const { chatPrompt } = require("../../chats"); class GeminiLLM { constructor(embedder = null, modelPreference = null) { if (!process.env.GEMINI_API_KEY) throw new Error("No Gemini API key was set."); // Docs: https://ai.google.dev/tutorials/node_quickstart const { GoogleGenerativeAI } = require("@google/generative-ai"); const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY); this.model = modelPreference || process.env.GEMINI_LLM_MODEL_PREF || "gemini-pro"; this.gemini = genAI.getGenerativeModel({ model: this.model }); this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; if (!embedder) throw new Error( "INVALID GEMINI LLM SETUP. No embedding engine has been set. Go to instance settings and set up an embedding interface to use Gemini as your LLM." ); this.embedder = embedder; this.defaultTemp = 0.7; // not used for Gemini } #appendContext(contextTexts = []) { if (!contextTexts || !contextTexts.length) return ""; return ( "\nContext:\n" + contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("") ); } streamingEnabled() { return "streamChat" in this && "streamGetChatCompletion" in this; } promptWindowLimit() { switch (this.model) { case "gemini-pro": return 30_720; default: return 30_720; // assume a gemini-pro model } } isValidChatCompletionModel(modelName = "") { const validModels = ["gemini-pro"]; return validModels.includes(modelName); } // Moderation cannot be done with Gemini. // Not implemented so must be stubbed async isSafe(_input = "") { return { safe: true, reasons: [] }; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${systemPrompt}${this.#appendContext(contextTexts)}`, }; return [ prompt, { role: "assistant", content: "Okay." }, ...chatHistory, { role: "USER_PROMPT", content: userPrompt }, ]; } // This will take an OpenAi format message array and only pluck valid roles from it. formatMessages(messages = []) { // Gemini roles are either user || model. // and all "content" is relabeled to "parts" return messages .map((message) => { if (message.role === "system") return { role: "user", parts: message.content }; if (message.role === "user") return { role: "user", parts: message.content }; if (message.role === "assistant") return { role: "model", parts: message.content }; return null; }) .filter((msg) => !!msg); } async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { if (!this.isValidChatCompletionModel(this.model)) throw new Error( `Gemini chat: ${this.model} is not valid for chat completion!` ); const compressedHistory = await this.compressMessages( { systemPrompt: chatPrompt(workspace), chatHistory, }, rawHistory ); const chatThread = this.gemini.startChat({ history: this.formatMessages(compressedHistory), }); const result = await chatThread.sendMessage(prompt); const response = result.response; const responseText = response.text(); if (!responseText) throw new Error("Gemini: No response could be parsed."); return responseText; } async getChatCompletion(messages = [], _opts = {}) { if (!this.isValidChatCompletionModel(this.model)) throw new Error( `Gemini chat: ${this.model} is not valid for chat completion!` ); const prompt = messages.find( (chat) => chat.role === "USER_PROMPT" )?.content; const chatThread = this.gemini.startChat({ history: this.formatMessages(messages), }); const result = await chatThread.sendMessage(prompt); const response = result.response; const responseText = response.text(); if (!responseText) throw new Error("Gemini: No response could be parsed."); return responseText; } async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { if (!this.isValidChatCompletionModel(this.model)) throw new Error( `Gemini chat: ${this.model} is not valid for chat completion!` ); const compressedHistory = await this.compressMessages( { systemPrompt: chatPrompt(workspace), chatHistory, }, rawHistory ); const chatThread = this.gemini.startChat({ history: this.formatMessages(compressedHistory), }); const responseStream = await chatThread.sendMessageStream(prompt); if (!responseStream.stream) throw new Error("Could not stream response stream from Gemini."); return { type: "geminiStream", ...responseStream }; } async streamGetChatCompletion(messages = [], _opts = {}) { if (!this.isValidChatCompletionModel(this.model)) throw new Error( `Gemini chat: ${this.model} is not valid for chat completion!` ); const prompt = messages.find( (chat) => chat.role === "USER_PROMPT" )?.content; const chatThread = this.gemini.startChat({ history: this.formatMessages(messages), }); const responseStream = await chatThread.sendMessageStream(prompt); if (!responseStream.stream) throw new Error("Could not stream response stream from Gemini."); return { type: "geminiStream", ...responseStream }; } async compressMessages(promptArgs = {}, rawHistory = []) { const { messageArrayCompressor } = require("../../helpers/chat"); const messageArray = this.constructPrompt(promptArgs); return await messageArrayCompressor(this, messageArray, rawHistory); } // 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); } } module.exports = { GeminiLLM, };