vLLM

Integrate vLLM-hosted custom models with Portkey and take them to production

Portkey provides a robust and secure platform to observe, govern, and manage your locally or privately hosted custom models using vLLM.

Here’s a list of all model architectures supported on vLLM.

Integrating Custom Models with Portkey SDK

1. Expose your vLLM Server

   Expose your vLLM server by using a tunneling service like ngrok or any other way you prefer. You can skip this step if you’re self-hosting the Gateway.

   ngrok http 11434 --host-header="localhost:8080"
   
2. Install the Portkey SDK

   NodeJS

   npm install --save portkey-ai
   

   Python

   pip install portkey-ai
   
3. Initialize Portkey with vLLM custom URL

   1. Pass your publicly-exposed vLLM server URL to Portkey with customHost (by default, vLLM is on http://localhost:8000/v1)
   2. Set target provider as openai since the server follows OpenAI API schema.

   NodeJS SDK

   import Portkey from 'portkey-ai'
   
   const portkey = new Portkey({
       apiKey: "PORTKEY_API_KEY",
       provider: "openai",
       customHost: "https://7cc4-3-235-157-146.ngrok-free.app" // Your vLLM ngrok URL
       Authorization: "AUTH_KEY", // If you need to pass auth
   })
   

   Python SDK

   from portkey_ai import Portkey
   
   portkey = Portkey(
       api_key="PORTKEY_API_KEY",
       provider="openai",
       custom_host="https://7cc4-3-235-157-146.ngrok-free.app" # Your vLLM ngrok URL
       Authorization="AUTH_KEY", # If you need to pass auth
   )
   

   More on custom_host here.

4. Invoke Chat Completions

   Use the Portkey SDK to invoke chat completions from your model, just as you would with any other provider:

   NodeJS SDK

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

   Python SDK

   completion = portkey.chat.completions.create(
       messages= [{ "role": 'user', "content": 'Say this is a test' }]
   )
   
   print(completion)
   

Using Virtual Keys

Virtual Keys serve as Portkey’s unified authentication system for all LLM interactions, simplifying the use of multiple providers and Portkey features within your application. For self-hosted LLMs, you can configure custom authentication requirements including authorization keys, bearer tokens, or any other headers needed to access your model:

1. Navigate to Virtual Keys in your Portkey dashboard
2. Click “Add Key” and enable the “Local/Privately hosted provider” toggle
3. Configure your deployment:
   • Select the matching provider API specification (typically OpenAI)
   • Enter your model’s base URL in the Custom Host field
   • Add required authentication headers and their values
4. Click “Create” to generate your virtual key

You can now use this virtual key in your requests:

NodeJS

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
    virtualKey: "YOUR_SELF_HOSTED_LLM_VIRTUAL_KEY"

async function main() {
  const response = await client.chat.completions.create({
    messages: [{ role: "user", content: "Bob the builder.." }],
    model: "your-self-hosted-model-name",
  });

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

Python

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    virtual_key="YOUR_SELF_HOSTED_LLM_VIRTUAL_KEY"
)

response = portkey.chat.completions.create(
  model="your-self-hosted-model-name",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
  ]

print(response)
)

For more information about managing self-hosted LLMs with Portkey, see Bring Your Own LLM.

Next Steps

Explore the complete list of features supported in the SDK:

SDK

---

You’ll find more information in the relevant sections:

1. Add metadata to your requests
2. Add gateway configs to your requests
3. Tracing requests
4. Setup a fallback from OpenAI to your local LLM