const { chatPrompt } = require("../../chats"); const { writeResponseChunk, clientAbortedHandler, } = require("../../helpers/chat/responses"); 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 }, { // Gemini-1.5-pro is only available on the v1beta API. apiVersion: this.model === "gemini-1.5-pro-latest" ? "v1beta" : "v1", } ); 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 ${i}]\n\n`; }) .join("") ); } streamingEnabled() { return "streamChat" in this && "streamGetChatCompletion" in this; } promptWindowLimit() { switch (this.model) { case "gemini-pro": return 30_720; case "gemini-1.5-pro-latest": return 1_048_576; default: return 30_720; // assume a gemini-pro model } } isValidChatCompletionModel(modelName = "") { const validModels = ["gemini-pro", "gemini-1.5-pro-latest"]; return validModels.includes(modelName); } // Moderation cannot be done with Gemini. // Not implemented so must be stubbed async isSafe(_input = "") { return { safe: true, reasons: [] }; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${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" const allMessages = messages .map((message) => { if (message.role === "system") return { role: "user", parts: [{ text: message.content }] }; if (message.role === "user") return { role: "user", parts: [{ text: message.content }] }; if (message.role === "assistant") return { role: "model", parts: [{ text: message.content }] }; return null; }) .filter((msg) => !!msg); // Specifically, Google cannot have the last sent message be from a user with no assistant reply // otherwise it will crash. So if the last item is from the user, it was not completed so pop it off // the history. if ( allMessages.length > 0 && allMessages[allMessages.length - 1].role === "user" ) allMessages.pop(); return allMessages; } 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 responseStream.stream; } 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 responseStream.stream; } async compressMessages(promptArgs = {}, rawHistory = []) { const { messageArrayCompressor } = require("../../helpers/chat"); const messageArray = this.constructPrompt(promptArgs); return await messageArrayCompressor(this, messageArray, rawHistory); } handleStream(response, stream, responseProps) { const { uuid = uuidv4(), sources = [] } = responseProps; return new Promise(async (resolve) => { let fullText = ""; // 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); for await (const chunk of stream) { let chunkText; try { // Due to content sensitivity we cannot always get the function .text(); // https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes#gemini-TASK-samples-nodejs // and it is not possible to unblock or disable this safety protocol without being allowlisted by Google. chunkText = chunk.text(); } catch (e) { chunkText = e.message; writeResponseChunk(response, { uuid, sources: [], type: "abort", textResponse: null, close: true, error: e.message, }); resolve(e.message); return; } fullText += chunkText; writeResponseChunk(response, { uuid, sources: [], type: "textResponseChunk", textResponse: chunk.text(), close: false, error: false, }); } 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); } } module.exports = { GeminiLLM, };