const { chatPrompt } = require("../../chats"); class LocalAiLLM { constructor(embedder = null) { if (!process.env.LOCAL_AI_BASE_PATH) throw new Error("No LocalAI Base Path was set."); const { Configuration, OpenAIApi } = require("openai"); const config = new Configuration({ basePath: process.env.LOCAL_AI_BASE_PATH, ...(!!process.env.LOCAL_AI_API_KEY ? { apiKey: process.env.LOCAL_AI_API_KEY, } : {}), }); this.openai = new OpenAIApi(config); this.model = process.env.LOCAL_AI_MODEL_PREF; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; if (!embedder) throw new Error( "INVALID LOCAL AI SETUP. No embedding engine has been set. Go to instance settings and set up an embedding interface to use LocalAI as your LLM." ); this.embedder = embedder; } streamingEnabled() { return "streamChat" in this && "streamGetChatCompletion" in this; } // Ensure the user set a value for the token limit // and if undefined - assume 4096 window. promptWindowLimit() { const limit = process.env.LOCAL_AI_MODEL_TOKEN_LIMIT || 4096; if (!limit || isNaN(Number(limit))) throw new Error("No LocalAi token context limit was set."); return Number(limit); } async isValidChatCompletionModel(_ = "") { return true; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${systemPrompt} Context: ${contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("")}`, }; return [prompt, ...chatHistory, { role: "user", content: userPrompt }]; } async isSafe(_input = "") { // Not implemented so must be stubbed return { safe: true, reasons: [] }; } async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `LocalAI chat: ${this.model} is not valid for chat completion!` ); const textResponse = await this.openai .createChatCompletion({ model: this.model, temperature: Number(workspace?.openAiTemp ?? 0.7), n: 1, messages: await this.compressMessages( { systemPrompt: chatPrompt(workspace), userPrompt: prompt, chatHistory, }, rawHistory ), }) .then((json) => { const res = json.data; if (!res.hasOwnProperty("choices")) throw new Error("LocalAI chat: No results!"); if (res.choices.length === 0) throw new Error("LocalAI chat: No results length!"); return res.choices[0].message.content; }) .catch((error) => { throw new Error( `LocalAI::createChatCompletion failed with: ${error.message}` ); }); return textResponse; } async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `LocalAI chat: ${this.model} is not valid for chat completion!` ); const streamRequest = await this.openai.createChatCompletion( { model: this.model, stream: true, temperature: Number(workspace?.openAiTemp ?? 0.7), n: 1, messages: await this.compressMessages( { systemPrompt: chatPrompt(workspace), userPrompt: prompt, chatHistory, }, rawHistory ), }, { responseType: "stream" } ); return streamRequest; } async getChatCompletion(messages = null, { temperature = 0.7 }) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `LocalAI chat: ${this.model} is not valid for chat completion!` ); const { data } = await this.openai.createChatCompletion({ model: this.model, messages, temperature, }); if (!data.hasOwnProperty("choices")) return null; return data.choices[0].message.content; } async streamGetChatCompletion(messages = null, { temperature = 0.7 }) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `LocalAi chat: ${this.model} is not valid for chat completion!` ); const streamRequest = await this.openai.createChatCompletion( { model: this.model, stream: true, messages, temperature, }, { responseType: "stream" } ); return streamRequest; } // Simple wrapper for dynamic embedder & normalize interface for all LLM implementations async embedTextInput(textInput) { return await this.embedder.embedTextInput(textInput); } async embedChunks(textChunks = []) { return await this.embedder.embedChunks(textChunks); } async compressMessages(promptArgs = {}, rawHistory = []) { const { messageArrayCompressor } = require("../../helpers/chat"); const messageArray = this.constructPrompt(promptArgs); return await messageArrayCompressor(this, messageArray, rawHistory); } } module.exports = { LocalAiLLM, };