const { NativeEmbedder } = require("../../EmbeddingEngines/native"); const { v4: uuidv4 } = require("uuid"); const { writeResponseChunk, clientAbortedHandler, } = require("../../helpers/chat/responses"); 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", "openrouter") : path.resolve(__dirname, `../../../storage/models/openrouter`) ); class OpenRouterLLM { constructor(embedder = null, modelPreference = null) { if (!process.env.OPENROUTER_API_KEY) throw new Error("No OpenRouter API key was set."); const { OpenAI: OpenAIApi } = require("openai"); this.basePath = "https://openrouter.ai/api/v1"; this.openai = new OpenAIApi({ baseURL: this.basePath, apiKey: process.env.OPENROUTER_API_KEY ?? null, defaultHeaders: { "HTTP-Referer": "https://useanything.com", "X-Title": "AnythingLLM", }, }); this.model = modelPreference || process.env.OPENROUTER_MODEL_PREF || "openrouter/auto"; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; this.embedder = embedder ?? new NativeEmbedder(); this.defaultTemp = 0.7; if (!fs.existsSync(cacheFolder)) fs.mkdirSync(cacheFolder, { recursive: true }); this.cacheModelPath = path.resolve(cacheFolder, "models.json"); this.cacheAtPath = path.resolve(cacheFolder, ".cached_at"); } log(text, ...args) { console.log(`\x1b[36m[${this.constructor.name}]\x1b[0m ${text}`, ...args); } // This checks if the .cached_at file has a timestamp that is more than 1Week (in millis) // from the current date. If it is, then we will refetch the API so that all the models are up // to date. #cacheIsStale() { const MAX_STALE = 6.048e8; // 1 Week in MS if (!fs.existsSync(this.cacheAtPath)) return true; const now = Number(new Date()); const timestampMs = Number(fs.readFileSync(this.cacheAtPath)); return now - timestampMs > MAX_STALE; } // The OpenRouter model API has a lot of models, so we cache this locally in the directory // as if the cache directory JSON file is stale or does not exist we will fetch from API and store it. // This might slow down the first request, but we need the proper token context window // for each model and this is a constructor property - so we can really only get it if this cache exists. // We used to have this as a chore, but given there is an API to get the info - this makes little sense. async #syncModels() { if (fs.existsSync(this.cacheModelPath) && !this.#cacheIsStale()) return false; this.log( "Model cache is not present or stale. Fetching from OpenRouter API." ); await fetchOpenRouterModels(); return; } #appendContext(contextTexts = []) { if (!contextTexts || !contextTexts.length) return ""; return ( "\nContext:\n" + contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("") ); } models() { if (!fs.existsSync(this.cacheModelPath)) return {}; return safeJsonParse( fs.readFileSync(this.cacheModelPath, { encoding: "utf-8" }), {} ); } streamingEnabled() { return "streamGetChatCompletion" in this; } promptWindowLimit() { const availableModels = this.models(); return availableModels[this.model]?.maxLength || 4096; } async isValidChatCompletionModel(model = "") { await this.#syncModels(); const availableModels = this.models(); return availableModels.hasOwnProperty(model); } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${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 getChatCompletion(messages = null, { temperature = 0.7 }) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `OpenRouter chat: ${this.model} is not valid for chat completion!` ); const result = await this.openai.chat.completions .create({ model: this.model, messages, temperature, }) .catch((e) => { throw new Error(e.response.data.error.message); }); if (!result.hasOwnProperty("choices") || result.choices.length === 0) return null; return result.choices[0].message.content; } async streamGetChatCompletion(messages = null, { temperature = 0.7 }) { if (!(await this.isValidChatCompletionModel(this.model))) throw new Error( `OpenRouter chat: ${this.model} is not valid for chat completion!` ); const streamRequest = await this.openai.chat.completions.create({ model: this.model, stream: true, messages, temperature, }); return streamRequest; } handleStream(response, stream, responseProps) { const timeoutThresholdMs = 500; const { uuid = uuidv4(), sources = [] } = responseProps; return new Promise(async (resolve) => { let fullText = ""; let lastChunkTime = null; // null when first token is still not received. // Establish listener to early-abort a streaming response // in case things go sideways or the user does not like the response. // We preserve the generated text but continue as if chat was completed // to preserve previously generated content. const handleAbort = () => clientAbortedHandler(resolve, fullText); response.on("close", handleAbort); // NOTICE: Not all OpenRouter models will return a stop reason // which keeps the connection open and so the model never finalizes the stream // like the traditional OpenAI response schema does. So in the case the response stream // never reaches a formal close state we maintain an interval timer that if we go >=timeoutThresholdMs with // no new chunks then we kill the stream and assume it to be complete. OpenRouter is quite fast // so this threshold should permit most responses, but we can adjust `timeoutThresholdMs` if // we find it is too aggressive. const timeoutCheck = setInterval(() => { if (lastChunkTime === null) return; const now = Number(new Date()); const diffMs = now - lastChunkTime; if (diffMs >= timeoutThresholdMs) { console.log( `OpenRouter stream did not self-close and has been stale for >${timeoutThresholdMs}ms. Closing response stream.` ); writeResponseChunk(response, { uuid, sources, type: "textResponseChunk", textResponse: "", close: true, error: false, }); clearInterval(timeoutCheck); response.removeListener("close", handleAbort); resolve(fullText); } }, 500); for await (const chunk of stream) { const message = chunk?.choices?.[0]; const token = message?.delta?.content; lastChunkTime = Number(new Date()); if (token) { fullText += token; writeResponseChunk(response, { uuid, sources: [], type: "textResponseChunk", textResponse: token, close: false, error: false, }); } if (message.finish_reason !== null) { writeResponseChunk(response, { uuid, sources, type: "textResponseChunk", textResponse: "", close: true, error: false, }); response.removeListener("close", handleAbort); resolve(fullText); } } }); } // 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); } } async function fetchOpenRouterModels() { return await fetch(`https://openrouter.ai/api/v1/models`, { method: "GET", headers: { "Content-Type": "application/json", }, }) .then((res) => res.json()) .then(({ data = [] }) => { const models = {}; data.forEach((model) => { models[model.id] = { id: model.id, name: model.name, organization: model.id.split("/")[0].charAt(0).toUpperCase() + model.id.split("/")[0].slice(1), maxLength: model.context_length, }; }); // Cache all response information if (!fs.existsSync(cacheFolder)) fs.mkdirSync(cacheFolder, { recursive: true }); fs.writeFileSync( path.resolve(cacheFolder, "models.json"), JSON.stringify(models), { encoding: "utf-8", } ); fs.writeFileSync( path.resolve(cacheFolder, ".cached_at"), String(Number(new Date())), { encoding: "utf-8", } ); return models; }) .catch((e) => { console.error(e); return {}; }); } module.exports = { OpenRouterLLM, fetchOpenRouterModels, };