358 lines
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HTML
358 lines
14 KiB
HTML
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<a href="https://uncloseai.com"><h1 class="unturf" style="font-family: 'ChunkFiveRegular';">uncloseai.</h1></a>
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<p>Node.js Examples - Free LLM & TTS AI Service</p>
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</header>
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<h2 id="nodejs-intro">Node.js Examples</h2>
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<p>This page demonstrates how to use the <strong>uncloseai.</strong> API endpoints with Node.js using the OpenAI client library. All examples use the same OpenAI-compatible API interface, making it easy to switch between different models and endpoints.</p>
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<p><strong>Available Endpoints:</strong></p>
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<ul>
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<li><strong>Hermes:</strong> <code>https://hermes.ai.unturf.com/v1</code> - General purpose conversational AI</li>
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<li><strong>Qwen 3 Coder:</strong> <code>https://qwen.ai.unturf.com/v1</code> - Specialized coding model</li>
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<li><strong>TTS:</strong> <code>https://speech.ai.unturf.com/v1</code> - Text-to-speech generation</li>
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</ul>
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<h3 id="nodejs-client">Node.js Client Installation</h3>
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<p>To install the OpenAI package for Node.js, you can use <code>npm</code> in your <code>package.json</code>:</p>
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<pre><code class="json">{
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"dependencies": {
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"openai": "6.3.0"
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}
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}
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</code></pre>
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<p>Run the following command to install it:</p>
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<pre><code class="bash">npm install</code></pre>
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<h2 id="nodejs-non-streaming">Non-Streaming Examples</h2>
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<p>Non-streaming mode waits for the complete response before returning. This is simpler to use but provides no intermediate feedback during generation.</p>
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<h3 id="nodejs-hermes-non-stream">Using Hermes (General Purpose)</h3>
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<pre><code class="javascript">const OpenAI = require('openai');
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const client = new OpenAI({
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baseURL: "https://hermes.ai.unturf.com/v1",
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apiKey: "dummy-api-key",
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});
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const MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic";
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const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];
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async function getResponse() {
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try {
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const response = await client.chat.completions.create({
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model: MODEL,
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messages: messages,
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temperature: 0.5,
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max_tokens: 150,
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});
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console.log(response.choices[0].message.content);
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} catch (error) {
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console.error("Error:", error.response ? error.response.data : error.message);
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}
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}
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getResponse();
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</code></pre>
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<h3 id="nodejs-qwen-non-stream">Using Qwen 3 Coder (Specialized for Coding)</h3>
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<pre><code class="javascript">const OpenAI = require('openai');
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const client = new OpenAI({
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baseURL: "https://qwen.ai.unturf.com/v1",
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apiKey: "dummy-api-key",
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});
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const MODEL = "hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M";
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const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];
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async function getResponse() {
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try {
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const response = await client.chat.completions.create({
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model: MODEL,
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messages: messages,
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temperature: 0.5,
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max_tokens: 150,
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});
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console.log(response.choices[0].message.content);
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} catch (error) {
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console.error("Error:", error.response ? error.response.data : error.message);
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}
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}
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getResponse();
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</code></pre>
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<h2 id="nodejs-streaming">Streaming Examples</h2>
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<p>Streaming mode returns chunks of the response as they are generated, providing real-time feedback. This is ideal for interactive applications and long responses.</p>
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<h3 id="nodejs-hermes-stream">Using Hermes (General Purpose)</h3>
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<pre><code class="javascript">const OpenAI = require('openai');
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const client = new OpenAI({
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baseURL: "https://hermes.ai.unturf.com/v1",
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apiKey: "dummy-api-key",
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});
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const MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic";
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const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];
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async function streamResponse() {
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try {
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const stream = await client.chat.completions.create({
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model: MODEL,
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messages: messages,
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temperature: 0.5,
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max_tokens: 150,
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stream: true, // Enable streaming
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});
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// Use async iterator to read each chunk
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for await (const chunk of stream) {
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const msg = chunk.choices[0].delta.content;
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process.stdout.write(msg); // Print each chunk as it arrives
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}
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} catch (error) {
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console.error("Error:", error.response ? error.response.data : error.message);
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}
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}
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streamResponse();
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</code></pre>
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<h3 id="nodejs-qwen-stream">Using Qwen 3 Coder (Specialized for Coding)</h3>
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<pre><code class="javascript">const OpenAI = require('openai');
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const client = new OpenAI({
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baseURL: "https://qwen.ai.unturf.com/v1",
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apiKey: "dummy-api-key",
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});
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const MODEL = "hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M";
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const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];
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async function streamResponse() {
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try {
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const stream = await client.chat.completions.create({
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model: MODEL,
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messages: messages,
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temperature: 0.5,
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max_tokens: 150,
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stream: true, // Enable streaming
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});
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// Use async iterator to read each chunk
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for await (const chunk of stream) {
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const msg = chunk.choices[0].delta.content;
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process.stdout.write(msg); // Print each chunk as it arrives
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}
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} catch (error) {
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console.error("Error:", error.response ? error.response.data : error.message);
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}
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}
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streamResponse();
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</code></pre>
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<h2 id="nodejs-tts">Text-to-Speech Example</h2>
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<p>Generate audio speech from text using the TTS endpoint. The audio is saved as an MP3 file.</p>
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<pre><code class="javascript">const OpenAI = require('openai');
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const client = new OpenAI({
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baseURL: "https://speech.ai.unturf.com/v1",
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apiKey: "YOLO",
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});
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async function getSpeech() {
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try {
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const response = await client.audio.speech.with_streaming_response.create({
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model: "tts-1",
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voice: "alloy",
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speed: 0.9,
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input: "I think so therefore, Today is a wonderful day to grow something people love!"
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});
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response.stream_to_file("speech.mp3");
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} catch (error) {
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console.error("Error:", error.response ? error.response.data : error.message);
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}
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}
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getSpeech();
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</code></pre>
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<li><a href="#nodejs-intro" class="active">Overview</a></li>
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