Deepbricks

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 Deepbricks Models

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

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1. Install the Portkey SDK

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

• NodeJS
• Python

npm install --save portkey-ai

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2. Initialize Portkey with the Virtual Key

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

• NodeJS SDK
• Python SDK

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "VIRTUAL_KEY" // Your Deepbricks
})

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3. Invoke Chat Completions with Deepbricks

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

• NodeJS SDK
• Python SDK

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'deepseek-ai/DeepSeek-V2-Chat',
});
console.log(chatCompletion.choices);

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Managing Deepbricks Prompts

You can manage all prompts to Deepbricks in the Prompt Library. All the current models of Deepbricks 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.

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 Deepbricks requests
3. Tracing Deepbricks requests
4. Setup a fallback from OpenAI to Deepbricks APIs