Merge pull request #666 from AnotiaWang/admin-password

feat: sync changes from master branch

.gitignore

@@ -4,9 +4,9 @@ npm-debug.log
 yarn-error.log
 
 # Build output
-.next/
+/.next/
-out/
+/out/
-dist/
+/dist/
 
 # IDE/Editor specific
 .vscode/

.prettierrc.js

@@ -6,6 +6,7 @@ const config = {
   endOfLine: 'auto',
   singleQuote: true,
   tabWidth: 2,
+  semi: true,
 };
 
 module.exports = config;

app.dockerfile

@@ -1,20 +1,13 @@
 FROM node:20.18.0-alpine
 
+ARG NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
+ARG NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
+ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
+ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
+
 WORKDIR /home/perplexica
 
-COPY src /home/perplexica/src
+COPY ui /home/perplexica/
-COPY public /home/perplexica/public
-COPY package.json /home/perplexica/package.json
-COPY yarn.lock /home/perplexica/yarn.lock
-COPY tsconfig.json /home/perplexica/tsconfig.json
-COPY next.config.mjs /home/perplexica/next.config.mjs
-COPY next-env.d.ts /home/perplexica/next-env.d.ts
-COPY postcss.config.js /home/perplexica/postcss.config.js
-COPY drizzle.config.ts /home/perplexica/drizzle.config.ts
-COPY tailwind.config.ts /home/perplexica/tailwind.config.ts
-
-RUN mkdir /home/perplexica/data
-RUN mkdir /home/perplexica/uploads
 
 RUN yarn install --frozen-lockfile
 RUN yarn build

backend.dockerfile

@@ -0,0 +1,17 @@
+FROM node:18-slim
+
+WORKDIR /home/perplexica
+
+COPY src /home/perplexica/src
+COPY tsconfig.json /home/perplexica/
+COPY drizzle.config.ts /home/perplexica/
+COPY package.json /home/perplexica/
+COPY yarn.lock /home/perplexica/
+
+RUN mkdir /home/perplexica/data
+RUN mkdir /home/perplexica/uploads
+
+RUN yarn install --frozen-lockfile --network-timeout 600000
+RUN yarn build
+
+CMD ["yarn", "start"]

docker-compose.yaml

@@ -9,20 +9,41 @@ services:
       - perplexica-network
     restart: unless-stopped
 
-  app:
+  perplexica-backend:
     build:
       context: .
-      dockerfile: app.dockerfile
+      dockerfile: backend.dockerfile
+    image: itzcrazykns1337/perplexica-backend:main
     environment:
       - SEARXNG_API_URL=http://searxng:8080
+    depends_on:
+      - searxng
     ports:
-      - 3000:3000
+      - 3001:3001
-    networks:
-      - perplexica-network
     volumes:
       - backend-dbstore:/home/perplexica/data
       - uploads:/home/perplexica/uploads
       - ./config.toml:/home/perplexica/config.toml
+    extra_hosts:
+      - 'host.docker.internal:host-gateway'
+    networks:
+      - perplexica-network
+    restart: unless-stopped
+
+  perplexica-frontend:
+    build:
+      context: .
+      dockerfile: app.dockerfile
+      args:
+        - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
+        - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
+    image: itzcrazykns1337/perplexica-frontend:main
+    depends_on:
+      - perplexica-backend
+    ports:
+      - 3000:3000
+    networks:
+      - perplexica-network
     restart: unless-stopped
 
 networks:

drizzle.config.ts

@@ -2,7 +2,7 @@ import { defineConfig } from 'drizzle-kit';
 
 export default defineConfig({
   dialect: 'sqlite',
-  schema: './src/lib/db/schema.ts',
+  schema: './src/db/schema.ts',
   out: './drizzle',
   dbCredentials: {
     url: './data/db.sqlite',

next-env.d.ts

@@ -1,5 +0,0 @@
-/// <reference types="next" />
-/// <reference types="next/image-types/global" />
-
-// NOTE: This file should not be edited
-// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.

package.json

@@ -1,62 +1,53 @@
 {
-  "name": "perplexica-frontend",
+  "name": "perplexica-backend",
   "version": "1.10.0-rc3",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
-    "dev": "next dev",
+    "start": "npm run db:push && node dist/app.js",
-    "build": "next build",
+    "build": "tsc",
-    "start": "npm run db:push && next start",
+    "dev": "nodemon --ignore uploads/ src/app.ts ",
-    "lint": "next lint",
+    "db:push": "drizzle-kit push sqlite",
-    "format:write": "prettier . --write",
+    "format": "prettier . --check",
-    "db:push": "drizzle-kit push"
+    "format:write": "prettier . --write"
-  },
-  "dependencies": {
-    "@headlessui/react": "^2.2.0",
-    "@iarna/toml": "^2.2.5",
-    "@icons-pack/react-simple-icons": "^12.3.0",
-    "@langchain/community": "^0.3.36",
-    "@langchain/core": "^0.3.42",
-    "@langchain/openai": "^0.0.25",
-    "@langchain/textsplitters": "^0.1.0",
-    "@tailwindcss/typography": "^0.5.12",
-    "axios": "^1.8.3",
-    "better-sqlite3": "^11.9.1",
-    "clsx": "^2.1.0",
-    "compute-cosine-similarity": "^1.1.0",
-    "compute-dot": "^1.1.0",
-    "drizzle-orm": "^0.40.1",
-    "html-to-text": "^9.0.5",
-    "langchain": "^0.1.30",
-    "lucide-react": "^0.363.0",
-    "markdown-to-jsx": "^7.7.2",
-    "next": "^15.2.2",
-    "next-themes": "^0.3.0",
-    "pdf-parse": "^1.1.1",
-    "react": "^18",
-    "react-dom": "^18",
-    "react-text-to-speech": "^0.14.5",
-    "react-textarea-autosize": "^8.5.3",
-    "sonner": "^1.4.41",
-    "tailwind-merge": "^2.2.2",
-    "winston": "^3.17.0",
-    "yet-another-react-lightbox": "^3.17.2",
-    "zod": "^3.22.4"
   },
   "devDependencies": {
-    "@types/better-sqlite3": "^7.6.12",
+    "@types/better-sqlite3": "^7.6.10",
+    "@types/cors": "^2.8.17",
+    "@types/express": "^4.17.21",
     "@types/html-to-text": "^9.0.4",
-    "@types/node": "^20",
+    "@types/multer": "^1.4.12",
     "@types/pdf-parse": "^1.1.4",
-    "@types/react": "^18",
+    "@types/readable-stream": "^4.0.11",
-    "@types/react-dom": "^18",
+    "@types/ws": "^8.5.12",
-    "autoprefixer": "^10.0.1",
+    "drizzle-kit": "^0.22.7",
-    "drizzle-kit": "^0.30.5",
+    "nodemon": "^3.1.0",
-    "eslint": "^8",
-    "eslint-config-next": "14.1.4",
-    "postcss": "^8",
     "prettier": "^3.2.5",
-    "tailwindcss": "^3.3.0",
+    "ts-node": "^10.9.2",
-    "typescript": "^5"
+    "typescript": "^5.4.3"
+  },
+  "dependencies": {
+    "@iarna/toml": "^2.2.5",
+    "@langchain/anthropic": "^0.2.3",
+    "@langchain/community": "^0.2.16",
+    "@langchain/openai": "^0.0.25",
+    "@langchain/google-genai": "^0.0.23",
+    "@xenova/transformers": "^2.17.1",
+    "axios": "^1.6.8",
+    "better-sqlite3": "^11.0.0",
+    "compute-cosine-similarity": "^1.1.0",
+    "compute-dot": "^1.1.0",
+    "cors": "^2.8.5",
+    "dotenv": "^16.4.5",
+    "drizzle-orm": "^0.31.2",
+    "express": "^4.19.2",
+    "html-to-text": "^9.0.5",
+    "langchain": "^0.1.30",
+    "mammoth": "^1.8.0",
+    "multer": "^1.4.5-lts.1",
+    "pdf-parse": "^1.1.1",
+    "winston": "^3.13.0",
+    "ws": "^8.17.1",
+    "zod": "^3.22.4"
   }
 }

sample.config.toml

@@ -1,6 +1,10 @@
 [GENERAL]
 PORT = 3001 # Port to run the server on
 SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
+CONFIG_PASSWORD = "lorem_ipsum" # Password to access config
+DISCOVER_ENABLED = true
+LIBRARY_ENABLED = true
+COPILOT_ENABLED = true
 KEEP_ALIVE = "5m" # How long to keep Ollama models loaded into memory. (Instead of using -1 use "-1m")
 
 [MODELS.OPENAI]
@@ -24,4 +28,4 @@ MODEL_NAME = ""
 API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
 [API_ENDPOINTS]
-SEARXNG = "" # SearxNG API URL - http://localhost:32768
+SEARXNG = "http://localhost:32768" # SearxNG API URL

src/app.ts

@@ -0,0 +1,38 @@
+import { startWebSocketServer } from './websocket';
+import express from 'express';
+import cors from 'cors';
+import http from 'http';
+import routes from './routes';
+import { getPort } from './config';
+import logger from './utils/logger';
+
+const port = getPort();
+
+const app = express();
+const server = http.createServer(app);
+
+const corsOptions = {
+  origin: '*',
+};
+
+app.use(cors(corsOptions));
+app.use(express.json());
+
+app.use('/api', routes);
+app.get('/api', (_, res) => {
+  res.status(200).json({ status: 'ok' });
+});
+
+server.listen(port, () => {
+  logger.info(`Server is running on port ${port}`);
+});
+
+startWebSocketServer(server);
+
+process.on('uncaughtException', (err, origin) => {
+  logger.error(`Uncaught Exception at ${origin}: ${err}`);
+});
+
+process.on('unhandledRejection', (reason, promise) => {
+  logger.error(`Unhandled Rejection at: ${promise}, reason: ${reason}`);
+});

src/app/api/chat/route.ts

@@ -1,360 +0,0 @@
-import prompts from '@/lib/prompts';
-import MetaSearchAgent from '@/lib/search/metaSearchAgent';
-import crypto from 'crypto';
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { EventEmitter } from 'stream';
-import {
-  chatModelProviders,
-  embeddingModelProviders,
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '@/lib/providers';
-import db from '@/lib/db';
-import { chats, messages as messagesSchema } from '@/lib/db/schema';
-import { and, eq, gt } from 'drizzle-orm';
-import { getFileDetails } from '@/lib/utils/files';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { ChatOpenAI } from '@langchain/openai';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '@/lib/config';
-
-export const runtime = 'nodejs';
-export const dynamic = 'force-dynamic';
-
-const searchHandlers: Record<string, MetaSearchAgent> = {
-  webSearch: new MetaSearchAgent({
-    activeEngines: [],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-    summarizer: true,
-  }),
-  academicSearch: new MetaSearchAgent({
-    activeEngines: ['arxiv', 'google scholar', 'pubmed'],
-    queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
-    responsePrompt: prompts.academicSearchResponsePrompt,
-    rerank: true,
-    rerankThreshold: 0,
-    searchWeb: true,
-    summarizer: false,
-  }),
-  writingAssistant: new MetaSearchAgent({
-    activeEngines: [],
-    queryGeneratorPrompt: '',
-    responsePrompt: prompts.writingAssistantPrompt,
-    rerank: true,
-    rerankThreshold: 0,
-    searchWeb: false,
-    summarizer: false,
-  }),
-  wolframAlphaSearch: new MetaSearchAgent({
-    activeEngines: ['wolframalpha'],
-    queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
-    responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
-    rerank: false,
-    rerankThreshold: 0,
-    searchWeb: true,
-    summarizer: false,
-  }),
-  youtubeSearch: new MetaSearchAgent({
-    activeEngines: ['youtube'],
-    queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
-    responsePrompt: prompts.youtubeSearchResponsePrompt,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-    summarizer: false,
-  }),
-  redditSearch: new MetaSearchAgent({
-    activeEngines: ['reddit'],
-    queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
-    responsePrompt: prompts.redditSearchResponsePrompt,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-    summarizer: false,
-  }),
-};
-
-type Message = {
-  messageId: string;
-  chatId: string;
-  content: string;
-};
-
-type ChatModel = {
-  provider: string;
-  name: string;
-};
-
-type EmbeddingModel = {
-  provider: string;
-  name: string;
-};
-
-type Body = {
-  message: Message;
-  optimizationMode: 'speed' | 'balanced' | 'quality';
-  focusMode: string;
-  history: Array<[string, string]>;
-  files: Array<string>;
-  chatModel: ChatModel;
-  embeddingModel: EmbeddingModel;
-};
-
-const handleEmitterEvents = async (
-  stream: EventEmitter,
-  writer: WritableStreamDefaultWriter,
-  encoder: TextEncoder,
-  aiMessageId: string,
-  chatId: string,
-) => {
-  let recievedMessage = '';
-  let sources: any[] = [];
-
-  stream.on('data', (data) => {
-    const parsedData = JSON.parse(data);
-    if (parsedData.type === 'response') {
-      writer.write(
-        encoder.encode(
-          JSON.stringify({
-            type: 'message',
-            data: parsedData.data,
-            messageId: aiMessageId,
-          }) + '\n',
-        ),
-      );
-
-      recievedMessage += parsedData.data;
-    } else if (parsedData.type === 'sources') {
-      writer.write(
-        encoder.encode(
-          JSON.stringify({
-            type: 'sources',
-            data: parsedData.data,
-            messageId: aiMessageId,
-          }) + '\n',
-        ),
-      );
-
-      sources = parsedData.data;
-    }
-  });
-  stream.on('end', () => {
-    writer.write(
-      encoder.encode(
-        JSON.stringify({
-          type: 'messageEnd',
-          messageId: aiMessageId,
-        }) + '\n',
-      ),
-    );
-    writer.close();
-
-    db.insert(messagesSchema)
-      .values({
-        content: recievedMessage,
-        chatId: chatId,
-        messageId: aiMessageId,
-        role: 'assistant',
-        metadata: JSON.stringify({
-          createdAt: new Date(),
-          ...(sources && sources.length > 0 && { sources }),
-        }),
-      })
-      .execute();
-  });
-  stream.on('error', (data) => {
-    const parsedData = JSON.parse(data);
-    writer.write(
-      encoder.encode(
-        JSON.stringify({
-          type: 'error',
-          data: parsedData.data,
-        }),
-      ),
-    );
-    writer.close();
-  });
-};
-
-const handleHistorySave = async (
-  message: Message,
-  humanMessageId: string,
-  focusMode: string,
-  files: string[],
-) => {
-  const chat = await db.query.chats.findFirst({
-    where: eq(chats.id, message.chatId),
-  });
-
-  if (!chat) {
-    await db
-      .insert(chats)
-      .values({
-        id: message.chatId,
-        title: message.content,
-        createdAt: new Date().toString(),
-        focusMode: focusMode,
-        files: files.map(getFileDetails),
-      })
-      .execute();
-  }
-
-  const messageExists = await db.query.messages.findFirst({
-    where: eq(messagesSchema.messageId, humanMessageId),
-  });
-
-  if (!messageExists) {
-    await db
-      .insert(messagesSchema)
-      .values({
-        content: message.content,
-        chatId: message.chatId,
-        messageId: humanMessageId,
-        role: 'user',
-        metadata: JSON.stringify({
-          createdAt: new Date(),
-        }),
-      })
-      .execute();
-  } else {
-    await db
-      .delete(messagesSchema)
-      .where(
-        and(
-          gt(messagesSchema.id, messageExists.id),
-          eq(messagesSchema.chatId, message.chatId),
-        ),
-      )
-      .execute();
-  }
-};
-
-export const POST = async (req: Request) => {
-  try {
-    const body = (await req.json()) as Body;
-    const { message } = body;
-
-    if (message.content === '') {
-      return Response.json(
-        {
-          message: 'Please provide a message to process',
-        },
-        { status: 400 },
-      );
-    }
-
-    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
-      getAvailableChatModelProviders(),
-      getAvailableEmbeddingModelProviders(),
-    ]);
-
-    const chatModelProvider =
-      chatModelProviders[
-        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
-      ];
-    const chatModel =
-      chatModelProvider[
-        body.chatModel?.name || Object.keys(chatModelProvider)[0]
-      ];
-
-    const embeddingProvider =
-      embeddingModelProviders[
-        body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0]
-      ];
-    const embeddingModel =
-      embeddingProvider[
-        body.embeddingModel?.name || Object.keys(embeddingProvider)[0]
-      ];
-
-    let llm: BaseChatModel | undefined;
-    let embedding = embeddingModel.model;
-
-    if (body.chatModel?.provider === 'custom_openai') {
-      llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
-        modelName: getCustomOpenaiModelName(),
-        temperature: 0.7,
-        configuration: {
-          baseURL: getCustomOpenaiApiUrl(),
-        },
-      }) as unknown as BaseChatModel;
-    } else if (chatModelProvider && chatModel) {
-      llm = chatModel.model;
-    }
-
-    if (!llm) {
-      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
-    }
-
-    if (!embedding) {
-      return Response.json(
-        { error: 'Invalid embedding model' },
-        { status: 400 },
-      );
-    }
-
-    const humanMessageId =
-      message.messageId ?? crypto.randomBytes(7).toString('hex');
-    const aiMessageId = crypto.randomBytes(7).toString('hex');
-
-    const history: BaseMessage[] = body.history.map((msg) => {
-      if (msg[0] === 'human') {
-        return new HumanMessage({
-          content: msg[1],
-        });
-      } else {
-        return new AIMessage({
-          content: msg[1],
-        });
-      }
-    });
-
-    const handler = searchHandlers[body.focusMode];
-
-    if (!handler) {
-      return Response.json(
-        {
-          message: 'Invalid focus mode',
-        },
-        { status: 400 },
-      );
-    }
-
-    const stream = await handler.searchAndAnswer(
-      message.content,
-      history,
-      llm,
-      embedding,
-      body.optimizationMode,
-      body.files,
-    );
-
-    const responseStream = new TransformStream();
-    const writer = responseStream.writable.getWriter();
-    const encoder = new TextEncoder();
-
-    handleEmitterEvents(stream, writer, encoder, aiMessageId, message.chatId);
-    handleHistorySave(message, humanMessageId, body.focusMode, body.files);
-
-    return new Response(responseStream.readable, {
-      headers: {
-        'Content-Type': 'text/event-stream',
-        Connection: 'keep-alive',
-        'Cache-Control': 'no-cache, no-transform',
-      },
-    });
-  } catch (err) {
-    console.error('An error ocurred while processing chat request:', err);
-    return Response.json(
-      { message: 'An error ocurred while processing chat request' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/chats/[id]/route.ts

@@ -1,69 +0,0 @@
-import db from '@/lib/db';
-import { chats, messages } from '@/lib/db/schema';
-import { eq } from 'drizzle-orm';
-
-export const GET = async (
-  req: Request,
-  { params }: { params: Promise<{ id: string }> },
-) => {
-  try {
-    const { id } = await params;
-
-    const chatExists = await db.query.chats.findFirst({
-      where: eq(chats.id, id),
-    });
-
-    if (!chatExists) {
-      return Response.json({ message: 'Chat not found' }, { status: 404 });
-    }
-
-    const chatMessages = await db.query.messages.findMany({
-      where: eq(messages.chatId, id),
-    });
-
-    return Response.json(
-      {
-        chat: chatExists,
-        messages: chatMessages,
-      },
-      { status: 200 },
-    );
-  } catch (err) {
-    console.error('Error in getting chat by id: ', err);
-    return Response.json(
-      { message: 'An error has occurred.' },
-      { status: 500 },
-    );
-  }
-};
-
-export const DELETE = async (
-  req: Request,
-  { params }: { params: Promise<{ id: string }> },
-) => {
-  try {
-    const { id } = await params;
-
-    const chatExists = await db.query.chats.findFirst({
-      where: eq(chats.id, id),
-    });
-
-    if (!chatExists) {
-      return Response.json({ message: 'Chat not found' }, { status: 404 });
-    }
-
-    await db.delete(chats).where(eq(chats.id, id)).execute();
-    await db.delete(messages).where(eq(messages.chatId, id)).execute();
-
-    return Response.json(
-      { message: 'Chat deleted successfully' },
-      { status: 200 },
-    );
-  } catch (err) {
-    console.error('Error in deleting chat by id: ', err);
-    return Response.json(
-      { message: 'An error has occurred.' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/chats/route.ts

@@ -1,15 +0,0 @@
-import db from '@/lib/db';
-
-export const GET = async (req: Request) => {
-  try {
-    let chats = await db.query.chats.findMany();
-    chats = chats.reverse();
-    return Response.json({ chats: chats }, { status: 200 });
-  } catch (err) {
-    console.error('Error in getting chats: ', err);
-    return Response.json(
-      { message: 'An error has occurred.' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/config/route.ts

@@ -1,109 +0,0 @@
-import {
-  getAnthropicApiKey,
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-  getGeminiApiKey,
-  getGroqApiKey,
-  getOllamaApiEndpoint,
-  getOpenaiApiKey,
-  updateConfig,
-} from '@/lib/config';
-import {
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '@/lib/providers';
-
-export const GET = async (req: Request) => {
-  try {
-    const config: Record<string, any> = {};
-
-    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
-      getAvailableChatModelProviders(),
-      getAvailableEmbeddingModelProviders(),
-    ]);
-
-    config['chatModelProviders'] = {};
-    config['embeddingModelProviders'] = {};
-
-    for (const provider in chatModelProviders) {
-      config['chatModelProviders'][provider] = Object.keys(
-        chatModelProviders[provider],
-      ).map((model) => {
-        return {
-          name: model,
-          displayName: chatModelProviders[provider][model].displayName,
-        };
-      });
-    }
-
-    for (const provider in embeddingModelProviders) {
-      config['embeddingModelProviders'][provider] = Object.keys(
-        embeddingModelProviders[provider],
-      ).map((model) => {
-        return {
-          name: model,
-          displayName: embeddingModelProviders[provider][model].displayName,
-        };
-      });
-    }
-
-    config['openaiApiKey'] = getOpenaiApiKey();
-    config['ollamaApiUrl'] = getOllamaApiEndpoint();
-    config['anthropicApiKey'] = getAnthropicApiKey();
-    config['groqApiKey'] = getGroqApiKey();
-    config['geminiApiKey'] = getGeminiApiKey();
-    config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
-    config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
-    config['customOpenaiModelName'] = getCustomOpenaiModelName();
-
-    return Response.json({ ...config }, { status: 200 });
-  } catch (err) {
-    console.error('An error ocurred while getting config:', err);
-    return Response.json(
-      { message: 'An error ocurred while getting config' },
-      { status: 500 },
-    );
-  }
-};
-
-export const POST = async (req: Request) => {
-  try {
-    const config = await req.json();
-
-    const updatedConfig = {
-      MODELS: {
-        OPENAI: {
-          API_KEY: config.openaiApiKey,
-        },
-        GROQ: {
-          API_KEY: config.groqApiKey,
-        },
-        ANTHROPIC: {
-          API_KEY: config.anthropicApiKey,
-        },
-        GEMINI: {
-          API_KEY: config.geminiApiKey,
-        },
-        OLLAMA: {
-          API_URL: config.ollamaApiUrl,
-        },
-        CUSTOM_OPENAI: {
-          API_URL: config.customOpenaiApiUrl,
-          API_KEY: config.customOpenaiApiKey,
-          MODEL_NAME: config.customOpenaiModelName,
-        },
-      },
-    };
-
-    updateConfig(updatedConfig);
-
-    return Response.json({ message: 'Config updated' }, { status: 200 });
-  } catch (err) {
-    console.error('An error ocurred while updating config:', err);
-    return Response.json(
-      { message: 'An error ocurred while updating config' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/discover/route.ts

@@ -1,61 +0,0 @@
-import { searchSearxng } from '@/lib/searxng';
-
-const articleWebsites = [
-  'yahoo.com',
-  'www.exchangewire.com',
-  'businessinsider.com',
-  /* 'wired.com',
-  'mashable.com',
-  'theverge.com',
-  'gizmodo.com',
-  'cnet.com',
-  'venturebeat.com', */
-];
-
-const topics = ['AI', 'tech']; /* TODO: Add UI to customize this */
-
-export const GET = async (req: Request) => {
-  try {
-    const data = (
-      await Promise.all([
-        ...new Array(articleWebsites.length * topics.length)
-          .fill(0)
-          .map(async (_, i) => {
-            return (
-              await searchSearxng(
-                `site:${articleWebsites[i % articleWebsites.length]} ${
-                  topics[i % topics.length]
-                }`,
-                {
-                  engines: ['bing news'],
-                  pageno: 1,
-                },
-              )
-            ).results;
-          }),
-      ])
-    )
-      .map((result) => result)
-      .flat()
-      .sort(() => Math.random() - 0.5);
-
-    return Response.json(
-      {
-        blogs: data,
-      },
-      {
-        status: 200,
-      },
-    );
-  } catch (err) {
-    console.error(`An error ocurred in discover route: ${err}`);
-    return Response.json(
-      {
-        message: 'An error has occurred',
-      },
-      {
-        status: 500,
-      },
-    );
-  }
-};

src/app/api/images/route.ts

@@ -1,83 +0,0 @@
-import handleImageSearch from '@/lib/chains/imageSearchAgent';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '@/lib/config';
-import { getAvailableChatModelProviders } from '@/lib/providers';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { ChatOpenAI } from '@langchain/openai';
-
-interface ChatModel {
-  provider: string;
-  model: string;
-}
-
-interface ImageSearchBody {
-  query: string;
-  chatHistory: any[];
-  chatModel?: ChatModel;
-}
-
-export const POST = async (req: Request) => {
-  try {
-    const body: ImageSearchBody = await req.json();
-
-    const chatHistory = body.chatHistory
-      .map((msg: any) => {
-        if (msg.role === 'user') {
-          return new HumanMessage(msg.content);
-        } else if (msg.role === 'assistant') {
-          return new AIMessage(msg.content);
-        }
-      })
-      .filter((msg) => msg !== undefined) as BaseMessage[];
-
-    const chatModelProviders = await getAvailableChatModelProviders();
-
-    const chatModelProvider =
-      chatModelProviders[
-        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
-      ];
-    const chatModel =
-      chatModelProvider[
-        body.chatModel?.model || Object.keys(chatModelProvider)[0]
-      ];
-
-    let llm: BaseChatModel | undefined;
-
-    if (body.chatModel?.provider === 'custom_openai') {
-      llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
-        modelName: getCustomOpenaiModelName(),
-        temperature: 0.7,
-        configuration: {
-          baseURL: getCustomOpenaiApiUrl(),
-        },
-      }) as unknown as BaseChatModel;
-    } else if (chatModelProvider && chatModel) {
-      llm = chatModel.model;
-    }
-
-    if (!llm) {
-      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
-    }
-
-    const images = await handleImageSearch(
-      {
-        chat_history: chatHistory,
-        query: body.query,
-      },
-      llm,
-    );
-
-    return Response.json({ images }, { status: 200 });
-  } catch (err) {
-    console.error(`An error ocurred while searching images: ${err}`);
-    return Response.json(
-      { message: 'An error ocurred while searching images' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/models/route.ts

@@ -1,47 +0,0 @@
-import {
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '@/lib/providers';
-
-export const GET = async (req: Request) => {
-  try {
-    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
-      getAvailableChatModelProviders(),
-      getAvailableEmbeddingModelProviders(),
-    ]);
-
-    Object.keys(chatModelProviders).forEach((provider) => {
-      Object.keys(chatModelProviders[provider]).forEach((model) => {
-        delete (chatModelProviders[provider][model] as { model?: unknown })
-          .model;
-      });
-    });
-
-    Object.keys(embeddingModelProviders).forEach((provider) => {
-      Object.keys(embeddingModelProviders[provider]).forEach((model) => {
-        delete (embeddingModelProviders[provider][model] as { model?: unknown })
-          .model;
-      });
-    });
-
-    return Response.json(
-      {
-        chatModelProviders,
-        embeddingModelProviders,
-      },
-      {
-        status: 200,
-      },
-    );
-  } catch (err) {
-    console.error('An error ocurred while fetching models', err);
-    return Response.json(
-      {
-        message: 'An error has occurred.',
-      },
-      {
-        status: 500,
-      },
-    );
-  }
-};

src/app/api/suggestions/route.ts

@@ -1,81 +0,0 @@
-import generateSuggestions from '@/lib/chains/suggestionGeneratorAgent';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '@/lib/config';
-import { getAvailableChatModelProviders } from '@/lib/providers';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { ChatOpenAI } from '@langchain/openai';
-
-interface ChatModel {
-  provider: string;
-  model: string;
-}
-
-interface SuggestionsGenerationBody {
-  chatHistory: any[];
-  chatModel?: ChatModel;
-}
-
-export const POST = async (req: Request) => {
-  try {
-    const body: SuggestionsGenerationBody = await req.json();
-
-    const chatHistory = body.chatHistory
-      .map((msg: any) => {
-        if (msg.role === 'user') {
-          return new HumanMessage(msg.content);
-        } else if (msg.role === 'assistant') {
-          return new AIMessage(msg.content);
-        }
-      })
-      .filter((msg) => msg !== undefined) as BaseMessage[];
-
-    const chatModelProviders = await getAvailableChatModelProviders();
-
-    const chatModelProvider =
-      chatModelProviders[
-        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
-      ];
-    const chatModel =
-      chatModelProvider[
-        body.chatModel?.model || Object.keys(chatModelProvider)[0]
-      ];
-
-    let llm: BaseChatModel | undefined;
-
-    if (body.chatModel?.provider === 'custom_openai') {
-      llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
-        modelName: getCustomOpenaiModelName(),
-        temperature: 0.7,
-        configuration: {
-          baseURL: getCustomOpenaiApiUrl(),
-        },
-      }) as unknown as BaseChatModel;
-    } else if (chatModelProvider && chatModel) {
-      llm = chatModel.model;
-    }
-
-    if (!llm) {
-      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
-    }
-
-    const suggestions = await generateSuggestions(
-      {
-        chat_history: chatHistory,
-      },
-      llm,
-    );
-
-    return Response.json({ suggestions }, { status: 200 });
-  } catch (err) {
-    console.error(`An error ocurred while generating suggestions: ${err}`);
-    return Response.json(
-      { message: 'An error ocurred while generating suggestions' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/uploads/route.ts

@@ -1,134 +0,0 @@
-import { NextResponse } from 'next/server';
-import fs from 'fs';
-import path from 'path';
-import crypto from 'crypto';
-import { getAvailableEmbeddingModelProviders } from '@/lib/providers';
-import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
-import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
-import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
-import { Document } from 'langchain/document';
-
-interface FileRes {
-  fileName: string;
-  fileExtension: string;
-  fileId: string;
-}
-
-const uploadDir = path.join(process.cwd(), 'uploads');
-
-if (!fs.existsSync(uploadDir)) {
-  fs.mkdirSync(uploadDir, { recursive: true });
-}
-
-const splitter = new RecursiveCharacterTextSplitter({
-  chunkSize: 500,
-  chunkOverlap: 100,
-});
-
-export async function POST(req: Request) {
-  try {
-    const formData = await req.formData();
-
-    const files = formData.getAll('files') as File[];
-    const embedding_model = formData.get('embedding_model');
-    const embedding_model_provider = formData.get('embedding_model_provider');
-
-    if (!embedding_model || !embedding_model_provider) {
-      return NextResponse.json(
-        { message: 'Missing embedding model or provider' },
-        { status: 400 },
-      );
-    }
-
-    const embeddingModels = await getAvailableEmbeddingModelProviders();
-    const provider =
-      embedding_model_provider ?? Object.keys(embeddingModels)[0];
-    const embeddingModel =
-      embedding_model ?? Object.keys(embeddingModels[provider as string])[0];
-
-    let embeddingsModel =
-      embeddingModels[provider as string]?.[embeddingModel as string]?.model;
-    if (!embeddingsModel) {
-      return NextResponse.json(
-        { message: 'Invalid embedding model selected' },
-        { status: 400 },
-      );
-    }
-
-    const processedFiles: FileRes[] = [];
-
-    await Promise.all(
-      files.map(async (file: any) => {
-        const fileExtension = file.name.split('.').pop();
-        if (!['pdf', 'docx', 'txt'].includes(fileExtension!)) {
-          return NextResponse.json(
-            { message: 'File type not supported' },
-            { status: 400 },
-          );
-        }
-
-        const uniqueFileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
-        const filePath = path.join(uploadDir, uniqueFileName);
-
-        const buffer = Buffer.from(await file.arrayBuffer());
-        fs.writeFileSync(filePath, new Uint8Array(buffer));
-
-        let docs: any[] = [];
-        if (fileExtension === 'pdf') {
-          const loader = new PDFLoader(filePath);
-          docs = await loader.load();
-        } else if (fileExtension === 'docx') {
-          const loader = new DocxLoader(filePath);
-          docs = await loader.load();
-        } else if (fileExtension === 'txt') {
-          const text = fs.readFileSync(filePath, 'utf-8');
-          docs = [
-            new Document({ pageContent: text, metadata: { title: file.name } }),
-          ];
-        }
-
-        const splitted = await splitter.splitDocuments(docs);
-
-        const extractedDataPath = filePath.replace(/\.\w+$/, '-extracted.json');
-        fs.writeFileSync(
-          extractedDataPath,
-          JSON.stringify({
-            title: file.name,
-            contents: splitted.map((doc) => doc.pageContent),
-          }),
-        );
-
-        const embeddings = await embeddingsModel.embedDocuments(
-          splitted.map((doc) => doc.pageContent),
-        );
-        const embeddingsDataPath = filePath.replace(
-          /\.\w+$/,
-          '-embeddings.json',
-        );
-        fs.writeFileSync(
-          embeddingsDataPath,
-          JSON.stringify({
-            title: file.name,
-            embeddings,
-          }),
-        );
-
-        processedFiles.push({
-          fileName: file.name,
-          fileExtension: fileExtension,
-          fileId: uniqueFileName.replace(/\.\w+$/, ''),
-        });
-      }),
-    );
-
-    return NextResponse.json({
-      files: processedFiles,
-    });
-  } catch (error) {
-    console.error('Error uploading file:', error);
-    return NextResponse.json(
-      { message: 'An error has occurred.' },
-      { status: 500 },
-    );
-  }
-}

src/app/api/videos/route.ts

@@ -1,83 +0,0 @@
-import handleVideoSearch from '@/lib/chains/videoSearchAgent';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '@/lib/config';
-import { getAvailableChatModelProviders } from '@/lib/providers';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { ChatOpenAI } from '@langchain/openai';
-
-interface ChatModel {
-  provider: string;
-  model: string;
-}
-
-interface VideoSearchBody {
-  query: string;
-  chatHistory: any[];
-  chatModel?: ChatModel;
-}
-
-export const POST = async (req: Request) => {
-  try {
-    const body: VideoSearchBody = await req.json();
-
-    const chatHistory = body.chatHistory
-      .map((msg: any) => {
-        if (msg.role === 'user') {
-          return new HumanMessage(msg.content);
-        } else if (msg.role === 'assistant') {
-          return new AIMessage(msg.content);
-        }
-      })
-      .filter((msg) => msg !== undefined) as BaseMessage[];
-
-    const chatModelProviders = await getAvailableChatModelProviders();
-
-    const chatModelProvider =
-      chatModelProviders[
-        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
-      ];
-    const chatModel =
-      chatModelProvider[
-        body.chatModel?.model || Object.keys(chatModelProvider)[0]
-      ];
-
-    let llm: BaseChatModel | undefined;
-
-    if (body.chatModel?.provider === 'custom_openai') {
-      llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
-        modelName: getCustomOpenaiModelName(),
-        temperature: 0.7,
-        configuration: {
-          baseURL: getCustomOpenaiApiUrl(),
-        },
-      }) as unknown as BaseChatModel;
-    } else if (chatModelProvider && chatModel) {
-      llm = chatModel.model;
-    }
-
-    if (!llm) {
-      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
-    }
-
-    const videos = await handleVideoSearch(
-      {
-        chat_history: chatHistory,
-        query: body.query,
-      },
-      llm,
-    );
-
-    return Response.json({ videos }, { status: 200 });
-  } catch (err) {
-    console.error(`An error ocurred while searching videos: ${err}`);
-    return Response.json(
-      { message: 'An error ocurred while searching videos' },
-      { status: 500 },
-    );
-  }
-};

src/app/c/[chatId]/page.tsx

@@ -1,9 +0,0 @@
-import ChatWindow from '@/components/ChatWindow';
-import React from 'react';
-
-const Page = ({ params }: { params: Promise<{ chatId: string }> }) => {
-  const { chatId } = React.use(params);
-  return <ChatWindow id={chatId} />;
-};
-
-export default Page;

src/app/library/layout.tsx

@@ -1,12 +0,0 @@
-import { Metadata } from 'next';
-import React from 'react';
-
-export const metadata: Metadata = {
-  title: 'Library - Perplexica',
-};
-
-const Layout = ({ children }: { children: React.ReactNode }) => {
-  return <div>{children}</div>;
-};
-
-export default Layout;

src/chains/imageSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const imageSearchChainPrompt = `
@@ -36,12 +36,6 @@ type ImageSearchChainInput = {
   query: string;
 };
 
-interface ImageSearchResult {
-  img_src: string;
-  url: string;
-  title: string;
-}
-
 const strParser = new StringOutputParser();
 
 const createImageSearchChain = (llm: BaseChatModel) => {
@@ -62,7 +56,7 @@ const createImageSearchChain = (llm: BaseChatModel) => {
         engines: ['bing images', 'google images'],
       });
 
-      const images: ImageSearchResult[] = [];
+      const images = [];
 
       res.results.forEach((result) => {
         if (result.img_src && result.url && result.title) {

src/chains/suggestionGeneratorAgent.ts

@@ -1,5 +1,5 @@
 import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
-import ListLineOutputParser from '../outputParsers/listLineOutputParser';
+import ListLineOutputParser from '../lib/outputParsers/listLineOutputParser';
 import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';

src/chains/videoSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const VideoSearchChainPrompt = `
@@ -36,13 +36,6 @@ type VideoSearchChainInput = {
   query: string;
 };
 
-interface VideoSearchResult {
-  img_src: string;
-  url: string;
-  title: string;
-  iframe_src: string;
-}
-
 const strParser = new StringOutputParser();
 
 const createVideoSearchChain = (llm: BaseChatModel) => {
@@ -63,7 +56,7 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
         engines: ['youtube'],
       });
 
-      const videos: VideoSearchResult[] = [];
+      const videos = [];
 
       res.results.forEach((result) => {
         if (

src/components/Sidebar.tsx

@@ -1,101 +0,0 @@
-'use client';
-
-import { cn } from '@/lib/utils';
-import { BookOpenText, Home, Search, SquarePen, Settings } from 'lucide-react';
-import Link from 'next/link';
-import { useSelectedLayoutSegments } from 'next/navigation';
-import React, { useState, type ReactNode } from 'react';
-import Layout from './Layout';
-
-const VerticalIconContainer = ({ children }: { children: ReactNode }) => {
-  return (
-    <div className="flex flex-col items-center gap-y-3 w-full">{children}</div>
-  );
-};
-
-const Sidebar = ({ children }: { children: React.ReactNode }) => {
-  const segments = useSelectedLayoutSegments();
-
-  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
-
-  const navLinks = [
-    {
-      icon: Home,
-      href: '/',
-      active: segments.length === 0 || segments.includes('c'),
-      label: 'Home',
-    },
-    {
-      icon: Search,
-      href: '/discover',
-      active: segments.includes('discover'),
-      label: 'Discover',
-    },
-    {
-      icon: BookOpenText,
-      href: '/library',
-      active: segments.includes('library'),
-      label: 'Library',
-    },
-  ];
-
-  return (
-    <div>
-      <div className="hidden lg:fixed lg:inset-y-0 lg:z-50 lg:flex lg:w-20 lg:flex-col">
-        <div className="flex grow flex-col items-center justify-between gap-y-5 overflow-y-auto bg-light-secondary dark:bg-dark-secondary px-2 py-8">
-          <a href="/">
-            <SquarePen className="cursor-pointer" />
-          </a>
-          <VerticalIconContainer>
-            {navLinks.map((link, i) => (
-              <Link
-                key={i}
-                href={link.href}
-                className={cn(
-                  'relative flex flex-row items-center justify-center cursor-pointer hover:bg-black/10 dark:hover:bg-white/10 duration-150 transition w-full py-2 rounded-lg',
-                  link.active
-                    ? 'text-black dark:text-white'
-                    : 'text-black/70 dark:text-white/70',
-                )}
-              >
-                <link.icon />
-                {link.active && (
-                  <div className="absolute right-0 -mr-2 h-full w-1 rounded-l-lg bg-black dark:bg-white" />
-                )}
-              </Link>
-            ))}
-          </VerticalIconContainer>
-
-          <Link href="/settings">
-            <Settings className="cursor-pointer" />
-          </Link>
-        </div>
-      </div>
-
-      <div className="fixed bottom-0 w-full z-50 flex flex-row items-center gap-x-6 bg-light-primary dark:bg-dark-primary px-4 py-4 shadow-sm lg:hidden">
-        {navLinks.map((link, i) => (
-          <Link
-            href={link.href}
-            key={i}
-            className={cn(
-              'relative flex flex-col items-center space-y-1 text-center w-full',
-              link.active
-                ? 'text-black dark:text-white'
-                : 'text-black dark:text-white/70',
-            )}
-          >
-            {link.active && (
-              <div className="absolute top-0 -mt-4 h-1 w-full rounded-b-lg bg-black dark:bg-white" />
-            )}
-            <link.icon />
-            <p className="text-xs">{link.label}</p>
-          </Link>
-        ))}
-      </div>
-
-      <Layout>{children}</Layout>
-    </div>
-  );
-};
-
-export default Sidebar;

src/config.ts

@@ -8,6 +8,10 @@ interface Config {
   GENERAL: {
     PORT: number;
     SIMILARITY_MEASURE: string;
+    CONFIG_PASSWORD: string;
+    DISCOVER_ENABLED: boolean;
+    LIBRARY_ENABLED: boolean;
+    COPILOT_ENABLED: boolean;
     KEEP_ALIVE: string;
   };
   MODELS: {
@@ -43,7 +47,7 @@ type RecursivePartial<T> = {
 
 const loadConfig = () =>
   toml.parse(
-    fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
+    fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
   ) as any as Config;
 
 export const getPort = () => loadConfig().GENERAL.PORT;
@@ -51,6 +55,14 @@ export const getPort = () => loadConfig().GENERAL.PORT;
 export const getSimilarityMeasure = () =>
   loadConfig().GENERAL.SIMILARITY_MEASURE;
 
+export const getConfigPassword = () => loadConfig().GENERAL.CONFIG_PASSWORD;
+
+export const isDiscoverEnabled = () => loadConfig().GENERAL.DISCOVER_ENABLED;
+
+export const isLibraryEnabled = () => loadConfig().GENERAL.LIBRARY_ENABLED;
+
+export const isCopilotEnabled = () => loadConfig().GENERAL.COPILOT_ENABLED;
+
 export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
 
 export const getOpenaiApiKey = () => loadConfig().MODELS.OPENAI.API_KEY;
@@ -62,7 +74,7 @@ export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
 export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
 
 export const getSearxngApiEndpoint = () =>
-  loadConfig().API_ENDPOINTS.SEARXNG || process.env.SEARXNG_API_URL;
+  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
 
 export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
 
@@ -109,8 +121,9 @@ const mergeConfigs = (current: any, update: any): any => {
 export const updateConfig = (config: RecursivePartial<Config>) => {
   const currentConfig = loadConfig();
   const mergedConfig = mergeConfigs(currentConfig, config);
+
   fs.writeFileSync(
-    path.join(path.join(process.cwd(), `${configFileName}`)),
+    path.join(__dirname, `../${configFileName}`),
     toml.stringify(mergedConfig),
   );
 };

src/db/index.ts

@@ -1,9 +1,8 @@
 import { drizzle } from 'drizzle-orm/better-sqlite3';
 import Database from 'better-sqlite3';
 import * as schema from './schema';
-import path from 'path';
 
-const sqlite = new Database(path.join(process.cwd(), 'data/db.sqlite'));
+const sqlite = new Database('data/db.sqlite');
 const db = drizzle(sqlite, {
   schema: schema,
 });

src/lib/huggingfaceTransformer.ts

@@ -0,0 +1,82 @@
+import { Embeddings, type EmbeddingsParams } from '@langchain/core/embeddings';
+import { chunkArray } from '@langchain/core/utils/chunk_array';
+
+export interface HuggingFaceTransformersEmbeddingsParams
+  extends EmbeddingsParams {
+  modelName: string;
+
+  model: string;
+
+  timeout?: number;
+
+  batchSize?: number;
+
+  stripNewLines?: boolean;
+}
+
+export class HuggingFaceTransformersEmbeddings
+  extends Embeddings
+  implements HuggingFaceTransformersEmbeddingsParams
+{
+  modelName = 'Xenova/all-MiniLM-L6-v2';
+
+  model = 'Xenova/all-MiniLM-L6-v2';
+
+  batchSize = 512;
+
+  stripNewLines = true;
+
+  timeout?: number;
+
+  private pipelinePromise: Promise<any>;
+
+  constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
+    super(fields ?? {});
+
+    this.modelName = fields?.model ?? fields?.modelName ?? this.model;
+    this.model = this.modelName;
+    this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
+    this.timeout = fields?.timeout;
+  }
+
+  async embedDocuments(texts: string[]): Promise<number[][]> {
+    const batches = chunkArray(
+      this.stripNewLines ? texts.map((t) => t.replace(/\n/g, ' ')) : texts,
+      this.batchSize,
+    );
+
+    const batchRequests = batches.map((batch) => this.runEmbedding(batch));
+    const batchResponses = await Promise.all(batchRequests);
+    const embeddings: number[][] = [];
+
+    for (let i = 0; i < batchResponses.length; i += 1) {
+      const batchResponse = batchResponses[i];
+      for (let j = 0; j < batchResponse.length; j += 1) {
+        embeddings.push(batchResponse[j]);
+      }
+    }
+
+    return embeddings;
+  }
+
+  async embedQuery(text: string): Promise<number[]> {
+    const data = await this.runEmbedding([
+      this.stripNewLines ? text.replace(/\n/g, ' ') : text,
+    ]);
+    return data[0];
+  }
+
+  private async runEmbedding(texts: string[]) {
+    const { pipeline } = await import('@xenova/transformers');
+
+    const pipe = await (this.pipelinePromise ??= pipeline(
+      'feature-extraction',
+      this.model,
+    ));
+
+    return this.caller.call(async () => {
+      const output = await pipe(texts, { pooling: 'mean', normalize: true });
+      return output.tolist();
+    });
+  }
+}

src/lib/outputParsers/lineOutputParser.ts

@@ -9,7 +9,7 @@ class LineOutputParser extends BaseOutputParser<string> {
 
   constructor(args?: LineOutputParserArgs) {
     super();
-    this.key = args?.key ?? this.key;
+    this.key = args.key ?? this.key;
   }
 
   static lc_name() {

src/lib/outputParsers/listLineOutputParser.ts

@@ -9,7 +9,7 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
 
   constructor(args?: LineListOutputParserArgs) {
     super();
-    this.key = args?.key ?? this.key;
+    this.key = args.key ?? this.key;
   }
 
   static lc_name() {

src/lib/providers/anthropic.ts

@@ -1,38 +1,6 @@
-import { ChatOpenAI } from '@langchain/openai';
+import { ChatAnthropic } from '@langchain/anthropic';
-import { ChatModel } from '.';
+import { getAnthropicApiKey } from '../../config';
-import { getAnthropicApiKey } from '../config';
+import logger from '../../utils/logger';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-const anthropicChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Claude 3.7 Sonnet',
-    key: 'claude-3-7-sonnet-20250219',
-  },
-  {
-    displayName: 'Claude 3.5 Haiku',
-    key: 'claude-3-5-haiku-20241022',
-  },
-  {
-    displayName: 'Claude 3.5 Sonnet v2',
-    key: 'claude-3-5-sonnet-20241022',
-  },
-  {
-    displayName: 'Claude 3.5 Sonnet',
-    key: 'claude-3-5-sonnet-20240620',
-  },
-  {
-    displayName: 'Claude 3 Opus',
-    key: 'claude-3-opus-20240229',
-  },
-  {
-    displayName: 'Claude 3 Sonnet',
-    key: 'claude-3-sonnet-20240229',
-  },
-  {
-    displayName: 'Claude 3 Haiku',
-    key: 'claude-3-haiku-20240307',
-  },
-];
 
 export const loadAnthropicChatModels = async () => {
   const anthropicApiKey = getAnthropicApiKey();
@@ -40,25 +8,52 @@ export const loadAnthropicChatModels = async () => {
   if (!anthropicApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
+    const chatModels = {
-
+      'claude-3-5-sonnet-20241022': {
-    anthropicChatModels.forEach((model) => {
+        displayName: 'Claude 3.5 Sonnet',
-      chatModels[model.key] = {
+        model: new ChatAnthropic({
-        displayName: model.displayName,
-        model: new ChatOpenAI({
-          openAIApiKey: anthropicApiKey,
-          modelName: model.key,
           temperature: 0.7,
-          configuration: {
+          anthropicApiKey: anthropicApiKey,
-            baseURL: 'https://api.anthropic.com/v1/',
+          model: 'claude-3-5-sonnet-20241022',
-          },
+        }),
-        }) as unknown as BaseChatModel,
+      },
-      };
+      'claude-3-5-haiku-20241022': {
-    });
+        displayName: 'Claude 3.5 Haiku',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-5-haiku-20241022',
+        }),
+      },
+      'claude-3-opus-20240229': {
+        displayName: 'Claude 3 Opus',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-opus-20240229',
+        }),
+      },
+      'claude-3-sonnet-20240229': {
+        displayName: 'Claude 3 Sonnet',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-sonnet-20240229',
+        }),
+      },
+      'claude-3-haiku-20240307': {
+        displayName: 'Claude 3 Haiku',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-haiku-20240307',
+        }),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Anthropic models: ${err}`);
+    logger.error(`Error loading Anthropic models: ${err}`);
     return {};
   }
 };

src/lib/providers/gemini.ts

@@ -1,42 +1,9 @@
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
+import {
-import { getGeminiApiKey } from '../config';
+  ChatGoogleGenerativeAI,
-import { ChatModel, EmbeddingModel } from '.';
+  GoogleGenerativeAIEmbeddings,
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+} from '@langchain/google-genai';
-import { Embeddings } from '@langchain/core/embeddings';
+import { getGeminiApiKey } from '../../config';
-
+import logger from '../../utils/logger';
-const geminiChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemini 2.0 Flash',
-    key: 'gemini-2.0-flash',
-  },
-  {
-    displayName: 'Gemini 2.0 Flash-Lite',
-    key: 'gemini-2.0-flash-lite',
-  },
-  {
-    displayName: 'Gemini 2.0 Pro Experimental',
-    key: 'gemini-2.0-pro-exp-02-05',
-  },
-  {
-    displayName: 'Gemini 1.5 Flash',
-    key: 'gemini-1.5-flash',
-  },
-  {
-    displayName: 'Gemini 1.5 Flash-8B',
-    key: 'gemini-1.5-flash-8b',
-  },
-  {
-    displayName: 'Gemini 1.5 Pro',
-    key: 'gemini-1.5-pro',
-  },
-];
-
-const geminiEmbeddingModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemini Embedding',
-    key: 'gemini-embedding-exp',
-  },
-];
 
 export const loadGeminiChatModels = async () => {
   const geminiApiKey = getGeminiApiKey();
@@ -44,53 +11,75 @@ export const loadGeminiChatModels = async () => {
   if (!geminiApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
+    const chatModels = {
-
+      'gemini-1.5-flash': {
-    geminiChatModels.forEach((model) => {
+        displayName: 'Gemini 1.5 Flash',
-      chatModels[model.key] = {
+        model: new ChatGoogleGenerativeAI({
-        displayName: model.displayName,
+          modelName: 'gemini-1.5-flash',
-        model: new ChatOpenAI({
-          openAIApiKey: geminiApiKey,
-          modelName: model.key,
           temperature: 0.7,
-          configuration: {
+          apiKey: geminiApiKey,
-            baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
+        }),
-          },
+      },
-        }) as unknown as BaseChatModel,
+      'gemini-1.5-flash-8b': {
-      };
+        displayName: 'Gemini 1.5 Flash 8B',
-    });
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-1.5-flash-8b',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-1.5-pro': {
+        displayName: 'Gemini 1.5 Pro',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-1.5-pro',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-2.0-flash-exp': {
+        displayName: 'Gemini 2.0 Flash Exp',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-2.0-flash-exp',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-2.0-flash-thinking-exp-01-21': {
+        displayName: 'Gemini 2.0 Flash Thinking Exp 01-21',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-2.0-flash-thinking-exp-01-21',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Gemini models: ${err}`);
+    logger.error(`Error loading Gemini models: ${err}`);
     return {};
   }
 };
 
-export const loadGeminiEmbeddingModels = async () => {
+export const loadGeminiEmbeddingsModels = async () => {
   const geminiApiKey = getGeminiApiKey();
 
   if (!geminiApiKey) return {};
 
   try {
-    const embeddingModels: Record<string, EmbeddingModel> = {};
+    const embeddingModels = {
-
+      'text-embedding-004': {
-    geminiEmbeddingModels.forEach((model) => {
+        displayName: 'Text Embedding',
-      embeddingModels[model.key] = {
+        model: new GoogleGenerativeAIEmbeddings({
-        displayName: model.displayName,
+          apiKey: geminiApiKey,
-        model: new OpenAIEmbeddings({
+          modelName: 'text-embedding-004',
-          openAIApiKey: geminiApiKey,
+        }),
-          modelName: model.key,
+      },
-          configuration: {
+    };
-            baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
-          },
-        }) as unknown as Embeddings,
-      };
-    });
 
     return embeddingModels;
   } catch (err) {
-    console.error(`Error loading OpenAI embeddings models: ${err}`);
+    logger.error(`Error loading Gemini embeddings model: ${err}`);
     return {};
   }
 };

src/lib/providers/groq.ts

@@ -1,82 +1,6 @@
 import { ChatOpenAI } from '@langchain/openai';
-import { getGroqApiKey } from '../config';
+import { getGroqApiKey } from '../../config';
-import { ChatModel } from '.';
+import logger from '../../utils/logger';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-const groqChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemma2 9B IT',
-    key: 'gemma2-9b-it',
-  },
-  {
-    displayName: 'Llama 3.3 70B Versatile',
-    key: 'llama-3.3-70b-versatile',
-  },
-  {
-    displayName: 'Llama 3.1 8B Instant',
-    key: 'llama-3.1-8b-instant',
-  },
-  {
-    displayName: 'Llama3 70B 8192',
-    key: 'llama3-70b-8192',
-  },
-  {
-    displayName: 'Llama3 8B 8192',
-    key: 'llama3-8b-8192',
-  },
-  {
-    displayName: 'Mixtral 8x7B 32768',
-    key: 'mixtral-8x7b-32768',
-  },
-  {
-    displayName: 'Qwen QWQ 32B (Preview)',
-    key: 'qwen-qwq-32b',
-  },
-  {
-    displayName: 'Mistral Saba 24B (Preview)',
-    key: 'mistral-saba-24b',
-  },
-  {
-    displayName: 'Qwen 2.5 Coder 32B (Preview)',
-    key: 'qwen-2.5-coder-32b',
-  },
-  {
-    displayName: 'Qwen 2.5 32B (Preview)',
-    key: 'qwen-2.5-32b',
-  },
-  {
-    displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
-    key: 'deepseek-r1-distill-qwen-32b',
-  },
-  {
-    displayName: 'DeepSeek R1 Distill Llama 70B SpecDec (Preview)',
-    key: 'deepseek-r1-distill-llama-70b-specdec',
-  },
-  {
-    displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
-    key: 'deepseek-r1-distill-llama-70b',
-  },
-  {
-    displayName: 'Llama 3.3 70B SpecDec (Preview)',
-    key: 'llama-3.3-70b-specdec',
-  },
-  {
-    displayName: 'Llama 3.2 1B Preview (Preview)',
-    key: 'llama-3.2-1b-preview',
-  },
-  {
-    displayName: 'Llama 3.2 3B Preview (Preview)',
-    key: 'llama-3.2-3b-preview',
-  },
-  {
-    displayName: 'Llama 3.2 11B Vision Preview (Preview)',
-    key: 'llama-3.2-11b-vision-preview',
-  },
-  {
-    displayName: 'Llama 3.2 90B Vision Preview (Preview)',
-    key: 'llama-3.2-90b-vision-preview',
-  },
-];
 
 export const loadGroqChatModels = async () => {
   const groqApiKey = getGroqApiKey();
@@ -84,25 +8,129 @@ export const loadGroqChatModels = async () => {
   if (!groqApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
+    const chatModels = {
-
+      'llama-3.3-70b-versatile': {
-    groqChatModels.forEach((model) => {
+        displayName: 'Llama 3.3 70B',
-      chatModels[model.key] = {
+        model: new ChatOpenAI(
-        displayName: model.displayName,
+          {
-        model: new ChatOpenAI({
+            openAIApiKey: groqApiKey,
-          openAIApiKey: groqApiKey,
+            modelName: 'llama-3.3-70b-versatile',
-          modelName: model.key,
+            temperature: 0.7,
-          temperature: 0.7,
+          },
-          configuration: {
+          {
             baseURL: 'https://api.groq.com/openai/v1',
           },
-        }) as unknown as BaseChatModel,
+        ),
-      };
+      },
-    });
+      'llama-3.2-3b-preview': {
+        displayName: 'Llama 3.2 3B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.2-3b-preview',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama-3.2-11b-vision-preview': {
+        displayName: 'Llama 3.2 11B Vision',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.2-11b-vision-preview',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama-3.2-90b-vision-preview': {
+        displayName: 'Llama 3.2 90B Vision',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.2-90b-vision-preview',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama-3.1-8b-instant': {
+        displayName: 'Llama 3.1 8B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.1-8b-instant',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama3-8b-8192': {
+        displayName: 'LLaMA3 8B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama3-8b-8192',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama3-70b-8192': {
+        displayName: 'LLaMA3 70B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama3-70b-8192',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'mixtral-8x7b-32768': {
+        displayName: 'Mixtral 8x7B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'mixtral-8x7b-32768',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'gemma2-9b-it': {
+        displayName: 'Gemma2 9B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'gemma2-9b-it',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Groq models: ${err}`);
+    logger.error(`Error loading Groq models: ${err}`);
     return {};
   }
 };

src/lib/providers/index.ts

@@ -1,49 +1,33 @@
-import { Embeddings } from '@langchain/core/embeddings';
+import { loadGroqChatModels } from './groq';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
-import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai';
+import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
+import { loadAnthropicChatModels } from './anthropic';
+import { loadTransformersEmbeddingsModels } from './transformers';
+import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
 import {
   getCustomOpenaiApiKey,
   getCustomOpenaiApiUrl,
   getCustomOpenaiModelName,
-} from '../config';
+} from '../../config';
 import { ChatOpenAI } from '@langchain/openai';
-import { loadOllamaChatModels, loadOllamaEmbeddingModels } from './ollama';
-import { loadGroqChatModels } from './groq';
-import { loadAnthropicChatModels } from './anthropic';
-import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini';
 
-export interface ChatModel {
+const chatModelProviders = {
-  displayName: string;
-  model: BaseChatModel;
-}
-
-export interface EmbeddingModel {
-  displayName: string;
-  model: Embeddings;
-}
-
-export const chatModelProviders: Record<
-  string,
-  () => Promise<Record<string, ChatModel>>
-> = {
   openai: loadOpenAIChatModels,
-  ollama: loadOllamaChatModels,
   groq: loadGroqChatModels,
+  ollama: loadOllamaChatModels,
   anthropic: loadAnthropicChatModels,
   gemini: loadGeminiChatModels,
 };
 
-export const embeddingModelProviders: Record<
+const embeddingModelProviders = {
-  string,
+  openai: loadOpenAIEmbeddingsModels,
-  () => Promise<Record<string, EmbeddingModel>>
+  local: loadTransformersEmbeddingsModels,
-> = {
+  ollama: loadOllamaEmbeddingsModels,
-  openai: loadOpenAIEmbeddingModels,
+  gemini: loadGeminiEmbeddingsModels,
-  ollama: loadOllamaEmbeddingModels,
-  gemini: loadGeminiEmbeddingModels,
 };
 
 export const getAvailableChatModelProviders = async () => {
-  const models: Record<string, Record<string, ChatModel>> = {};
+  const models = {};
 
   for (const provider in chatModelProviders) {
     const providerModels = await chatModelProviders[provider]();
@@ -68,7 +52,7 @@ export const getAvailableChatModelProviders = async () => {
               configuration: {
                 baseURL: customOpenAiApiUrl,
               },
-            }) as unknown as BaseChatModel,
+            }),
           },
         }
       : {}),
@@ -78,7 +62,7 @@ export const getAvailableChatModelProviders = async () => {
 };
 
 export const getAvailableEmbeddingModelProviders = async () => {
-  const models: Record<string, Record<string, EmbeddingModel>> = {};
+  const models = {};
 
   for (const provider in embeddingModelProviders) {
     const providerModels = await embeddingModelProviders[provider]();

src/lib/providers/ollama.ts

@@ -1,73 +1,74 @@
-import axios from 'axios';
-import { getKeepAlive, getOllamaApiEndpoint } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
 import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
+import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
+import logger from '../../utils/logger';
+import { ChatOllama } from '@langchain/community/chat_models/ollama';
+import axios from 'axios';
 
 export const loadOllamaChatModels = async () => {
-  const ollamaApiEndpoint = getOllamaApiEndpoint();
+  const ollamaEndpoint = getOllamaApiEndpoint();
+  const keepAlive = getKeepAlive();
 
-  if (!ollamaApiEndpoint) return {};
+  if (!ollamaEndpoint) return {};
 
   try {
-    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
+    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models } = res.data;
+    const { models: ollamaModels } = response.data;
 
-    const chatModels: Record<string, ChatModel> = {};
+    const chatModels = ollamaModels.reduce((acc, model) => {
-
+      acc[model.model] = {
-    models.forEach((model: any) => {
-      chatModels[model.model] = {
         displayName: model.name,
         model: new ChatOllama({
-          baseUrl: ollamaApiEndpoint,
+          baseUrl: ollamaEndpoint,
           model: model.model,
           temperature: 0.7,
-          keepAlive: getKeepAlive(),
+          keepAlive: keepAlive,
         }),
       };
-    });
+
+      return acc;
+    }, {});
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Ollama models: ${err}`);
+    logger.error(`Error loading Ollama models: ${err}`);
     return {};
   }
 };
 
-export const loadOllamaEmbeddingModels = async () => {
+export const loadOllamaEmbeddingsModels = async () => {
-  const ollamaApiEndpoint = getOllamaApiEndpoint();
+  const ollamaEndpoint = getOllamaApiEndpoint();
 
-  if (!ollamaApiEndpoint) return {};
+  if (!ollamaEndpoint) return {};
 
   try {
-    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
+    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models } = res.data;
+    const { models: ollamaModels } = response.data;
 
-    const embeddingModels: Record<string, EmbeddingModel> = {};
+    const embeddingsModels = ollamaModels.reduce((acc, model) => {
-
+      acc[model.model] = {
-    models.forEach((model: any) => {
-      embeddingModels[model.model] = {
         displayName: model.name,
         model: new OllamaEmbeddings({
-          baseUrl: ollamaApiEndpoint,
+          baseUrl: ollamaEndpoint,
           model: model.model,
         }),
       };
-    });
 
-    return embeddingModels;
+      return acc;
+    }, {});
+
+    return embeddingsModels;
   } catch (err) {
-    console.error(`Error loading Ollama embeddings models: ${err}`);
+    logger.error(`Error loading Ollama embeddings model: ${err}`);
     return {};
   }
 };

src/lib/providers/openai.ts

@@ -1,90 +1,89 @@
 import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { getOpenaiApiKey } from '../config';
+import { getOpenaiApiKey } from '../../config';
-import { ChatModel, EmbeddingModel } from '.';
+import logger from '../../utils/logger';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Embeddings } from '@langchain/core/embeddings';
-
-const openaiChatModels: Record<string, string>[] = [
-  {
-    displayName: 'GPT-3.5 Turbo',
-    key: 'gpt-3.5-turbo',
-  },
-  {
-    displayName: 'GPT-4',
-    key: 'gpt-4',
-  },
-  {
-    displayName: 'GPT-4 turbo',
-    key: 'gpt-4-turbo',
-  },
-  {
-    displayName: 'GPT-4 omni',
-    key: 'gpt-4o',
-  },
-  {
-    displayName: 'GPT-4 omni mini',
-    key: 'gpt-4o-mini',
-  },
-];
-
-const openaiEmbeddingModels: Record<string, string>[] = [
-  {
-    displayName: 'Text Embedding 3 Small',
-    key: 'text-embedding-3-small',
-  },
-  {
-    displayName: 'Text Embedding 3 Large',
-    key: 'text-embedding-3-large',
-  },
-];
 
 export const loadOpenAIChatModels = async () => {
-  const openaiApiKey = getOpenaiApiKey();
+  const openAIApiKey = getOpenaiApiKey();
 
-  if (!openaiApiKey) return {};
+  if (!openAIApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
+    const chatModels = {
-
+      'gpt-3.5-turbo': {
-    openaiChatModels.forEach((model) => {
+        displayName: 'GPT-3.5 Turbo',
-      chatModels[model.key] = {
-        displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey: openaiApiKey,
+          openAIApiKey,
-          modelName: model.key,
+          modelName: 'gpt-3.5-turbo',
           temperature: 0.7,
-        }) as unknown as BaseChatModel,
+        }),
-      };
+      },
-    });
+      'gpt-4': {
+        displayName: 'GPT-4',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4',
+          temperature: 0.7,
+        }),
+      },
+      'gpt-4-turbo': {
+        displayName: 'GPT-4 turbo',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4-turbo',
+          temperature: 0.7,
+        }),
+      },
+      'gpt-4o': {
+        displayName: 'GPT-4 omni',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4o',
+          temperature: 0.7,
+        }),
+      },
+      'gpt-4o-mini': {
+        displayName: 'GPT-4 omni mini',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4o-mini',
+          temperature: 0.7,
+        }),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading OpenAI models: ${err}`);
+    logger.error(`Error loading OpenAI models: ${err}`);
     return {};
   }
 };
 
-export const loadOpenAIEmbeddingModels = async () => {
+export const loadOpenAIEmbeddingsModels = async () => {
-  const openaiApiKey = getOpenaiApiKey();
+  const openAIApiKey = getOpenaiApiKey();
 
-  if (!openaiApiKey) return {};
+  if (!openAIApiKey) return {};
 
   try {
-    const embeddingModels: Record<string, EmbeddingModel> = {};
+    const embeddingModels = {
-
+      'text-embedding-3-small': {
-    openaiEmbeddingModels.forEach((model) => {
+        displayName: 'Text Embedding 3 Small',
-      embeddingModels[model.key] = {
-        displayName: model.displayName,
         model: new OpenAIEmbeddings({
-          openAIApiKey: openaiApiKey,
+          openAIApiKey,
-          modelName: model.key,
+          modelName: 'text-embedding-3-small',
-        }) as unknown as Embeddings,
+        }),
-      };
+      },
-    });
+      'text-embedding-3-large': {
+        displayName: 'Text Embedding 3 Large',
+        model: new OpenAIEmbeddings({
+          openAIApiKey,
+          modelName: 'text-embedding-3-large',
+        }),
+      },
+    };
 
     return embeddingModels;
   } catch (err) {
-    console.error(`Error loading OpenAI embeddings models: ${err}`);
+    logger.error(`Error loading OpenAI embeddings model: ${err}`);
     return {};
   }
 };

src/lib/providers/transformers.ts

@@ -0,0 +1,32 @@
+import logger from '../../utils/logger';
+import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
+
+export const loadTransformersEmbeddingsModels = async () => {
+  try {
+    const embeddingModels = {
+      'xenova-bge-small-en-v1.5': {
+        displayName: 'BGE Small',
+        model: new HuggingFaceTransformersEmbeddings({
+          modelName: 'Xenova/bge-small-en-v1.5',
+        }),
+      },
+      'xenova-gte-small': {
+        displayName: 'GTE Small',
+        model: new HuggingFaceTransformersEmbeddings({
+          modelName: 'Xenova/gte-small',
+        }),
+      },
+      'xenova-bert-base-multilingual-uncased': {
+        displayName: 'Bert Multilingual',
+        model: new HuggingFaceTransformersEmbeddings({
+          modelName: 'Xenova/bert-base-multilingual-uncased',
+        }),
+      },
+    };
+
+    return embeddingModels;
+  } catch (err) {
+    logger.error(`Error loading Transformers embeddings model: ${err}`);
+    return {};
+  }
+};

src/lib/searxng.ts

@@ -1,5 +1,5 @@
 import axios from 'axios';
-import { getSearxngApiEndpoint } from './config';
+import { getSearxngApiEndpoint } from '../config';
 
 interface SearxngSearchOptions {
   categories?: string[];
@@ -30,12 +30,11 @@ export const searchSearxng = async (
 
   if (opts) {
     Object.keys(opts).forEach((key) => {
-      const value = opts[key as keyof SearxngSearchOptions];
+      if (Array.isArray(opts[key])) {
-      if (Array.isArray(value)) {
+        url.searchParams.append(key, opts[key].join(','));
-        url.searchParams.append(key, value.join(','));
         return;
       }
-      url.searchParams.append(key, value as string);
+      url.searchParams.append(key, opts[key]);
     });
   }
 

src/lib/types/compute-dot.d.ts

@@ -1,5 +0,0 @@
-declare function computeDot(vectorA: number[], vectorB: number[]): number;
-
-declare module 'compute-dot' {
-  export default computeDot;
-}

src/routes/chats.ts

@@ -0,0 +1,66 @@
+import express from 'express';
+import logger from '../utils/logger';
+import db from '../db/index';
+import { eq } from 'drizzle-orm';
+import { chats, messages } from '../db/schema';
+
+const router = express.Router();
+
+router.get('/', async (_, res) => {
+  try {
+    let chats = await db.query.chats.findMany();
+
+    chats = chats.reverse();
+
+    return res.status(200).json({ chats: chats });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error in getting chats: ${err.message}`);
+  }
+});
+
+router.get('/:id', async (req, res) => {
+  try {
+    const chatExists = await db.query.chats.findFirst({
+      where: eq(chats.id, req.params.id),
+    });
+
+    if (!chatExists) {
+      return res.status(404).json({ message: 'Chat not found' });
+    }
+
+    const chatMessages = await db.query.messages.findMany({
+      where: eq(messages.chatId, req.params.id),
+    });
+
+    return res.status(200).json({ chat: chatExists, messages: chatMessages });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error in getting chat: ${err.message}`);
+  }
+});
+
+router.delete(`/:id`, async (req, res) => {
+  try {
+    const chatExists = await db.query.chats.findFirst({
+      where: eq(chats.id, req.params.id),
+    });
+
+    if (!chatExists) {
+      return res.status(404).json({ message: 'Chat not found' });
+    }
+
+    await db.delete(chats).where(eq(chats.id, req.params.id)).execute();
+    await db
+      .delete(messages)
+      .where(eq(messages.chatId, req.params.id))
+      .execute();
+
+    return res.status(200).json({ message: 'Chat deleted successfully' });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error in deleting chat: ${err.message}`);
+  }
+});
+
+export default router;

src/routes/config.ts

@@ -0,0 +1,139 @@
+import express from 'express';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+import {
+  getGroqApiKey,
+  getOllamaApiEndpoint,
+  getAnthropicApiKey,
+  getGeminiApiKey,
+  getOpenaiApiKey,
+  updateConfig,
+  getConfigPassword,
+  isLibraryEnabled,
+  isCopilotEnabled,
+  isDiscoverEnabled,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiApiKey,
+  getCustomOpenaiModelName,
+} from '../config';
+import logger from '../utils/logger';
+
+const router = express.Router();
+
+router.get('/', async (req, res) => {
+  try {
+    const authHeader = req.headers['authorization']?.split(' ')[1];
+    const password = getConfigPassword();
+
+    if (authHeader !== password) {
+      res.status(401).json({ message: 'Unauthorized' });
+      return;
+    }
+
+    const config = {};
+
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    config['chatModelProviders'] = {};
+    config['embeddingModelProviders'] = {};
+
+    for (const provider in chatModelProviders) {
+      config['chatModelProviders'][provider] = Object.keys(
+        chatModelProviders[provider],
+      ).map((model) => {
+        return {
+          name: model,
+          displayName: chatModelProviders[provider][model].displayName,
+        };
+      });
+    }
+
+    for (const provider in embeddingModelProviders) {
+      config['embeddingModelProviders'][provider] = Object.keys(
+        embeddingModelProviders[provider],
+      ).map((model) => {
+        return {
+          name: model,
+          displayName: embeddingModelProviders[provider][model].displayName,
+        };
+      });
+    }
+
+    config['openaiApiKey'] = getOpenaiApiKey();
+    config['ollamaApiUrl'] = getOllamaApiEndpoint();
+    config['anthropicApiKey'] = getAnthropicApiKey();
+    config['groqApiKey'] = getGroqApiKey();
+    config['geminiApiKey'] = getGeminiApiKey();
+    config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
+    config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
+    config['customOpenaiModelName'] = getCustomOpenaiModelName();
+
+    res.status(200).json(config);
+  } catch (err: any) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error getting config: ${err.message}`);
+  }
+});
+
+router.post('/', async (req, res) => {
+  const authHeader = req.headers['authorization']?.split(' ')[1];
+  const password = getConfigPassword();
+
+  if (authHeader !== password) {
+    res.status(401).json({ message: 'Unauthorized' });
+    return;
+  }
+
+  const config = req.body;
+
+  const updatedConfig = {
+    GENERAL: {
+      DISCOVER_ENABLED: config.isDiscoverEnabled,
+      LIBRARY_ENABLED: config.isLibraryEnabled,
+      COPILOT_ENABLED: config.isCopilotEnabled,
+    },
+    MODELS: {
+      OPENAI: {
+        API_KEY: config.openaiApiKey,
+      },
+      GROQ: {
+        API_KEY: config.groqApiKey,
+      },
+      ANTHROPIC: {
+        API_KEY: config.anthropicApiKey,
+      },
+      GEMINI: {
+        API_KEY: config.geminiApiKey,
+      },
+      OLLAMA: {
+        API_URL: config.ollamaApiUrl,
+      },
+      CUSTOM_OPENAI: {
+        API_URL: config.customOpenaiApiUrl,
+        API_KEY: config.customOpenaiApiKey,
+        MODEL_NAME: config.customOpenaiModelName,
+      },
+    },
+  };
+
+  updateConfig(updatedConfig);
+
+  res.status(200).json({ message: 'Config updated' });
+});
+
+router.get('/preferences', (_, res) => {
+  const preferences = {
+    isLibraryEnabled: isLibraryEnabled(),
+    isCopilotEnabled: isCopilotEnabled(),
+    isDiscoverEnabled: isDiscoverEnabled(),
+  };
+
+  res.status(200).json(preferences);
+});
+
+export default router;

src/routes/discover.ts

@@ -0,0 +1,48 @@
+import express from 'express';
+import { searchSearxng } from '../lib/searxng';
+import logger from '../utils/logger';
+
+const router = express.Router();
+
+router.get('/', async (req, res) => {
+  try {
+    const data = (
+      await Promise.all([
+        searchSearxng('site:businessinsider.com AI', {
+          engines: ['bing news'],
+          pageno: 1,
+        }),
+        searchSearxng('site:www.exchangewire.com AI', {
+          engines: ['bing news'],
+          pageno: 1,
+        }),
+        searchSearxng('site:yahoo.com AI', {
+          engines: ['bing news'],
+          pageno: 1,
+        }),
+        searchSearxng('site:businessinsider.com tech', {
+          engines: ['bing news'],
+          pageno: 1,
+        }),
+        searchSearxng('site:www.exchangewire.com tech', {
+          engines: ['bing news'],
+          pageno: 1,
+        }),
+        searchSearxng('site:yahoo.com tech', {
+          engines: ['bing news'],
+          pageno: 1,
+        }),
+      ])
+    )
+      .map((result) => result.results)
+      .flat()
+      .sort(() => Math.random() - 0.5);
+
+    return res.json({ blogs: data });
+  } catch (err: any) {
+    logger.error(`Error in discover route: ${err.message}`);
+    return res.status(500).json({ message: 'An error has occurred' });
+  }
+});
+
+export default router;

src/routes/images.ts

@@ -0,0 +1,82 @@
+import express from 'express';
+import handleImageSearch from '../chains/imageSearchAgent';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { getAvailableChatModelProviders } from '../lib/providers';
+import { HumanMessage, AIMessage } from '@langchain/core/messages';
+import logger from '../utils/logger';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '../config';
+
+const router = express.Router();
+
+interface ChatModel {
+  provider: string;
+  model: string;
+}
+
+interface ImageSearchBody {
+  query: string;
+  chatHistory: any[];
+  chatModel?: ChatModel;
+}
+
+router.post('/', async (req, res) => {
+  try {
+    let body: ImageSearchBody = req.body;
+
+    const chatHistory = body.chatHistory.map((msg: any) => {
+      if (msg.role === 'user') {
+        return new HumanMessage(msg.content);
+      } else if (msg.role === 'assistant') {
+        return new AIMessage(msg.content);
+      }
+    });
+
+    const chatModelProviders = await getAvailableChatModelProviders();
+
+    const chatModelProvider =
+      body.chatModel?.provider || Object.keys(chatModelProviders)[0];
+    const chatModel =
+      body.chatModel?.model ||
+      Object.keys(chatModelProviders[chatModelProvider])[0];
+
+    let llm: BaseChatModel | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        modelName: getCustomOpenaiModelName(),
+        openAIApiKey: getCustomOpenaiApiKey(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (
+      chatModelProviders[chatModelProvider] &&
+      chatModelProviders[chatModelProvider][chatModel]
+    ) {
+      llm = chatModelProviders[chatModelProvider][chatModel]
+        .model as unknown as BaseChatModel | undefined;
+    }
+
+    if (!llm) {
+      return res.status(400).json({ message: 'Invalid model selected' });
+    }
+
+    const images = await handleImageSearch(
+      { query: body.query, chat_history: chatHistory },
+      llm,
+    );
+
+    res.status(200).json({ images });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error in image search: ${err.message}`);
+  }
+});
+
+export default router;

src/routes/index.ts

@@ -0,0 +1,24 @@
+import express from 'express';
+import imagesRouter from './images';
+import videosRouter from './videos';
+import configRouter from './config';
+import modelsRouter from './models';
+import suggestionsRouter from './suggestions';
+import chatsRouter from './chats';
+import searchRouter from './search';
+import discoverRouter from './discover';
+import uploadsRouter from './uploads';
+
+const router = express.Router();
+
+router.use('/images', imagesRouter);
+router.use('/videos', videosRouter);
+router.use('/config', configRouter);
+router.use('/models', modelsRouter);
+router.use('/suggestions', suggestionsRouter);
+router.use('/chats', chatsRouter);
+router.use('/search', searchRouter);
+router.use('/discover', discoverRouter);
+router.use('/uploads', uploadsRouter);
+
+export default router;

src/routes/models.ts

@@ -0,0 +1,59 @@
+import express from 'express';
+import logger from '../utils/logger';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+
+const router = express.Router();
+
+router.get('/', async (req, res) => {
+  try {
+    const [chatModelProvidersRaw, embeddingModelProvidersRaw] =
+      await Promise.all([
+        getAvailableChatModelProviders(),
+        getAvailableEmbeddingModelProviders(),
+      ]);
+
+    const chatModelProviders = {};
+
+    const chatModelProvidersKeys = Object.keys(chatModelProvidersRaw);
+    chatModelProvidersKeys.forEach((provider) => {
+      chatModelProviders[provider] = {};
+      const models = Object.keys(chatModelProvidersRaw[provider]);
+      models.forEach((model) => {
+        chatModelProviders[provider][model] = {};
+      });
+    });
+
+    const embeddingModelProviders = {};
+
+    const embeddingModelProvidersKeys = Object.keys(embeddingModelProvidersRaw);
+    embeddingModelProvidersKeys.forEach((provider) => {
+      embeddingModelProviders[provider] = {};
+      const models = Object.keys(embeddingModelProvidersRaw[provider]);
+      models.forEach((model) => {
+        embeddingModelProviders[provider][model] = {};
+      });
+    });
+
+    Object.keys(chatModelProviders).forEach((provider) => {
+      Object.keys(chatModelProviders[provider]).forEach((model) => {
+        delete chatModelProviders[provider][model].model;
+      });
+    });
+
+    Object.keys(embeddingModelProviders).forEach((provider) => {
+      Object.keys(embeddingModelProviders[provider]).forEach((model) => {
+        delete embeddingModelProviders[provider][model].model;
+      });
+    });
+
+    res.status(200).json({ chatModelProviders, embeddingModelProviders });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(err.message);
+  }
+});
+
+export default router;

src/routes/search.ts

@@ -0,0 +1,158 @@
+import express from 'express';
+import logger from '../utils/logger';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+import { searchHandlers } from '../websocket/messageHandler';
+import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
+import { MetaSearchAgentType } from '../search/metaSearchAgent';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '../config';
+
+const router = express.Router();
+
+interface chatModel {
+  provider: string;
+  model: string;
+  customOpenAIKey?: string;
+  customOpenAIBaseURL?: string;
+}
+
+interface embeddingModel {
+  provider: string;
+  model: string;
+}
+
+interface ChatRequestBody {
+  optimizationMode: 'speed' | 'balanced';
+  focusMode: string;
+  chatModel?: chatModel;
+  embeddingModel?: embeddingModel;
+  query: string;
+  history: Array<[string, string]>;
+}
+
+router.post('/', async (req, res) => {
+  try {
+    const body: ChatRequestBody = req.body;
+
+    if (!body.focusMode || !body.query) {
+      return res.status(400).json({ message: 'Missing focus mode or query' });
+    }
+
+    body.history = body.history || [];
+    body.optimizationMode = body.optimizationMode || 'balanced';
+
+    const history: BaseMessage[] = body.history.map((msg) => {
+      if (msg[0] === 'human') {
+        return new HumanMessage({
+          content: msg[1],
+        });
+      } else {
+        return new AIMessage({
+          content: msg[1],
+        });
+      }
+    });
+
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    const chatModelProvider =
+      body.chatModel?.provider || Object.keys(chatModelProviders)[0];
+    const chatModel =
+      body.chatModel?.model ||
+      Object.keys(chatModelProviders[chatModelProvider])[0];
+
+    const embeddingModelProvider =
+      body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0];
+    const embeddingModel =
+      body.embeddingModel?.model ||
+      Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
+
+    let llm: BaseChatModel | undefined;
+    let embeddings: Embeddings | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        modelName: body.chatModel?.model || getCustomOpenaiModelName(),
+        openAIApiKey:
+          body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
+        temperature: 0.7,
+        configuration: {
+          baseURL:
+            body.chatModel?.customOpenAIBaseURL || getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (
+      chatModelProviders[chatModelProvider] &&
+      chatModelProviders[chatModelProvider][chatModel]
+    ) {
+      llm = chatModelProviders[chatModelProvider][chatModel]
+        .model as unknown as BaseChatModel | undefined;
+    }
+
+    if (
+      embeddingModelProviders[embeddingModelProvider] &&
+      embeddingModelProviders[embeddingModelProvider][embeddingModel]
+    ) {
+      embeddings = embeddingModelProviders[embeddingModelProvider][
+        embeddingModel
+      ].model as Embeddings | undefined;
+    }
+
+    if (!llm || !embeddings) {
+      return res.status(400).json({ message: 'Invalid model selected' });
+    }
+
+    const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
+
+    if (!searchHandler) {
+      return res.status(400).json({ message: 'Invalid focus mode' });
+    }
+
+    const emitter = await searchHandler.searchAndAnswer(
+      body.query,
+      history,
+      llm,
+      embeddings,
+      body.optimizationMode,
+      [],
+    );
+
+    let message = '';
+    let sources = [];
+
+    emitter.on('data', (data) => {
+      const parsedData = JSON.parse(data);
+      if (parsedData.type === 'response') {
+        message += parsedData.data;
+      } else if (parsedData.type === 'sources') {
+        sources = parsedData.data;
+      }
+    });
+
+    emitter.on('end', () => {
+      res.status(200).json({ message, sources });
+    });
+
+    emitter.on('error', (data) => {
+      const parsedData = JSON.parse(data);
+      res.status(500).json({ message: parsedData.data });
+    });
+  } catch (err: any) {
+    logger.error(`Error in getting search results: ${err.message}`);
+    res.status(500).json({ message: 'An error has occurred.' });
+  }
+});
+
+export default router;

src/routes/suggestions.ts

@@ -0,0 +1,81 @@
+import express from 'express';
+import generateSuggestions from '../chains/suggestionGeneratorAgent';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { getAvailableChatModelProviders } from '../lib/providers';
+import { HumanMessage, AIMessage } from '@langchain/core/messages';
+import logger from '../utils/logger';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '../config';
+
+const router = express.Router();
+
+interface ChatModel {
+  provider: string;
+  model: string;
+}
+
+interface SuggestionsBody {
+  chatHistory: any[];
+  chatModel?: ChatModel;
+}
+
+router.post('/', async (req, res) => {
+  try {
+    let body: SuggestionsBody = req.body;
+
+    const chatHistory = body.chatHistory.map((msg: any) => {
+      if (msg.role === 'user') {
+        return new HumanMessage(msg.content);
+      } else if (msg.role === 'assistant') {
+        return new AIMessage(msg.content);
+      }
+    });
+
+    const chatModelProviders = await getAvailableChatModelProviders();
+
+    const chatModelProvider =
+      body.chatModel?.provider || Object.keys(chatModelProviders)[0];
+    const chatModel =
+      body.chatModel?.model ||
+      Object.keys(chatModelProviders[chatModelProvider])[0];
+
+    let llm: BaseChatModel | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        modelName: getCustomOpenaiModelName(),
+        openAIApiKey: getCustomOpenaiApiKey(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (
+      chatModelProviders[chatModelProvider] &&
+      chatModelProviders[chatModelProvider][chatModel]
+    ) {
+      llm = chatModelProviders[chatModelProvider][chatModel]
+        .model as unknown as BaseChatModel | undefined;
+    }
+
+    if (!llm) {
+      return res.status(400).json({ message: 'Invalid model selected' });
+    }
+
+    const suggestions = await generateSuggestions(
+      { chat_history: chatHistory },
+      llm,
+    );
+
+    res.status(200).json({ suggestions: suggestions });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error in generating suggestions: ${err.message}`);
+  }
+});
+
+export default router;

src/routes/uploads.ts

@@ -0,0 +1,151 @@
+import express from 'express';
+import logger from '../utils/logger';
+import multer from 'multer';
+import path from 'path';
+import crypto from 'crypto';
+import fs from 'fs';
+import { Embeddings } from '@langchain/core/embeddings';
+import { getAvailableEmbeddingModelProviders } from '../lib/providers';
+import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
+import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
+import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
+import { Document } from 'langchain/document';
+
+const router = express.Router();
+
+const splitter = new RecursiveCharacterTextSplitter({
+  chunkSize: 500,
+  chunkOverlap: 100,
+});
+
+const storage = multer.diskStorage({
+  destination: (req, file, cb) => {
+    cb(null, path.join(process.cwd(), './uploads'));
+  },
+  filename: (req, file, cb) => {
+    const splitedFileName = file.originalname.split('.');
+    const fileExtension = splitedFileName[splitedFileName.length - 1];
+    if (!['pdf', 'docx', 'txt'].includes(fileExtension)) {
+      return cb(new Error('File type is not supported'), '');
+    }
+    cb(null, `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`);
+  },
+});
+
+const upload = multer({ storage });
+
+router.post(
+  '/',
+  upload.fields([
+    { name: 'files' },
+    { name: 'embedding_model', maxCount: 1 },
+    { name: 'embedding_model_provider', maxCount: 1 },
+  ]),
+  async (req, res) => {
+    try {
+      const { embedding_model, embedding_model_provider } = req.body;
+
+      if (!embedding_model || !embedding_model_provider) {
+        res
+          .status(400)
+          .json({ message: 'Missing embedding model or provider' });
+        return;
+      }
+
+      const embeddingModels = await getAvailableEmbeddingModelProviders();
+      const provider =
+        embedding_model_provider ?? Object.keys(embeddingModels)[0];
+      const embeddingModel: Embeddings =
+        embedding_model ?? Object.keys(embeddingModels[provider])[0];
+
+      let embeddingsModel: Embeddings | undefined;
+
+      if (
+        embeddingModels[provider] &&
+        embeddingModels[provider][embeddingModel]
+      ) {
+        embeddingsModel = embeddingModels[provider][embeddingModel].model as
+          | Embeddings
+          | undefined;
+      }
+
+      if (!embeddingsModel) {
+        res.status(400).json({ message: 'Invalid LLM model selected' });
+        return;
+      }
+
+      const files = req.files['files'] as Express.Multer.File[];
+      if (!files || files.length === 0) {
+        res.status(400).json({ message: 'No files uploaded' });
+        return;
+      }
+
+      await Promise.all(
+        files.map(async (file) => {
+          let docs: Document[] = [];
+
+          if (file.mimetype === 'application/pdf') {
+            const loader = new PDFLoader(file.path);
+            docs = await loader.load();
+          } else if (
+            file.mimetype ===
+            'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
+          ) {
+            const loader = new DocxLoader(file.path);
+            docs = await loader.load();
+          } else if (file.mimetype === 'text/plain') {
+            const text = fs.readFileSync(file.path, 'utf-8');
+            docs = [
+              new Document({
+                pageContent: text,
+                metadata: {
+                  title: file.originalname,
+                },
+              }),
+            ];
+          }
+
+          const splitted = await splitter.splitDocuments(docs);
+
+          const json = JSON.stringify({
+            title: file.originalname,
+            contents: splitted.map((doc) => doc.pageContent),
+          });
+
+          const pathToSave = file.path.replace(/\.\w+$/, '-extracted.json');
+          fs.writeFileSync(pathToSave, json);
+
+          const embeddings = await embeddingsModel.embedDocuments(
+            splitted.map((doc) => doc.pageContent),
+          );
+
+          const embeddingsJSON = JSON.stringify({
+            title: file.originalname,
+            embeddings: embeddings,
+          });
+
+          const pathToSaveEmbeddings = file.path.replace(
+            /\.\w+$/,
+            '-embeddings.json',
+          );
+          fs.writeFileSync(pathToSaveEmbeddings, embeddingsJSON);
+        }),
+      );
+
+      res.status(200).json({
+        files: files.map((file) => {
+          return {
+            fileName: file.originalname,
+            fileExtension: file.filename.split('.').pop(),
+            fileId: file.filename.replace(/\.\w+$/, ''),
+          };
+        }),
+      });
+    } catch (err: any) {
+      logger.error(`Error in uploading file results: ${err.message}`);
+      res.status(500).json({ message: 'An error has occurred.' });
+    }
+  },
+);
+
+export default router;

src/routes/videos.ts

@@ -0,0 +1,82 @@
+import express from 'express';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { getAvailableChatModelProviders } from '../lib/providers';
+import { HumanMessage, AIMessage } from '@langchain/core/messages';
+import logger from '../utils/logger';
+import handleVideoSearch from '../chains/videoSearchAgent';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '../config';
+
+const router = express.Router();
+
+interface ChatModel {
+  provider: string;
+  model: string;
+}
+
+interface VideoSearchBody {
+  query: string;
+  chatHistory: any[];
+  chatModel?: ChatModel;
+}
+
+router.post('/', async (req, res) => {
+  try {
+    let body: VideoSearchBody = req.body;
+
+    const chatHistory = body.chatHistory.map((msg: any) => {
+      if (msg.role === 'user') {
+        return new HumanMessage(msg.content);
+      } else if (msg.role === 'assistant') {
+        return new AIMessage(msg.content);
+      }
+    });
+
+    const chatModelProviders = await getAvailableChatModelProviders();
+
+    const chatModelProvider =
+      body.chatModel?.provider || Object.keys(chatModelProviders)[0];
+    const chatModel =
+      body.chatModel?.model ||
+      Object.keys(chatModelProviders[chatModelProvider])[0];
+
+    let llm: BaseChatModel | undefined;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        modelName: getCustomOpenaiModelName(),
+        openAIApiKey: getCustomOpenaiApiKey(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (
+      chatModelProviders[chatModelProvider] &&
+      chatModelProviders[chatModelProvider][chatModel]
+    ) {
+      llm = chatModelProviders[chatModelProvider][chatModel]
+        .model as unknown as BaseChatModel | undefined;
+    }
+
+    if (!llm) {
+      return res.status(400).json({ message: 'Invalid model selected' });
+    }
+
+    const videos = await handleVideoSearch(
+      { chat_history: chatHistory, query: body.query },
+      llm,
+    );
+
+    res.status(200).json({ videos });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error in video search: ${err.message}`);
+  }
+});
+
+export default router;

src/search/metaSearchAgent.ts

@@ -13,17 +13,18 @@ import {
 } from '@langchain/core/runnables';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import LineListOutputParser from '../outputParsers/listLineOutputParser';
+import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
-import LineOutputParser from '../outputParsers/lineOutputParser';
+import LineOutputParser from '../lib/outputParsers/lineOutputParser';
 import { getDocumentsFromLinks } from '../utils/documents';
 import { Document } from 'langchain/document';
-import { searchSearxng } from '../searxng';
+import { searchSearxng } from '../lib/searxng';
-import path from 'node:path';
+import path from 'path';
-import fs from 'node:fs';
+import fs from 'fs';
 import computeSimilarity from '../utils/computeSimilarity';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import { StreamEvent } from '@langchain/core/tracers/log_stream';
+import { IterableReadableStream } from '@langchain/core/utils/stream';
 
 export interface MetaSearchAgentType {
   searchAndAnswer: (
@@ -89,7 +90,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
             question = 'summarize';
           }
 
-          let docs: Document[] = [];
+          let docs = [];
 
           const linkDocs = await getDocumentsFromLinks({ links });
 
@@ -310,7 +311,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
         const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
 
         const fileSimilaritySearchObject = content.contents.map(
-          (c: string, i: number) => {
+          (c: string, i) => {
             return {
               fileName: content.title,
               content: c,
@@ -413,8 +414,6 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
       return sortedDocs;
     }
-
-    return [];
   }
 
   private processDocs(docs: Document[]) {
@@ -427,7 +426,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
   }
 
   private async handleStream(
-    stream: AsyncGenerator<StreamEvent, any, any>,
+    stream: IterableReadableStream<StreamEvent>,
     emitter: eventEmitter,
   ) {
     for await (const event of stream) {

src/utils/computeSimilarity.ts

@@ -6,7 +6,7 @@ const computeSimilarity = (x: number[], y: number[]): number => {
   const similarityMeasure = getSimilarityMeasure();
 
   if (similarityMeasure === 'cosine') {
-    return cosineSimilarity(x, y) as number;
+    return cosineSimilarity(x, y);
   } else if (similarityMeasure === 'dot') {
     return dot(x, y);
   }

src/utils/documents.ts

@@ -3,6 +3,7 @@ import { htmlToText } from 'html-to-text';
 import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
 import { Document } from '@langchain/core/documents';
 import pdfParse from 'pdf-parse';
+import logger from './logger';
 
 export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
   const splitter = new RecursiveCharacterTextSplitter();
@@ -78,13 +79,12 @@ export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
 
         docs.push(...linkDocs);
       } catch (err) {
-        console.error(
+        logger.error(
-          'An error occurred while getting documents from links: ',
+          `Error at generating documents from links: ${err.message}`,
-          err,
         );
         docs.push(
           new Document({
-            pageContent: `Failed to retrieve content from the link: ${err}`,
+            pageContent: `Failed to retrieve content from the link: ${err.message}`,
             metadata: {
               title: 'Failed to retrieve content',
               url: link,

src/utils/logger.ts

@@ -0,0 +1,22 @@
+import winston from 'winston';
+
+const logger = winston.createLogger({
+  level: 'info',
+  transports: [
+    new winston.transports.Console({
+      format: winston.format.combine(
+        winston.format.colorize(),
+        winston.format.simple(),
+      ),
+    }),
+    new winston.transports.File({
+      filename: 'app.log',
+      format: winston.format.combine(
+        winston.format.timestamp(),
+        winston.format.json(),
+      ),
+    }),
+  ],
+});
+
+export default logger;

src/websocket/connectionManager.ts

@@ -0,0 +1,122 @@
+import { WebSocket } from 'ws';
+import { handleMessage } from './messageHandler';
+import {
+  getAvailableEmbeddingModelProviders,
+  getAvailableChatModelProviders,
+} from '../lib/providers';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import type { IncomingMessage } from 'http';
+import logger from '../utils/logger';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '../config';
+
+export const handleConnection = async (
+  ws: WebSocket,
+  request: IncomingMessage,
+) => {
+  try {
+    const searchParams = new URL(request.url, `http://${request.headers.host}`)
+      .searchParams;
+
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    const chatModelProvider =
+      searchParams.get('chatModelProvider') ||
+      Object.keys(chatModelProviders)[0];
+    const chatModel =
+      searchParams.get('chatModel') ||
+      Object.keys(chatModelProviders[chatModelProvider])[0];
+
+    const embeddingModelProvider =
+      searchParams.get('embeddingModelProvider') ||
+      Object.keys(embeddingModelProviders)[0];
+    const embeddingModel =
+      searchParams.get('embeddingModel') ||
+      Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
+
+    let llm: BaseChatModel | undefined;
+    let embeddings: Embeddings | undefined;
+
+    if (
+      chatModelProviders[chatModelProvider] &&
+      chatModelProviders[chatModelProvider][chatModel] &&
+      chatModelProvider != 'custom_openai'
+    ) {
+      llm = chatModelProviders[chatModelProvider][chatModel]
+        .model as unknown as BaseChatModel | undefined;
+    } else if (chatModelProvider == 'custom_openai') {
+      const customOpenaiApiKey = getCustomOpenaiApiKey();
+      const customOpenaiApiUrl = getCustomOpenaiApiUrl();
+      const customOpenaiModelName = getCustomOpenaiModelName();
+
+      if (customOpenaiApiKey && customOpenaiApiUrl && customOpenaiModelName) {
+        llm = new ChatOpenAI({
+          modelName: customOpenaiModelName,
+          openAIApiKey: customOpenaiApiKey,
+          temperature: 0.7,
+          configuration: {
+            baseURL: customOpenaiApiUrl,
+          },
+        }) as unknown as BaseChatModel;
+      }
+    }
+
+    if (
+      embeddingModelProviders[embeddingModelProvider] &&
+      embeddingModelProviders[embeddingModelProvider][embeddingModel]
+    ) {
+      embeddings = embeddingModelProviders[embeddingModelProvider][
+        embeddingModel
+      ].model as Embeddings | undefined;
+    }
+
+    if (!llm || !embeddings) {
+      ws.send(
+        JSON.stringify({
+          type: 'error',
+          data: 'Invalid LLM or embeddings model selected, please refresh the page and try again.',
+          key: 'INVALID_MODEL_SELECTED',
+        }),
+      );
+      ws.close();
+    }
+
+    const interval = setInterval(() => {
+      if (ws.readyState === ws.OPEN) {
+        ws.send(
+          JSON.stringify({
+            type: 'signal',
+            data: 'open',
+          }),
+        );
+        clearInterval(interval);
+      }
+    }, 5);
+
+    ws.on(
+      'message',
+      async (message) =>
+        await handleMessage(message.toString(), ws, llm, embeddings),
+    );
+
+    ws.on('close', () => logger.debug('Connection closed'));
+  } catch (err) {
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: 'Internal server error.',
+        key: 'INTERNAL_SERVER_ERROR',
+      }),
+    );
+    ws.close();
+    logger.error(err);
+  }
+};

src/websocket/index.ts

@@ -0,0 +1,8 @@
+import { initServer } from './websocketServer';
+import http from 'http';
+
+export const startWebSocketServer = (
+  server: http.Server<typeof http.IncomingMessage, typeof http.ServerResponse>,
+) => {
+  initServer(server);
+};

src/websocket/messageHandler.ts

@@ -0,0 +1,281 @@
+import { EventEmitter, WebSocket } from 'ws';
+import { BaseMessage, AIMessage, HumanMessage } from '@langchain/core/messages';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import logger from '../utils/logger';
+import db from '../db';
+import { chats, messages as messagesSchema } from '../db/schema';
+import { eq, gt, and } from 'drizzle-orm';
+import crypto from 'crypto';
+import { isLibraryEnabled } from '../config';
+import { getFileDetails } from '../utils/files';
+import MetaSearchAgent, {
+  MetaSearchAgentType,
+} from '../search/metaSearchAgent';
+import prompts from '../prompts';
+
+type Message = {
+  messageId: string;
+  chatId: string;
+  content: string;
+};
+
+type WSMessage = {
+  message: Message;
+  optimizationMode: 'speed' | 'balanced' | 'quality';
+  type: string;
+  focusMode: string;
+  history: Array<[string, string]>;
+  files: Array<string>;
+};
+
+export const searchHandlers = {
+  webSearch: new MetaSearchAgent({
+    activeEngines: [],
+    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
+    responsePrompt: prompts.webSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: true,
+  }),
+  academicSearch: new MetaSearchAgent({
+    activeEngines: ['arxiv', 'google scholar', 'pubmed'],
+    queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
+    responsePrompt: prompts.academicSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  writingAssistant: new MetaSearchAgent({
+    activeEngines: [],
+    queryGeneratorPrompt: '',
+    responsePrompt: prompts.writingAssistantPrompt,
+    rerank: true,
+    rerankThreshold: 0,
+    searchWeb: false,
+    summarizer: false,
+  }),
+  wolframAlphaSearch: new MetaSearchAgent({
+    activeEngines: ['wolframalpha'],
+    queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
+    responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
+    rerank: false,
+    rerankThreshold: 0,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  youtubeSearch: new MetaSearchAgent({
+    activeEngines: ['youtube'],
+    queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
+    responsePrompt: prompts.youtubeSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  redditSearch: new MetaSearchAgent({
+    activeEngines: ['reddit'],
+    queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
+    responsePrompt: prompts.redditSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: false,
+  }),
+};
+
+const handleEmitterEvents = (
+  emitter: EventEmitter,
+  ws: WebSocket,
+  messageId: string,
+  chatId: string,
+) => {
+  let recievedMessage = '';
+  let sources = [];
+
+  const libraryEnabled = isLibraryEnabled();
+
+  emitter.on('data', (data) => {
+    const parsedData = JSON.parse(data);
+    if (parsedData.type === 'response') {
+      ws.send(
+        JSON.stringify({
+          type: 'message',
+          data: parsedData.data,
+          messageId: messageId,
+        }),
+      );
+      recievedMessage += parsedData.data;
+    } else if (parsedData.type === 'sources') {
+      ws.send(
+        JSON.stringify({
+          type: 'sources',
+          data: parsedData.data,
+          messageId: messageId,
+        }),
+      );
+      sources = parsedData.data;
+    }
+  });
+  emitter.on('end', () => {
+    ws.send(JSON.stringify({ type: 'messageEnd', messageId: messageId }));
+
+    if (libraryEnabled) {
+      db.insert(messagesSchema)
+        .values({
+          content: recievedMessage,
+          chatId: chatId,
+          messageId: messageId,
+          role: 'assistant',
+          metadata: JSON.stringify({
+            createdAt: new Date(),
+            ...(sources && sources.length > 0 && { sources }),
+          }),
+        })
+        .execute();
+    }
+  });
+  emitter.on('error', (data) => {
+    const parsedData = JSON.parse(data);
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: parsedData.data,
+        key: 'CHAIN_ERROR',
+      }),
+    );
+  });
+};
+
+export const handleMessage = async (
+  message: string,
+  ws: WebSocket,
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  try {
+    const parsedWSMessage = JSON.parse(message) as WSMessage;
+    const parsedMessage = parsedWSMessage.message;
+
+    if (parsedWSMessage.files.length > 0) {
+      /* TODO: Implement uploads in other classes/single meta class system*/
+      parsedWSMessage.focusMode = 'webSearch';
+    }
+
+    const humanMessageId =
+      parsedMessage.messageId ?? crypto.randomBytes(7).toString('hex');
+    const aiMessageId = crypto.randomBytes(7).toString('hex');
+
+    if (!parsedMessage.content)
+      return ws.send(
+        JSON.stringify({
+          type: 'error',
+          data: 'Invalid message format',
+          key: 'INVALID_FORMAT',
+        }),
+      );
+
+    const history: BaseMessage[] = parsedWSMessage.history.map((msg) => {
+      if (msg[0] === 'human') {
+        return new HumanMessage({
+          content: msg[1],
+        });
+      } else {
+        return new AIMessage({
+          content: msg[1],
+        });
+      }
+    });
+
+    if (parsedWSMessage.type === 'message') {
+      const handler: MetaSearchAgentType =
+        searchHandlers[parsedWSMessage.focusMode];
+
+      const libraryEnabled = isLibraryEnabled();
+
+      if (handler) {
+        try {
+          const emitter = await handler.searchAndAnswer(
+            parsedMessage.content,
+            history,
+            llm,
+            embeddings,
+            parsedWSMessage.optimizationMode,
+            parsedWSMessage.files,
+          );
+
+          handleEmitterEvents(emitter, ws, aiMessageId, parsedMessage.chatId);
+
+          if (libraryEnabled) {
+            const chat = await db.query.chats.findFirst({
+              where: eq(chats.id, parsedMessage.chatId),
+            });
+
+            if (!chat) {
+              await db
+                .insert(chats)
+                .values({
+                  id: parsedMessage.chatId,
+                  title: parsedMessage.content,
+                  createdAt: new Date().toString(),
+                  focusMode: parsedWSMessage.focusMode,
+                  files: parsedWSMessage.files.map(getFileDetails),
+                })
+                .execute();
+            }
+
+            const messageExists = await db.query.messages.findFirst({
+              where: eq(messagesSchema.messageId, humanMessageId),
+            });
+
+            if (!messageExists) {
+              await db
+                .insert(messagesSchema)
+                .values({
+                  content: parsedMessage.content,
+                  chatId: parsedMessage.chatId,
+                  messageId: humanMessageId,
+                  role: 'user',
+                  metadata: JSON.stringify({
+                    createdAt: new Date(),
+                  }),
+                })
+                .execute();
+            } else {
+              await db
+                .delete(messagesSchema)
+                .where(
+                  and(
+                    gt(messagesSchema.id, messageExists.id),
+                    eq(messagesSchema.chatId, parsedMessage.chatId),
+                  ),
+                )
+                .execute();
+            }
+          }
+        } catch (err) {
+          console.log(err);
+        }
+      } else {
+        ws.send(
+          JSON.stringify({
+            type: 'error',
+            data: 'Invalid focus mode',
+            key: 'INVALID_FOCUS_MODE',
+          }),
+        );
+      }
+    }
+  } catch (err) {
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: 'Invalid message format',
+        key: 'INVALID_FORMAT',
+      }),
+    );
+    logger.error(`Failed to handle message: ${err}`);
+  }
+};

src/websocket/websocketServer.ts

@@ -0,0 +1,16 @@
+import { WebSocketServer } from 'ws';
+import { handleConnection } from './connectionManager';
+import http from 'http';
+import { getPort } from '../config';
+import logger from '../utils/logger';
+
+export const initServer = (
+  server: http.Server<typeof http.IncomingMessage, typeof http.ServerResponse>,
+) => {
+  const port = getPort();
+  const wss = new WebSocketServer({ server });
+
+  wss.on('connection', handleConnection);
+
+  logger.info(`WebSocket server started on port ${port}`);
+};

tsconfig.json

@@ -1,27 +1,18 @@
 {
   "compilerOptions": {
-    "lib": ["dom", "dom.iterable", "esnext"],
+    "lib": ["ESNext"],
-    "allowJs": true,
+    "module": "Node16",
-    "skipLibCheck": true,
+    "moduleResolution": "Node16",
-    "strict": true,
+    "target": "ESNext",
-    "noEmit": true,
+    "outDir": "dist",
+    "sourceMap": false,
     "esModuleInterop": true,
-    "module": "esnext",
+    "experimentalDecorators": true,
-    "moduleResolution": "bundler",
+    "emitDecoratorMetadata": true,
-    "resolveJsonModule": true,
+    "allowSyntheticDefaultImports": true,
-    "isolatedModules": true,
+    "skipLibCheck": true,
-    "jsx": "preserve",
+    "skipDefaultLibCheck": true
-    "incremental": true,
-    "plugins": [
-      {
-        "name": "next"
-      }
-    ],
-    "paths": {
-      "@/*": ["./src/*"]
-    },
-    "target": "ES2017"
   },
-  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+  "include": ["src"],
-  "exclude": ["node_modules"]
+  "exclude": ["node_modules", "**/*.spec.ts"]
 }

ui/.env.example

@@ -0,0 +1,2 @@
+NEXT_PUBLIC_WS_URL=ws://localhost:3001
+NEXT_PUBLIC_API_URL=http://localhost:3001/api

ui/.gitignore

@@ -0,0 +1,34 @@
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+.yarn/install-state.gz
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# local env files
+.env*.local
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts

ui/.prettierrc.js

@@ -0,0 +1,11 @@
+/** @type {import("prettier").Config} */
+
+const config = {
+  printWidth: 80,
+  trailingComma: 'all',
+  endOfLine: 'auto',
+  singleQuote: true,
+  tabWidth: 2,
+};
+
+module.exports = config;

ui/app/c/[chatId]/page.tsx

@@ -0,0 +1,7 @@
+import ChatWindow from '@/components/ChatWindow';
+
+const Page = ({ params }: { params: { chatId: string } }) => {
+  return <ChatWindow id={params.chatId} />;
+};
+
+export default Page;

ui/app/discover/page.tsx

@@ -19,7 +19,7 @@ const Page = () => {
   useEffect(() => {
     const fetchData = async () => {
       try {
-        const res = await fetch(`/api/discover`, {
+        const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/discover`, {
           method: 'GET',
           headers: {
             'Content-Type': 'application/json',

ui/app/library/layout.tsx

@@ -0,0 +1,36 @@
+import { Metadata } from 'next';
+import React from 'react';
+
+export const metadata: Metadata = {
+  title: 'Library - Perplexica',
+};
+
+const Layout = async ({ children }: { children: React.ReactNode }) => {
+  const res = await fetch(
+    `${process.env.NEXT_PUBLIC_API_URL}/config/preferences`,
+    {
+      method: 'GET',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    },
+  );
+
+  const data = await res.json();
+
+  const { isLibraryEnabled } = data;
+
+  if (!isLibraryEnabled) {
+    return (
+      <div className="flex flex-row items-center justify-center min-h-screen w-full">
+        <p className="text-lg dark:text-white/70 text-black/70">
+          Library is disabled
+        </p>
+      </div>
+    );
+  }
+
+  return <div>{children}</div>;
+};
+
+export default Layout;

ui/app/library/page.tsx

@@ -1,8 +1,9 @@
 'use client';
 
 import DeleteChat from '@/components/DeleteChat';
-import { cn, formatTimeDifference } from '@/lib/utils';
+import { formatTimeDifference } from '@/lib/utils';
-import { BookOpenText, ClockIcon, Delete, ScanEye } from 'lucide-react';
+import { cn } from '@/lib/utils';
+import { BookOpenText, ClockIcon } from 'lucide-react';
 import Link from 'next/link';
 import { useEffect, useState } from 'react';
 
@@ -21,7 +22,7 @@ const Page = () => {
     const fetchChats = async () => {
       setLoading(true);
 
-      const res = await fetch(`/api/chats`, {
+      const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chats`, {
         method: 'GET',
         headers: {
           'Content-Type': 'application/json',

ui/app/settings/page.tsx

@@ -113,12 +113,90 @@ const Page = () => {
   const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
   const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
   
+  const [password, setPassword] = useState('');
+  const [passwordSubmitted, setPasswordSubmitted] = useState(false);
+  const [isPasswordValid, setIsPasswordValid] = useState(true);
+
+  const handlePasswordSubmit = async () => {
+    setIsLoading(true);
+    setPasswordSubmitted(true);
+
+    const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
+      headers: {
+        'Content-Type': 'application/json',
+        Authorization: `Bearer ${password}`,
+      },
+    });
+
+    if (res.status === 401) {
+      setIsPasswordValid(false);
+      setPasswordSubmitted(false);
+      setIsLoading(false);
+      return;
+    } else {
+      setIsPasswordValid(true);
+    }
+
+    const data = (await res.json()) as SettingsType;
+    setConfig(data);
+
+    const chatModelProvidersKeys = Object.keys(data.chatModelProviders || {});
+    const embeddingModelProvidersKeys = Object.keys(
+      data.embeddingModelProviders || {},
+    );
+
+    const defaultChatModelProvider =
+      chatModelProvidersKeys.length > 0 ? chatModelProvidersKeys[0] : '';
+    const defaultEmbeddingModelProvider =
+      embeddingModelProvidersKeys.length > 0
+        ? embeddingModelProvidersKeys[0]
+        : '';
+
+    const chatModelProvider =
+      localStorage.getItem('chatModelProvider') ||
+      defaultChatModelProvider ||
+      '';
+    const chatModel =
+      localStorage.getItem('chatModel') ||
+      (data.chatModelProviders &&
+      data.chatModelProviders[chatModelProvider]?.length > 0
+        ? data.chatModelProviders[chatModelProvider][0].name
+        : undefined) ||
+      '';
+    const embeddingModelProvider =
+      localStorage.getItem('embeddingModelProvider') ||
+      defaultEmbeddingModelProvider ||
+      '';
+    const embeddingModel =
+      localStorage.getItem('embeddingModel') ||
+      (data.embeddingModelProviders &&
+        data.embeddingModelProviders[embeddingModelProvider]?.[0].name) ||
+      '';
+
+    setSelectedChatModelProvider(chatModelProvider);
+    setSelectedChatModel(chatModel);
+    setSelectedEmbeddingModelProvider(embeddingModelProvider);
+    setSelectedEmbeddingModel(embeddingModel);
+    setChatModels(data.chatModelProviders || {});
+    setEmbeddingModels(data.embeddingModelProviders || {});
+
+    setAutomaticImageSearch(
+      localStorage.getItem('autoImageSearch') === 'true',
+    );
+    setAutomaticVideoSearch(
+      localStorage.getItem('autoVideoSearch') === 'true',
+    );
+
+    setIsLoading(false);
+  };
+
   useEffect(() => {
     const fetchConfig = async () => {
       setIsLoading(true);
-      const res = await fetch(`/api/config`, {
+      const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
         headers: {
           'Content-Type': 'application/json',
+          Authorization: `Bearer ${password}`,
         },
       });
 
@@ -187,13 +265,21 @@ const Page = () => {
         [key]: value,
       } as SettingsType;
 
-      const response = await fetch(`/api/config`, {
+      const response = await fetch(
-        method: 'POST',
+        `${process.env.NEXT_PUBLIC_API_URL}/config`,
-        headers: {
+        {
-          'Content-Type': 'application/json',
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            Authorization: `Bearer ${password}`,
+          },
+          body: JSON.stringify(updatedConfig),
         },
-        body: JSON.stringify(updatedConfig),
+      );
-      });
+
+      if (response.status === 401) {
+        throw new Error('Unauthorized');
+      }
 
       if (!response.ok) {
         throw new Error('Failed to update config');
@@ -205,9 +291,10 @@ const Page = () => {
         key.toLowerCase().includes('api') ||
         key.toLowerCase().includes('url')
       ) {
-        const res = await fetch(`/api/config`, {
+        const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
           headers: {
             'Content-Type': 'application/json',
+            Authorization: `Bearer ${password}`,
           },
         });
 
@@ -373,6 +460,33 @@ const Page = () => {
             />
           </svg>
         </div>
+      ) : !passwordSubmitted ? (
+        <div className="flex flex-col max-w-md mx-auto mt-10 p-6 bg-light-secondary dark:bg-dark-secondary border border-light-200 dark:border-dark-200 rounded-2xl">
+          <h2 className="text-sm text-black/80 dark:text-white/80">
+          Enter the password to access the settings
+          </h2>
+          <div className="flex flex-col">
+            <Input
+              type="password"
+              placeholder="Password"
+              className="mt-4"
+              disabled={isLoading}
+              onChange={(e) => setPassword(e.target.value)}
+            />
+          </div>
+          {!isPasswordValid && (
+            <p className="text-xs text-red-500 mt-2">
+              Password is incorrect
+            </p>
+          )}
+          <button
+            onClick={handlePasswordSubmit}
+            disabled={isLoading}
+            className="bg-[#24A0ED] flex flex-row items-center text-xs mt-4 text-white disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#ececec21] rounded-full px-4 py-2"
+          >
+            Submit
+          </button>
+        </div>
       ) : (
         config && (
           <div className="flex flex-col space-y-6 pb-28 lg:pb-8">

ui/components/Chat.tsx

@@ -48,17 +48,11 @@ const Chat = ({
   });
 
   useEffect(() => {
-    const scroll = () => {
+    messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
-      messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
-    };
 
     if (messages.length === 1) {
       document.title = `${messages[0].content.substring(0, 30)} - Perplexica`;
     }
-
-    if (messages[messages.length - 1]?.role == 'user') {
-      scroll();
-    }
   }, [messages]);
 
   return (

ui/components/ChatWindow.tsx

@@ -29,154 +29,280 @@ export interface File {
   fileId: string;
 }
 
-interface ChatModelProvider {
+const useSocket = (
-  name: string;
+  url: string,
-  provider: string;
+  setIsWSReady: (ready: boolean) => void,
-}
+  setError: (error: boolean) => void,
-
-interface EmbeddingModelProvider {
-  name: string;
-  provider: string;
-}
-
-const checkConfig = async (
-  setChatModelProvider: (provider: ChatModelProvider) => void,
-  setEmbeddingModelProvider: (provider: EmbeddingModelProvider) => void,
-  setIsConfigReady: (ready: boolean) => void,
-  setHasError: (hasError: boolean) => void,
 ) => {
-  try {
+  const wsRef = useRef<WebSocket | null>(null);
-    let chatModel = localStorage.getItem('chatModel');
+  const reconnectTimeoutRef = useRef<NodeJS.Timeout>();
-    let chatModelProvider = localStorage.getItem('chatModelProvider');
+  const retryCountRef = useRef(0);
-    let embeddingModel = localStorage.getItem('embeddingModel');
+  const isCleaningUpRef = useRef(false);
-    let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
+  const MAX_RETRIES = 3;
+  const INITIAL_BACKOFF = 1000; // 1 second
+  const isConnectionErrorRef = useRef(false);
 
-    const autoImageSearch = localStorage.getItem('autoImageSearch');
+  const getBackoffDelay = (retryCount: number) => {
-    const autoVideoSearch = localStorage.getItem('autoVideoSearch');
+    return Math.min(INITIAL_BACKOFF * Math.pow(2, retryCount), 10000); // Cap at 10 seconds
+  };
 
-    if (!autoImageSearch) {
+  useEffect(() => {
-      localStorage.setItem('autoImageSearch', 'true');
+    const connectWs = async () => {
-    }
+      if (wsRef.current?.readyState === WebSocket.OPEN) {
-
+        wsRef.current.close();
-    if (!autoVideoSearch) {
-      localStorage.setItem('autoVideoSearch', 'false');
-    }
-
-    const providers = await fetch(`/api/models`, {
-      headers: {
-        'Content-Type': 'application/json',
-      },
-    }).then(async (res) => {
-      if (!res.ok)
-        throw new Error(
-          `Failed to fetch models: ${res.status} ${res.statusText}`,
-        );
-      return res.json();
-    });
-
-    if (
-      !chatModel ||
-      !chatModelProvider ||
-      !embeddingModel ||
-      !embeddingModelProvider
-    ) {
-      if (!chatModel || !chatModelProvider) {
-        const chatModelProviders = providers.chatModelProviders;
-
-        chatModelProvider =
-          chatModelProvider || Object.keys(chatModelProviders)[0];
-
-        chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
-
-        if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
-          return toast.error('No chat models available');
       }
 
-      if (!embeddingModel || !embeddingModelProvider) {
+      try {
-        const embeddingModelProviders = providers.embeddingModelProviders;
+        let chatModel = localStorage.getItem('chatModel');
+        let chatModelProvider = localStorage.getItem('chatModelProvider');
+        let embeddingModel = localStorage.getItem('embeddingModel');
+        let embeddingModelProvider = localStorage.getItem(
+          'embeddingModelProvider',
+        );
+
+        const autoImageSearch = localStorage.getItem('autoImageSearch');
+        const autoVideoSearch = localStorage.getItem('autoVideoSearch');
+
+        if (!autoImageSearch) {
+          localStorage.setItem('autoImageSearch', 'true');
+        }
+
+        if (!autoVideoSearch) {
+          localStorage.setItem('autoVideoSearch', 'false');
+        }
+
+        const providers = await fetch(
+          `${process.env.NEXT_PUBLIC_API_URL}/models`,
+          {
+            headers: {
+              'Content-Type': 'application/json',
+            },
+          },
+        ).then(async (res) => {
+          if (!res.ok)
+            throw new Error(
+              `Failed to fetch models: ${res.status} ${res.statusText}`,
+            );
+          return res.json();
+        });
 
         if (
-          !embeddingModelProviders ||
+          !chatModel ||
-          Object.keys(embeddingModelProviders).length === 0
+          !chatModelProvider ||
-        )
+          !embeddingModel ||
-          return toast.error('No embedding models available');
+          !embeddingModelProvider
+        ) {
+          if (!chatModel || !chatModelProvider) {
+            const chatModelProviders = providers.chatModelProviders;
 
-        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+            chatModelProvider =
-        embeddingModel = Object.keys(
+              chatModelProvider || Object.keys(chatModelProviders)[0];
-          embeddingModelProviders[embeddingModelProvider],
+
-        )[0];
+            chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+
+            if (
+              !chatModelProviders ||
+              Object.keys(chatModelProviders).length === 0
+            )
+              return toast.error('No chat models available');
+          }
+
+          if (!embeddingModel || !embeddingModelProvider) {
+            const embeddingModelProviders = providers.embeddingModelProviders;
+
+            if (
+              !embeddingModelProviders ||
+              Object.keys(embeddingModelProviders).length === 0
+            )
+              return toast.error('No embedding models available');
+
+            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+            embeddingModel = Object.keys(
+              embeddingModelProviders[embeddingModelProvider],
+            )[0];
+          }
+
+          localStorage.setItem('chatModel', chatModel!);
+          localStorage.setItem('chatModelProvider', chatModelProvider);
+          localStorage.setItem('embeddingModel', embeddingModel!);
+          localStorage.setItem(
+            'embeddingModelProvider',
+            embeddingModelProvider,
+          );
+        } else {
+          const chatModelProviders = providers.chatModelProviders;
+          const embeddingModelProviders = providers.embeddingModelProviders;
+
+          if (
+            Object.keys(chatModelProviders).length > 0 &&
+            !chatModelProviders[chatModelProvider]
+          ) {
+            const chatModelProvidersKeys = Object.keys(chatModelProviders);
+            chatModelProvider =
+              chatModelProvidersKeys.find(
+                (key) => Object.keys(chatModelProviders[key]).length > 0,
+              ) || chatModelProvidersKeys[0];
+
+            localStorage.setItem('chatModelProvider', chatModelProvider);
+          }
+
+          if (
+            chatModelProvider &&
+            !chatModelProviders[chatModelProvider][chatModel]
+          ) {
+            chatModel = Object.keys(
+              chatModelProviders[
+                Object.keys(chatModelProviders[chatModelProvider]).length > 0
+                  ? chatModelProvider
+                  : Object.keys(chatModelProviders)[0]
+              ],
+            )[0];
+            localStorage.setItem('chatModel', chatModel);
+          }
+
+          if (
+            Object.keys(embeddingModelProviders).length > 0 &&
+            !embeddingModelProviders[embeddingModelProvider]
+          ) {
+            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+            localStorage.setItem(
+              'embeddingModelProvider',
+              embeddingModelProvider,
+            );
+          }
+
+          if (
+            embeddingModelProvider &&
+            !embeddingModelProviders[embeddingModelProvider][embeddingModel]
+          ) {
+            embeddingModel = Object.keys(
+              embeddingModelProviders[embeddingModelProvider],
+            )[0];
+            localStorage.setItem('embeddingModel', embeddingModel);
+          }
+        }
+
+        const wsURL = new URL(url);
+        const searchParams = new URLSearchParams({});
+
+        searchParams.append('chatModel', chatModel!);
+        searchParams.append('chatModelProvider', chatModelProvider);
+
+        if (chatModelProvider === 'custom_openai') {
+          searchParams.append(
+            'openAIApiKey',
+            localStorage.getItem('openAIApiKey')!,
+          );
+          searchParams.append(
+            'openAIBaseURL',
+            localStorage.getItem('openAIBaseURL')!,
+          );
+        }
+
+        searchParams.append('embeddingModel', embeddingModel!);
+        searchParams.append('embeddingModelProvider', embeddingModelProvider);
+
+        wsURL.search = searchParams.toString();
+
+        const ws = new WebSocket(wsURL.toString());
+        wsRef.current = ws;
+
+        const timeoutId = setTimeout(() => {
+          if (ws.readyState !== 1) {
+            toast.error(
+              'Failed to connect to the server. Please try again later.',
+            );
+          }
+        }, 10000);
+
+        ws.addEventListener('message', (e) => {
+          const data = JSON.parse(e.data);
+          if (data.type === 'signal' && data.data === 'open') {
+            const interval = setInterval(() => {
+              if (ws.readyState === 1) {
+                setIsWSReady(true);
+                setError(false);
+                if (retryCountRef.current > 0) {
+                  toast.success('Connection restored.');
+                }
+                retryCountRef.current = 0;
+                clearInterval(interval);
+              }
+            }, 5);
+            clearTimeout(timeoutId);
+            console.debug(new Date(), 'ws:connected');
+          }
+          if (data.type === 'error') {
+            isConnectionErrorRef.current = true;
+            setError(true);
+            toast.error(data.data);
+          }
+        });
+
+        ws.onerror = () => {
+          clearTimeout(timeoutId);
+          setIsWSReady(false);
+          toast.error('WebSocket connection error.');
+        };
+
+        ws.onclose = () => {
+          clearTimeout(timeoutId);
+          setIsWSReady(false);
+          console.debug(new Date(), 'ws:disconnected');
+          if (!isCleaningUpRef.current && !isConnectionErrorRef.current) {
+            toast.error('Connection lost. Attempting to reconnect...');
+            attemptReconnect();
+          }
+        };
+      } catch (error) {
+        console.debug(new Date(), 'ws:error', error);
+        setIsWSReady(false);
+        attemptReconnect();
+      }
+    };
+
+    const attemptReconnect = () => {
+      retryCountRef.current += 1;
+
+      if (retryCountRef.current > MAX_RETRIES) {
+        console.debug(new Date(), 'ws:max_retries');
+        setError(true);
+        toast.error(
+          'Unable to connect to server after multiple attempts. Please refresh the page to try again.',
+        );
+        return;
       }
 
-      localStorage.setItem('chatModel', chatModel!);
+      const backoffDelay = getBackoffDelay(retryCountRef.current);
-      localStorage.setItem('chatModelProvider', chatModelProvider);
+      console.debug(
-      localStorage.setItem('embeddingModel', embeddingModel!);
+        new Date(),
-      localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
+        `ws:retry attempt=${retryCountRef.current}/${MAX_RETRIES} delay=${backoffDelay}ms`,
-    } else {
+      );
-      const chatModelProviders = providers.chatModelProviders;
-      const embeddingModelProviders = providers.embeddingModelProviders;
 
-      if (
+      if (reconnectTimeoutRef.current) {
-        Object.keys(chatModelProviders).length > 0 &&
+        clearTimeout(reconnectTimeoutRef.current);
-        !chatModelProviders[chatModelProvider]
-      ) {
-        const chatModelProvidersKeys = Object.keys(chatModelProviders);
-        chatModelProvider =
-          chatModelProvidersKeys.find(
-            (key) => Object.keys(chatModelProviders[key]).length > 0,
-          ) || chatModelProvidersKeys[0];
-
-        localStorage.setItem('chatModelProvider', chatModelProvider);
       }
 
-      if (
+      reconnectTimeoutRef.current = setTimeout(() => {
-        chatModelProvider &&
+        connectWs();
-        !chatModelProviders[chatModelProvider][chatModel]
+      }, backoffDelay);
-      ) {
+    };
-        chatModel = Object.keys(
+
-          chatModelProviders[
+    connectWs();
-            Object.keys(chatModelProviders[chatModelProvider]).length > 0
+
-              ? chatModelProvider
+    return () => {
-              : Object.keys(chatModelProviders)[0]
+      if (reconnectTimeoutRef.current) {
-          ],
+        clearTimeout(reconnectTimeoutRef.current);
-        )[0];
-        localStorage.setItem('chatModel', chatModel);
       }
-
+      if (wsRef.current?.readyState === WebSocket.OPEN) {
-      if (
+        wsRef.current.close();
-        Object.keys(embeddingModelProviders).length > 0 &&
+        isCleaningUpRef.current = true;
-        !embeddingModelProviders[embeddingModelProvider]
+        console.debug(new Date(), 'ws:cleanup');
-      ) {
-        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-        localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
       }
+    };
+  }, [url, setIsWSReady, setError]);
 
-      if (
+  return wsRef.current;
-        embeddingModelProvider &&
-        !embeddingModelProviders[embeddingModelProvider][embeddingModel]
-      ) {
-        embeddingModel = Object.keys(
-          embeddingModelProviders[embeddingModelProvider],
-        )[0];
-        localStorage.setItem('embeddingModel', embeddingModel);
-      }
-    }
-
-    setChatModelProvider({
-      name: chatModel!,
-      provider: chatModelProvider,
-    });
-
-    setEmbeddingModelProvider({
-      name: embeddingModel!,
-      provider: embeddingModelProvider,
-    });
-
-    setIsConfigReady(true);
-  } catch (err) {
-    console.error('An error occurred while checking the configuration:', err);
-    setIsConfigReady(false);
-    setHasError(true);
-  }
 };
 
 const loadMessages = async (
@@ -189,12 +315,15 @@ const loadMessages = async (
   setFiles: (files: File[]) => void,
   setFileIds: (fileIds: string[]) => void,
 ) => {
-  const res = await fetch(`/api/chats/${chatId}`, {
+  const res = await fetch(
-    method: 'GET',
+    `${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
-    headers: {
+    {
-      'Content-Type': 'application/json',
+      method: 'GET',
+      headers: {
+        'Content-Type': 'application/json',
+      },
     },
-  });
+  );
 
   if (res.status === 404) {
     setNotFound(true);
@@ -244,32 +373,15 @@ const ChatWindow = ({ id }: { id?: string }) => {
   const [chatId, setChatId] = useState<string | undefined>(id);
   const [newChatCreated, setNewChatCreated] = useState(false);
 
-  const [chatModelProvider, setChatModelProvider] = useState<ChatModelProvider>(
-    {
-      name: '',
-      provider: '',
-    },
-  );
-
-  const [embeddingModelProvider, setEmbeddingModelProvider] =
-    useState<EmbeddingModelProvider>({
-      name: '',
-      provider: '',
-    });
-
-  const [isConfigReady, setIsConfigReady] = useState(false);
   const [hasError, setHasError] = useState(false);
   const [isReady, setIsReady] = useState(false);
 
-  useEffect(() => {
+  const [isWSReady, setIsWSReady] = useState(false);
-    checkConfig(
+  const ws = useSocket(
-      setChatModelProvider,
+    process.env.NEXT_PUBLIC_WS_URL!,
-      setEmbeddingModelProvider,
+    setIsWSReady,
-      setIsConfigReady,
+    setHasError,
-      setHasError,
+  );
-    );
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, []);
 
   const [loading, setLoading] = useState(false);
   const [messageAppeared, setMessageAppeared] = useState(false);
@@ -287,6 +399,8 @@ const ChatWindow = ({ id }: { id?: string }) => {
 
   const [notFound, setNotFound] = useState(false);
 
+  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
+
   useEffect(() => {
     if (
       chatId &&
@@ -312,6 +426,16 @@ const ChatWindow = ({ id }: { id?: string }) => {
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, []);
 
+  useEffect(() => {
+    return () => {
+      if (ws?.readyState === 1) {
+        ws.close();
+        console.debug(new Date(), 'ws:cleanup');
+      }
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
   const messagesRef = useRef<Message[]>([]);
 
   useEffect(() => {
@@ -319,18 +443,18 @@ const ChatWindow = ({ id }: { id?: string }) => {
   }, [messages]);
 
   useEffect(() => {
-    if (isMessagesLoaded && isConfigReady) {
+    if (isMessagesLoaded && isWSReady) {
       setIsReady(true);
       console.debug(new Date(), 'app:ready');
     } else {
       setIsReady(false);
     }
-  }, [isMessagesLoaded, isConfigReady]);
+  }, [isMessagesLoaded, isWSReady]);
 
   const sendMessage = async (message: string, messageId?: string) => {
     if (loading) return;
-    if (!isConfigReady) {
+    if (!ws || ws.readyState !== WebSocket.OPEN) {
-      toast.error('Cannot send message before the configuration is ready');
+      toast.error('Cannot send message while disconnected');
       return;
     }
 
@@ -343,27 +467,18 @@ const ChatWindow = ({ id }: { id?: string }) => {
 
     messageId = messageId ?? crypto.randomBytes(7).toString('hex');
 
-    console.log(
+    ws.send(
       JSON.stringify({
-        content: message,
+        type: 'message',
         message: {
           messageId: messageId,
           chatId: chatId!,
           content: message,
         },
-        chatId: chatId!,
         files: fileIds,
         focusMode: focusMode,
         optimizationMode: optimizationMode,
-        history: chatHistory,
+        history: [...chatHistory, ['human', message]],
-        chatModel: {
-          name: chatModelProvider.name,
-          provider: chatModelProvider.provider,
-        },
-        embeddingModel: {
-          name: embeddingModelProvider.name,
-          provider: embeddingModelProvider.provider,
-        },
       }),
     );
 
@@ -378,7 +493,9 @@ const ChatWindow = ({ id }: { id?: string }) => {
       },
     ]);
 
-    const messageHandler = async (data: any) => {
+    const messageHandler = async (e: MessageEvent) => {
+      const data = JSON.parse(e.data);
+
       if (data.type === 'error') {
         toast.error(data.data);
         setLoading(false);
@@ -441,6 +558,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
           ['assistant', recievedMessage],
         ]);
 
+        ws?.removeEventListener('message', messageHandler);
         setLoading(false);
 
         const lastMsg = messagesRef.current[messagesRef.current.length - 1];
@@ -466,72 +584,16 @@ const ChatWindow = ({ id }: { id?: string }) => {
         const autoVideoSearch = localStorage.getItem('autoVideoSearch');
 
         if (autoImageSearch === 'true') {
-          document
+          document.getElementById('search-images')?.click();
-            .getElementById(`search-images-${lastMsg.messageId}`)
-            ?.click();
         }
 
         if (autoVideoSearch === 'true') {
-          document
+          document.getElementById('search-videos')?.click();
-            .getElementById(`search-videos-${lastMsg.messageId}`)
-            ?.click();
         }
       }
     };
 
-    const res = await fetch('/api/chat', {
+    ws?.addEventListener('message', messageHandler);
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      body: JSON.stringify({
-        content: message,
-        message: {
-          messageId: messageId,
-          chatId: chatId!,
-          content: message,
-        },
-        chatId: chatId!,
-        files: fileIds,
-        focusMode: focusMode,
-        optimizationMode: optimizationMode,
-        history: chatHistory,
-        chatModel: {
-          name: chatModelProvider.name,
-          provider: chatModelProvider.provider,
-        },
-        embeddingModel: {
-          name: embeddingModelProvider.name,
-          provider: embeddingModelProvider.provider,
-        },
-      }),
-    });
-
-    if (!res.body) throw new Error('No response body');
-
-    const reader = res.body?.getReader();
-    const decoder = new TextDecoder('utf-8');
-
-    let partialChunk = '';
-
-    while (true) {
-      const { value, done } = await reader.read();
-      if (done) break;
-
-      partialChunk += decoder.decode(value, { stream: true });
-
-      try {
-        const messages = partialChunk.split('\n');
-        for (const msg of messages) {
-          if (!msg.trim()) continue;
-          const json = JSON.parse(msg);
-          messageHandler(json);
-        }
-        partialChunk = '';
-      } catch (error) {
-        console.warn('Incomplete JSON, waiting for next chunk...');
-      }
-    }
   };
 
   const rewrite = (messageId: string) => {
@@ -552,11 +614,11 @@ const ChatWindow = ({ id }: { id?: string }) => {
   };
 
   useEffect(() => {
-    if (isReady && initialMessage && isConfigReady) {
+    if (isReady && initialMessage && ws?.readyState === 1) {
       sendMessage(initialMessage);
     }
     // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [isConfigReady, isReady, initialMessage]);
+  }, [ws?.readyState, isReady, initialMessage, isWSReady]);
 
   if (hasError) {
     return (

ui/components/DeleteChat.tsx

@@ -29,12 +29,15 @@ const DeleteChat = ({
   const handleDelete = async () => {
     setLoading(true);
     try {
-      const res = await fetch(`/api/chats/${chatId}`, {
+      const res = await fetch(
-        method: 'DELETE',
+        `${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
-        headers: {
+        {
-          'Content-Type': 'application/json',
+          method: 'DELETE',
+          headers: {
+            'Content-Type': 'application/json',
+          },
         },
-      });
+      );
 
       if (res.status != 200) {
         throw new Error('Failed to delete chat');

ui/components/MessageBox.tsx

@@ -68,13 +68,7 @@ const MessageBox = ({
   return (
     <div>
       {message.role === 'user' && (
-        <div
+        <div className={cn('w-full', messageIndex === 0 ? 'pt-16' : 'pt-8', 'break-words')}>
-          className={cn(
-            'w-full',
-            messageIndex === 0 ? 'pt-16' : 'pt-8',
-            'break-words',
-          )}
-        >
           <h2 className="text-black dark:text-white font-medium text-3xl lg:w-9/12">
             {message.content}
           </h2>
@@ -193,12 +187,10 @@ const MessageBox = ({
             <SearchImages
               query={history[messageIndex - 1].content}
               chatHistory={history.slice(0, messageIndex - 1)}
-              messageId={message.messageId}
             />
             <SearchVideos
               chatHistory={history.slice(0, messageIndex - 1)}
               query={history[messageIndex - 1].content}
-              messageId={message.messageId}
             />
           </div>
         </div>

ui/components/MessageInputActions/Attach.tsx

@@ -41,7 +41,7 @@ const Attach = ({
     data.append('embedding_model_provider', embeddingModelProvider!);
     data.append('embedding_model', embeddingModel!);
 
-    const res = await fetch(`/api/uploads`, {
+    const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
       method: 'POST',
       body: data,
     });

ui/components/MessageInputActions/AttachSmall.tsx

@@ -39,7 +39,7 @@ const AttachSmall = ({
     data.append('embedding_model_provider', embeddingModelProvider!);
     data.append('embedding_model', embeddingModel!);
 
-    const res = await fetch(`/api/uploads`, {
+    const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
       method: 'POST',
       body: data,
     });

ui/components/MessageInputActions/Copilot.tsx

@@ -1,5 +1,6 @@
 import { cn } from '@/lib/utils';
 import { Switch } from '@headlessui/react';
+import { useEffect } from 'react';
 
 const CopilotToggle = ({
   copilotEnabled,
@@ -8,11 +9,33 @@ const CopilotToggle = ({
   copilotEnabled: boolean;
   setCopilotEnabled: (enabled: boolean) => void;
 }) => {
+  const fetchAndSetCopilotEnabled = async () => {
+    const res = await fetch(
+      `${process.env.NEXT_PUBLIC_API_URL}/config/preferences`,
+      {
+        method: 'GET',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+      },
+    );
+
+    const preferences = await res.json();
+
+    setCopilotEnabled(preferences.isCopilotEnabled);
+  };
+
+  useEffect(() => {
+    fetchAndSetCopilotEnabled();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
   return (
     <div className="group flex flex-row items-center space-x-1 active:scale-95 duration-200 transition cursor-pointer">
       <Switch
         checked={copilotEnabled}
         onChange={setCopilotEnabled}
+        disabled={true}
         className="bg-light-secondary dark:bg-dark-secondary border border-light-200/70 dark:border-dark-200 relative inline-flex h-5 w-10 sm:h-6 sm:w-11 items-center rounded-full"
       >
         <span className="sr-only">Copilot</span>

ui/components/MessageInputActions/Focus.tsx

@@ -45,13 +45,25 @@ const focusModes = [
     key: 'youtubeSearch',
     title: 'Youtube',
     description: 'Search and watch videos',
-    icon: <SiYoutube className="h-5 w-auto mr-0.5" />,
+    icon: (
+      <SiYoutube
+        className="h-5 w-auto mr-0.5"
+        onPointerEnterCapture={undefined}
+        onPointerLeaveCapture={undefined}
+      />
+    ),
   },
   {
     key: 'redditSearch',
     title: 'Reddit',
     description: 'Search for discussions and opinions',
-    icon: <SiReddit className="h-5 w-auto mr-0.5" />,
+    icon: (
+      <SiReddit
+        className="h-5 w-auto mr-0.5"
+        onPointerEnterCapture={undefined}
+        onPointerLeaveCapture={undefined}
+      />
+    ),
   },
 ];
 

ui/components/MessageSources.tsx

@@ -69,15 +69,11 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
           <div className="flex flex-row items-center space-x-1">
             {sources.slice(3, 6).map((source, i) => {
               return source.metadata.url === 'File' ? (
-                <div
+                <div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
-                  key={i}
-                  className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full"
-                >
                   <File size={12} className="text-white/70" />
                 </div>
               ) : (
                 <img
-                  key={i}
                   src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
                   width={16}
                   height={16}