Integrating Router

OpenAI Python

Using OpenAI's SDK, the following function.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_UMAMIAI_API_KEY",  # defaults to os.environ.get("UMAMIAI_API_KEY")
)

chat_completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
)

becomes

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_UMAMIAI_API_KEY",  # defaults to os.environ.get("UMAMIAI_API_KEY")
    base_url="https://api.umamiai.xyz/router",
)

chat_completion = client.chat.completions.create(
    model="router",
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
)

Setting Parameters

To route across a subset of models, specifiy the array of whitelisted origins:

chat_completion = client.chat.completions.create(
    model="router",
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
    extra_body={
        "models": ["gpt-3.5-turbo", "claude-2.1"]
    }
)

To specify a max cost and willingness to pay for a 10% improvement on model quality on each request, set the parameters in the following way:\

client.chat.completions.create(
    model="router",
    messages=[
        {"role": "user", "content": "Hello world!"}
    ],
    extra_body={
        "models": ["gpt-3.5-turbo", "claude-2.1"],
        "max_cost": 0.02, 
        "willingness_to_pay": 0.01 
    },
)

Finally, to associate metadata (for instance about a user or region) with each request, set the extra body parameter like so:

client.chat.completions.create(
    model="router",
    messages=[
        {"role": "user", "content": "Write small poem"},
    ],
    extra_body={
        "models": ["gpt-3.5-turbo", "claude-2.1"],
        "max_cost": 0.02,
        "willingness_to_pay": 0.01,
        "extra": {
            "ip": "123.123.123.123",
            "Timezone": "UTC+0",
            "Country": "US",
            "City": "New York",
        }
    },
)

OpenAI Node

Using OpenAI's SDK, the following function

import OpenAI from 'openai';

const openai = new OpenAI({
    apiKey: 'YOUR_UMAMIAI_API_KEY', // defaults to process.env["UMAMIAI_API_KEY"]
});

async function main() {
    const chatCompletion = await openai.chat.completions.create({
        model: 'gpt-3.5-turbo',
        messages: [{ role: 'user', content: 'Say this is a test' }],
    });
}

main();

becomes

import OpenAI from 'openai';

const openai = new OpenAI({
    apiKey: 'YOUR_UMAMIAI_API_KEY', // defaults to process.env["UMAMIAI_API_KEY"]
    baseURL: "https://api.umamiai.xyz/router"
});

async function main() {
    const chatCompletion = await openai.chat.completions.create({
        model: 'router',
        messages: [{ role: 'user', content: 'Say this is a test' }],
    });
}

main();

OpenAI cURL

Usage

The following request

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "Hello world!"
    },
    {
      "role": "assistant",
      "content": "Hello! How can I assist you today?"
    }
  ],
}'

becomes

curl https://api.umamiai.xyz/router/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_UMAMIAI_API_KEY" \
  -d '{
  "model": "router",
  "messages": [
    {
      "role": "user",
      "content": "Hello world!"
    },
    {
      "role": "assistant",
      "content": "Hello! How can I assist you today?"
    }
  ],
}'

LangChain Python

Usage

The following code snippet

from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

llm = ChatOpenAI(
    model_name="gpt-3.5-turbo",
    openai_api_key="YOUR_UMAMIAI_API_KEY"
)

messages = [HumanMessage(content="Say this is a test")]

llm.invoke(messages)

becomes

from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

llm = ChatOpenAI(
    model_name="router",
    openai_api_base="https://api.umamiai.xyz/router",
    openai_api_key="YOUR_UMAMIAI_API_KEY"
)

messages = [HumanMessage(content="Say this is a test")]

llm.invoke(messages)

LangChain JS

Usage

The following code snippet

import { ChatOpenAI } from "langchain/chat_models/openai";
import { HumanMessage } from "langchain/schema";

async function main() {
  const chat = new ChatOpenAI({
    modelName: "gpt-3.5-turbo",
    openAIApiKey: "YOUR_UMAMIAI_API_KEY",
  });

  await chat.call([new HumanMessage("Say this is a test")]);
}

main();

becomes

import { ChatOpenAI } from "langchain/chat_models/openai";
import { HumanMessage } from "langchain/schema";

async function main() {
    const chat = new ChatOpenAI({
        modelName: "router",
        configuration: {
            baseURL: "https://api.umamiai.xyz/router",
        },
        openAIApiKey: "YOUR_UMAMIAI_API_KEY",
    });

    await chat.call([new HumanMessage("Say this is a test")]);
}

main();