const { NativeEmbedder } = require("../../EmbeddingEngines/native"); const { handleDefaultStreamResponseV2, } = require("../../helpers/chat/responses"); const { LLMPerformanceMonitor, } = require("../../helpers/chat/LLMPerformanceMonitor"); const fs = require("fs"); const path = require("path"); const { safeJsonParse } = require("../../http"); const cacheFolder = path.resolve( process.env.STORAGE_DIR ? path.resolve(process.env.STORAGE_DIR, "models", "togetherAi") : path.resolve(__dirname, `../../../storage/models/togetherAi`) ); async function togetherAiModels(apiKey = null) { const cacheModelPath = path.resolve(cacheFolder, "models.json"); const cacheAtPath = path.resolve(cacheFolder, ".cached_at"); // If cache exists and is less than 1 week old, use it if (fs.existsSync(cacheModelPath) && fs.existsSync(cacheAtPath)) { const now = Number(new Date()); const timestampMs = Number(fs.readFileSync(cacheAtPath)); if (now - timestampMs <= 6.048e8) { // 1 Week in MS return safeJsonParse( fs.readFileSync(cacheModelPath, { encoding: "utf-8" }), [] ); } } try { const { OpenAI: OpenAIApi } = require("openai"); const openai = new OpenAIApi({ baseURL: "https://api.together.xyz/v1", apiKey: apiKey || process.env.TOGETHER_AI_API_KEY || null, }); const response = await openai.models.list(); // Filter and transform models into the expected format // Only include chat models const validModels = response.body .filter((model) => ["chat"].includes(model.type)) .map((model) => ({ id: model.id, name: model.display_name || model.id, organization: model.organization || "Unknown", type: model.type, maxLength: model.context_length || 4096, })); // Cache the results if (!fs.existsSync(cacheFolder)) fs.mkdirSync(cacheFolder, { recursive: true }); fs.writeFileSync(cacheModelPath, JSON.stringify(validModels), { encoding: "utf-8", }); fs.writeFileSync(cacheAtPath, String(Number(new Date())), { encoding: "utf-8", }); return validModels; } catch (error) { console.error("Error fetching Together AI models:", error); // If cache exists but is stale, still use it as fallback if (fs.existsSync(cacheModelPath)) { return safeJsonParse( fs.readFileSync(cacheModelPath, { encoding: "utf-8" }), [] ); } return []; } } class TogetherAiLLM { constructor(embedder = null, modelPreference = null) { if (!process.env.TOGETHER_AI_API_KEY) throw new Error("No TogetherAI API key was set."); const { OpenAI: OpenAIApi } = require("openai"); this.openai = new OpenAIApi({ baseURL: "https://api.together.xyz/v1", apiKey: process.env.TOGETHER_AI_API_KEY ?? null, }); this.model = modelPreference || process.env.TOGETHER_AI_MODEL_PREF; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; this.embedder = !embedder ? new NativeEmbedder() : embedder; this.defaultTemp = 0.7; } #appendContext(contextTexts = []) { if (!contextTexts || !contextTexts.length) return ""; return ( "\nContext:\n" + contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("") ); } #generateContent({ userPrompt, attachments = [] }) { if (!attachments.length) { return userPrompt; } const content = [{ type: "text", text: userPrompt }]; for (let attachment of attachments) { content.push({ type: "image_url", image_url: { url: attachment.contentString, }, }); } return content.flat(); } async allModelInformation() { const models = await togetherAiModels(); return models.reduce((acc, model) => { acc[model.id] = model; return acc; }, {}); } streamingEnabled() { return "streamGetChatCompletion" in this; } static async promptWindowLimit(modelName) { const models = await togetherAiModels(); const model = models.find((m) => m.id === modelName); return model?.maxLength || 4096; } async promptWindowLimit() { const models = await togetherAiModels(); const model = models.find((m) => m.id === this.model); return model?.maxLength || 4096; } async isValidChatCompletionModel(model = "") { const models = await togetherAiModels(); const foundModel = models.find((m) => m.id === model); return foundModel && foundModel.type === "chat"; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", attachments = [], }) { const prompt = { role: "system", content: `${systemPrompt}${this.#appendContext(contextTexts)}`, }; return [ prompt, ...chatHistory, { role: "user", content: this.#generateContent({ userPrompt, attachments }), }, ]; } async getChatCompletion(messages = null, { temperature = 0.7 }) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `TogetherAI chat: ${this.model} is not valid for chat completion!` ); const result = await LLMPerformanceMonitor.measureAsyncFunction( this.openai.chat.completions .create({ model: this.model, messages, temperature, }) .catch((e) => { throw new Error(e.message); }) ); if ( !result.output.hasOwnProperty("choices") || result.output.choices.length === 0 ) return null; return { textResponse: result.output.choices[0].message.content, metrics: { prompt_tokens: result.output.usage?.prompt_tokens || 0, completion_tokens: result.output.usage?.completion_tokens || 0, total_tokens: result.output.usage?.total_tokens || 0, outputTps: result.output.usage?.completion_tokens / result.duration, duration: result.duration, }, }; } async streamGetChatCompletion(messages = null, { temperature = 0.7 }) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `TogetherAI chat: ${this.model} is not valid for chat completion!` ); const measuredStreamRequest = await LLMPerformanceMonitor.measureStream( this.openai.chat.completions.create({ model: this.model, stream: true, messages, temperature, }), messages, false ); return measuredStreamRequest; } handleStream(response, stream, responseProps) { return handleDefaultStreamResponseV2(response, stream, responseProps); } // 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 = { TogetherAiLLM, togetherAiModels, };