Google Vertex AI

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 virtual key system

​

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

npm install --save portkey-ai

pip install portkey-ai

​

2. Initialize Portkey with the Virtual Key

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 Virtual key.

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"]
    virtualKey: "VERTEX_VIRTUAL_KEY", // Your Vertex AI Virtual Key
})

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "VERTEX_VIRTUAL_KEY", // Your Vertex AI Virtual Key
})

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VERTEX_VIRTUAL_KEY"   # Replace with your virtual key for Google
)

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 and Gemini

Use the Portkey instance to send requests to Gemini models hosted on Vertex AI. You can also override the virtual key directly in the API call if needed.

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gemini-1.5-pro-latest',
}, {Authorization: "Bearer $YOUR_VERTEX_ACCESS_TOKEN"});

console.log(chatCompletion.choices);

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gemini-1.5-pro-latest',
}, {Authorization: "Bearer $YOUR_VERTEX_ACCESS_TOKEN"});

console.log(chatCompletion.choices);

completion = portkey.with_options(Authorization="Bearer $YOUR_VERTEX_ACCESS_TOKEN").chat.completions.create(
    messages= [{ "role": 'user', "content": 'Say this is a test' }],
    model= 'gemini-1.5-pro-latest'
)

print(completion)

​

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:

1. Model Naming Convention: When making requests to your self-deployed model, you must prefix the model name with endpoints.

   endpoints.my_endpoint_name

2. Required Permissions: The Google Cloud service account used in your Portkey virtual key 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);

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);

completion = portkey.with_options(Authorization="Bearer $YOUR_VERTEX_ACCESS_TOKEN").chat.completions.create(
    messages= [{ "role": 'user', "content": 'Say this is a test' }],
    model= 'endpoints.my_huggingface_model' # Notice the 'endpoints.' prefix
)

print(completion)

​

Document, Video, Audio Processing

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

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
});

​

Extended Thinking (Reasoning Models) (Beta)

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 client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY",   # Add your provider's virtual key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="anthropic.claude-3-7-sonnet@20250219",
  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)

"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
    virtual_key="VIRTUAL_KEY",   # Add your provider's virtual key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="anthropic.claude-3-7-sonnet@20250219",
  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....."

​

Text Embedding Models

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

import Portkey from 'portkey-ai';

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

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

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

import Portkey from 'portkey-ai';

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

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

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

from portkey_ai import Portkey

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

# Generate embeddings
def get_embeddings():
    embeddings = portkey.with_options(Authorization="Bearer $YOUR_VERTEX_ACCESS_TOKEN").embeddings.create(
        input='The vector representation for this text',
        model='text-embedding-004',
        task_type="CLASSIFICATION" # Optional
    )
    print(embeddings)

get_embeddings()

curl 'https://api.portkey.ai/v1/embeddings' \
    -H 'Content-Type: application/json' \
    -H 'x-portkey-api-key: PORTKEY_API_KEY' \
    -H 'x-portkey-provider: vertex-ai' \
    -H 'Authorization: Bearer VERTEX_AI_ACCESS_TOKEN' \
    -H 'x-portkey-virtual-key: $VERTEX_VIRTUAL_KEY' \
    --data-raw '{
        "model": "textembedding-004",
        "input": "A HTTP 246 code is used to signify an AI response containing hallucinations or other inaccuracies",
        "task_type": "CLASSIFICATION"
    }'

​

Function Calling

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

​

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-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY" \
  -d '{
    "prompt": "Cat flying to mars from moon",
    "model":"imagen-3.0-generate-001"
  }'

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
        }
    }]

​

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.

​

Making Requests Without Virtual Keys

You can also pass your Vertex AI details & secrets directly without using the Virtual Keys in Portkey.

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);

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);

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    vertex_project_id="sample-55646",
    vertex_region="us-central1",
    provider="vertex_ai",
    Authorization="$GCLOUD AUTH PRINT-ACCESS-TOKEN"
)

completion = portkey.chat.completions.create(
    messages= [{ "role": 'user', "content": 'Say this is a test' }],
    model= 'gemini-1.5-pro-latest'
)

print(completion)

import OpenAI from "openai";
import { PORTKEY_GATEWAY_URL, createHeaders } from "portkey-ai";

const portkey = new OpenAI({
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    apiKey: "PORTKEY_API_KEY",
    provider: "vertex-ai",
    vertexRegion: "us-central1",
    vertexProjectId: "xxx"
    Authorization: "Bearer $GCLOUD AUTH PRINT-ACCESS-TOKEN",
    // forwardHeaders: ["Authorization"] // You can also directly forward the auth token to Google
  }),
});

async function main() {
  const response = await portkey.chat.completions.create({
    messages: [{ role: "user", content: "1729" }],
    model: "gemini-1.5-flash-001",
    max_tokens: 32,
  });

  console.log(response.choices[0].message.content);
}

main();

curl 'https://api.portkey.ai/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'x-portkey-api-key: PORTKEY_API_KEY' \
-H 'x-portkey-provider: vertex-ai' \
-H 'Authorization: Bearer VERTEX_AI_ACCESS_TOKEN' \
-H 'x-portkey-vertex-project-id: sample-94994' \
-H 'x-portkey-vertex-region: us-central1' \
--data '{
    "model": "gemini-1.5-pro",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant"
      },
      {
        "role": "user",
        "content": "what is a portkey?"
      }
    ]
}'

For further questions on custom Vertex AI deployments or fine-grained access tokens, reach out to us on [email protected]

​

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:

1. Upload your Google Cloud service account JSON file
2. 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.

​

Using Project ID and Region Authentication

For standard Vertex AI models, you can simply provide:

1. Your Vertex Project ID (found in your Google Cloud console)
2. 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.

You’ll find more information in the relevant sections:

1. Add metadata to your requests
2. Add gateway configs to your Vertex AI requests
3. Tracing Vertex AI requests
4. Setup a fallback from OpenAI to Vertex AI APIs