feat(package): update scripts

.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

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

app.dockerfile

@@ -1,13 +1,20 @@
 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 ui /home/perplexica/
+COPY src /home/perplexica/src
+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

@@ -1,17 +0,0 @@
-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

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

drizzle.config.ts

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

next-env.d.ts

@@ -0,0 +1,5 @@
+/// <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

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

sample.config.toml

@@ -24,4 +24,4 @@ MODEL_NAME = ""
 API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
 [API_ENDPOINTS]
-SEARXNG = "http://localhost:32768" # SearxNG API URL
+SEARXNG = "" # SearxNG API URL - http://localhost:32768

src/app.ts

@@ -1,38 +0,0 @@
-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

@@ -0,0 +1,360 @@
+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

@@ -0,0 +1,69 @@
+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

@@ -0,0 +1,15 @@
+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/discover/route.ts

@@ -0,0 +1,61 @@
+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

@@ -0,0 +1,83 @@
+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

@@ -0,0 +1,47 @@
+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

@@ -0,0 +1,81 @@
+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

@@ -0,0 +1,134 @@
+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

@@ -0,0 +1,83 @@
+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

@@ -0,0 +1,9 @@
+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/discover/page.tsx

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

src/app/library/page.tsx

@@ -21,7 +21,7 @@ const Page = () => {
     const fetchChats = async () => {
       setLoading(true);
 
-      const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chats`, {
+      const res = await fetch(`/api/chats`, {
         method: 'GET',
         headers: {
           'Content-Type': 'application/json',

src/components/ChatWindow.tsx

@@ -29,36 +29,27 @@ export interface File {
   fileId: string;
 }
 
-const useSocket = (
+interface ChatModelProvider {
-  url: string,
+  name: string;
-  setIsWSReady: (ready: boolean) => void,
+  provider: string;
-  setError: (error: boolean) => void,
-) => {
-  const wsRef = useRef<WebSocket | null>(null);
-  const reconnectTimeoutRef = useRef<NodeJS.Timeout>();
-  const retryCountRef = useRef(0);
-  const isCleaningUpRef = useRef(false);
-  const MAX_RETRIES = 3;
-  const INITIAL_BACKOFF = 1000; // 1 second
-  const isConnectionErrorRef = useRef(false);
-
-  const getBackoffDelay = (retryCount: number) => {
-    return Math.min(INITIAL_BACKOFF * Math.pow(2, retryCount), 10000); // Cap at 10 seconds
-  };
-
-  useEffect(() => {
-    const connectWs = async () => {
-      if (wsRef.current?.readyState === WebSocket.OPEN) {
-        wsRef.current.close();
 }
 
+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 {
     let chatModel = localStorage.getItem('chatModel');
     let chatModelProvider = localStorage.getItem('chatModelProvider');
     let embeddingModel = localStorage.getItem('embeddingModel');
-        let embeddingModelProvider = localStorage.getItem(
+    let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
-          'embeddingModelProvider',
-        );
 
     const autoImageSearch = localStorage.getItem('autoImageSearch');
     const autoVideoSearch = localStorage.getItem('autoVideoSearch');
@@ -71,14 +62,11 @@ const useSocket = (
       localStorage.setItem('autoVideoSearch', 'false');
     }
 
-        const providers = await fetch(
+    const providers = await fetch(`/api/models`, {
-          `${process.env.NEXT_PUBLIC_API_URL}/models`,
-          {
       headers: {
         'Content-Type': 'application/json',
       },
-          },
+    }).then(async (res) => {
-        ).then(async (res) => {
       if (!res.ok)
         throw new Error(
           `Failed to fetch models: ${res.status} ${res.statusText}`,
@@ -100,10 +88,7 @@ const useSocket = (
 
         chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
 
-            if (
+        if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
-              !chatModelProviders ||
-              Object.keys(chatModelProviders).length === 0
-            )
           return toast.error('No chat models available');
       }
 
@@ -125,10 +110,7 @@ const useSocket = (
       localStorage.setItem('chatModel', chatModel!);
       localStorage.setItem('chatModelProvider', chatModelProvider);
       localStorage.setItem('embeddingModel', embeddingModel!);
-          localStorage.setItem(
+      localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
-            'embeddingModelProvider',
-            embeddingModelProvider,
-          );
     } else {
       const chatModelProviders = providers.chatModelProviders;
       const embeddingModelProviders = providers.embeddingModelProviders;
@@ -165,10 +147,7 @@ const useSocket = (
         !embeddingModelProviders[embeddingModelProvider]
       ) {
         embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-            localStorage.setItem(
+        localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
-              'embeddingModelProvider',
-              embeddingModelProvider,
-            );
       }
 
       if (
@@ -182,128 +161,23 @@ const useSocket = (
       }
     }
 
-        const wsURL = new URL(url);
+    setChatModelProvider({
-        const searchParams = new URLSearchParams({});
+      name: chatModel!,
-
+      provider: chatModelProvider,
-        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 = () => {
+    setEmbeddingModelProvider({
-          clearTimeout(timeoutId);
+      name: embeddingModel!,
-          setIsWSReady(false);
+      provider: embeddingModelProvider,
-          toast.error('WebSocket connection error.');
+    });
-        };
 
-        ws.onclose = () => {
+    setIsConfigReady(true);
-          clearTimeout(timeoutId);
+  } catch (err) {
-          setIsWSReady(false);
+    console.error('An error occurred while checking the configuration:', err);
-          console.debug(new Date(), 'ws:disconnected');
+    setIsConfigReady(false);
-          if (!isCleaningUpRef.current && !isConnectionErrorRef.current) {
+    setHasError(true);
-            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;
-      }
-
-      const backoffDelay = getBackoffDelay(retryCountRef.current);
-      console.debug(
-        new Date(),
-        `ws:retry attempt=${retryCountRef.current}/${MAX_RETRIES} delay=${backoffDelay}ms`,
-      );
-
-      if (reconnectTimeoutRef.current) {
-        clearTimeout(reconnectTimeoutRef.current);
-      }
-
-      reconnectTimeoutRef.current = setTimeout(() => {
-        connectWs();
-      }, backoffDelay);
-    };
-
-    connectWs();
-
-    return () => {
-      if (reconnectTimeoutRef.current) {
-        clearTimeout(reconnectTimeoutRef.current);
-      }
-      if (wsRef.current?.readyState === WebSocket.OPEN) {
-        wsRef.current.close();
-        isCleaningUpRef.current = true;
-        console.debug(new Date(), 'ws:cleanup');
-      }
-    };
-  }, [url, setIsWSReady, setError]);
-
-  return wsRef.current;
-};
 
 const loadMessages = async (
   chatId: string,
@@ -315,15 +189,12 @@ const loadMessages = async (
   setFiles: (files: File[]) => void,
   setFileIds: (fileIds: string[]) => void,
 ) => {
-  const res = await fetch(
+  const res = await fetch(`/api/chats/${chatId}`, {
-    `${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
-    {
     method: 'GET',
     headers: {
       'Content-Type': 'application/json',
     },
-    },
+  });
-  );
 
   if (res.status === 404) {
     setNotFound(true);
@@ -373,15 +244,32 @@ 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);
 
-  const [isWSReady, setIsWSReady] = useState(false);
+  useEffect(() => {
-  const ws = useSocket(
+    checkConfig(
-    process.env.NEXT_PUBLIC_WS_URL!,
+      setChatModelProvider,
-    setIsWSReady,
+      setEmbeddingModelProvider,
+      setIsConfigReady,
       setHasError,
     );
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
 
   const [loading, setLoading] = useState(false);
   const [messageAppeared, setMessageAppeared] = useState(false);
@@ -399,8 +287,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
 
   const [notFound, setNotFound] = useState(false);
 
-  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
-
   useEffect(() => {
     if (
       chatId &&
@@ -426,16 +312,6 @@ 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(() => {
@@ -443,18 +319,18 @@ const ChatWindow = ({ id }: { id?: string }) => {
   }, [messages]);
 
   useEffect(() => {
-    if (isMessagesLoaded && isWSReady) {
+    if (isMessagesLoaded && isConfigReady) {
       setIsReady(true);
       console.debug(new Date(), 'app:ready');
     } else {
       setIsReady(false);
     }
-  }, [isMessagesLoaded, isWSReady]);
+  }, [isMessagesLoaded, isConfigReady]);
 
   const sendMessage = async (message: string, messageId?: string) => {
     if (loading) return;
-    if (!ws || ws.readyState !== WebSocket.OPEN) {
+    if (!isConfigReady) {
-      toast.error('Cannot send message while disconnected');
+      toast.error('Cannot send message before the configuration is ready');
       return;
     }
 
@@ -467,18 +343,27 @@ const ChatWindow = ({ id }: { id?: string }) => {
 
     messageId = messageId ?? crypto.randomBytes(7).toString('hex');
 
-    ws.send(
+    console.log(
       JSON.stringify({
-        type: 'message',
+        content: message,
         message: {
           messageId: messageId,
           chatId: chatId!,
           content: message,
         },
+        chatId: chatId!,
         files: fileIds,
         focusMode: focusMode,
         optimizationMode: optimizationMode,
-        history: [...chatHistory, ['human', message]],
+        history: chatHistory,
+        chatModel: {
+          name: chatModelProvider.name,
+          provider: chatModelProvider.provider,
+        },
+        embeddingModel: {
+          name: embeddingModelProvider.name,
+          provider: embeddingModelProvider.provider,
+        },
       }),
     );
 
@@ -493,9 +378,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
       },
     ]);
 
-    const messageHandler = async (e: MessageEvent) => {
+    const messageHandler = async (data: any) => {
-      const data = JSON.parse(e.data);
-
       if (data.type === 'error') {
         toast.error(data.data);
         setLoading(false);
@@ -558,7 +441,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
           ['assistant', recievedMessage],
         ]);
 
-        ws?.removeEventListener('message', messageHandler);
         setLoading(false);
 
         const lastMsg = messagesRef.current[messagesRef.current.length - 1];
@@ -584,16 +466,72 @@ const ChatWindow = ({ id }: { id?: string }) => {
         const autoVideoSearch = localStorage.getItem('autoVideoSearch');
 
         if (autoImageSearch === 'true') {
-          document.getElementById('search-images')?.click();
+          document
+            .getElementById(`search-images-${lastMsg.messageId}`)
+            ?.click();
         }
 
         if (autoVideoSearch === 'true') {
-          document.getElementById('search-videos')?.click();
+          document
+            .getElementById(`search-videos-${lastMsg.messageId}`)
+            ?.click();
         }
       }
     };
 
-    ws?.addEventListener('message', messageHandler);
+    const res = await fetch('/api/chat', {
+      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) => {
@@ -614,11 +552,11 @@ const ChatWindow = ({ id }: { id?: string }) => {
   };
 
   useEffect(() => {
-    if (isReady && initialMessage && ws?.readyState === 1) {
+    if (isReady && initialMessage && isConfigReady) {
       sendMessage(initialMessage);
     }
     // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [ws?.readyState, isReady, initialMessage, isWSReady]);
+  }, [isConfigReady, isReady, initialMessage]);
 
   if (hasError) {
     return (

src/components/DeleteChat.tsx

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

src/components/MessageBox.tsx

@@ -193,10 +193,12 @@ 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>

src/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(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
+    const res = await fetch(`/api/uploads`, {
       method: 'POST',
       body: data,
     });

src/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(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
+    const res = await fetch(`/api/uploads`, {
       method: 'POST',
       body: data,
     });

src/components/MessageInputActions/Focus.tsx

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

src/components/MessageSources.tsx

@@ -69,11 +69,15 @@ 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 className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
+                <div
+                  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}

src/components/SearchImages.tsx

@@ -14,9 +14,11 @@ type Image = {
 const SearchImages = ({
   query,
   chatHistory,
+  messageId,
 }: {
   query: string;
   chatHistory: Message[];
+  messageId: string;
 }) => {
   const [images, setImages] = useState<Image[] | null>(null);
   const [loading, setLoading] = useState(false);
@@ -27,7 +29,7 @@ const SearchImages = ({
     <>
       {!loading && images === null && (
         <button
-          id="search-images"
+          id={`search-images-${messageId}`}
           onClick={async () => {
             setLoading(true);
 
@@ -37,9 +39,7 @@ const SearchImages = ({
             const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
             const customOpenAIKey = localStorage.getItem('openAIApiKey');
 
-            const res = await fetch(
+            const res = await fetch(`/api/images`, {
-              `${process.env.NEXT_PUBLIC_API_URL}/images`,
-              {
               method: 'POST',
               headers: {
                 'Content-Type': 'application/json',
@@ -56,8 +56,7 @@ const SearchImages = ({
                   }),
                 },
               }),
-              },
+            });
-            );
 
             const data = await res.json();
 

src/components/SearchVideos.tsx

@@ -27,9 +27,11 @@ declare module 'yet-another-react-lightbox' {
 const Searchvideos = ({
   query,
   chatHistory,
+  messageId,
 }: {
   query: string;
   chatHistory: Message[];
+  messageId: string;
 }) => {
   const [videos, setVideos] = useState<Video[] | null>(null);
   const [loading, setLoading] = useState(false);
@@ -42,7 +44,7 @@ const Searchvideos = ({
     <>
       {!loading && videos === null && (
         <button
-          id="search-videos"
+          id={`search-videos-${messageId}`}
           onClick={async () => {
             setLoading(true);
 
@@ -52,9 +54,7 @@ const Searchvideos = ({
             const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
             const customOpenAIKey = localStorage.getItem('openAIApiKey');
 
-            const res = await fetch(
+            const res = await fetch(`/api/videos`, {
-              `${process.env.NEXT_PUBLIC_API_URL}/videos`,
-              {
               method: 'POST',
               headers: {
                 'Content-Type': 'application/json',
@@ -71,8 +71,7 @@ const Searchvideos = ({
                   }),
                 },
               }),
-              },
+            });
-            );
 
             const data = await res.json();
 

src/config.ts

@@ -1,117 +0,0 @@
-import fs from 'fs';
-import path from 'path';
-import toml from '@iarna/toml';
-
-const configFileName = 'config.toml';
-
-interface Config {
-  GENERAL: {
-    PORT: number;
-    SIMILARITY_MEASURE: string;
-    KEEP_ALIVE: string;
-  };
-  MODELS: {
-    OPENAI: {
-      API_KEY: string;
-    };
-    GROQ: {
-      API_KEY: string;
-    };
-    ANTHROPIC: {
-      API_KEY: string;
-    };
-    GEMINI: {
-      API_KEY: string;
-    };
-    OLLAMA: {
-      API_URL: string;
-    };
-    CUSTOM_OPENAI: {
-      API_URL: string;
-      API_KEY: string;
-      MODEL_NAME: string;
-    };
-  };
-  API_ENDPOINTS: {
-    SEARXNG: string;
-  };
-}
-
-type RecursivePartial<T> = {
-  [P in keyof T]?: RecursivePartial<T[P]>;
-};
-
-const loadConfig = () =>
-  toml.parse(
-    fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
-  ) as any as Config;
-
-export const getPort = () => loadConfig().GENERAL.PORT;
-
-export const getSimilarityMeasure = () =>
-  loadConfig().GENERAL.SIMILARITY_MEASURE;
-
-export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
-
-export const getOpenaiApiKey = () => loadConfig().MODELS.OPENAI.API_KEY;
-
-export const getGroqApiKey = () => loadConfig().MODELS.GROQ.API_KEY;
-
-export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
-
-export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
-
-export const getSearxngApiEndpoint = () =>
-  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
-
-export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
-
-export const getCustomOpenaiApiKey = () =>
-  loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
-
-export const getCustomOpenaiApiUrl = () =>
-  loadConfig().MODELS.CUSTOM_OPENAI.API_URL;
-
-export const getCustomOpenaiModelName = () =>
-  loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
-
-const mergeConfigs = (current: any, update: any): any => {
-  if (update === null || update === undefined) {
-    return current;
-  }
-
-  if (typeof current !== 'object' || current === null) {
-    return update;
-  }
-
-  const result = { ...current };
-
-  for (const key in update) {
-    if (Object.prototype.hasOwnProperty.call(update, key)) {
-      const updateValue = update[key];
-
-      if (
-        typeof updateValue === 'object' &&
-        updateValue !== null &&
-        typeof result[key] === 'object' &&
-        result[key] !== null
-      ) {
-        result[key] = mergeConfigs(result[key], updateValue);
-      } else if (updateValue !== undefined) {
-        result[key] = updateValue;
-      }
-    }
-  }
-
-  return result;
-};
-
-export const updateConfig = (config: RecursivePartial<Config>) => {
-  const currentConfig = loadConfig();
-  const mergedConfig = mergeConfigs(currentConfig, config);
-
-  fs.writeFileSync(
-    path.join(__dirname, `../${configFileName}`),
-    toml.stringify(mergedConfig),
-  );
-};

src/lib/actions.ts

@@ -7,7 +7,7 @@ export const getSuggestions = async (chatHisory: Message[]) => {
   const customOpenAIKey = localStorage.getItem('openAIApiKey');
   const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
 
-  const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/suggestions`, {
+  const res = await fetch(`/api/suggestions`, {
     method: 'POST',
     headers: {
       'Content-Type': 'application/json',

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

src/lib/chains/suggestionGeneratorAgent.ts

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

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

src/lib/config.ts

@@ -62,7 +62,7 @@ export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
 export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
 
 export const getSearxngApiEndpoint = () =>
-  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
+  loadConfig().API_ENDPOINTS.SEARXNG || process.env.SEARXNG_API_URL;
 
 export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
 
@@ -109,7 +109,6 @@ const mergeConfigs = (current: any, update: any): any => {
 export const updateConfig = (config: RecursivePartial<Config>) => {
   const currentConfig = loadConfig();
   const mergedConfig = mergeConfigs(currentConfig, config);
-  console.log(mergedConfig);
   fs.writeFileSync(
     path.join(path.join(process.cwd(), `${configFileName}`)),
     toml.stringify(mergedConfig),

src/lib/db/index.ts

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

src/lib/huggingfaceTransformer.ts

@@ -1,82 +0,0 @@
-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

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

src/lib/providers/gemini.ts

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

src/lib/providers/groq.ts

@@ -1,6 +1,82 @@
 import { ChatOpenAI } from '@langchain/openai';
-import { getGroqApiKey } from '../../config';
+import { getGroqApiKey } from '../config';
-import logger from '../../utils/logger';
+import { ChatModel } from '.';
+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();
@@ -8,129 +84,25 @@ export const loadGroqChatModels = async () => {
   if (!groqApiKey) return {};
 
   try {
-    const chatModels = {
+    const chatModels: Record<string, ChatModel> = {};
-      'llama-3.3-70b-versatile': {
+
-        displayName: 'Llama 3.3 70B',
+    groqChatModels.forEach((model) => {
-        model: new ChatOpenAI(
+      chatModels[model.key] = {
-          {
+        displayName: model.displayName,
+        model: new ChatOpenAI({
           openAIApiKey: groqApiKey,
-            modelName: 'llama-3.3-70b-versatile',
+          modelName: model.key,
           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) {
-    logger.error(`Error loading Groq models: ${err}`);
+    console.error(`Error loading Groq models: ${err}`);
     return {};
   }
 };

src/lib/providers/index.ts

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

src/lib/providers/ollama.ts

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

src/lib/providers/openai.ts

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

src/lib/providers/transformers.ts

@@ -1,32 +0,0 @@
-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[];
@@ -30,11 +30,12 @@ export const searchSearxng = async (
 
   if (opts) {
     Object.keys(opts).forEach((key) => {
-      if (Array.isArray(opts[key])) {
+      const value = opts[key as keyof SearxngSearchOptions];
-        url.searchParams.append(key, opts[key].join(','));
+      if (Array.isArray(value)) {
+        url.searchParams.append(key, value.join(','));
         return;
       }
-      url.searchParams.append(key, opts[key]);
+      url.searchParams.append(key, value as string);
     });
   }
 

src/lib/utils/documents.ts

@@ -3,7 +3,6 @@ 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();

src/routes/chats.ts

@@ -1,66 +0,0 @@
-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

@@ -1,104 +0,0 @@
-import express from 'express';
-import {
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '../lib/providers';
-import {
-  getGroqApiKey,
-  getOllamaApiEndpoint,
-  getAnthropicApiKey,
-  getGeminiApiKey,
-  getOpenaiApiKey,
-  updateConfig,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiApiKey,
-  getCustomOpenaiModelName,
-} from '../config';
-import logger from '../utils/logger';
-
-const router = express.Router();
-
-router.get('/', async (_, res) => {
-  try {
-    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 config = req.body;
-
-  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);
-
-  res.status(200).json({ message: 'Config updated' });
-});
-
-export default router;

src/routes/discover.ts

@@ -1,48 +0,0 @@
-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

@@ -1,82 +0,0 @@
-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

@@ -1,24 +0,0 @@
-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

@@ -1,36 +0,0 @@
-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 [chatModelProviders, embeddingModelProviders] = await Promise.all([
-      getAvailableChatModelProviders(),
-      getAvailableEmbeddingModelProviders(),
-    ]);
-
-    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

@@ -1,158 +0,0 @@
-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

@@ -1,81 +0,0 @@
-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

@@ -1,151 +0,0 @@
-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;