Anthropic

Portkey provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including Anthropic's Claude 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.

Provider Slug: anthropic

Portkey SDK Integration with Anthropic

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

1. Install the Portkey SDK

Add the Portkey SDK to your application to interact with Anthropic's API through Portkey's gateway.

npm install --save portkey-ai

pip install portkey-ai

2. Initialize Portkey with the Virtual Key

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

import Portkey from 'portkey-ai'
 
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "VIRTUAL_KEY" // Your Anthropic Virtual Key
})

from portkey_ai import Portkey

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

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key="ANTHROPIC_API_KEY",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        api_key="PORTKEY_API_KEY",
        provider="anthropic"
    )
)

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

const client = new OpenAI({
  apiKey: "ANTHROPIC_API_KEY",
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "anthropic",
    apiKey: "PORTKEY_API_KEY",
  }),
});

3. Invoke Chat Completions with Anthropic

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

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'claude-3-opus-20240229',
    max_tokens: 250 // Required field for Anthropic
});

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

chat_completion = portkey.chat.completions.create(
    messages= [{ "role": 'user', "content": 'Say this is a test' }],
    model= 'claude-3-opus-20240229',
    max_tokens=250 # Required field for Anthropic
)
    
print(chat_completion.choices[0].message.content)

chat_completion = client.chat.completions.create(
    messages = [{ "role": 'user', "content": 'Say this is a test' }],
    model = 'claude-3-opus-20240229',
    max_tokens = 250
)

print(chat_completion.choices[0].message.content)

async function main() {
    const chatCompletion = await client.chat.completions.create({
        model: "claude-3-opus-20240229",
        max_tokens: 1024,
        messages: [{ role: "user", content: "Hello, Claude" }],
    });
    console.log(chatCompletion.choices[0].message.content);
}

main();

How to Use Anthropic System Prompt

With Portkey, we make Anthropic models interoperable with the OpenAI schema and SDK methods. So, instead of passing the system prompt separately, you can pass it as part of the messages body, similar to OpenAI:

const chatCompletion = await portkey.chat.completions.create({
    messages: [
        { role: 'system', content: 'Your system prompt' },
        { role: 'user', content: 'Say this is a test' }
    ],
    model: 'claude-3-opus-20240229',
    max_tokens: 250
});

console.log(chatCompletion.choices);

completion = portkey.chat.completions.create(
    messages= [
        { "role": 'system', "content": 'Your system prompt' },
        { "role": 'user', "content": 'Say this is a test' }
    ],
    model= 'claude-3-opus-20240229',
    max_tokens=250 # Required field for Anthropic
)
    
print(completion.choices)

Vision Chat Completion Usage

Portkey's multimodal Gateway fully supports Anthropic's vision models claude-3-sonnet, claude-3-haiku, claude-3-opus, and the newest claude-3.5-sonnet. Images are made available to Anthropic's model by passing the base64 encoded image directly in the request.

Here's an example using Anthropic claude-3.5-sonnet model

import base64
import httpx
from portkey_ai import Portkey

# Fetch and encode the image
image1_url = "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
image1_data = base64.b64encode(httpx.get(image1_url).content).decode("utf-8")

// Initialize the Portkey client
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
    virtualKey: "VIRTUAL_KEY"   // Add your provider's virtual key
});

# Create the request
response = portkey.chat.completions.create(
    model="claude-3-5-sonnet-20240620",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant, who describes imagse"
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{image1_data}"
                    }
                }
            ]
        }
    ],
    max_tokens=1400,
)
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
  virtualKey: "VIRTUAL_KEY" // Add your anthropic's virtual key
});

// Generate a chat completion
async function getChatCompletionFunctions() {
    const response = await portkey.chat.completions.create({
      model: "claude-3-5-sonnet-20240620",
      messages: [
        {
          role: "system",
          content: "You are a helpful assistant who describes images."
        },
        {
          role: "user",
          content: [
            { type: "text", text: "What's in this image?" },
            {
              type: "image_url",
              image_url: {
                url: "data:image/jpeg;base64,BASE64_IMAGE_DATA"
              }
            }
          ]
        }
      ],
      max_tokens: 300
    });
    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: 'ANTHROPIC_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "openai",
    apiKey: "PORTKEY_API_KEY" // defaults to process.env["PORTKEY_API_KEY"]
  })
});

// Generate a chat completion with streaming
async function getChatCompletionFunctions(){
  const response = await openai.chat.completions.create({
    model: "gpt-4-vision-preview",
    messages: [
      {
        role: "user",
        content: [
          { type: "text", text: "What’s in this image?" },
          {
            type: "image_url",
            image_url:
              "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
          },
        ],
      },
    ],
  });
  
  console.log(response)

}
await getChatCompletionFunctions();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='Anthropic_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="anthropic",
        api_key="PORTKEY_API_KEY"
    )
)


response = openai.chat.completions.create(
   model="claude-3-5-sonnet-20240620",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant, who describes imagse"
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{base_64_encoded_image}"
                    }
                }
            ]
        }
    ],
    max_tokens=1400,
)

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-provider: anthropic" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -d '{
    "model": "claude-3-5-sonnet-20240620",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant who describes images."
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What's in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "data:image/jpeg;base64,BASE64_IMAGE_DATA"
            }
          }
        ]
      }
    ],
    "max_tokens": 300
  }'

API Reference

On completion, the request will get logged in the logs UI where any image inputs or outputs can be viewed. Portkey will automatically load the base64 images making for a great debugging experience with vision models.

For more info, check out this guide:

Vision

Prompt Caching

Portkey also works with Anthropic's new prompt caching feature and helps you save time & money for all your Anthropic requests. Refer to this guide to learn how to enable it:

Prompt Caching

Managing Anthropic Prompts

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

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:

PreviousStructued Outputs NextPrompt Caching

Last updated 3 hours ago