Test with Deno 2

Author: ariya

.github/workflows/test-deno.yml

@@ -0,0 +1,25 @@
+name: Test with Deno
+
+on: [push, pull_request, workflow_dispatch]
+
+jobs:
+  test:
+    runs-on: ubuntu-22.04
+    timeout-minutes: 10
+    steps:
+      - uses: actions/checkout@v4
+      - uses: denoland/setup-deno@v2
+        with:
+          deno-version: v2.x
+
+      - run: deno --version
+
+      - name: Prepare LLM
+        uses: ./.github/actions/prepare-llm
+        timeout-minutes: 3
+
+      - run: echo 'Which planet in our solar system is the largest?' | ./ask-llm.ts | grep -i jupiter
+        timeout-minutes: 7
+        env:
+          LLM_API_BASE_URL: 'http://127.0.0.1:8080/v1'
+          LLM_DEBUG: 1

README.md

@@ -8,6 +8,7 @@ It is available in several flavors:
 
 * Python version. Compatible with [CPython](https://python.org) or [PyPy](https://pypy.org),  v3.10 or higher.
 * JavaScript version. Compatible with [Node.js](https://nodejs.org) (>= v18) or [Bun](https://bun.sh) (>= v1.0).
+* TypeScript version. Compatible with [Deno](https://deno.com/) (>= 2.0) or [Bun](https://bun.sh) (>= v1.0).
 * Clojure version. Compatible with [Babashka](https://babashka.org/) (>= 1.3).
 * Swift version. Compatible with [Swift](https://www.swift.org), v5.10 or higher.
 * Go version. Compatible with [Go](https://golang.org), v1.19 or higher.
@@ -18,6 +19,7 @@ Interact with the LLM with:
 ```bash
 ./ask-llm.py         # for Python user
 ./ask-llm.js         # for Node.js user
+./ask-llm.ts         # for Deno user
 ./ask-llm.clj        # for Clojure user
 ./ask-llm.swift      # for Swift user
 go run ask-llm.go    # for Go user

ask-llm.ts

@@ -0,0 +1,152 @@
+#!/usr/bin/env -S deno run --allow-env --allow-net
+
+import readline from 'node:readline';
+
+const LLM_API_BASE_URL = process.env.LLM_API_BASE_URL || 'https://api.openai.com/v1';
+const LLM_API_KEY = process.env.LLM_API_KEY || process.env.OPENAI_API_KEY;
+const LLM_CHAT_MODEL = process.env.LLM_CHAT_MODEL;
+const LLM_STREAMING = process.env.LLM_STREAMING !== 'no';
+
+const LLM_DEBUG = process.env.LLM_DEBUG;
+
+/**
+ * Represents a chat message.
+ *
+ * @typedef {Object} Message
+ * @property {'system'|'user'|'assistant'} role
+ * @property {string} content
+ */
+
+/**
+ * A callback function to stream then completion.
+ *
+ * @callback CompletionHandler
+ * @param {string} text
+ * @returns {void}
+ */
+
+/**
+ * Generates a chat completion using a RESTful LLM API service.
+ *
+ * @param {Array<Message>} messages - List of chat messages.
+ * @param {CompletionHandler=} handler - An optional callback to stream the completion.
+ * @returns {Promise<string>} The completion generated by the LLM.
+ */
+const chat = async (messages, handler) => {
+    const url = `${LLM_API_BASE_URL}/chat/completions`;
+    const auth = LLM_API_KEY ? { 'Authorization': `Bearer ${LLM_API_KEY}` } : {};
+    const model = LLM_CHAT_MODEL || 'gpt-4o-mini';
+    const stop = ['<|im_end|>', '<|end|>', '<|eot_id|>'];
+    const max_tokens = 200;
+    const temperature = 0;
+    const stream = LLM_STREAMING && typeof handler === 'function';
+    const response = await fetch(url, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json', ...auth },
+        body: JSON.stringify({ messages, model, stop, max_tokens, temperature, stream })
+    });
+    if (!response.ok) {
+        throw new Error(`HTTP error with the status: ${response.status} ${response.statusText}`);
+    }
+
+    if (!stream) {
+        const data = await response.json();
+        const { choices } = data;
+        const first = choices[0];
+        const { message } = first;
+        const { content } = message;
+        const answer = content.trim();
+        handler && handler(answer);
+        return answer;
+    }
+
+    const parse = (line) => {
+        let partial = null;
+        const prefix = line.substring(0, 6);
+        if (prefix === 'data: ') {
+            const payload = line.substring(6);
+            try {
+                const { choices } = JSON.parse(payload);
+                const [choice] = choices;
+                const { delta } = choice;
+                partial = delta?.content;
+            } catch (e) {
+                // ignore
+            } finally {
+                return partial;
+            }
+        }
+        return partial;
+    }
+
+    const reader = response.body.getReader();
+    const decoder = new TextDecoder();
+
+    let answer = '';
+    let buffer = '';
+    while (true) {
+        const { value, done } = await reader.read();
+        if (done) {
+            break;
+        }
+        const lines = decoder.decode(value).split('\n');
+        for (let i = 0; i < lines.length; ++i) {
+            const line = buffer + lines[i];
+            if (line[0] === ':') {
+                buffer = '';
+                continue;
+            }
+            if (line === 'data: [DONE]') {
+                break;
+            }
+            if (line.length > 0) {
+                const partial = parse(line.trim());
+                if (partial === null) {
+                    buffer = line;
+                } else if (partial && partial.length > 0) {
+                    buffer = '';
+                    if (answer.length < 1) {
+                        const leading = partial.trim();
+                        answer = leading;
+                        handler && (leading.length > 0) && handler(leading);
+                    } else {
+                        answer += partial;
+                        handler && handler(partial);
+                    }
+                }
+            }
+        }
+    }
+    return answer;
+}
+
+const SYSTEM_PROMPT = 'Answer the question politely and concisely.';
+
+(async () => {
+    console.log(`Using LLM at ${LLM_API_BASE_URL}.`);
+    console.log('Press Ctrl+D to exit.')
+    console.log();
+
+    const messages = [];
+    messages.push({ role: 'system', content: SYSTEM_PROMPT });
+
+    let loop = true;
+    const io = readline.createInterface({ input: process.stdin, output: process.stdout });
+    io.on('close', () => { loop = false; });
+
+    const qa = () => {
+        io.question('>> ', async (question) => {
+            messages.push({ role: 'user', content: question });
+            const start = Date.now();
+            const answer = await chat(messages, (str) => process.stdout.write(str));
+            messages.push({ role: 'assistant', content: answer.trim() });
+            console.log();
+            const elapsed = Date.now() - start;
+            LLM_DEBUG && console.log(`[${elapsed} ms]`);
+            console.log();
+            loop && qa();
+        })
+    }
+
+    qa();
+})();