Batch of LLM-assisted fixes/feature updates
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Cam Spry committed
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@@ -1,90 +1,156 @@
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'use strict';
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const fs = require('fs');
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const path = require('path');
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const OpenAI = require('openai');
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// read optional config file
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const configPath = path.join(__dirname, 'llm_config.json');
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const llmOptions = fs.existsSync(configPath)
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? JSON.parse(fs.readFileSync(configPath, 'utf8'))
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: {};
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const DEFAULT_MODEL = 'meta-llama/Llama-3.2-3B-Instruct-Turbo';
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const CONFIG_PATH = path.join(__dirname, 'llm_config.json');
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const PERSONALITY_PATH = path.join(__dirname, 'bot_personalities_dumb.txt');
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console.log(`Read config from ${configPath}`);
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const FALLBACK_PERSONALITY = 'You are a friendly bot playing a collaborative story game';
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function generateBotPersonality() {
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const personalities = fs.readFileSync(path.join(__dirname, 'bot_personalities_dumb.txt'), 'utf8')
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// Read the optional config file once, tolerating missing/corrupt files.
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function loadConfig() {
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if (!fs.existsSync(CONFIG_PATH)) return {};
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try {
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const parsed = JSON.parse(fs.readFileSync(CONFIG_PATH, 'utf8'));
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if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) return parsed;
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console.warn(`[LLMBot] ${CONFIG_PATH} must contain a JSON object; ignoring it.`);
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} catch (err) {
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console.warn(`[LLMBot] Could not parse ${CONFIG_PATH}: ${err.message}`);
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}
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return {};
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}
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function readLines(file) {
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try {
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return fs
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.readFileSync(file, 'utf8')
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.split('\n')
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.map(personality => personality.trim())
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.filter(personality => personality.length > 0);
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if (personalities.length === 0) {
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return 'You are a friendly bot'; // Fallback if no personalities are found
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.map((line) => line.trim())
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.filter((line) => line.length > 0);
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} catch (err) {
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console.warn(`[LLMBot] Could not read ${file}: ${err.message}`);
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return [];
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}
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const randomIndex = Math.floor(Math.random() * personalities.length);
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return personalities[randomIndex];
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}
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function resolvePersonalitiesFile(configured) {
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const requested = configured || process.env.YARN_BOT_PERSONALITIES;
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if (!requested) return PERSONALITY_PATH;
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return path.isAbsolute(requested) ? requested : path.join(__dirname, requested);
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}
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function readPersonalities(file = PERSONALITY_PATH) {
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return readLines(file);
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}
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function generateBotPersonality(file = PERSONALITY_PATH) {
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const personalities = readPersonalities(file);
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if (!personalities.length) return FALLBACK_PERSONALITY;
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return personalities[Math.floor(Math.random() * personalities.length)];
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}
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function firstContent(response) {
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const content = response?.choices?.[0]?.message?.content;
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return typeof content === 'string' ? content.trim() : '';
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}
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/**
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* LLM-backed bot. The constructor throws when the client cannot be
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* configured (for example, no API key); BotFactory catches that and
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* falls back to RandomBot so a misconfigured server keeps working.
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*/
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class LLMBot {
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constructor() {
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// Instantiate the OpenAI client with the config options we just read
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this.llm = new OpenAI(llmOptions);
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constructor(config) {
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const configPathExists = fs.existsSync(CONFIG_PATH);
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if (configPathExists) console.log(`[LLMBot] Read config from ${CONFIG_PATH}`);
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// Prompt fragments we’ll reuse
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this.writePrompt =
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`You are playing a collaborative storytelling game.
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Continue the following story with exactly ONE short, creative sentence or sentence fragment.
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Do NOT repeat what’s already written.`;
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this.options = config && typeof config === 'object' ? config : loadConfig();
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this.model = this.options.model || process.env.OPENAI_MODEL || DEFAULT_MODEL;
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this.votePrompt =
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`You are playing a collaborative storytelling game.
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Below is the story so far, followed by a list of possible next sentences.
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Reply with ONLY the number (0-indexed) of the sentence you like best.`;
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// Throws if OPENAI_API_KEY (or an apiKey in llm_config.json) is missing.
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this.llm = new OpenAI(this.options);
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this.banterPrompt =
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`You are a playful, slightly sarcastic AI taking part in a writing-game chat.
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Keep it short, fun, and on-topic.`;
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this.writePrompt = [
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'You are playing a collaborative storytelling game.',
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'Continue the following story with exactly ONE short, creative sentence or sentence fragment.',
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'Do NOT repeat what is already written.',
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'Reply with the sentence only, no quotes, no preamble.'
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].join('\n');
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this.limitPrompt = "Please do not exceed 25 words."
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this.votePrompt = [
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'You are playing a collaborative storytelling game.',
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'Below is the story so far, followed by a list of possible next sentences.',
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'Reply with ONLY the number of the sentence you like best. No other text.'
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].join('\n');
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this.personality = generateBotPersonality();
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this.banterPrompt = [
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'You are a playful, slightly sarcastic AI taking part in a writing-game chat.',
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'Keep it short, fun, and on-topic (one sentence, under 20 words).'
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].join('\n');
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this.limitPrompt = 'Please do not exceed 25 words.';
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// Defaults to bot_personalities_dumb.txt; override with `personalitiesFile`
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// in the config or the YARN_BOT_PERSONALITIES environment variable.
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this.personalitiesFile = resolvePersonalitiesFile(this.options.personalitiesFile);
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this.personality = generateBotPersonality(this.personalitiesFile);
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}
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async Write(story_so_far) {
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const msg = `${this.personality}. ${this.writePrompt} ${this.limitPrompt}\n\nStory so far:\n${story_so_far}`;
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async complete(messages, { temperature = 0.8, maxTokens = 80 } = {}) {
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const response = await this.llm.chat.completions.create({
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model: llmOptions.model || 'meta-llama/Llama-3.2-3B-Instruct-Turbo',
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messages: [{ role: 'user', content: msg }],
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temperature: llmOptions.temperature ?? 0.8,
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max_tokens: llmOptions.max_tokens ?? 80,
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model: this.model,
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messages,
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temperature,
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max_tokens: maxTokens
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});
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//console.log(`Received response from LLM: ${response.choices[0].message.content}`);
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return response.choices[0].message.content.trim();
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return firstContent(response);
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}
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async Vote(story_so_far, choices) {
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const choicesBlock = choices.map((c, i) => `${i}: ${(c.entry_text)}`).join('\n');
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const msg = `${this.personality}. ${this.votePrompt}\n\nStory:\n${story_so_far}\n\nChoices:\n${choicesBlock}`;
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//console.log(msg);
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const response = await this.llm.chat.completions.create({
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model: llmOptions.model || 'meta-llama/Llama-3.2-3B-Instruct-Turbo',
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messages: [{ role: 'user', content: msg }],
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async Write(storySoFar) {
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const msg = `${this.personality}.\n${this.writePrompt}\n${this.limitPrompt}\n\nStory so far:\n${storySoFar || '(the story has not started yet)'}`;
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const text = await this.complete([{ role: 'user', content: msg }], {
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temperature: this.options.temperature ?? 0.8,
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maxTokens: this.options.max_tokens ?? 80
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});
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// Strip wrapping quotes and stray newlines so entries read naturally.
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return text.replace(/^["'\s]+|["'\s]+$/g, '');
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}
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async Vote(storySoFar, choices) {
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const list = Array.isArray(choices) ? choices : [];
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if (!list.length) return null;
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const choicesBlock = list
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.map((choice, i) => `${choice.index ?? i}: ${choice.entry_text}`)
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.join('\n');
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const msg = `${this.personality}.\n${this.votePrompt}\n\nStory:\n${storySoFar}\n\nChoices:\n${choicesBlock}`;
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const reply = await this.complete([{ role: 'user', content: msg }], {
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temperature: 0.2,
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max_tokens: 5,
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maxTokens: 8
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});
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const reply = response.choices[0].message.content.trim();
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const match = reply.match(/\d+/);
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return match ? parseInt(match[0], 10) : 0;
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if (!match) return null;
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const parsed = Number.parseInt(match[0], 10);
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return Number.isInteger(parsed) ? parsed : null;
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}
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async Banter(chat_so_far) {
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const msg = `${this.banterPrompt}\n\nChat so far:\n${chat_so_far}`;
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const response = await this.llm.chat.completions.create({
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model: llmOptions.model || 'meta-llama/Llama-3.2-3B-Instruct-Turbo',
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messages: [{ role: 'user', content: msg }],
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temperature: llmOptions.temperature ?? 0.8,
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max_tokens: llmOptions.max_tokens ?? 60,
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async Banter(chatSoFar) {
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const msg = `${this.banterPrompt}\n\nRecent chat:\n${chatSoFar || '(nobody has spoken yet)'}`;
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return this.complete([{ role: 'user', content: msg }], {
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temperature: this.options.temperature ?? 0.8,
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maxTokens: this.options.max_tokens ?? 60
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});
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return response.choices[0].message.content.trim();
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}
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}
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module.exports = LLMBot;
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LLMBot.DEFAULT_MODEL = DEFAULT_MODEL;
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LLMBot.CONFIG_PATH = CONFIG_PATH;
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LLMBot.PERSONALITY_PATH = PERSONALITY_PATH;
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LLMBot.readPersonalities = readPersonalities;
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LLMBot.generateBotPersonality = generateBotPersonality;
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module.exports = LLMBot;
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