Google Gemini

Portkey provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including Google Gemini APIs.

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 LLM API keys through a virtual key system.

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Portkey SDK Integration with Google Gemini Models

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

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1. Install the Portkey SDK

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

npm install --save portkey-ai

npm install --save portkey-ai

pip install portkey-ai

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2. Initialize Portkey with the Virtual Key

To use Gemini with Portkey, get your API key from here, then add it to Portkey to create the virtual key.

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    provider:"@PROVIDER" // Your Google Virtual Key
})

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    provider:"@PROVIDER" // Your Google Virtual Key
})

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    provider="@PROVIDER"   # Replace with your virtual key for Google
)

​

3. Invoke Chat Completions with Google Gemini

Use the Portkey instance to send requests to Google Gemini. You can also override the virtual key directly in the API call if needed.

const chatCompletion = await portkey.chat.completions.create({
    messages: [
        { role: 'system', content: 'You are not a helpful assistant' },
        { role: 'user', content: 'Say this is a test' }
    ],
    model: 'gemini-1.5-pro',
});

console.log(chatCompletion.choices);

const chatCompletion = await portkey.chat.completions.create({
    messages: [
        { role: 'system', content: 'You are not a helpful assistant' },
        { role: 'user', content: 'Say this is a test' }
    ],
    model: 'gemini-1.5-pro',
});

console.log(chatCompletion.choices);

completion = portkey.chat.completions.create(
    messages= [
        { "role": 'system', "content": 'You are not a helpful assistant' },
        { "role": 'user', "content": 'Say this is a test' }
    ],
    model= 'gemini-1.5-pro'
)

print(completion)

​

Function Calling

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

Function Calling

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Document, Video, Audio Processing with Gemini

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

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

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

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".

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Sending base64 Image

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

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

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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
        }
    }]

​

Extended Thinking (Reasoning Models) (Beta)

Models like gemini-2.5-flash-preview-04-17 gemini-2.5-flash-preview-04-17 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.

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Single turn conversation

from portkey_ai import Portkey

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

# Create the request
response = portkey.chat.completions.create(
  model="gemini-2.5-flash-preview-04-17",
  max_tokens=3000,
  thinking={
      "type": "enabled",
      "budget_tokens": 2030
  },
  stream=True,
  messages=[
      {
          "role": "user",
          "content": [
              {
                  "type": "text",
                  "text": "when does the flight from new york to bengaluru land tomorrow, what time, what is its flight number, and what is its baggage belt?"
              }
          ]
      }
  ]
)
print(response)
# in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
# response = portkey.chat.completions.create(
#   ...same config as above but with stream: true
# )
# for chunk in response:
#     if chunk.choices[0].delta:
#         content_blocks = chunk.choices[0].delta.get("content_blocks")
#         if content_blocks is not None:
#             for content_block in content_blocks:
#                 print(content_block)

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

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Next Steps

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

You’ll find more information in the relevant sections:

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