number, and what is its baggage belt?"
              }
          ]
      }
  ]
)
print(response)
# in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
# response = portkey.chat.completions.create(
#   ...same config as above but with stream: true
# )
# for chunk in response:
#     if chunk.choices[0].delta:
#         content_blocks = chunk.choices[0].delta.get("content_blocks")
#         if content_blocks is not None:
#             for content_block in content_blocks:
#                 print(content_block)

To disable thinking for gemini models like google.gemini-2.5-flash-preview-04-17, you are required to explicitly set budget_tokens to 0.

"thinking": {
    "type": "enabled",
    "budget_tokens": 0
}

Multi turn conversation

from portkey_ai import Portkey

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="@VERTEX_PROVIDER/anthropic.claude-3-7-sonnet@20250219", # your model slug from Portkey's Model Catalog
  max_tokens=3000,
  thinking={
      "type": "enabled",
      "budget_tokens": 2030
  },
  stream=True,
  messages=[
      {
          "role": "user",
          "content": [
              {
                  "type": "text",
                  "text": "when does the flight from baroda to bangalore land tomorrow, what time, what is its flight number, and what is its baggage belt?"
              }
          ]
      },
      {
          "role": "assistant",
          "content": [
                  {
                      "type": "thinking",
                      "thinking": "The user is asking several questions about a flight from Baroda (also known as Vadodara) to Bangalore:\n1. When does the flight land tomorrow\n2. What time does it land\n3. What is the flight number\n4. What is the baggage belt number at the arrival airport\n\nTo properly answer these questions, I would need access to airline flight schedules and airport information systems. However, I don't have:\n- Real-time or scheduled flight information\n- Access to airport baggage claim allocation systems\n- Information about specific flights between these cities\n- The ability to look up tomorrow's specific flight schedules\n\nThis question requires current, specific flight information that I don't have access to. Instead of guessing or providing potentially incorrect information, I should explain this limitation and suggest ways the user could find this information.",
                      "signature": "EqoBCkgIARABGAIiQBVA7FBNLRtWarDSy9TAjwtOpcTSYHJ+2GYEoaorq3V+d3eapde04bvEfykD/66xZXjJ5yyqogJ8DEkNMotspRsSDKzuUJ9FKhSNt/3PdxoMaFZuH+1z1aLF8OeQIjCrA1+T2lsErrbgrve6eDWeMvP+1sqVqv/JcIn1jOmuzrPi2tNz5M0oqkOO9txJf7QqEPPw6RG3JLO2h7nV1BMN6wE="
                  }
          ]
      },
      {
          "role": "user",
          "content": "thanks that's good to know, how about to chennai?"
      }
  ]
)
print(response)

Sending `base64` Image

Here, you can send the base64 image data along with the url field too:

"url": "data:image/png;base64,UklGRkacAABXRUJQVlA4IDqcAAC....."

This same message format also works for all other media types — just send your media file in the url field, like "url": "gs://cloud-samples-data/video/animals.mp4" for google cloud urls and "url":"https://download.samplelib.com/mp3/sample-3s.mp3" for public urlsYour URL should have the file extension, this is used for inferring MIME_TYPE which is a required parameter for prompting Gemini models with files

Text Embedding Models

You can use any of Vertex AI’s English and Multilingual models through Portkey, in the familar OpenAI-schema.

The Gemini-specific parameter task_type is also supported on Portkey.

import Portkey from 'portkey-ai';

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
});

// Generate embeddings
async function getEmbeddings() {
    const embeddings = await portkey.embeddings.create({
        input: "embed this",
        model: "@VERTEX_PROVIDER/text-multilingual-embedding-002", // your model slug from Portkey's Model Catalog
        // @ts-ignore (if using typescript)
        task_type: "CLASSIFICATION", // Optional
    }, {Authorization: "Bearer $YOUR_VERTEX_ACCESS_TOKEN"});

    console.log(embeddings);
}
await getEmbeddings();

Function Calling

Portkey supports function calling mode on Google’s Gemini Models. Explore this Cookbook for a deep dive and examples:

Function Calling

Managing Vertex AI Prompts

You can manage all prompts to Google Gemini in the Prompt Library. All the models in the model garden are supported and you can easily start testing different prompts. Once you’re ready with your prompt, you can use the portkey.prompts.completions.create interface to use the prompt in your application.

Image Generation Models

Portkey supports the Imagen API on Vertex AI for image generations, letting you easily make requests in the familar OpenAI-compliant schema.

curl https://api.portkey.ai/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: $PORTKEY_PROVIDER" \
  -d '{
    "prompt": "Cat flying to mars from moon",
    "model":"@your-model-slug"
  }'

Image Generation API Reference

List of Supported Imagen Models

imagen-3.0-generate-001
imagen-3.0-fast-generate-001
imagegeneration@006
imagegeneration@005
imagegeneration@002

Grounding with Google Search

Vertex AI supports grounding with Google Search. This is a feature that allows you to ground your LLM responses with real-time search results. Grounding is invoked by passing the google_search tool (for newer models like gemini-2.0-flash-001), and google_search_retrieval (for older models like gemini-1.5-flash) in the tools array.

"tools": [
    {
        "type": "function",
        "function": {
            "name": "google_search" // or google_search_retrieval for older models
        }
    }]

If you mix regular tools with grounding tools, vertex might throw an error saying only one tool can be used at a time.

gemini-2.0-flash-thinking-exp and other thinking/reasoning models

gemini-2.0-flash-thinking-exp models return a Chain of Thought response along with the actual inference text, this is not openai compatible, however, Portkey supports this by adding a \r\n\r\n and appending the two responses together. You can split the response along this pattern to get the Chain of Thought response and the actual inference text. If you require the Chain of Thought response along with the actual inference text, pass the strict open ai compliance flag as false in the request. If you want to get the inference text only, pass the strict open ai compliance flag as true in the request.

Multiple Modalities on chat completions endpoint

gemini-2.5-flash-image (nano banana)

The image data is available in the content_parts field in the response and it can be plugged back in for multi turn conversations

single turn conversation

from portkey_ai import Portkey

# Initialize the Portkey clien
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="gemini-2.5-flash-image-preview", # your model slug from Portkey's Model Catalog
  max_tokens=32768,
  stream=False,
  modalities=["text", "image"],
  messages= [
      {
        "role": "system",
        "content": "You are a helpful assistant"
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Add some chocolate drizzle to the croissants. Include text across the top of the image that says \"Made Fresh Daily\"."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "gs://cloud-samples-data/generative-ai/image/croissant.jpeg"
            }
          }
        ]
      }
    ]
)
print(response)
# in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
# response = portkey.chat.completions.create(
#   ...same config as above but with stream: true
# )
# for chunk in response:
#     if chunk.choices[0].delta:
#         content_blocks = chunk.choices[0].delta.get("content_blocks")
#         if content_blocks is not None:
#             for content_block in content_blocks:
#                 print(content_block)

multi turn conversation

from portkey_ai import Portkey

# Initialize the Portkey clien
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="gemini-2.5-flash-image-preview", # your model slug from Portkey's Model Catalog
  max_tokens=32768,
  stream=False,
  modalities=["text", "image"],
  messages= [
      {
        "role": "system",
        "content": "You are a helpful assistant"
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Add some chocolate drizzle to the croissants. Include text across the top of the image that says \"Made Fresh Daily\"."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "gs://cloud-samples-data/generative-ai/image/croissant.jpeg"
            }
          }
        ]
      },
        {
        "role": "assistant",
        "content": [
                {
                    "type": "text",
                    "text": "Here are the croissants with chocolate drizzle and the requested text: "
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/jpeg;base64,UKDhasdhj....."
                    }
                }
            ]
        },
        {
            "role": "user",
            "content": "looking good, thanks fam"
        }

    ]
)
print(response)
# in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
# response = portkey.chat.completions.create(
#   ...same config as above but with stream: true
# )
# for chunk in response:
#     if chunk.choices[0].delta:
#         content_blocks = chunk.choices[0].delta.get("content_blocks")
#         if content_blocks is not None:
#             for content_block in content_blocks:
#                 print(content_block)

Making Requests Without Portkey’s Model Catalog

You can also pass your Vertex AI details & secrets directly without using the Portkey’s Model Catalog. Vertex AI expects a region, a project ID and the access token in the request for a successful completion request. This is how you can specify these fields directly in your requests:

Example Request

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
    vertexProjectId: "sample-55646",
    vertexRegion: "us-central1",
    provider:"vertex-ai",
    Authorization: "$GCLOUD AUTH PRINT-ACCESS-TOKEN"
})

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gemini-pro',
});

console.log(chatCompletion.choices);

For further questions on custom Vertex AI deployments or fine-grained access tokens, reach out to us on support@portkey.ai

How to Find Your Google Vertex Project Details

To obtain your Vertex Project ID and Region, navigate to Google Vertex Dashboard.

You can copy the Project ID located at the top left corner of your screen.
Find the Region dropdown on the same page to get your Vertex Region.

Get Your Service Account JSON

Follow this process to get your Service Account JSON.

When selecting Service Account File as your authentication method, you’ll need to:

Upload your Google Cloud service account JSON file
Specify the Vertex Region

This method is particularly important for using self-deployed models, as your service account must have the aiplatform.endpoints.predict permission to access custom endpoints. Learn more about permission on your Vertex IAM key here.

For Self-Deployed Models: Your service account must have the aiplatform.endpoints.predict permission in Google Cloud IAM. Without this specific permission, requests to custom endpoints will fail.

Using Project ID and Region Authentication

For standard Vertex AI models, you can simply provide:

Your Vertex Project ID (found in your Google Cloud console)
The Vertex Region where your models are deployed

This method is simpler but may not have all the permissions needed for custom endpoints.

Next Steps

The complete list of features supported in the SDK are available on the link below.

SDK

You’ll find more information in the relevant sections:

Ecosystem

LLM Integrations

Cloud Platforms

Guardrails

Plugins

Vector Databases

Agents

AI Apps

Libraries

Tracing Providers

MCP Servers

​Portkey SDK Integration with Google Vertex AI

​1. Install the Portkey SDK

​2. Initialize Portkey Client

​3. Invoke Chat Completions with Vertex AI

​Using the /messages Route with Vertex AI Models

Counting Tokens

​Using Self-Deployed Models on Vertex AI (Hugging Face, Custom Models)

​Document, Video, Audio Processing

​Document Processing (PDF)

Gemini Documents Understanding Docs

​Extended Thinking (Reasoning Models) (Beta)

​Single turn conversation

​Multi turn conversation

​Sending base64 Image

​Text Embedding Models

​Function Calling

​Managing Vertex AI Prompts

​Image Generation Models

​List of Supported Imagen Models

​Grounding with Google Search

​gemini-2.0-flash-thinking-exp and other thinking/reasoning models

​Multiple Modalities on chat completions endpoint

​gemini-2.5-flash-image (nano banana)

​single turn conversation

​multi turn conversation

​Making Requests Without Portkey’s Model Catalog

​Example Request

​How to Find Your Google Vertex Project Details

​Get Your Service Account JSON

​Using Project ID and Region Authentication

​Next Steps

SDK

Portkey SDK Integration with Google Vertex AI

1. Install the Portkey SDK

2. Initialize Portkey Client

3. Invoke Chat Completions with Vertex AI

Using the /messages Route with Vertex AI Models

Using Self-Deployed Models on Vertex AI (Hugging Face, Custom Models)

Document, Video, Audio Processing

Document Processing (PDF)

Extended Thinking (Reasoning Models) (Beta)

Single turn conversation

Multi turn conversation

Sending `base64` Image

Text Embedding Models

Function Calling

Managing Vertex AI Prompts

Image Generation Models

List of Supported Imagen Models

Grounding with Google Search

gemini-2.0-flash-thinking-exp and other thinking/reasoning models

Multiple Modalities on chat completions endpoint

gemini-2.5-flash-image (nano banana)

single turn conversation

multi turn conversation

Making Requests Without Portkey’s Model Catalog

Example Request

How to Find Your Google Vertex Project Details

Get Your Service Account JSON

Using Project ID and Region Authentication

Next Steps