Refactor components codes (#538)
* remove deprecated code * refactor code * refactor components codes * fix renderer tests
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an-lee
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@@ -113,45 +113,12 @@ class ConversationsHandler {
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});
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}
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private async ask(
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event: IpcMainEvent,
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id: string,
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params: {
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messageId: string;
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content?: string;
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file?: string;
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blob?: {
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type: string;
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arrayBuffer: ArrayBuffer;
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};
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}
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) {
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const conversation = await Conversation.findOne({
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where: { id },
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});
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if (!conversation) {
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event.sender.send("on-notification", {
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type: "error",
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message: t("models.conversation.notFound"),
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});
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return;
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}
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return conversation.ask(params).catch((err) => {
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event.sender.send("on-notification", {
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type: "error",
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message: err.message,
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});
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});
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}
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register() {
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ipcMain.handle("conversations-find-all", this.findAll);
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ipcMain.handle("conversations-find-one", this.findOne);
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ipcMain.handle("conversations-create", this.create);
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ipcMain.handle("conversations-update", this.update);
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ipcMain.handle("conversations-destroy", this.destroy);
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ipcMain.handle("conversations-ask", this.ask);
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}
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}
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@@ -1,4 +1,3 @@
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import { app } from "electron";
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import {
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AfterCreate,
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AfterDestroy,
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@@ -13,28 +12,8 @@ import {
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AllowNull,
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} from "sequelize-typescript";
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import { Message, Speech } from "@main/db/models";
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import { ChatMessageHistory, BufferMemory } from "langchain/memory";
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import { ConversationChain } from "langchain/chains";
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import { ChatOpenAI } from "@langchain/openai";
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import { ChatOllama } from "@langchain/community/chat_models/ollama";
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import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
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import {
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ChatPromptTemplate,
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MessagesPlaceholder,
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} from "@langchain/core/prompts";
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import { type Generation } from "langchain/dist/schema";
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import settings from "@main/settings";
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import db from "@main/db";
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import mainWindow from "@main/window";
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import { t } from "i18next";
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import log from "@main/logger";
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import fs from "fs-extra";
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import path from "path";
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import Ffmpeg from "@main/ffmpeg";
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import whisper from "@main/whisper";
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import { hashFile } from "@main/utils";
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import { WEB_API_URL } from "@/constants";
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import proxyAgent from "@main/proxy-agent";
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const logger = log.scope("db/models/conversation");
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@Table({
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@@ -68,7 +47,7 @@ export class Conversation extends Model<Conversation> {
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} & { [key: string]: any };
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@Column(DataType.VIRTUAL)
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get type(): 'gpt' | 'tts' {
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get type(): "gpt" | "tts" {
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return this.getDataValue("configuration").type || "gpt";
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}
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@@ -117,263 +96,4 @@ export class Conversation extends Model<Conversation> {
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record: conversation.toJSON(),
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});
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}
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// convert messages to chat history
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async chatHistory() {
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const chatMessageHistory = new ChatMessageHistory();
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let limit = this.configuration.historyBufferSize;
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if (!limit || limit < 0) {
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limit = 0;
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}
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const _messages = await Message.findAll({
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where: { conversationId: this.id },
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order: [["createdAt", "DESC"]],
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limit,
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});
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logger.debug(_messages);
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_messages
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.sort((a, b) => a.createdAt - b.createdAt)
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.forEach((message) => {
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if (message.role === "user") {
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chatMessageHistory.addUserMessage(message.content);
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} else if (message.role === "assistant") {
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chatMessageHistory.addAIChatMessage(message.content);
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}
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});
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return chatMessageHistory;
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}
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// choose llm based on engine
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llm() {
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const { httpAgent, fetch } = proxyAgent();
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if (this.engine === "enjoyai") {
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return new ChatOpenAI(
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{
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openAIApiKey: settings.getSync("user.accessToken") as string,
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modelName: this.model,
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configuration: {
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baseURL: `${process.env.WEB_API_URL || WEB_API_URL}/api/ai`,
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},
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temperature: this.configuration.temperature,
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n: this.configuration.numberOfChoices,
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maxTokens: this.configuration.maxTokens,
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frequencyPenalty: this.configuration.frequencyPenalty,
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presencePenalty: this.configuration.presencePenalty,
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},
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{
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httpAgent,
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// @ts-ignore
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fetch,
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}
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);
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} else if (this.engine === "openai") {
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const key = settings.getSync("openai.key") as string;
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if (!key) {
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throw new Error(t("openaiKeyRequired"));
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}
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return new ChatOpenAI(
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{
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openAIApiKey: key,
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modelName: this.model,
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configuration: {
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baseURL: this.configuration.baseUrl,
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},
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temperature: this.configuration.temperature,
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n: this.configuration.numberOfChoices,
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maxTokens: this.configuration.maxTokens,
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frequencyPenalty: this.configuration.frequencyPenalty,
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presencePenalty: this.configuration.presencePenalty,
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},
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{
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httpAgent,
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// @ts-ignore
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fetch,
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}
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);
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} else if (this.engine === "googleGenerativeAi") {
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const key = settings.getSync("googleGenerativeAi.key") as string;
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if (!key) {
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throw new Error(t("googleGenerativeAiKeyRequired"));
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}
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return new ChatGoogleGenerativeAI({
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apiKey: key,
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modelName: this.model,
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temperature: this.configuration.temperature,
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maxOutputTokens: this.configuration.maxTokens,
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});
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} else if (this.engine == "ollama") {
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return new ChatOllama({
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baseUrl: this.configuration.baseUrl,
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model: this.model,
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temperature: this.configuration.temperature,
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frequencyPenalty: this.configuration.frequencyPenalty,
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presencePenalty: this.configuration.presencePenalty,
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});
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}
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}
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// choose memory based on conversation scenario
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async memory() {
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const chatHistory = await this.chatHistory();
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return new BufferMemory({
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chatHistory,
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memoryKey: "history",
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returnMessages: true,
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});
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}
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chatPrompt() {
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return ChatPromptTemplate.fromMessages([
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["system", this.roleDefinition],
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new MessagesPlaceholder("history"),
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["human", "{input}"],
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]);
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}
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async chain() {
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return new ConversationChain({
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llm: this.llm(),
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memory: await this.memory(),
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prompt: this.chatPrompt(),
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verbose: app.isPackaged ? false : true,
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});
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}
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async ask(params: {
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messageId?: string;
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content?: string;
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file?: string;
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blob?: {
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type: string;
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arrayBuffer: ArrayBuffer;
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};
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}) {
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let { content, file, blob, messageId } = params;
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if (!content && !blob) {
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throw new Error(t("models.conversation.contentRequired"));
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}
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let md5 = "";
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let extname = ".wav";
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if (file) {
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extname = path.extname(file);
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md5 = await hashFile(file, { algo: "md5" });
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fs.copySync(
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file,
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path.join(settings.userDataPath(), "speeches", `${md5}${extname}`)
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);
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} else if (blob) {
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const filename = `${Date.now()}${extname}`;
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const format = blob.type.split("/")[1];
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const tempfile = path.join(
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settings.cachePath(),
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`${Date.now()}.${format}`
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);
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await fs.outputFile(tempfile, Buffer.from(blob.arrayBuffer));
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const wavFile = path.join(settings.userDataPath(), "speeches", filename);
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const ffmpeg = new Ffmpeg();
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await ffmpeg.convertToWav(tempfile, wavFile);
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md5 = await hashFile(wavFile, { algo: "md5" });
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fs.renameSync(
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wavFile,
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path.join(path.dirname(wavFile), `${md5}${extname}`)
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);
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const previousMessage = await Message.findOne({
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where: { conversationId: this.id },
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order: [["createdAt", "DESC"]],
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});
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let prompt = "";
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if (previousMessage?.content) {
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prompt = previousMessage.content.replace(/"/g, '\\"');
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}
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const { transcription } = await whisper.transcribe(wavFile, {
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force: true,
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extra: [`--prompt "${prompt}"`],
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});
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content = transcription
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.map((t: TranscriptionResultSegmentType) => t.text)
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.join(" ")
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.trim();
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logger.debug("transcription", transcription);
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}
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const chain = await this.chain();
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let response: Generation[] = [];
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const result = await chain.call({ input: content }, [
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{
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handleLLMEnd: async (output) => {
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response = output.generations[0];
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},
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},
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]);
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logger.debug("LLM result:", result);
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if (!response) {
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throw new Error(t("models.conversation.failedToGenerateResponse"));
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}
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const transaction = await db.connection.transaction();
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await Message.create(
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{
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id: messageId,
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conversationId: this.id,
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role: "user",
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content,
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},
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{
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include: [Conversation],
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transaction,
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}
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);
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const replies = await Promise.all(
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response.map(async (generation) => {
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if (!generation?.text) {
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throw new Error(t("models.conversation.failedToGenerateResponse"));
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}
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return await Message.create(
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{
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conversationId: this.id,
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role: "assistant",
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content: generation.text,
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},
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{
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include: [Conversation],
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transaction,
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}
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);
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})
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);
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if (md5) {
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await Speech.create(
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{
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sourceId: messageId,
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sourceType: "message",
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text: content,
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md5,
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extname,
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configuration: {
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engine: "Human",
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},
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},
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{
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include: [Message],
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transaction,
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}
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);
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}
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await transaction.commit();
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return replies.map((reply) => reply.toJSON());
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}
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}
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