const { chatPrompt } = require("../../chats"); // hybrid of openAi LLM chat completion for LMStudio class LMStudioLLM { constructor(embedder = null) { if (!process.env.LMSTUDIO_BASE_PATH) throw new Error("No LMStudio API Base Path was set."); const { Configuration, OpenAIApi } = require("openai"); const config = new Configuration({ basePath: process.env.LMSTUDIO_BASE_PATH?.replace(/\/+$/, ""), // here is the URL to your LMStudio instance }); this.lmstudio = new OpenAIApi(config); // When using LMStudios inference server - the model param is not required so // we can stub it here. this.model = "model-placeholder"; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; if (!embedder) throw new Error( "INVALID LM STUDIO SETUP. No embedding engine has been set. Go to instance settings and set up an embedding interface to use LMStudio as your LLM." ); this.embedder = embedder; } streamingEnabled() { return "streamChat" in this && "streamGetChatCompletion" in this; } // Ensure the user set a value for the token limit // and if undefined - assume 4096 window. promptWindowLimit() { const limit = process.env.LMSTUDIO_MODEL_TOKEN_LIMIT || 4096; if (!limit || isNaN(Number(limit))) throw new Error("No LMStudio token context limit was set."); return Number(limit); } async isValidChatCompletionModel(_ = "") { // LMStudio may be anything. The user must do it correctly. // See comment about this.model declaration in constructor return true; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${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 (!this.model) throw new Error( `LMStudio chat: ${this.model} is not valid or defined for chat completion!` ); const textResponse = await this.lmstudio .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("LMStudio chat: No results!"); if (res.choices.length === 0) throw new Error("LMStudio chat: No results length!"); return res.choices[0].message.content; }) .catch((error) => { throw new Error( `LMStudio::createChatCompletion failed with: ${error.message}` ); }); return textResponse; } async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { if (!this.model) throw new Error( `LMStudio chat: ${this.model} is not valid or defined for chat completion!` ); const streamRequest = await this.lmstudio.createChatCompletion( { model: this.model, temperature: Number(workspace?.openAiTemp ?? 0.7), n: 1, stream: true, messages: await this.compressMessages( { systemPrompt: chatPrompt(workspace), userPrompt: prompt, chatHistory, }, rawHistory ), }, { responseType: "stream" } ); return streamRequest; } async getChatCompletion(messages = null, { temperature = 0.7 }) { if (!this.model) throw new Error( `LMStudio chat: ${this.model} is not valid or defined model for chat completion!` ); const { data } = await this.lmstudio.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 (!this.model) throw new Error( `LMStudio chat: ${this.model} is not valid or defined model for chat completion!` ); const streamRequest = await this.lmstudio.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 = { LMStudioLLM, };