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Nitro with openai-node

You can migrate from OAI API or Azure OpenAI to Nitro using your existing NodeJS code quickly

The ONLY thing you need to do is to override baseURL in openai init with Nitro URL

Chat Completion​

Engine Typescript Code
Nitro 
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: '', // defaults to process.env["OPENAI_API_KEY"]
  baseURL: "http://localhost:3928/v1/" // https://api.openai.com/v1
});

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

  stream.on('content', (delta, snapshot) => {
    process.stdout.write(delta);
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }

  const chatCompletion = await stream.finalChatCompletion();
  console.log(chatCompletion); // {id: "…", choices: […], …}
}
chatCompletion()
OAI 
import OpenAI from 'openai';

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

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

  stream.on('content', (delta, snapshot) => {
    process.stdout.write(delta);
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }

  const chatCompletion = await stream.finalChatCompletion();
  console.log(chatCompletion); // {id: "…", choices: […], …}
}
chatCompletion()
Azure OAI 
import OpenAI from 'openai';

const resource = '<your resource name>';
const model = '<your model>';
const apiVersion = '2023-06-01-preview';
const apiKey = process.env['AZURE_OPENAI_API_KEY'];

if (!apiKey) {
  throw new Error('The AZURE_OPENAI_API_KEY variable is missing.');
}

const baseURL = `https://${resource}.openai.azure.com/openai/` +
                `deployments/${model}`;

const openai = new OpenAI({
  apiKey,
  baseURL,
  defaultQuery: { 'api-version': apiVersion },
  defaultHeaders: { 'api-key': apiKey },
});

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

    stream.on('content', (delta, snapshot) => {
      process.stdout.write(delta);
    });

    for await (const chunk of stream) {
      process.stdout.write(chunk.choices[0]?.delta?.content || '');
    }

    const chatCompletion = await stream.finalChatCompletion();
    console.log(chatCompletion); // Log the final completion
  } catch (error) {
    console.error('Error in chat completion:', error);
  }
}

chatCompletion();

Resource:

Embedding​

Engine Embedding
Nitro 
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: '', // Defaults to process.env["OPENAI_API_KEY"]
  baseURL: 'http://localhost:3928/v1/'
  // 'https://api.openai.com/v1'
});

async function embedding() {
  try {
    const response = await openai.embeddings.create({
      input: 'Hello How are you?',
      model: 'text-embedding-ada-002'
    });
    console.log(response); // Log the response
  } catch (error) {
    console.error('Error in fetching embedding:', error);
  }
}

embedding();
OAI 
import OpenAI from 'openai';

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

async function embedding() {
  const embedding = await openai.embeddings.create({
    input: 'Hello How are you?',
    model: 'text-embedding-ada-002'
  });
  console.log(embedding); // {object: "list", data: […], …}
}

embedding();
Azure OAI 
import OpenAI from 'openai';

const resource = '<your resource name>';
const model = '<your model>';
const apiVersion = '2023-06-01-preview';
const apiKey = process.env['AZURE_OPENAI_API_KEY'];

if (!apiKey) {
  throw new Error('The AZURE_OPENAI_API_KEY variable is missing.');
}

// Splitting the baseURL into concatenated parts for readability
const baseURL = `https://${resource}.openai.azure.com/openai/` +
                `deployments/${model}`;

const openai = new OpenAI({
  apiKey,
  baseURL,
  defaultQuery: { 'api-version': apiVersion },
  defaultHeaders: { 'api-key': apiKey },
});

async function embedding() {
  const embedding = await openai.embeddings.create({
    input: 'Hello How are you?',
    model: 'text-embedding-ada-002'
  });
  console.log(embedding); // {object: "list", data: […], …}
}

embedding();

Audio​

Coming soon

How to reproduce​

Step 1: Dependencies installation

npm install --save openai typescript
# or
yarn add openai

Step 2: Fill tsconfig.json

{
  "compilerOptions": {
    "moduleResolution": "node",
    "sourceMap": true,
    "outDir": "dist",
    "target": "es2020",
    "lib": ["es2020"],
    "module": "commonjs",
  },
  "lib": ["es2015"]
}

Step 3: Fill index.ts file with code.

Step 4: Build with npx tsc.

Step 5: Run the code with node dist/index.js.