const { NativeEmbedder } = require("../../EmbeddingEngines/native"); const { OpenAiEmbedder } = require("../../EmbeddingEngines/openAi"); const { handleDefaultStreamResponseV2, } = require("../../helpers/chat/responses"); class HuggingFaceLLM { constructor(embedder = null, _modelPreference = null) { if (!process.env.HUGGING_FACE_LLM_ENDPOINT) throw new Error("No HuggingFace Inference Endpoint was set."); if (!process.env.HUGGING_FACE_LLM_API_KEY) throw new Error("No HuggingFace Access Token was set."); const { OpenAI: OpenAIApi } = require("openai"); this.openai = new OpenAIApi({ baseURL: `${process.env.HUGGING_FACE_LLM_ENDPOINT}/v1`, apiKey: process.env.HUGGING_FACE_LLM_API_KEY, }); // When using HF inference server - the model param is not required so // we can stub it here. HF Endpoints can only run one model at a time. // We set to 'tgi' so that endpoint for HF can accept message format this.model = "tgi"; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; if (!embedder) console.warn( "No embedding provider defined for HuggingFaceLLM - falling back to Native for embedding!" ); this.embedder = !embedder ? new OpenAiEmbedder() : new NativeEmbedder(); this.defaultTemp = 0.2; } #appendContext(contextTexts = []) { if (!contextTexts || !contextTexts.length) return ""; return ( "\nContext:\n" + contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("") ); } streamingEnabled() { return "streamGetChatCompletion" in this; } promptWindowLimit() { const limit = process.env.HUGGING_FACE_LLM_TOKEN_LIMIT || 4096; if (!limit || isNaN(Number(limit))) throw new Error("No HuggingFace token context limit was set."); return Number(limit); } async isValidChatCompletionModel(_ = "") { return true; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { // System prompt it not enabled for HF model chats const prompt = { role: "user", content: `${systemPrompt}${this.#appendContext(contextTexts)}`, }; const assistantResponse = { role: "assistant", content: "Okay, I will follow those instructions", }; return [ prompt, assistantResponse, ...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 }) { const result = await this.openai.createChatCompletion({ model: this.model, messages, temperature, }); if (!result.hasOwnProperty("choices") || result.choices.length === 0) return null; return result.choices[0].message.content; } async streamGetChatCompletion(messages = null, { temperature = 0.7 }) { const streamRequest = await this.openai.chat.completions.create({ model: this.model, stream: true, messages, temperature, }); return streamRequest; } 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 = { HuggingFaceLLM, };