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 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.

NodeJS

npm install --save portkey-ai

Python

pip install 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.

NodeJS SDK

import Portkey from 'portkey-ai'

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

})

Python SDK

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your 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.

NodeJS SDK

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);

Python SDK

completion = portkey.with_options(Authorization="Bearer $YOUR_VERTEX_ACCESS_TOKEN").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
)

print(completion)

To use Anthopic models on Vertex AI, prepend anthropic. to the model name.
Example: @VERTEX_PROVIDER/anthropic.claude-3-5-sonnet@20240620

Similarly, for Meta models, prepend meta. to the model name.
Example: @VERTEX_PROVIDER/meta.llama-3-8b-8192

Anthropic Beta Header Support: When using Anthropic models on Vertex AI, you can pass the anthropic-beta header (or x-portkey-anthropic-beta) to enable beta features. This header is forwarded to the underlying Anthropic API.

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

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"
        }
    ]
}'

Python SDK

    Coming Soon!

NodeJS SDK

    Coming Soon!

Anthropic Python SDK

Anthropic Python SDK

  import anthropic

  client = anthropic.Anthropic(
      api_key="dummy", # we will use portkey's provider slug
      default_headers={"x-portkey-api-key": "YOUR_PORTKEY_API_KEY"},
      base_url="https://api.portkey.ai/v1"
  )
  message = client.messages.create(
      model="@your-provider-slug/your-model-name",
      max_tokens=250,
      messages=[
          {"role": "user", "content": "Hello, Claude"}
      ],
  )
  print(message.content)

Anthropic TypeScript SDK

Anthropic TS SDK

   import Anthropic from '@anthropic-ai/sdk';

   const anthropic = new Anthropic({
     apiKey: 'dummy', // we will use portkey's provider slug
     baseURL: "https://api.portkey.ai/v1",
     defaultHeaders: { "x-portkey-api-key": "YOUR_PORTKEY_API_KEY" }
   });

   const msg = await anthropic.messages.create({
     model: "@your-provider-slug/your-model-name",
     max_tokens: 1024,
     messages: [{ role: "user", content: "Hello, Claude" }],
   });
   console.log(msg);

Counting Tokens

Portkey supports the Google Vertex AI CountTokens API to estimate token usage before sending requests. Check out the count-tokens guide for more details.

Explicit context caching

Vertex AI supports context caching to reduce costs and latency for repeated prompts with large amounts of context. You can explicitly create a cache and then reference it in subsequent inference requests.

This is different from Portkey’s gateway caching. Vertex AI context caching is a native Google feature that caches large context (system instructions, documents, etc.) on Google’s servers. Portkey’s simple and semantic caching caches complete request-response pairs at the gateway level. Use Vertex AI context caching when you have large, reusable context; use Portkey’s gateway caching for repeated identical or similar requests.

Gemini models only. Context caching is only available for Gemini models on Vertex AI (e.g., gemini-1.5-pro, gemini-1.5-flash). It is not supported for Anthropic, Meta, or other models hosted on Vertex AI.

Pricing

Context caching can significantly reduce costs for applications with large, reusable context:

Token type	Price per token
Cache write (input)	$0.000625
Cache read (input)	$0.00005

Cache reads are ~12x cheaper than cache writes, making this cost-effective for scenarios where you reference the same cached content multiple times.

Step 1: Create a context cache

Use Portkey’s proxy capability with the x-portkey-custom-host header to call Vertex AI’s native caching endpoints. This routes your request through Portkey while targeting Vertex AI’s cachedContents API directly.

cURL

curl --location 'https://api.portkey.ai/v1/projects/{{YOUR_PROJECT_ID}}/locations/{{LOCATION}}/cachedContents' \
--header 'x-portkey-provider: {{@my-vertex-ai-provider}}' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: {{your_api_key}}' \
--header 'x-portkey-custom-host: https://aiplatform.googleapis.com/v1' \
--data '{
  "model": "projects/{{YOUR_PROJECT_ID}}/locations/{{LOCATION}}/publishers/google/models/{{MODEL_ID}}",
  "displayName": "{{my-cache-display-name}}",
  "contents": [{
    "role": "user",
      "parts": [{
        "text": "This is sample text to demonstrate explicit caching. (you need a minimum of 1024 tokens)"
      }]
  },
  {
    "role": "model",
      "parts": [{
        "text": "thankyou I am your helpful assistant"
      }]
  }],
  "ttl": "3600s"
}'

Python

import requests

YOUR_PROJECT_ID = "your-project-id"
LOCATION = "us-central1"
MODEL_ID = "gemini-1.5-pro-001"

url = f"https://api.portkey.ai/v1/projects/{YOUR_PROJECT_ID}/locations/{LOCATION}/cachedContents"

headers = {
    "x-portkey-provider": "@my-vertex-ai-provider",
    "Content-Type": "application/json",
    "x-portkey-api-key": "your_portkey_api_key",
    "x-portkey-custom-host": "https://aiplatform.googleapis.com/v1"
}

payload = {
    "model": f"projects/{YOUR_PROJECT_ID}/locations/{LOCATION}/publishers/google/models/{MODEL_ID}",
    "displayName": "my-cache-display-name",
    "contents": [
        {
            "role": "user",
            "parts": [{"text": "This is sample text to demonstrate explicit caching. (minimum 1024 tokens required)"}]
        },
        {
            "role": "model",
            "parts": [{"text": "Thank you, I am your helpful assistant"}]
        }
    ],
    "ttl": "3600s"
}

response = requests.post(url, headers=headers, json=payload)
cache_name = response.json().get("name")  # Save this for inference requests
print(f"Cache created: {cache_name}")

NodeJS

const YOUR_PROJECT_ID = "your-project-id";
const LOCATION = "us-central1";
const MODEL_ID = "gemini-1.5-pro-001";

const response = await fetch(
  `https://api.portkey.ai/v1/projects/${YOUR_PROJECT_ID}/locations/${LOCATION}/cachedContents`,
  {
    method: 'POST',
    headers: {
      'x-portkey-provider': '@my-vertex-ai-provider',
      'Content-Type': 'application/json',
      'x-portkey-api-key': 'your_portkey_api_key',
      'x-portkey-custom-host': 'https://aiplatform.googleapis.com/v1'
    },
    body: JSON.stringify({
      model: `projects/${YOUR_PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}`,
      displayName: 'my-cache-display-name',
      contents: [
        {
          role: 'user',
          parts: [{ text: 'This is sample text to demonstrate explicit caching. (minimum 1024 tokens required)' }]
        },
        {
          role: 'model',
          parts: [{ text: 'Thank you, I am your helpful assistant' }]
        }
      ],
      ttl: '3600s'
    })
  }
);

const data = await response.json();
const cacheName = data.name; // Save this for inference requests
console.log(`Cache created: ${cacheName}`);

Request variables:

Variable	Description
YOUR_PROJECT_ID	Your Google Cloud project ID
LOCATION	The region where your model is deployed (e.g., us-central1)
MODEL_ID	The Gemini model identifier (e.g., gemini-1.5-pro-001)
my-cache-display-name	A unique name to identify your cache
your_api_key	Your Portkey API key
@my-vertex-ai-provider	Your Vertex AI provider slug from Portkey’s Model Catalog
ttl	Cache time-to-live (e.g., 3600s for 1 hour, 86400s for 1 day)

Context caching requires a minimum of 1024 tokens in the cached content. The cache has a default TTL (time-to-live) which you can configure using the ttl parameter.

Proxy pattern limitation: Creating caches uses Portkey’s proxy capability to forward requests to Vertex AI’s native endpoints. Native endpoint support within Portkey’s standard API is not planned since context caching is Gemini-specific.

Step 2: Use the cache in inference requests

Once the cache is created, reference it in your chat completion requests using the cached_content parameter:

cURL

curl 'https://api.portkey.ai/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'x-portkey-api-key: {{your_api_key}}' \
--data '{
    "model": "@my-vertex-ai-provider/gemini-1.5-pro-001",
    "cached_content": "{{my-cache-display-name}}",
    "messages": [
      {
        "role": "user",
        "content": "Based on the context I provided earlier, answer my question."
      }
    ]
}'

Python SDK

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
)

completion = portkey.chat.completions.create(
    model="@my-vertex-ai-provider/gemini-1.5-pro-001",
    cached_content="my-cache-display-name",
    messages=[
        {"role": "user", "content": "Based on the context I provided earlier, answer my question."}
    ]
)

print(completion)

NodeJS SDK

import Portkey from 'portkey-ai';

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

const completion = await portkey.chat.completions.create({
    model: "@my-vertex-ai-provider/gemini-1.5-pro-001",
    cached_content: "my-cache-display-name",
    messages: [
        { role: "user", content: "Based on the context I provided earlier, answer my question." }
    ]
});

console.log(completion);

The model and region used in the inference request must match the model and region used when creating the cache.

For more details on context caching options like TTL configuration and cache management, refer to the Vertex AI context caching documentation.

---

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 Model Catalog must have the aiplatform.endpoints.predict permission.

NodeJS SDK

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);

Python SDK

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)

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 various file types to your Gemini messages including documents (pdf), images (jpg, png), videos (webm, mp4), and audio files.

Supported Audio Formats: mp3, wav, opus, ogg, flac, pcm, aac, m4a, mpeg, mpga, mp4, webm

Gemini Docs:

• Document Processing
• Video & Image Processing
• Audio Processing

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

JavaScript

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
});

completion = 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
)

print(completion)

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: vertex-ai' \
--header 'x-portkey-vertex-region: us-central1' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: PORTKEY_API_KEY' \
--header 'Authorization: VERTEX_AI_ACCESS_TOKEN' \
--data '{
    "model": "gemini-1.5-pro-001",
    "max_tokens": 200,
    "stream": false,
    "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 this image"
                }
            ]
        }
    ]
}'

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.

NodeJS

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);

completion = 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." }
        ]
    }],
)
print(completion.choices[0].message.content)

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: google' \
--header 'x-portkey-api-key: YOUR_PORTKEY_API_KEY' \
--header 'Content-Type: application/json' \
--header 'Authorization: VERTEX_AI_ACCESS_TOKEN' \
--data '{
    "model": "@VERTEX_PROVIDER/MODEL_NAME",
    "messages": [{
        "role": "user",
        "content": [
            {"type": "text", "text": "Summarize the key findings of this research paper."},
            {"type": "image_url", "image_url": {"url": "https://generativelanguage.googleapis.com/v1beta/files/your-pdf-file-id"}}
        ]
    }]
}'

Method 2: Sending a Local Document as Base64 Data

This is suitable for smaller, local PDF files.

NodeJS

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);

import base64

with open("whitepaper.pdf", "rb") as pdf_file:
    pdf_bytes = pdf_file.read()

base64_pdf = base64.b64encode(pdf_bytes).decode('utf-8')
pdf_uri = f"data:application/pdf;base64,{base64_pdf}"

completion = portkey.chat.completions.create(
    model='@VERTEX_PROVIDER/MODEL_NAME',
    messages=[{
        "role": "user",
        "content": [
            { "type": "image_url", "image_url": { "url": pdf_uri }},
            { "type": "text", "text": "What is the main conclusion of this document?" }
        ]
    }],
)
print(completion.choices[0].message.content)

# First, encode your PDF file to base64
# For example: base64 -i whitepaper.pdf -o pdf.b64
# Then use the encoded content in the request

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: google' \
--header 'x-portkey-api-key: YOUR_PORTKEY_API_KEY' \
--header 'Content-Type: application/json' \
--header 'Authorization: VERTEX_AI_ACCESS_TOKEN' \
--data '{
    "model": "@VERTEX_PROVIDER/MODEL_NAME",
    "messages": [{
        "role": "user",
        "content": [
            {"type": "text", "text": "What is the main conclusion of this document?"},
            {"type": "image_url", "image_url": {"url": "data:application/pdf;base64,YOUR_BASE64_PDF_DATA"}}
        ]
    }]
}'

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.

---

Media Resolution

The media_resolution parameter allows you to control token allocation for media inputs (images, videos, PDFs) when using Gemini models on Vertex AI. This helps balance between processing detail and cost/speed.

Supported values

Value	Description
MEDIA_RESOLUTION_LOW	Reduced tokens for faster, cheaper processing
MEDIA_RESOLUTION_MEDIUM	Balanced approach between detail and cost
MEDIA_RESOLUTION_HIGH	Maximum tokens for detailed analysis
MEDIA_RESOLUTION_ULTRA_HIGH	Highest resolution (per-part only, for specialized tasks)

Top-level configuration

Apply media resolution globally to all media in the request:

NodeJS

const chatCompletion = await portkey.chat.completions.create({
    model: 'gemini-1.5-pro',
    media_resolution: 'MEDIA_RESOLUTION_HIGH',
    messages: [{
        role: 'user',
        content: [
            {
                type: 'image_url',
                image_url: {
                    url: 'gs://cloud-samples-data/generative-ai/image/scones.jpg'
                }
            },
            { type: 'text', text: 'Analyze this image in detail.' }
        ]
    }]
}, {Authorization: "Bearer $YOUR_VERTEX_ACCESS_TOKEN"});

completion = portkey.with_options(Authorization="Bearer $YOUR_VERTEX_ACCESS_TOKEN").chat.completions.create(
    model='gemini-1.5-pro',
    media_resolution='MEDIA_RESOLUTION_HIGH',
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "gs://cloud-samples-data/generative-ai/image/scones.jpg"
                }
            },
            { "type": "text", "text": "Analyze this image in detail." }
        ]
    }]
)

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: vertex-ai' \
--header 'x-portkey-vertex-region: us-central1' \
--header 'x-portkey-api-key: PORTKEY_API_KEY' \
--header 'Authorization: VERTEX_AI_ACCESS_TOKEN' \
--header 'Content-Type: application/json' \
--data '{
    "model": "gemini-1.5-pro",
    "media_resolution": "MEDIA_RESOLUTION_HIGH",
    "messages": [{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "gs://cloud-samples-data/generative-ai/image/scones.jpg"}},
            {"type": "text", "text": "Analyze this image in detail."}
        ]
    }]
}'

Per-part configuration (Gemini 3 only)

For Gemini 3 models, you can specify media resolution on individual media parts. Per-part settings take precedence over global settings when both are specified.

NodeJS

const chatCompletion = await portkey.chat.completions.create({
    model: 'gemini-3.0-pro',
    messages: [{
        role: 'user',
        content: [
            {
                type: 'image_url',
                image_url: {
                    url: 'gs://cloud-samples-data/generative-ai/image/scones.jpg',
                    media_resolution: 'MEDIA_RESOLUTION_HIGH'
                }
            },
            { type: 'text', text: 'Analyze this image in detail.' }
        ]
    }]
}, {Authorization: "Bearer $YOUR_VERTEX_ACCESS_TOKEN"});

completion = portkey.with_options(Authorization="Bearer $YOUR_VERTEX_ACCESS_TOKEN").chat.completions.create(
    model='gemini-3.0-pro',
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "gs://cloud-samples-data/generative-ai/image/scones.jpg",
                    "media_resolution": "MEDIA_RESOLUTION_HIGH"
                }
            },
            { "type": "text", "text": "Analyze this image in detail." }
        ]
    }]
)

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: vertex-ai' \
--header 'x-portkey-vertex-region: us-central1' \
--header 'x-portkey-api-key: PORTKEY_API_KEY' \
--header 'Authorization: VERTEX_AI_ACCESS_TOKEN' \
--header 'Content-Type: application/json' \
--data '{
    "model": "gemini-3.0-pro",
    "messages": [{
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "gs://cloud-samples-data/generative-ai/image/scones.jpg",
                    "media_resolution": "MEDIA_RESOLUTION_HIGH"
                }
            },
            {"type": "text", "text": "Analyze this image in detail."}
        ]
    }]
}'

Google Vertex AI Media Resolution Documentation

---

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

Python

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)

import Portkey from 'portkey-ai';

// Initialize the Portkey client
const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
  strictOpenAiCompliance: false
});

// Generate a chat completion
async function getChatCompletionFunctions() {
    const response = await 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?"
                  }
              ]
          }
      ]
    });
    console.log(response);
  // in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
  // const response = await portkey.chat.completions.create({
  //   ...same config as above but with stream: true
  // });
  // for await (const chunk of response) {
  //   if (chunk.choices[0].delta?.content_blocks) {
  //     for (const contentBlock of chunk.choices[0].delta.content_blocks) {
  //       console.log(contentBlock);
  //     }
  //   }
  // }
  }
// Call the function
getChatCompletionFunctions();

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    strictOpenAiCompliance: false
  })
});

// Generate a chat completion with streaming
async function getChatCompletionFunctions(){
  const response = await openai.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?"
                }
            ]
        }
    ],
  });

  console.log(response)
  // in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
  // const response = await openai.chat.completions.create({
  //   ...same config as above but with stream: true
  // });
  // for await (const chunk of response) {
  //   if (chunk.choices[0].delta?.content_blocks) {
  //     for (const contentBlock of chunk.choices[0].delta.content_blocks) {
  //       console.log(contentBlock);
  //     }
  //   }
  // }
}
await getChatCompletionFunctions();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        strict_open_ai_compliance=False
    )
)


response = openai.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)

curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-strict-open-ai-compliance: false" \
  -d '{
    "model": "@VERTEX_PROVIDER/MODEL_NAME_FROM_PORTKEY_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?"
          }
        ]
      }
    ]
  }'

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
}

Using reasoning_effort Parameter

You can also control thinking using the OpenAI-compatible reasoning_effort parameter instead of thinking.budget_tokens:

response = portkey.chat.completions.create(
    model="@VERTEX_PROVIDER/google.gemini-2.5-flash-preview-04-17",
    max_tokens=3000,
    reasoning_effort="medium",  # Options: "none", "low", "medium", "high"
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

Gemini 2.5 Models

For Gemini 2.5 models, reasoning_effort maps to thinking_budget with specific token allocations:

reasoning_effort	thinking_budget (tokens)
none	Disabled
low	1,024
medium	8,192
high	24,576

Gemini 3.0+ Models

For Gemini 3.0 and later models, reasoning_effort maps directly to thinkingLevel:

reasoning_effort	Vertex thinkingLevel
none	Disabled
minimal	minimal
low	low
medium	medium
high	high

Multi turn conversation

Python

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)

import Portkey from 'portkey-ai';

// Initialize the Portkey client
const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
  strictOpenAiCompliance: false
});

// Generate a chat completion
async function getChatCompletionFunctions() {
    const response = await 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?"
          }
      ]
    });
    console.log(response);
  }
// Call the function
getChatCompletionFunctions();

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    strictOpenAiCompliance: false
  })
});

// Generate a chat completion with streaming
async function getChatCompletionFunctions(){
  const response = await openai.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?"
        }
    ],
  });

  console.log(response)

}
await getChatCompletionFunctions();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        strict_open_ai_compliance=False
    )
)


response = openai.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)

curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-strict-open-ai-compliance: false" \
  -d '{
    "model": "@VERTEX_PROVIDER/MODEL_NAME_FROM_PORTKEY_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?"
      }
    ]
  }'

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 urls

Your 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.

NodeJS

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();

Python

from portkey_ai import Portkey

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API 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='@VERTEX_PROVIDER/text-embedding-004', # your model slug from Portkey's Model Catalog
        task_type="CLASSIFICATION" # Optional
    )
    print(embeddings)

get_embeddings()

cURL

 curl 'https://api.portkey.ai/v1/embeddings' \
    -H 'Content-Type: application/json' \
    -H 'x-portkey-api-key: PORTKEY_API_KEY' \
    --data-raw '{
        "model": "@VERTEX_PROVIDER/text-embedding-004", # your model slug from Portkey's Model Catalog
        "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:

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

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"
  }'

from portkey_ai import Portkey

client = Portkey(
  api_key = "PORTKEY_API_KEY",
)

client.images.generate(
  prompt = "Cat flying to mars from moon",
  model = "@VERTEX_PROVIDER/imagen-3.0-generate-001" # your model slug from Portkey's Model Catalog
)

import Portkey from 'portkey-ai';

const client = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
});

async function main() {
  const image = await client.images.generate({
    prompt: "Cat flying to mars from moon",
    model: "@VERTEX_PROVIDER/imagen-3.0-generate-001" # your model slug from Portkey's Model Catalog
  });

  console.log(image.data);
}
main();

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

Custom Metadata Labels

The recommended way to attribute costs and track usage is to use metadata which allows your workflows to be vendor agnostic

Vertex AI supports adding custom labels to your API calls for attribution. Pass labels in your request body or configure them in your gateway config using override_params.

Python

completion = portkey.chat.completions.create(
    messages=[{"role": "user", "content": "Say this is a test"}],
    model="@VERTEX_PROVIDER/gemini-1.5-pro-latest",
    labels={"service_id": "backend-api", "environment": "production"}
)

NodeJS

const completion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: '@VERTEX_PROVIDER/gemini-1.5-pro-latest',
    labels: { service_id: "backend-api", environment: "production" }
});

Config

{
  "provider": "vertex-ai",
  "override_params": {
    "labels": { "service_id": "backend-api", "environment": "production" }
  }
}

---

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
        }
    }]

Grounding with Google Maps

Vertex AI supports grounding with Google Maps for location-based queries—places, directions, ratings, and geographic information.

Pass the google_maps (or googleMaps) tool in the tools array:

cURL

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-api-key: YOUR_PORTKEY_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "model": "@YOUR_VERTEX_PROVIDER/gemini-2.5-pro",
    "messages": [{"role": "user", "content": "What are the best Italian restaurants near Times Square?"}],
    "tools": [{"type": "function", "function": {"name": "google_maps"}}]
}'

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_PORTKEY_API_KEY")

response = portkey.chat.completions.create(
    model="@YOUR_VERTEX_PROVIDER/gemini-2.5-pro",
    messages=[{"role": "user", "content": "What are the best Italian restaurants near Times Square?"}],
    tools=[{"type": "function", "function": {"name": "google_maps"}}]
)

print(response)

import Portkey from 'portkey-ai';

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

const response = await portkey.chat.completions.create({
    model: "@YOUR_VERTEX_PROVIDER/gemini-2.5-pro",
    messages: [{ role: "user", content: "What are the best Italian restaurants near Times Square?" }],
    tools: [{ type: "function", function: { name: "google_maps" } }]
});

console.log(response);

With Retrieval Configuration

Optionally configure location coordinates, language, and widget options by passing them inside the function parameters:

cURL

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-api-key: YOUR_PORTKEY_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "model": "@YOUR_VERTEX_PROVIDER/gemini-2.5-pro",
    "messages": [{"role": "user", "content": "What are the best coffee shops nearby?"}],
    "tools": [{
        "type": "function",
        "function": {
            "name": "google_maps",
            "parameters": {
                "enableWidget": true,
                "retrievalConfig": {
                    "latLng": {"latitude": 37.7749, "longitude": -122.4194},
                    "languageCode": "en_US"
                }
            }
        }
    }]
}'

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_PORTKEY_API_KEY")

response = portkey.chat.completions.create(
    model="@YOUR_VERTEX_PROVIDER/gemini-2.5-pro",
    messages=[{"role": "user", "content": "What are the best coffee shops nearby?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "google_maps",
            "parameters": {
                "enableWidget": True,
                "retrievalConfig": {
                    "latLng": {"latitude": 37.7749, "longitude": -122.4194},
                    "languageCode": "en_US"
                }
            }
        }
    }]
)

print(response)

import Portkey from 'portkey-ai';

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

const response = await portkey.chat.completions.create({
    model: "@YOUR_VERTEX_PROVIDER/gemini-2.5-pro",
    messages: [{ role: "user", content: "What are the best coffee shops nearby?" }],
    tools: [{
        type: "function",
        function: {
            name: "google_maps",
            parameters: {
                enableWidget: true,
                retrievalConfig: {
                    latLng: { latitude: 37.7749, longitude: -122.4194 },
                    languageCode: "en_US"
                }
            }
        }
    }]
});

console.log(response);

Parameter	Description
enableWidget	Return a token to enable the Google Maps widget (default: false)
retrievalConfig.latLng.latitude	Latitude (e.g., 37.7749 for San Francisco)
retrievalConfig.latLng.longitude	Longitude (e.g., -122.4194 for San Francisco)
retrievalConfig.languageCode	Language code for results (e.g., en_US)

Mixing regular tools with grounding tools may cause errors—use only one tool type per request.

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

Python

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"],
  image_config={    # optional
    "aspect_ratio": "16:9",
    "image_size": "2K"
  }
  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)

import Portkey from 'portkey-ai';

// Initialize the Portkey client
const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
  strictOpenAiCompliance: false
});

// Generate a chat completion
async function getChatCompletionFunctions() {
    const response = await 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"],
      image_config: {    // optional
        aspect_ratio: "16:9",
        image_size: "2K"
      },
      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"
            }
          }
        ]
      }
    ]
    });
    console.log(response);
  // in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
  // const response = await portkey.chat.completions.create({
  //   ...same config as above but with stream: true
  // });
  // for await (const chunk of response) {
  //   if (chunk.choices[0].delta?.content_blocks) {
  //     for (const contentBlock of chunk.choices[0].delta.content_blocks) {
  //       console.log(contentBlock);
  //     }
  //   }
  // }
  }
// Call the function
getChatCompletionFunctions();

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    strictOpenAiCompliance: false
  })
});

async function run() {
  const response = await openai.chat.completions.create({
    model: "gemini-2.5-flash-image-preview",
    max_tokens: 32768,
    stream: false,
    modalities: ["text", "image"],
    image_config: {    // optional
      aspect_ratio: "16:9",
      image_size: "2K"
    },
    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"
            }
          }
        ]
      }
    ]
  });

  console.log(response);
}

run();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        strict_open_ai_compliance=False
    )
)

response = openai.chat.completions.create(
    model="gemini-2.5-flash-image-preview",
    max_tokens=32768,
    stream=False,
    modalities=["text", "image"],
    image_config = {    # optional
      aspect_ratio: "16:9",
      image_size: "2K" 
    },
    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)

curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-strict-open-ai-compliance: false" \
  -d '{
"model": "gemini-2.5-flash-image-preview",
"max_tokens": 32768,
"stream": false,
"modalities": ["text", "image"],
"image_config": { 
  "aspect_ratio": "16:9",
  "image_size": "2K"
},
"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"
                }
            }
        ]
    }
]
}'

---

Thought Signatures (Tool Calling Verification)

Set x-portkey-strict-open-ai-compliance to false to receive the thought_signature in the response. This header must be included in all requests when using thought signatures.

Google’s Gemini 3 Pro model requires passing a thought_signature parameter in tool calling conversations for verifying the payload. This signature is returned by the model in the assistant’s tool call response and must be included when continuing multi-turn conversations.

Google Gemini Thought Signatures Documentation

Single turn conversation

In a single-turn conversation, you make a request with tools defined, and the model returns tool calls with thought signatures.

cURL

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: @my-vertex-ai-provider' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: your-api-key' \
--header 'x-portkey-strict-open-ai-compliance: false' \
--data '{
    "model": "gemini-3-pro-preview",
    "max_tokens": 1000,
    "stream": true,
    "messages": [
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are a helpful assistant"
                }
            ]
        },
        {
            "role": "user",
            "content": "What is the current time in Bombay?"
        }
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Get the current time for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        }
    ]
}'

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@VERTEX_PROVIDER",
    strict_open_ai_compliance=False
)

response = portkey.chat.completions.create(
    model="gemini-3-pro-preview",
    max_tokens=1000,
    stream=True,
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are a helpful assistant"
                }
            ]
        },
        {
            "role": "user",
            "content": "What is the current time in Bombay?"
        }
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Get the current time for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        }
    ]
)
print(response)

import Portkey from 'portkey-ai';

const portkey = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  provider: '@VERTEX_PROVIDER',
  strictOpenAiCompliance: false
});

const response = await portkey.chat.completions.create({
  model: 'gemini-3-pro-preview',
  max_tokens: 1000,
  stream: true,
  messages: [
    {
      role: 'system',
      content: [
        {
          type: 'text',
          text: 'You are a helpful assistant'
        }
      ]
    },
    {
      role: 'user',
      content: 'What is the current time in Bombay?'
    }
  ],
  tools: [
    {
      type: 'function',
      function: {
        name: 'get_current_time',
        description: 'Get the current time for a specific location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'The city and state, e.g., San Francisco, CA'
            }
          },
          required: [
            'location'
          ]
        }
      }
    }
  ]
});
console.log(response);

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider='@VERTEX_PROVIDER',
        strict_open_ai_compliance=False
    )
)

response = openai.chat.completions.create(
    model='gemini-3-pro-preview',
    max_tokens=1000,
    stream=True,
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are a helpful assistant"
                }
            ]
        },
        {
            "role": "user",
            "content": "What is the current time in Bombay?"
        }
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Get the current time for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        }
    ]
)
print(response)

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

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY',
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: '@VERTEX_PROVIDER',
    strictOpenAiCompliance: false
  })
});

const response = await openai.chat.completions.create({
  model: 'gemini-3-pro-preview',
  max_tokens: 1000,
  stream: true,
  messages: [
    {
      role: 'system',
      content: [
        {
          type: 'text',
          text: 'You are a helpful assistant'
        }
      ]
    },
    {
      role: 'user',
      content: 'What is the current time in Bombay?'
    }
  ],
  tools: [
    {
      type: 'function',
      function: {
        name: 'get_current_time',
        description: 'Get the current time for a specific location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'The city and state, e.g., San Francisco, CA'
            }
          },
          required: [
            'location'
          ]
        }
      }
    }
  ]
});
console.log(response);

Multi turn conversation

In multi-turn conversations, you must include the thought_signature field in the assistant’s tool call when continuing the conversation.

cURL

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: @my-vertex-ai-provider' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: your-api-key' \
--header 'x-portkey-strict-open-ai-compliance: false' \
--data '{
    "model": "gemini-3-pro-preview",
    "max_tokens": 1000,
    "stream": true,
    "messages": [
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are a helpful assistant"
                }
            ]
        },
        {
            "role": "user",
            "content": "Check the time in Chennai and if it is later than 9Pm get the temperature"
        },
        {
            "role": "assistant",
            "tool_calls": [
                {
                    "id": "portkey-1dcd51a0-a20a-482d-b244-2d4aff5aebdb",
                    "type": "function",
                    "function": {
                        "name": "get_current_time",
                        "arguments": "{\"location\":\"Chennai, India\"}",
                        "thought_signature": "CtQBAePx/17ARdotHH1RN31zOtCF+YpuOFTpU//tJRF4dEvegfDKLUaZnuG38II1POmVFdzBbzt87cTDr0TsEKHyHScN9PURHrhRer7liusjRrLR5QF4n1ZYJJYF3C+3bgC9YJsJyQhY/HAgVZQ53gq7n4I63CgXhYA+tzNN3CnHqdStgY0wLK0mCu/tb1kReSrXYMbre27SB5t2eRA7Wl+OKasKCOk7sYCJ8VkT+NaD+s6+NVTX2Au3RmUGVxYdjapo0vc7nnjvfmpTJHviyGJZIGIdXWw="
                    }
                }
            ]
        },
        {
            "role": "tool",
            "content": "{ '\''time'\'': '\''10PM'\'' }",
            "tool_call_id": "toolu_014jEfKqGbfFvRaKfiauxgPv"
        }
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Get the current time for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        },
        {
            "type": "function",
            "function": {
                "name": "get_current_temperature",
                "description": "Get the current temperature for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        },
                        "unit": {
                            "type": "string",
                            "enum": [
                                "Celsius",
                                "Fahrenheit"
                            ],
                            "description": "The temperature unit to use. Infer this from the user'\''s location."
                        }
                    },
                    "required": [
                        "location",
                        "unit"
                    ]
                }
            }
        }
    ]
}'

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@VERTEX_PROVIDER",
    strict_open_ai_compliance=False
)

response = portkey.chat.completions.create(
    model="gemini-3-pro-preview",
    max_tokens=1000,
    stream=True,
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are a helpful assistant"
                }
            ]
        },
        {
            "role": "user",
            "content": "Check the time in Chennai and if it is later than 9Pm get the temperature"
        },
        {
            "role": "assistant",
            "tool_calls": [
                {
                    "id": "portkey-1dcd51a0-a20a-482d-b244-2d4aff5aebdb",
                    "type": "function",
                    "function": {
                        "name": "get_current_time",
                        "arguments": "{\"location\":\"Chennai, India\"}",
                        "thought_signature": "CtQBAePx/17ARdotHH1RN31zOtCF+YpuOFTpU//tJRF4dEvegfDKLUaZnuG38II1POmVFdzBbzt87cTDr0TsEKHyHScN9PURHrhRer7liusjRrLR5QF4n1ZYJJYF3C+3bgC9YJsJyQhY/HAgVZQ53gq7n4I63CgXhYA+tzNN3CnHqdStgY0wLK0mCu/tb1kReSrXYMbre27SB5t2eRA7Wl+OKasKCOk7sYCJ8VkT+NaD+s6+NVTX2Au3RmUGVxYdjapo0vc7nnjvfmpTJHviyGJZIGIdXWw="
                    }
                }
            ]
        },
        {
            "role": "tool",
            "content": "{ 'time': '10PM' }",
            "tool_call_id": "toolu_014jEfKqGbfFvRaKfiauxgPv"
        }
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Get the current time for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        },
        {
            "type": "function",
            "function": {
                "name": "get_current_temperature",
                "description": "Get the current temperature for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        },
                        "unit": {
                            "type": "string",
                            "enum": [
                                "Celsius",
                                "Fahrenheit"
                            ],
                            "description": "The temperature unit to use. Infer this from the user's location."
                        }
                    },
                    "required": [
                        "location",
                        "unit"
                    ]
                }
            }
        }
    ]
)
print(response)

import Portkey from 'portkey-ai';

const portkey = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  provider: '@VERTEX_PROVIDER'
});

const response = await portkey.chat.completions.create({
  model: 'gemini-3-pro-preview',
  max_tokens: 1000,
  stream: true,
  messages: [
    {
      role: 'system',
      content: [
        {
          type: 'text',
          text: 'You are a helpful assistant'
        }
      ]
    },
    {
      role: 'user',
      content: 'Check the time in Chennai and if it is later than 9Pm get the temperature'
    },
    {
      role: 'assistant',
      tool_calls: [
        {
          id: 'portkey-1dcd51a0-a20a-482d-b244-2d4aff5aebdb',
          type: 'function',
          function: {
            name: 'get_current_time',
            arguments: '{"location":"Chennai, India"}',
            thought_signature: 'CtQBAePx/17ARdotHH1RN31zOtCF+YpuOFTpU//tJRF4dEvegfDKLUaZnuG38II1POmVFdzBbzt87cTDr0TsEKHyHScN9PURHrhRer7liusjRrLR5QF4n1ZYJJYF3C+3bgC9YJsJyQhY/HAgVZQ53gq7n4I63CgXhYA+tzNN3CnHqdStgY0wLK0mCu/tb1kReSrXYMbre27SB5t2eRA7Wl+OKasKCOk7sYCJ8VkT+NaD+s6+NVTX2Au3RmUGVxYdjapo0vc7nnjvfmpTJHviyGJZIGIdXWw='
          }
        }
      ]
    },
    {
      role: 'tool',
      content: "{ 'time': '10PM' }",
      tool_call_id: 'toolu_014jEfKqGbfFvRaKfiauxgPv'
    }
  ],
  tools: [
    {
      type: 'function',
      function: {
        name: 'get_current_time',
        description: 'Get the current time for a specific location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'The city and state, e.g., San Francisco, CA'
            }
          },
          required: [
            'location'
          ]
        }
      }
    },
    {
      type: 'function',
      function: {
        name: 'get_current_temperature',
        description: 'Get the current temperature for a specific location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'The city and state, e.g., San Francisco, CA'
            },
            unit: {
              type: 'string',
              enum: [
                'Celsius',
                'Fahrenheit'
              ],
              description: 'The temperature unit to use. Infer this from the user\'s location.'
            }
          },
          required: [
            'location',
            'unit'
          ]
        }
      }
    }
  ]
});
console.log(response);

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider='@VERTEX_PROVIDER'
    )
)

response = openai.chat.completions.create(
    model='gemini-3-pro-preview',
    max_tokens=1000,
    stream=True,
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are a helpful assistant"
                }
            ]
        },
        {
            "role": "user",
            "content": "Check the time in Chennai and if it is later than 9Pm get the temperature"
        },
        {
            "role": "assistant",
            "tool_calls": [
                {
                    "id": "portkey-1dcd51a0-a20a-482d-b244-2d4aff5aebdb",
                    "type": "function",
                    "function": {
                        "name": "get_current_time",
                        "arguments": "{\"location\":\"Chennai, India\"}",
                        "thought_signature": "CtQBAePx/17ARdotHH1RN31zOtCF+YpuOFTpU//tJRF4dEvegfDKLUaZnuG38II1POmVFdzBbzt87cTDr0TsEKHyHScN9PURHrhRer7liusjRrLR5QF4n1ZYJJYF3C+3bgC9YJsJyQhY/HAgVZQ53gq7n4I63CgXhYA+tzNN3CnHqdStgY0wLK0mCu/tb1kReSrXYMbre27SB5t2eRA7Wl+OKasKCOk7sYCJ8VkT+NaD+s6+NVTX2Au3RmUGVxYdjapo0vc7nnjvfmpTJHviyGJZIGIdXWw="
                    }
                }
            ]
        },
        {
            "role": "tool",
            "content": "{ 'time': '10PM' }",
            "tool_call_id": "toolu_014jEfKqGbfFvRaKfiauxgPv"
        }
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Get the current time for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        },
        {
            "type": "function",
            "function": {
                "name": "get_current_temperature",
                "description": "Get the current temperature for a specific location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g., San Francisco, CA"
                        },
                        "unit": {
                            "type": "string",
                            "enum": [
                                "Celsius",
                                "Fahrenheit"
                            ],
                            "description": "The temperature unit to use. Infer this from the user's location."
                        }
                    },
                    "required": [
                        "location",
                        "unit"
                    ]
                }
            }
        }
    ]
)
print(response)

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

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY',
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: '@VERTEX_PROVIDER'
  })
});

const response = await openai.chat.completions.create({
  model: 'gemini-3-pro-preview',
  max_tokens: 1000,
  stream: true,
  messages: [
    {
      role: 'system',
      content: [
        {
          type: 'text',
          text: 'You are a helpful assistant'
        }
      ]
    },
    {
      role: 'user',
      content: 'Check the time in Chennai and if it is later than 9Pm get the temperature'
    },
    {
      role: 'assistant',
      tool_calls: [
        {
          id: 'portkey-1dcd51a0-a20a-482d-b244-2d4aff5aebdb',
          type: 'function',
          function: {
            name: 'get_current_time',
            arguments: '{"location":"Chennai, India"}',
            thought_signature: 'CtQBAePx/17ARdotHH1RN31zOtCF+YpuOFTpU//tJRF4dEvegfDKLUaZnuG38II1POmVFdzBbzt87cTDr0TsEKHyHScN9PURHrhRer7liusjRrLR5QF4n1ZYJJYF3C+3bgC9YJsJyQhY/HAgVZQ53gq7n4I63CgXhYA+tzNN3CnHqdStgY0wLK0mCu/tb1kReSrXYMbre27SB5t2eRA7Wl+OKasKCOk7sYCJ8VkT+NaD+s6+NVTX2Au3RmUGVxYdjapo0vc7nnjvfmpTJHviyGJZIGIdXWw='
          }
        }
      ]
    },
    {
      role: 'tool',
      content: "{ 'time': '10PM' }",
      tool_call_id: 'toolu_014jEfKqGbfFvRaKfiauxgPv'
    }
  ],
  tools: [
    {
      type: 'function',
      function: {
        name: 'get_current_time',
        description: 'Get the current time for a specific location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'The city and state, e.g., San Francisco, CA'
            }
          },
          required: [
            'location'
          ]
        }
      }
    },
    {
      type: 'function',
      function: {
        name: 'get_current_temperature',
        description: 'Get the current temperature for a specific location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'The city and state, e.g., San Francisco, CA'
            },
            unit: {
              type: 'string',
              enum: [
                'Celsius',
                'Fahrenheit'
              ],
              description: 'The temperature unit to use. Infer this from the user\'s location.'
            }
          },
          required: [
            'location',
            'unit'
          ]
        }
      }
    }
  ]
});
console.log(response);

The thought_signature is automatically generated by the model and returned in the tool call response. You must preserve this signature when including the assistant’s message in subsequent requests.

---

Computer Use (Browser Automation) (Preview)

This uses the Gemini computer-use preview model. Set strict_open_ai_compliance to false.

Single turn conversation

NodeJS

import Portkey from 'portkey-ai';

const portkey = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  provider: '@VERTEX_PROVIDER',
  strictOpenAiCompliance: false
});

const response = await portkey.chat.completions.create({
  model: 'gemini-2.5-computer-use-preview-10-2025',
  stream: false,
  messages: [
    { role: 'system', content: 'You are a helpful assistant' },
    { role: 'user', content: "Go to google.com and search for 'weather in New York'" }
  ],
  tools: [
    {
      type: 'function',
      function: {
        name: 'computer_use',
        parameters: { environment: 'ENVIRONMENT_BROWSER' }
      }
    }
  ]
});
console.log(response);

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@VERTEX_PROVIDER",
    strict_open_ai_compliance=False
)

response = portkey.chat.completions.create(
    model="gemini-2.5-computer-use-preview-10-2025",
    stream=False,
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Go to google.com and search for 'weather in New York'"}
    ],
    tools=[{
        "type": "function",
        "function": {"name": "computer_use", "parameters": {"environment": "ENVIRONMENT_BROWSER"}}
    }]
)
print(response)

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

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY',
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({ provider: 'vertex-ai', strictOpenAiCompliance: false })
});

const response = await openai.chat.completions.create({
  model: 'gemini-2.5-computer-use-preview-10-2025',
  stream: false,
  messages: [
    { role: 'system', content: 'You are a helpful assistant' },
    { role: 'user', content: "Go to google.com and search for 'weather in New York'" }
  ],
  tools: [{ type: 'function', function: { name: 'computer_use', parameters: { environment: 'ENVIRONMENT_BROWSER' } } }]
});
console.log(response);

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(provider='vertex-ai', strict_open_ai_compliance=False)
)

response = openai.chat.completions.create(
    model='gemini-2.5-computer-use-preview-10-2025',
    stream=False,
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Go to google.com and search for 'weather in New York'"}
    ],
    tools=[{ "type": "function", "function": { "name": "computer_use", "parameters": { "environment": "ENVIRONMENT_BROWSER" } } }]
)
print(response)

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: @my-vertex-ai-provider' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: your-api-key' \
--header 'x-portkey-strict-open-ai-compliance: false' \
--data '{
    "model": "gemini-2.5-computer-use-preview-10-2025",
    "stream": false,
    "messages": [
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Go to google.com and search for '\''weather in New York'\''"}
    ],
    "tools": [
        {"type": "function", "function": {"name": "computer_use", "parameters": {"environment": "ENVIRONMENT_BROWSER"}}}
    ]
}'

Multi turn conversation

NodeJS

import Portkey from 'portkey-ai';

const portkey = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  provider: '@VERTEX_PROVIDER',
  strictOpenAiCompliance: false
});

const response = await portkey.chat.completions.create({
  model: 'gemini-2.5-computer-use-preview-10-2025',
  stream: false,
  messages: [
    { role: 'system', content: 'You are a helpful assistant' },
    { role: 'user', content: "Go to google.com and search for 'weather in New York'" },
    { role: 'assistant', tool_calls: [ { id: 'portkey-50925c03-b8cc-4057-948b-13a9d9de19e0', type: 'function', function: { name: 'open_web_browser', arguments: '{}' } } ] },
    { role: 'user', content: "I've opened the browser" }
  ],
  tools: [{ type: 'function', function: { name: 'computerUse', parameters: { environment: 'ENVIRONMENT_BROWSER' } } }]
});
console.log(response);

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@VERTEX_PROVIDER",
    strict_open_ai_compliance=False
)

response = portkey.chat.completions.create(
    model="gemini-2.5-computer-use-preview-10-2025",
    stream=False,
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Go to google.com and search for 'weather in New York'"},
        {"role": "assistant", "tool_calls": [{"id": "portkey-50925c03-b8cc-4057-948b-13a9d9de19e0", "type": "function", "function": {"name": "open_web_browser", "arguments": "{}"}}]},
        {"role": "user", "content": "I've opened the browser"}
    ],
    tools=[{ "type": "function", "function": { "name": "computerUse", "parameters": { "environment": "ENVIRONMENT_BROWSER" } } }]
)
print(response)

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

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY',
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({ provider: 'vertex-ai', strictOpenAiCompliance: false })
});

const response = await openai.chat.completions.create({
  model: 'gemini-2.5-computer-use-preview-10-2025',
  stream: false,
  messages: [
    { role: 'system', content: 'You are a helpful assistant' },
    { role: 'user', content: "Go to google.com and search for 'weather in New York'" },
    { role: 'assistant', tool_calls: [{ id: 'portkey-50925c03-b8cc-4057-948b-13a9d9de19e0', type: 'function', function: { name: 'open_web_browser', arguments: '{}' } }] },
    { role: 'user', content: "I've opened the browser" }
  ],
  tools: [{ type: 'function', function: { name: 'computerUse', parameters: { environment: 'ENVIRONMENT_BROWSER' } } }]
});
console.log(response);

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(provider='vertex-ai', strict_open_ai_compliance=False)
)

response = openai.chat.completions.create(
    model='gemini-2.5-computer-use-preview-10-2025',
    stream=False,
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Go to google.com and search for 'weather in New York'"},
        {"role": "assistant", "tool_calls": [{"id": "portkey-50925c03-b8cc-4057-948b-13a9d9de19e0", "type": "function", "function": {"name": "open_web_browser", "arguments": "{}"}}]},
        {"role": "user", "content": "I've opened the browser"}
    ],
    tools=[{ "type": "function", "function": { "name": "computerUse", "parameters": { "environment": "ENVIRONMENT_BROWSER" } } }]
)
print(response)

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'x-portkey-provider: @my-vertex-ai-provider' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: your-api-key' \
--header 'x-portkey-strict-open-ai-compliance: false' \
--data '{
    "model": "gemini-2.5-computer-use-preview-10-2025",
    "stream": false,
    "messages": [
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Go to google.com and search for 'weather in New York'"},
        {"role": "assistant", "tool_calls": [{"id": "portkey-50925c03-b8cc-4057-948b-13a9d9de19e0", "type": "function", "function": {"name": "open_web_browser", "arguments": "{}"}}]},
        {"role": "user", "content": "I've opened the browser"}
    ],
    "tools": [{"type": "function", "function": {"name": "computerUse", "parameters": {"environment": "ENVIRONMENT_BROWSER"}}}]
}'

multi turn conversation

Python

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"],
  image_config={    # optional
    "aspect_ratio": "16:9",
    "image_size": "2K"
  },
  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)

import Portkey from 'portkey-ai';

// Initialize the Portkey client
const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
  strictOpenAiCompliance: false
});

// Generate a chat completion
async function getChatCompletionFunctions() {
    const response = await 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"],
      image_config: {    // optional
        "aspect_ratio": "16:9",
        "image_size": "2K"
      },
      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"
    }

    ]
    });
    console.log(response);
  // in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
  // const response = await portkey.chat.completions.create({
  //   ...same config as above but with stream: true
  // });
  // for await (const chunk of response) {
  //   if (chunk.choices[0].delta?.content_blocks) {
  //     for (const contentBlock of chunk.choices[0].delta.content_blocks) {
  //       console.log(contentBlock);
  //     }
  //   }
  // }
  }
// Call the function
getChatCompletionFunctions();

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const openai = new OpenAI({
  apiKey: 'PORTKEY_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    strictOpenAiCompliance: false
  })
});

async function run() {
  const response = await openai.chat.completions.create({
    model: "gemini-2.5-flash-image-preview",
    max_tokens: 32768,
    stream: false,
    modalities: ["text", "image"],
    image_config: {    // optional
      "aspect_ratio": "16:9",
      "image_size": "2K"
    },
    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"
    }
    ]
  });

  console.log(response);
}

run();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='PORTKEY_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        strict_open_ai_compliance=False
    )
)

response = openai.chat.completions.create(
    model="gemini-2.5-flash-image-preview",
    max_tokens=32768,
    stream=False,
    modalities=["text", "image"],
    image_config={    # optional
      aspect_ratio: "16:9",
      image_size: "2K"
    },
    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)

curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-strict-open-ai-compliance: false" \
  -d '{
"model": "gemini-2.5-flash-image-preview",
"max_tokens": 32768,
"stream": false,
"modalities": ["text", "image"],
"image_config": {
  "aspect_ratio": "16:9",
  "image_size": "2K"
},
"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"
    }

]
}'

Safety settings

Gemini models support configuring safety settings to block potentially harmful responses

TypeScript

import Portkey from 'portkey-ai';

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

async function main() {
const response = await portkey.chat.completions.create({
  model: "@my-vertex-provider/gemini-2.5-flash",
  temperature: 0,
  stream: false,
  messages: [
    {
      role: "user",
      content: "Speak explicitly like a gangster harrasing someone for money"
    }
  ],
  safety_settings: [
    {
      category: "HARM_CATEGORY_SEXUALLY_EXPLICIT",
      threshold: "BLOCK_LOW_AND_ABOVE"
    },
    {
      category: "HARM_CATEGORY_HATE_SPEECH",
      threshold: "BLOCK_LOW_AND_ABOVE"
    },
    {
      category: "HARM_CATEGORY_HARASSMENT",
      threshold: "BLOCK_LOW_AND_ABOVE"
    },
    {
      category: "HARM_CATEGORY_DANGEROUS_CONTENT",
      threshold: "BLOCK_LOW_AND_ABOVE"
    }
  ]
});

console.log(response.choices);
}

main();

from portkey_ai import Portkey

portkey = Portkey(
  api_key="PORTKEY_API_KEY",
)

response = portkey.chat.completions.create(
  model="@my-vertex-provider/gemini-2.5-flash",
  temperature=0,
  stream=False,
  messages=[
      {
          "role": "user",
          "content": "Speak explicitly like a gangster harrasing someone for money"
      }
  ],
  safety_settings=[
      {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_HARASSMENT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      }
  ]
)

print(response.choices)

import OpenAI from "openai";

const openai = new OpenAI({
apiKey: process.env.PORTKEY_API_KEY, // Portkey API Key
baseURL: "https://api.portkey.ai/v1",
defaultHeaders: {
  "x-portkey-strict-open-ai-compliance": "false",
},
});

async function main() {
const response = await openai.chat.completions.create({
  model: "@my-vertex-provider/gemini-2.5-flash",
  temperature: 0,
  stream: false,
  messages: [
    {
      role: "user",
      content: "Speak explicitly like a gangster harrasing someone for money"
    }
  ],
  safety_settings: [
    {
      category: "HARM_CATEGORY_SEXUALLY_EXPLICIT",
      threshold: "BLOCK_LOW_AND_ABOVE"
    },
    {
      category: "HARM_CATEGORY_HATE_SPEECH",
      threshold: "BLOCK_LOW_AND_ABOVE"
    },
    {
      category: "HARM_CATEGORY_HARASSMENT",
      threshold: "BLOCK_LOW_AND_ABOVE"
    },
    {
      category: "HARM_CATEGORY_DANGEROUS_CONTENT",
      threshold: "BLOCK_LOW_AND_ABOVE"
    }
  ]
});

console.log(response.choices);
}

main();

import openai

client = openai.OpenAI(
  api_key="PORTKEY_API_KEY",
  base_url="https://api.portkey.ai/v1",
  default_headers={
      "x-portkey-strict-open-ai-compliance": "false"
  }
)

response = client.chat.completions.create(
  model="@my-vertex-provider/gemini-2.5-flash",
  temperature=0,
  stream=False,
  messages=[
      {
          "role": "user",
          "content": "Speak explicitly like a gangster harrasing someone for money"
      }
  ],
  safety_settings=[
      {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_HARASSMENT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      }
  ]
)

print(response.choices)

curl --location 'https://api.portkey.ai/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: $PORTKEY_API_KEY' \
--header 'x-portkey-strict-open-ai-compliance: false' \
--data '{
  "model": "@my-vertex-provider/gemini-2.5-flash",
  "temperature": 0,
  "stream": false,
  "messages": [
      {
          "role": "user",
          "content": "Speak explicitly like a gangster harrasing someone for money"
      }
  ],
  "safety_settings": [
      {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_HARASSMENT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "threshold": "BLOCK_LOW_AND_ABOVE"
      }
  ]
}'

The response contains the saftetyRatings keys in the choice object of the response. You only need to pass the strict-open-ai-compliance flag if you need the safetyRatings in response.

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

NodeJS SDK

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);

Python SDK

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)

OpenAI Node SDK

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

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 [e-mail]

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.

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:

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.

SDK

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