const fs = require("fs"); const path = require("path"); const { NativeEmbedder } = require("../../EmbeddingEngines/native"); const { chatPrompt } = require("../../chats"); // Docs: https://api.js.langchain.com/classes/chat_models_llama_cpp.ChatLlamaCpp.html const ChatLlamaCpp = (...args) => import("langchain/chat_models/llama_cpp").then( ({ ChatLlamaCpp }) => new ChatLlamaCpp(...args) ); class NativeLLM { constructor(embedder = null) { if (!process.env.NATIVE_LLM_MODEL_PREF) throw new Error("No local Llama model was set."); this.model = process.env.NATIVE_LLM_MODEL_PREF || null; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; this.embedder = embedder || new NativeEmbedder(); this.cacheDir = path.resolve( process.env.STORAGE_DIR ? path.resolve(process.env.STORAGE_DIR, "models", "downloaded") : path.resolve(__dirname, `../../../storage/models/downloaded`) ); // Make directory when it does not exist in existing installations if (!fs.existsSync(this.cacheDir)) fs.mkdirSync(this.cacheDir); } async #initializeLlamaModel(temperature = 0.7) { const modelPath = path.join(this.cacheDir, this.model); if (!fs.existsSync(modelPath)) throw new Error( `Local Llama model ${this.model} was not found in storage!` ); global.llamaModelInstance = await ChatLlamaCpp({ modelPath, temperature, useMlock: true, }); } // If the model has been loaded once, it is in the memory now // so we can skip re-loading it and instead go straight to inference. // Note: this will break temperature setting hopping between workspaces with different temps. async #llamaClient({ temperature = 0.7 }) { if (global.llamaModelInstance) return global.llamaModelInstance; await this.#initializeLlamaModel(temperature); return global.llamaModelInstance; } #convertToLangchainPrototypes(chats = []) { const { HumanMessage, SystemMessage, AIMessage, } = require("langchain/schema"); const langchainChats = []; const roleToMessageMap = { system: SystemMessage, user: HumanMessage, assistant: AIMessage, }; for (const chat of chats) { if (!roleToMessageMap.hasOwnProperty(chat.role)) continue; const MessageClass = roleToMessageMap[chat.role]; langchainChats.push(new MessageClass({ content: chat.content })); } return langchainChats; } #appendContext(contextTexts = []) { if (!contextTexts || !contextTexts.length) return ""; return ( "\nContext:\n" + contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("") ); } streamingEnabled() { return "streamChat" in this && "streamGetChatCompletion" in this; } // Ensure the user set a value for the token limit // and if undefined - assume 4096 window. // DEV: Currently this ENV is not configurable. promptWindowLimit() { const limit = process.env.NATIVE_LLM_MODEL_TOKEN_LIMIT || 4096; if (!limit || isNaN(Number(limit))) throw new Error("No NativeAI token context limit was set."); return Number(limit); } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${systemPrompt}${this.#appendContext(contextTexts)}`, }; 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 = []) { try { const messages = await this.compressMessages( { systemPrompt: chatPrompt(workspace), userPrompt: prompt, chatHistory, }, rawHistory ); const model = await this.#llamaClient({ temperature: Number(workspace?.openAiTemp ?? 0.7), }); const response = await model.call(messages); return response.content; } catch (error) { throw new Error( `NativeLLM::createChatCompletion failed with: ${error.message}` ); } } async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { const model = await this.#llamaClient({ temperature: Number(workspace?.openAiTemp ?? 0.7), }); const messages = await this.compressMessages( { systemPrompt: chatPrompt(workspace), userPrompt: prompt, chatHistory, }, rawHistory ); const responseStream = await model.stream(messages); return responseStream; } async getChatCompletion(messages = null, { temperature = 0.7 }) { const model = await this.#llamaClient({ temperature }); const response = await model.call(messages); return response.content; } async streamGetChatCompletion(messages = null, { temperature = 0.7 }) { const model = await this.#llamaClient({ temperature }); const responseStream = await model.stream(messages); return responseStream; } // 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); const compressedMessages = await messageArrayCompressor( this, messageArray, rawHistory ); return this.#convertToLangchainPrototypes(compressedMessages); } } module.exports = { NativeLLM, };