feat(chat-window): remove debugging code,

Only set scrollIntoView for user msg.

.github/workflows/docker-build.yaml

@@ -10,9 +10,6 @@ on:
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        service: [backend, app]
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
@@ -36,17 +33,12 @@ jobs:
         id: version
         run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
 
-      - name: Build and push Docker image for ${{ matrix.service }}
+      - name: Build and push Docker image
         if: github.ref == 'refs/heads/master' && github.event_name == 'push'
         run: |
           docker buildx create --use
-          if [[ "${{ matrix.service }}" == "backend" ]]; then \
+          DOCKERFILE=app.dockerfile; \
-            DOCKERFILE=backend.dockerfile; \
+          IMAGE_NAME=perplexica; \
-            IMAGE_NAME=perplexica-backend; \
-          else \
-            DOCKERFILE=app.dockerfile; \
-            IMAGE_NAME=perplexica-frontend; \
-          fi
           docker buildx build --platform linux/amd64,linux/arm64 \
             --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:main \
             --cache-to=type=inline \
@@ -54,17 +46,12 @@ jobs:
             -t itzcrazykns1337/${IMAGE_NAME}:main \
             --push .
 
-      - name: Build and push release Docker image for ${{ matrix.service }}
+      - name: Build and push release Docker image
         if: github.event_name == 'release'
         run: |
           docker buildx create --use
-          if [[ "${{ matrix.service }}" == "backend" ]]; then \
+          DOCKERFILE=app.dockerfile; \
-            DOCKERFILE=backend.dockerfile; \
+          IMAGE_NAME=perplexica; \
-            IMAGE_NAME=perplexica-backend; \
-          else \
-            DOCKERFILE=app.dockerfile; \
-            IMAGE_NAME=perplexica-frontend; \
-          fi
           docker buildx build --platform linux/amd64,linux/arm64 \
             --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
             --cache-to=type=inline \

.gitignore

@@ -2,12 +2,11 @@
 node_modules/
 npm-debug.log
 yarn-error.log
-package-lock.json
 
 # Build output
-/.next/
+.next/
-/out/
+out/
-/dist/
+dist/
 
 # IDE/Editor specific
 .vscode/
@@ -38,6 +37,3 @@ Thumbs.db
 # Db
 db.sqlite
 /searxng
-
-# Dev
-docker-compose-dev.yaml

.prettierrc.js

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

README.md

@@ -1,5 +1,21 @@
 # 🚀 Perplexica - An AI-powered search engine 🔎 <!-- omit in toc -->
 
+<div align="center" markdown="1">
+   <sup>Special thanks to:</sup>
+   <br>
+   <br>
+   <a href="https://www.warp.dev/perplexica">
+      <img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/775dd593-9b5f-40f1-bf48-479faff4c27b">
+   </a>
+
+### [Warp, the AI Devtool that lives in your terminal](https://www.warp.dev/perplexica)
+
+[Available for MacOS, Linux, & Windows](https://www.warp.dev/perplexica)
+
+</div>
+
+<hr/>
+
 [![Discord](https://dcbadge.vercel.app/api/server/26aArMy8tT?style=flat&compact=true)](https://discord.gg/26aArMy8tT)
 
 ![preview](.assets/perplexica-screenshot.png?)

app.dockerfile

@@ -1,15 +1,27 @@
-FROM node:20.18.0-alpine
+FROM node:20.18.0-alpine AS builder
-
-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 package.json yarn.lock ./
-
 RUN yarn install --frozen-lockfile
+
+COPY tsconfig.json next.config.mjs next-env.d.ts postcss.config.js drizzle.config.ts tailwind.config.ts ./
+COPY src ./src
+COPY public ./public
+
+RUN mkdir -p /home/perplexica/data
 RUN yarn build
 
-CMD ["yarn", "start"]
+FROM node:20.18.0-alpine
+
+WORKDIR /home/perplexica
+
+COPY --from=builder /home/perplexica/public ./public
+COPY --from=builder /home/perplexica/.next/static ./public/_next/static
+
+COPY --from=builder /home/perplexica/.next/standalone ./
+COPY --from=builder /home/perplexica/data ./data
+
+RUN mkdir /home/perplexica/uploads
+
+CMD ["node", "server.js"]

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

@@ -4,46 +4,26 @@ services:
     volumes:
       - ./searxng:/etc/searxng:rw
     ports:
-      - '4000:8080'
+      - 4000:8080
     networks:
       - perplexica-network
     restart: unless-stopped
 
-  perplexica-backend:
+  app:
+    image: itzcrazykns1337/perplexica:main
     build:
       context: .
-      dockerfile: backend.dockerfile
+      dockerfile: app.dockerfile
-    image: itzcrazykns1337/perplexica-backend:main
     environment:
       - SEARXNG_API_URL=http://searxng:8080
-    depends_on:
-      - searxng
     ports:
-      - '3001:3001'
+      - 3000:3000
+    networks:
+      - perplexica-network
     volumes:
       - backend-dbstore:/home/perplexica/data
       - uploads:/home/perplexica/uploads
       - ./config.toml:/home/perplexica/config.toml
-    extra_hosts:
-      - 'host.docker.internal:host-gateway'
-    networks:
-      - perplexica-network
-    restart: unless-stopped
-
-  perplexica-frontend:
-    build:
-      context: .
-      dockerfile: app.dockerfile
-      args:
-        - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
-        - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-    image: itzcrazykns1337/perplexica-frontend:main
-    depends_on:
-      - perplexica-backend
-    ports:
-      - '3000:3000'
-    networks:
-      - perplexica-network
     restart: unless-stopped
 
 networks:

drizzle.config.ts

@@ -2,7 +2,7 @@ import { defineConfig } from 'drizzle-kit';
 
 export default defineConfig({
   dialect: 'sqlite',
-  schema: './src/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.

next.config.mjs

@@ -1,5 +1,6 @@
 /** @type {import('next').NextConfig} */
 const nextConfig = {
+  output: 'standalone',
   images: {
     remotePatterns: [
       {
@@ -7,6 +8,7 @@ const nextConfig = {
       },
     ],
   },
+  serverExternalPackages: ['pdf-parse'],
 };
 
 export default nextConfig;

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": "npm run db:push && next build",
-    "dev": "nodemon --ignore uploads/ src/app.ts ",
+    "start": "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/google-genai": "^0.0.23",
+    "@langchain/core": "^0.3.42",
     "@langchain/openai": "^0.0.25",
-    "@xenova/transformers": "^2.17.1",
+    "@langchain/textsplitters": "^0.1.0",
-    "axios": "^1.6.8",
+    "@tailwindcss/typography": "^0.5.12",
-    "better-sqlite3": "^11.0.0",
+    "axios": "^1.8.3",
+    "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

@@ -3,12 +3,6 @@ PORT = 3001 # Port to run the server on
 SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
 KEEP_ALIVE = "5m" # How long to keep Ollama models loaded into memory. (Instead of using -1 use "-1m")
 
-[SEARCH_ENGINE_BACKENDS] # "google" | "searxng" | "bing" | "brave" | "yacy"
-SEARCH = "searxng"
-IMAGE = "searxng"
-VIDEO = "searxng"
-NEWS = "searxng"
-
 [MODELS.OPENAI]
 API_KEY = ""
 
@@ -24,22 +18,10 @@ API_KEY = ""
 [MODELS.CUSTOM_OPENAI]
 API_KEY = ""
 API_URL = ""
+MODEL_NAME = ""
 
 [MODELS.OLLAMA]
 API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
-[SEARCH_ENGINES.GOOGLE]
+[API_ENDPOINTS]
-API_KEY = ""
+SEARXNG = "" # SearxNG API URL - http://localhost:32768
-CSE_ID = ""
-
-[SEARCH_ENGINES.SEARXNG]
-ENDPOINT = ""
-
-[SEARCH_ENGINES.BING]
-SUBSCRIPTION_KEY = ""
-
-[SEARCH_ENGINES.BRAVE]
-API_KEY = ""
-
-[SEARCH_ENGINES.YACY]
-ENDPOINT = ""

searxng/settings.yml

@@ -15,5 +15,3 @@ server:
 engines:
   - name: wolframalpha
     disabled: false
-  - name: qwant
-    disabled: true

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/config/route.ts

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

src/app/api/discover/route.ts

@@ -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/app/settings/page.tsx

@@ -23,18 +23,6 @@ interface SettingsType {
   customOpenaiApiKey: string;
   customOpenaiApiUrl: string;
   customOpenaiModelName: string;
-  searchEngineBackends: {
-    search: string;
-    image: string;
-    video: string;
-    news: string;
-  };
-  searxngEndpoint: string;
-  googleApiKey: string;
-  googleCseId: string;
-  bingSubscriptionKey: string;
-  braveApiKey: string;
-  yacyEndpoint: string;
 }
 
 interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
@@ -124,17 +112,11 @@ const Page = () => {
   const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
   const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
   const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
-  const [searchEngineBackends, setSearchEngineBackends] = useState({
-    search: '',
-    image: '',
-    video: '',
-    news: '',
-  });
 
   useEffect(() => {
     const fetchConfig = async () => {
       setIsLoading(true);
-      const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
+      const res = await fetch(`/api/config`, {
         headers: {
           'Content-Type': 'application/json',
         },
@@ -143,16 +125,6 @@ const Page = () => {
       const data = (await res.json()) as SettingsType;
       setConfig(data);
 
-      // Set search engine backends if they exist in the response
-      if (data.searchEngineBackends) {
-        setSearchEngineBackends({
-          search: data.searchEngineBackends.search || '',
-          image: data.searchEngineBackends.image || '',
-          video: data.searchEngineBackends.video || '',
-          news: data.searchEngineBackends.news || '',
-        });
-      }
-
       const chatModelProvidersKeys = Object.keys(data.chatModelProviders || {});
       const embeddingModelProvidersKeys = Object.keys(
         data.embeddingModelProviders || {},
@@ -215,16 +187,13 @@ const Page = () => {
         [key]: value,
       } as SettingsType;
 
-      const response = await fetch(
+      const response = await fetch(`/api/config`, {
-        `${process.env.NEXT_PUBLIC_API_URL}/config`,
+        method: 'POST',
-        {
+        headers: {
-          method: 'POST',
+          'Content-Type': 'application/json',
-          headers: {
-            'Content-Type': 'application/json',
-          },
-          body: JSON.stringify(updatedConfig),
         },
-      );
+        body: JSON.stringify(updatedConfig),
+      });
 
       if (!response.ok) {
         throw new Error('Failed to update config');
@@ -236,7 +205,7 @@ const Page = () => {
         key.toLowerCase().includes('api') ||
         key.toLowerCase().includes('url')
       ) {
-        const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
+        const res = await fetch(`/api/config`, {
           headers: {
             'Content-Type': 'application/json',
           },
@@ -359,8 +328,6 @@ const Page = () => {
         localStorage.setItem('embeddingModelProvider', value);
       } else if (key === 'embeddingModel') {
         localStorage.setItem('embeddingModel', value);
-      } else if (key === 'searchEngineBackends') {
-        localStorage.setItem('searchEngineBackends', value);
       }
     } catch (err) {
       console.error('Failed to save:', err);
@@ -823,234 +790,6 @@ const Page = () => {
                 </div>
               </div>
             </SettingsSection>
-
-            <SettingsSection title="Search Engine Settings">
-              <div className="flex flex-col space-y-4">
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Default Search Engine
-                  </p>
-                  <Select
-                    value={searchEngineBackends.search}
-                    onChange={(e) => {
-                      const value = e.target.value;
-                      setSearchEngineBackends((prev) => ({
-                        ...prev,
-                        search: value,
-                      }));
-                      saveConfig('searchEngineBackends', {
-                        ...searchEngineBackends,
-                        search: value,
-                      });
-                    }}
-                    options={[
-                      { value: 'searxng', label: 'SearXNG' },
-                      { value: 'google', label: 'Google' },
-                      { value: 'bing', label: 'Bing' },
-                      { value: 'brave', label: 'Brave' },
-                      { value: 'yacy', label: 'YaCy' },
-                    ]}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Image Search Engine
-                  </p>
-                  <Select
-                    value={searchEngineBackends.image}
-                    onChange={(e) => {
-                      const value = e.target.value;
-                      setSearchEngineBackends((prev) => ({
-                        ...prev,
-                        image: value,
-                      }));
-                      saveConfig('searchEngineBackends', {
-                        ...searchEngineBackends,
-                        image: value,
-                      });
-                    }}
-                    options={[
-                      { value: '', label: 'Use Default Search Engine' },
-                      { value: 'searxng', label: 'SearXNG' },
-                      { value: 'google', label: 'Google' },
-                      { value: 'bing', label: 'Bing' },
-                      { value: 'brave', label: 'Brave' },
-                    ]}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Video Search Engine
-                  </p>
-                  <Select
-                    value={searchEngineBackends.video}
-                    onChange={(e) => {
-                      const value = e.target.value;
-                      setSearchEngineBackends((prev) => ({
-                        ...prev,
-                        video: value,
-                      }));
-                      saveConfig('searchEngineBackends', {
-                        ...searchEngineBackends,
-                        video: value,
-                      });
-                    }}
-                    options={[
-                      { value: '', label: 'Use Default Search Engine' },
-                      { value: 'searxng', label: 'SearXNG' },
-                      { value: 'google', label: 'Google' },
-                      { value: 'bing', label: 'Bing' },
-                      { value: 'brave', label: 'Brave' },
-                    ]}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    News Search Engine
-                  </p>
-                  <Select
-                    value={searchEngineBackends.news}
-                    onChange={(e) => {
-                      const value = e.target.value;
-                      setSearchEngineBackends((prev) => ({
-                        ...prev,
-                        news: value,
-                      }));
-                      saveConfig('searchEngineBackends', {
-                        ...searchEngineBackends,
-                        news: value,
-                      });
-                    }}
-                    options={[
-                      { value: '', label: 'Use Default Search Engine' },
-                      { value: 'searxng', label: 'SearXNG' },
-                      { value: 'google', label: 'Google' },
-                      { value: 'bing', label: 'Bing' },
-                      { value: 'brave', label: 'Brave' },
-                    ]}
-                  />
-                </div>
-
-                <div className="pt-4 border-t border-light-200 dark:border-dark-200">
-                  <div className="flex flex-col space-y-1">
-                    <p className="text-black/70 dark:text-white/70 text-sm">
-                      SearXNG Endpoint
-                    </p>
-                    <Input
-                      type="text"
-                      placeholder="SearXNG API Endpoint"
-                      value={config.searxngEndpoint || ''}
-                      isSaving={savingStates['searxngEndpoint']}
-                      onChange={(e) => {
-                        setConfig((prev) => ({
-                          ...prev!,
-                          searxngEndpoint: e.target.value,
-                        }));
-                      }}
-                      onSave={(value) => saveConfig('searxngEndpoint', value)}
-                    />
-                  </div>
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Google API Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Google API Key"
-                    value={config.googleApiKey || ''}
-                    isSaving={savingStates['googleApiKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        googleApiKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('googleApiKey', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Google CSE ID
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Google Custom Search Engine ID"
-                    value={config.googleCseId || ''}
-                    isSaving={savingStates['googleCseId']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        googleCseId: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('googleCseId', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Bing Subscription Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Bing Subscription Key"
-                    value={config.bingSubscriptionKey || ''}
-                    isSaving={savingStates['bingSubscriptionKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        bingSubscriptionKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('bingSubscriptionKey', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Brave API Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Brave API Key"
-                    value={config.braveApiKey || ''}
-                    isSaving={savingStates['braveApiKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        braveApiKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('braveApiKey', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    YaCy Endpoint
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="YaCy API Endpoint"
-                    value={config.yacyEndpoint || ''}
-                    isSaving={savingStates['yacyEndpoint']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        yacyEndpoint: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('yacyEndpoint', value)}
-                  />
-                </div>
-              </div>
-            </SettingsSection>
           </div>
         )
       )}

src/chains/imageSearchAgent.ts

@@ -1,171 +0,0 @@
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-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/searchEngines/searxng';
-import { searchGooglePSE } from '../lib/searchEngines/google_pse';
-import { searchBraveAPI } from '../lib/searchEngines/brave';
-import { searchYaCy } from '../lib/searchEngines/yacy';
-import { searchBingAPI } from '../lib/searchEngines/bing';
-import { getImageSearchEngineBackend } from '../config';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-const imageSearchChainPrompt = `
-You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search the web for images.
-You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
-
-Example:
-1. Follow up question: What is a cat?
-Rephrased: A cat
-
-2. Follow up question: What is a car? How does it works?
-Rephrased: Car working
-
-3. Follow up question: How does an AC work?
-Rephrased: AC working
-
-Conversation:
-{chat_history}
-
-Follow up question: {query}
-Rephrased question:
-`;
-
-type ImageSearchChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-async function performImageSearch(query: string) {
-  const searchEngine = getImageSearchEngineBackend();
-  let images = [];
-
-  switch (searchEngine) {
-    case 'google': {
-      const googleResult = await searchGooglePSE(query);
-      images = googleResult.results
-        .map((result) => {
-          if (result.img_src && result.url && result.title) {
-            return {
-              img_src: result.img_src,
-              url: result.url,
-              title: result.title,
-              source: result.displayLink,
-            };
-          }
-        })
-        .filter(Boolean);
-      break;
-    }
-
-    case 'searxng': {
-      const searxResult = await searchSearxng(query, {
-        engines: ['google images', 'bing images'],
-        pageno: 1,
-      });
-      searxResult.results.forEach((result) => {
-        if (result.img_src && result.url && result.title) {
-          images.push({
-            img_src: result.img_src,
-            url: result.url,
-            title: result.title,
-          });
-        }
-      });
-      break;
-    }
-
-    case 'brave': {
-      const braveResult = await searchBraveAPI(query);
-      images = braveResult.results
-        .map((result) => {
-          if (result.img_src && result.url && result.title) {
-            return {
-              img_src: result.img_src,
-              url: result.url,
-              title: result.title,
-              source: result.url,
-            };
-          }
-        })
-        .filter(Boolean);
-      break;
-    }
-
-    case 'yacy': {
-      const yacyResult = await searchYaCy(query);
-      images = yacyResult.results
-        .map((result) => {
-          if (result.img_src && result.url && result.title) {
-            return {
-              img_src: result.img_src,
-              url: result.url,
-              title: result.title,
-              source: result.url,
-            };
-          }
-        })
-        .filter(Boolean);
-      break;
-    }
-
-    case 'bing': {
-      const bingResult = await searchBingAPI(query);
-      images = bingResult.results
-        .map((result) => {
-          if (result.img_src && result.url && result.title) {
-            return {
-              img_src: result.img_src,
-              url: result.url,
-              title: result.title,
-              source: result.url,
-            };
-          }
-        })
-        .filter(Boolean);
-      break;
-    }
-
-    default:
-      throw new Error(`Unknown search engine ${searchEngine}`);
-  }
-
-  return images;
-}
-
-const strParser = new StringOutputParser();
-
-const createImageSearchChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    RunnableMap.from({
-      chat_history: (input: ImageSearchChainInput) => {
-        return formatChatHistoryAsString(input.chat_history);
-      },
-      query: (input: ImageSearchChainInput) => {
-        return input.query;
-      },
-    }),
-    PromptTemplate.fromTemplate(imageSearchChainPrompt),
-    llm,
-    strParser,
-    RunnableLambda.from(async (input: string) => {
-      const images = await performImageSearch(input);
-      return images.slice(0, 10);
-    }),
-  ]);
-};
-
-const handleImageSearch = (
-  input: ImageSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const imageSearchChain = createImageSearchChain(llm);
-  return imageSearchChain.invoke(input);
-};
-
-export default handleImageSearch;

src/chains/videoSearchAgent.ts

@@ -1,169 +0,0 @@
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-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/searchEngines/searxng';
-import { searchGooglePSE } from '../lib/searchEngines/google_pse';
-import { searchBraveAPI } from '../lib/searchEngines/brave';
-import { searchBingAPI } from '../lib/searchEngines/bing';
-import { getVideoSearchEngineBackend } from '../config';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-const VideoSearchChainPrompt = `
-  You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
-  You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
-
-  Example:
-  1. Follow up question: How does a car work?
-  Rephrased: How does a car work?
-
-  2. Follow up question: What is the theory of relativity?
-  Rephrased: What is theory of relativity
-
-  3. Follow up question: How does an AC work?
-  Rephrased: How does an AC work
-
-  Conversation:
-  {chat_history}
-
-  Follow up question: {query}
-  Rephrased question:
-  `;
-
-type VideoSearchChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const strParser = new StringOutputParser();
-
-async function performVideoSearch(query: string) {
-  const searchEngine = getVideoSearchEngineBackend();
-  const youtubeQuery = `${query} site:youtube.com`;
-  let videos = [];
-
-  switch (searchEngine) {
-    case 'google': {
-      const googleResult = await searchGooglePSE(youtubeQuery);
-      googleResult.results.forEach((result) => {
-        // Use .results instead of .originalres
-        if (result.img_src && result.url && result.title) {
-          const videoId = new URL(result.url).searchParams.get('v');
-          videos.push({
-            img_src: result.img_src,
-            url: result.url,
-            title: result.title,
-            iframe_src: videoId
-              ? `https://www.youtube.com/embed/${videoId}`
-              : null,
-          });
-        }
-      });
-      break;
-    }
-
-    case 'searxng': {
-      const searxResult = await searchSearxng(query, {
-        engines: ['youtube'],
-      });
-      searxResult.results.forEach((result) => {
-        if (
-          result.thumbnail &&
-          result.url &&
-          result.title &&
-          result.iframe_src
-        ) {
-          videos.push({
-            img_src: result.thumbnail,
-            url: result.url,
-            title: result.title,
-            iframe_src: result.iframe_src,
-          });
-        }
-      });
-      break;
-    }
-
-    case 'brave': {
-      const braveResult = await searchBraveAPI(youtubeQuery);
-      braveResult.results.forEach((result) => {
-        if (result.img_src && result.url && result.title) {
-          const videoId = new URL(result.url).searchParams.get('v');
-          videos.push({
-            img_src: result.img_src,
-            url: result.url,
-            title: result.title,
-            iframe_src: videoId
-              ? `https://www.youtube.com/embed/${videoId}`
-              : null,
-          });
-        }
-      });
-      break;
-    }
-
-    case 'yacy': {
-      console.log('Not available for yacy');
-      videos = [];
-      break;
-    }
-
-    case 'bing': {
-      const bingResult = await searchBingAPI(youtubeQuery);
-      bingResult.results.forEach((result) => {
-        if (result.img_src && result.url && result.title) {
-          const videoId = new URL(result.url).searchParams.get('v');
-          videos.push({
-            img_src: result.img_src,
-            url: result.url,
-            title: result.title,
-            iframe_src: videoId
-              ? `https://www.youtube.com/embed/${videoId}`
-              : null,
-          });
-        }
-      });
-      break;
-    }
-
-    default:
-      throw new Error(`Unknown search engine ${searchEngine}`);
-  }
-
-  return videos;
-}
-
-const createVideoSearchChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    RunnableMap.from({
-      chat_history: (input: VideoSearchChainInput) => {
-        return formatChatHistoryAsString(input.chat_history);
-      },
-      query: (input: VideoSearchChainInput) => {
-        return input.query;
-      },
-    }),
-    PromptTemplate.fromTemplate(VideoSearchChainPrompt),
-    llm,
-    strParser,
-    RunnableLambda.from(async (input: string) => {
-      const videos = await performVideoSearch(input);
-      return videos.slice(0, 10);
-    }),
-  ]);
-};
-
-const handleVideoSearch = (
-  input: VideoSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const VideoSearchChain = createVideoSearchChain(llm);
-  return VideoSearchChain.invoke(input);
-};
-
-export default handleVideoSearch;

src/components/Chat.tsx

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

src/components/ChatWindow.tsx

@@ -29,280 +29,154 @@ export interface File {
   fileId: string;
 }
 
-const useSocket = (
+interface ChatModelProvider {
-  url: string,
+  name: string;
-  setIsWSReady: (ready: boolean) => void,
+  provider: string;
-  setError: (error: boolean) => void,
+}
+
+interface EmbeddingModelProvider {
+  name: string;
+  provider: string;
+}
+
+const checkConfig = async (
+  setChatModelProvider: (provider: ChatModelProvider) => void,
+  setEmbeddingModelProvider: (provider: EmbeddingModelProvider) => void,
+  setIsConfigReady: (ready: boolean) => void,
+  setHasError: (hasError: boolean) => void,
 ) => {
-  const wsRef = useRef<WebSocket | null>(null);
+  try {
-  const reconnectTimeoutRef = useRef<NodeJS.Timeout>();
+    let chatModel = localStorage.getItem('chatModel');
-  const retryCountRef = useRef(0);
+    let chatModelProvider = localStorage.getItem('chatModelProvider');
-  const isCleaningUpRef = useRef(false);
+    let embeddingModel = localStorage.getItem('embeddingModel');
-  const MAX_RETRIES = 3;
+    let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
-  const INITIAL_BACKOFF = 1000; // 1 second
-  const isConnectionErrorRef = useRef(false);
 
-  const getBackoffDelay = (retryCount: number) => {
+    const autoImageSearch = localStorage.getItem('autoImageSearch');
-    return Math.min(INITIAL_BACKOFF * Math.pow(2, retryCount), 10000); // Cap at 10 seconds
+    const autoVideoSearch = localStorage.getItem('autoVideoSearch');
-  };
 
-  useEffect(() => {
+    if (!autoImageSearch) {
-    const connectWs = async () => {
+      localStorage.setItem('autoImageSearch', 'true');
-      if (wsRef.current?.readyState === WebSocket.OPEN) {
+    }
-        wsRef.current.close();
+
+    if (!autoVideoSearch) {
+      localStorage.setItem('autoVideoSearch', 'false');
+    }
+
+    const providers = await fetch(`/api/models`, {
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    }).then(async (res) => {
+      if (!res.ok)
+        throw new Error(
+          `Failed to fetch models: ${res.status} ${res.statusText}`,
+        );
+      return res.json();
+    });
+
+    if (
+      !chatModel ||
+      !chatModelProvider ||
+      !embeddingModel ||
+      !embeddingModelProvider
+    ) {
+      if (!chatModel || !chatModelProvider) {
+        const chatModelProviders = providers.chatModelProviders;
+
+        chatModelProvider =
+          chatModelProvider || Object.keys(chatModelProviders)[0];
+
+        chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+
+        if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
+          return toast.error('No chat models available');
       }
 
-      try {
+      if (!embeddingModel || !embeddingModelProvider) {
-        let chatModel = localStorage.getItem('chatModel');
+        const embeddingModelProviders = providers.embeddingModelProviders;
-        let chatModelProvider = localStorage.getItem('chatModelProvider');
-        let embeddingModel = localStorage.getItem('embeddingModel');
-        let embeddingModelProvider = localStorage.getItem(
-          'embeddingModelProvider',
-        );
-
-        const autoImageSearch = localStorage.getItem('autoImageSearch');
-        const autoVideoSearch = localStorage.getItem('autoVideoSearch');
-
-        if (!autoImageSearch) {
-          localStorage.setItem('autoImageSearch', 'true');
-        }
-
-        if (!autoVideoSearch) {
-          localStorage.setItem('autoVideoSearch', 'false');
-        }
-
-        const providers = await fetch(
-          `${process.env.NEXT_PUBLIC_API_URL}/models`,
-          {
-            headers: {
-              'Content-Type': 'application/json',
-            },
-          },
-        ).then(async (res) => {
-          if (!res.ok)
-            throw new Error(
-              `Failed to fetch models: ${res.status} ${res.statusText}`,
-            );
-          return res.json();
-        });
 
         if (
-          !chatModel ||
+          !embeddingModelProviders ||
-          !chatModelProvider ||
+          Object.keys(embeddingModelProviders).length === 0
-          !embeddingModel ||
+        )
-          !embeddingModelProvider
+          return toast.error('No embedding models available');
-        ) {
-          if (!chatModel || !chatModelProvider) {
-            const chatModelProviders = providers.chatModelProviders;
 
-            chatModelProvider =
+        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-              chatModelProvider || Object.keys(chatModelProviders)[0];
+        embeddingModel = Object.keys(
-
+          embeddingModelProviders[embeddingModelProvider],
-            chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+        )[0];
-
-            if (
-              !chatModelProviders ||
-              Object.keys(chatModelProviders).length === 0
-            )
-              return toast.error('No chat models available');
-          }
-
-          if (!embeddingModel || !embeddingModelProvider) {
-            const embeddingModelProviders = providers.embeddingModelProviders;
-
-            if (
-              !embeddingModelProviders ||
-              Object.keys(embeddingModelProviders).length === 0
-            )
-              return toast.error('No embedding models available');
-
-            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-            embeddingModel = Object.keys(
-              embeddingModelProviders[embeddingModelProvider],
-            )[0];
-          }
-
-          localStorage.setItem('chatModel', chatModel!);
-          localStorage.setItem('chatModelProvider', chatModelProvider);
-          localStorage.setItem('embeddingModel', embeddingModel!);
-          localStorage.setItem(
-            'embeddingModelProvider',
-            embeddingModelProvider,
-          );
-        } else {
-          const chatModelProviders = providers.chatModelProviders;
-          const embeddingModelProviders = providers.embeddingModelProviders;
-
-          if (
-            Object.keys(chatModelProviders).length > 0 &&
-            !chatModelProviders[chatModelProvider]
-          ) {
-            const chatModelProvidersKeys = Object.keys(chatModelProviders);
-            chatModelProvider =
-              chatModelProvidersKeys.find(
-                (key) => Object.keys(chatModelProviders[key]).length > 0,
-              ) || chatModelProvidersKeys[0];
-
-            localStorage.setItem('chatModelProvider', chatModelProvider);
-          }
-
-          if (
-            chatModelProvider &&
-            !chatModelProviders[chatModelProvider][chatModel]
-          ) {
-            chatModel = Object.keys(
-              chatModelProviders[
-                Object.keys(chatModelProviders[chatModelProvider]).length > 0
-                  ? chatModelProvider
-                  : Object.keys(chatModelProviders)[0]
-              ],
-            )[0];
-            localStorage.setItem('chatModel', chatModel);
-          }
-
-          if (
-            Object.keys(embeddingModelProviders).length > 0 &&
-            !embeddingModelProviders[embeddingModelProvider]
-          ) {
-            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-            localStorage.setItem(
-              'embeddingModelProvider',
-              embeddingModelProvider,
-            );
-          }
-
-          if (
-            embeddingModelProvider &&
-            !embeddingModelProviders[embeddingModelProvider][embeddingModel]
-          ) {
-            embeddingModel = Object.keys(
-              embeddingModelProviders[embeddingModelProvider],
-            )[0];
-            localStorage.setItem('embeddingModel', embeddingModel);
-          }
-        }
-
-        const wsURL = new URL(url);
-        const searchParams = new URLSearchParams({});
-
-        searchParams.append('chatModel', chatModel!);
-        searchParams.append('chatModelProvider', chatModelProvider);
-
-        if (chatModelProvider === 'custom_openai') {
-          searchParams.append(
-            'openAIApiKey',
-            localStorage.getItem('openAIApiKey')!,
-          );
-          searchParams.append(
-            'openAIBaseURL',
-            localStorage.getItem('openAIBaseURL')!,
-          );
-        }
-
-        searchParams.append('embeddingModel', embeddingModel!);
-        searchParams.append('embeddingModelProvider', embeddingModelProvider);
-
-        wsURL.search = searchParams.toString();
-
-        const ws = new WebSocket(wsURL.toString());
-        wsRef.current = ws;
-
-        const timeoutId = setTimeout(() => {
-          if (ws.readyState !== 1) {
-            toast.error(
-              'Failed to connect to the server. Please try again later.',
-            );
-          }
-        }, 10000);
-
-        ws.addEventListener('message', (e) => {
-          const data = JSON.parse(e.data);
-          if (data.type === 'signal' && data.data === 'open') {
-            const interval = setInterval(() => {
-              if (ws.readyState === 1) {
-                setIsWSReady(true);
-                setError(false);
-                if (retryCountRef.current > 0) {
-                  toast.success('Connection restored.');
-                }
-                retryCountRef.current = 0;
-                clearInterval(interval);
-              }
-            }, 5);
-            clearTimeout(timeoutId);
-            console.debug(new Date(), 'ws:connected');
-          }
-          if (data.type === 'error') {
-            isConnectionErrorRef.current = true;
-            setError(true);
-            toast.error(data.data);
-          }
-        });
-
-        ws.onerror = () => {
-          clearTimeout(timeoutId);
-          setIsWSReady(false);
-          toast.error('WebSocket connection error.');
-        };
-
-        ws.onclose = () => {
-          clearTimeout(timeoutId);
-          setIsWSReady(false);
-          console.debug(new Date(), 'ws:disconnected');
-          if (!isCleaningUpRef.current && !isConnectionErrorRef.current) {
-            toast.error('Connection lost. Attempting to reconnect...');
-            attemptReconnect();
-          }
-        };
-      } catch (error) {
-        console.debug(new Date(), 'ws:error', error);
-        setIsWSReady(false);
-        attemptReconnect();
-      }
-    };
-
-    const attemptReconnect = () => {
-      retryCountRef.current += 1;
-
-      if (retryCountRef.current > MAX_RETRIES) {
-        console.debug(new Date(), 'ws:max_retries');
-        setError(true);
-        toast.error(
-          'Unable to connect to server after multiple attempts. Please refresh the page to try again.',
-        );
-        return;
       }
 
-      const backoffDelay = getBackoffDelay(retryCountRef.current);
+      localStorage.setItem('chatModel', chatModel!);
-      console.debug(
+      localStorage.setItem('chatModelProvider', chatModelProvider);
-        new Date(),
+      localStorage.setItem('embeddingModel', embeddingModel!);
-        `ws:retry attempt=${retryCountRef.current}/${MAX_RETRIES} delay=${backoffDelay}ms`,
+      localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
-      );
+    } else {
+      const chatModelProviders = providers.chatModelProviders;
+      const embeddingModelProviders = providers.embeddingModelProviders;
 
-      if (reconnectTimeoutRef.current) {
+      if (
-        clearTimeout(reconnectTimeoutRef.current);
+        Object.keys(chatModelProviders).length > 0 &&
+        !chatModelProviders[chatModelProvider]
+      ) {
+        const chatModelProvidersKeys = Object.keys(chatModelProviders);
+        chatModelProvider =
+          chatModelProvidersKeys.find(
+            (key) => Object.keys(chatModelProviders[key]).length > 0,
+          ) || chatModelProvidersKeys[0];
+
+        localStorage.setItem('chatModelProvider', chatModelProvider);
       }
 
-      reconnectTimeoutRef.current = setTimeout(() => {
+      if (
-        connectWs();
+        chatModelProvider &&
-      }, backoffDelay);
+        !chatModelProviders[chatModelProvider][chatModel]
-    };
+      ) {
-
+        chatModel = Object.keys(
-    connectWs();
+          chatModelProviders[
-
+            Object.keys(chatModelProviders[chatModelProvider]).length > 0
-    return () => {
+              ? chatModelProvider
-      if (reconnectTimeoutRef.current) {
+              : Object.keys(chatModelProviders)[0]
-        clearTimeout(reconnectTimeoutRef.current);
+          ],
+        )[0];
+        localStorage.setItem('chatModel', chatModel);
       }
-      if (wsRef.current?.readyState === WebSocket.OPEN) {
-        wsRef.current.close();
-        isCleaningUpRef.current = true;
-        console.debug(new Date(), 'ws:cleanup');
-      }
-    };
-  }, [url, setIsWSReady, setError]);
 
-  return wsRef.current;
+      if (
+        Object.keys(embeddingModelProviders).length > 0 &&
+        !embeddingModelProviders[embeddingModelProvider]
+      ) {
+        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+        localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
+      }
+
+      if (
+        embeddingModelProvider &&
+        !embeddingModelProviders[embeddingModelProvider][embeddingModel]
+      ) {
+        embeddingModel = Object.keys(
+          embeddingModelProviders[embeddingModelProvider],
+        )[0];
+        localStorage.setItem('embeddingModel', embeddingModel);
+      }
+    }
+
+    setChatModelProvider({
+      name: chatModel!,
+      provider: chatModelProvider,
+    });
+
+    setEmbeddingModelProvider({
+      name: embeddingModel!,
+      provider: embeddingModelProvider,
+    });
+
+    setIsConfigReady(true);
+  } catch (err) {
+    console.error('An error occurred while checking the configuration:', err);
+    setIsConfigReady(false);
+    setHasError(true);
+  }
 };
 
 const loadMessages = async (
@@ -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: {
-      method: 'GET',
+      'Content-Type': 'application/json',
-      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,
-    setHasError,
+      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;
     }
 
@@ -467,21 +343,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
 
     messageId = messageId ?? crypto.randomBytes(7).toString('hex');
 
-    ws.send(
-      JSON.stringify({
-        type: 'message',
-        message: {
-          messageId: messageId,
-          chatId: chatId!,
-          content: message,
-        },
-        files: fileIds,
-        focusMode: focusMode,
-        optimizationMode: optimizationMode,
-        history: [...chatHistory, ['human', message]],
-      }),
-    );
-
     setMessages((prevMessages) => [
       ...prevMessages,
       {
@@ -493,9 +354,7 @@ 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);
@@ -558,11 +417,25 @@ const ChatWindow = ({ id }: { id?: string }) => {
           ['assistant', recievedMessage],
         ]);
 
-        ws?.removeEventListener('message', messageHandler);
         setLoading(false);
 
         const lastMsg = messagesRef.current[messagesRef.current.length - 1];
 
+        const autoImageSearch = localStorage.getItem('autoImageSearch');
+        const autoVideoSearch = localStorage.getItem('autoVideoSearch');
+
+        if (autoImageSearch === 'true') {
+          document
+            .getElementById(`search-images-${lastMsg.messageId}`)
+            ?.click();
+        }
+
+        if (autoVideoSearch === 'true') {
+          document
+            .getElementById(`search-videos-${lastMsg.messageId}`)
+            ?.click();
+        }
+
         if (
           lastMsg.role === 'assistant' &&
           lastMsg.sources &&
@@ -579,21 +452,62 @@ const ChatWindow = ({ id }: { id?: string }) => {
             }),
           );
         }
-
-        const autoImageSearch = localStorage.getItem('autoImageSearch');
-        const autoVideoSearch = localStorage.getItem('autoVideoSearch');
-
-        if (autoImageSearch === 'true') {
-          document.getElementById('search-images')?.click();
-        }
-
-        if (autoVideoSearch === 'true') {
-          document.getElementById('search-videos')?.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 +528,11 @@ 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: {
-          method: 'DELETE',
+          'Content-Type': 'application/json',
-          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);
 
@@ -37,27 +39,24 @@ 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: {
-                method: 'POST',
+                'Content-Type': 'application/json',
-                headers: {
-                  'Content-Type': 'application/json',
-                },
-                body: JSON.stringify({
-                  query: query,
-                  chatHistory: chatHistory,
-                  chatModel: {
-                    provider: chatModelProvider,
-                    model: chatModel,
-                    ...(chatModelProvider === 'custom_openai' && {
-                      customOpenAIBaseURL: customOpenAIBaseURL,
-                      customOpenAIKey: customOpenAIKey,
-                    }),
-                  },
-                }),
               },
-            );
+              body: JSON.stringify({
+                query: query,
+                chatHistory: chatHistory,
+                chatModel: {
+                  provider: chatModelProvider,
+                  model: chatModel,
+                  ...(chatModelProvider === 'custom_openai' && {
+                    customOpenAIBaseURL: customOpenAIBaseURL,
+                    customOpenAIKey: customOpenAIKey,
+                  }),
+                },
+              }),
+            });
 
             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);
 
@@ -52,27 +54,24 @@ 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: {
-                method: 'POST',
+                'Content-Type': 'application/json',
-                headers: {
-                  'Content-Type': 'application/json',
-                },
-                body: JSON.stringify({
-                  query: query,
-                  chatHistory: chatHistory,
-                  chatModel: {
-                    provider: chatModelProvider,
-                    model: chatModel,
-                    ...(chatModelProvider === 'custom_openai' && {
-                      customOpenAIBaseURL: customOpenAIBaseURL,
-                      customOpenAIKey: customOpenAIKey,
-                    }),
-                  },
-                }),
               },
-            );
+              body: JSON.stringify({
+                query: query,
+                chatHistory: chatHistory,
+                chatModel: {
+                  provider: chatModelProvider,
+                  model: chatModel,
+                  ...(chatModelProvider === 'custom_openai' && {
+                    customOpenAIBaseURL: customOpenAIBaseURL,
+                    customOpenAIKey: customOpenAIKey,
+                  }),
+                },
+              }),
+            });
 
             const data = await res.json();
 

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

@@ -0,0 +1,90 @@
+import {
+  RunnableSequence,
+  RunnableMap,
+  RunnableLambda,
+} from '@langchain/core/runnables';
+import { PromptTemplate } from '@langchain/core/prompts';
+import formatChatHistoryAsString from '../utils/formatHistory';
+import { BaseMessage } from '@langchain/core/messages';
+import { StringOutputParser } from '@langchain/core/output_parsers';
+import { searchSearxng } from '../searxng';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+const imageSearchChainPrompt = `
+You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search the web for images.
+You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
+
+Example:
+1. Follow up question: What is a cat?
+Rephrased: A cat
+
+2. Follow up question: What is a car? How does it works?
+Rephrased: Car working
+
+3. Follow up question: How does an AC work?
+Rephrased: AC working
+
+Conversation:
+{chat_history}
+
+Follow up question: {query}
+Rephrased question:
+`;
+
+type ImageSearchChainInput = {
+  chat_history: BaseMessage[];
+  query: string;
+};
+
+interface ImageSearchResult {
+  img_src: string;
+  url: string;
+  title: string;
+}
+
+const strParser = new StringOutputParser();
+
+const createImageSearchChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    RunnableMap.from({
+      chat_history: (input: ImageSearchChainInput) => {
+        return formatChatHistoryAsString(input.chat_history);
+      },
+      query: (input: ImageSearchChainInput) => {
+        return input.query;
+      },
+    }),
+    PromptTemplate.fromTemplate(imageSearchChainPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      const res = await searchSearxng(input, {
+        engines: ['bing images', 'google images'],
+      });
+
+      const images: ImageSearchResult[] = [];
+
+      res.results.forEach((result) => {
+        if (result.img_src && result.url && result.title) {
+          images.push({
+            img_src: result.img_src,
+            url: result.url,
+            title: result.title,
+          });
+        }
+      });
+
+      return images.slice(0, 10);
+    }),
+  ]);
+};
+
+const handleImageSearch = (
+  input: ImageSearchChainInput,
+  llm: BaseChatModel,
+) => {
+  const imageSearchChain = createImageSearchChain(llm);
+  return imageSearchChain.invoke(input);
+};
+
+export default handleImageSearch;

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

@@ -0,0 +1,97 @@
+import {
+  RunnableSequence,
+  RunnableMap,
+  RunnableLambda,
+} from '@langchain/core/runnables';
+import { PromptTemplate } from '@langchain/core/prompts';
+import formatChatHistoryAsString from '../utils/formatHistory';
+import { BaseMessage } from '@langchain/core/messages';
+import { StringOutputParser } from '@langchain/core/output_parsers';
+import { searchSearxng } from '../searxng';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+const VideoSearchChainPrompt = `
+  You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
+  You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
+  
+  Example:
+  1. Follow up question: How does a car work?
+  Rephrased: How does a car work?
+  
+  2. Follow up question: What is the theory of relativity?
+  Rephrased: What is theory of relativity
+  
+  3. Follow up question: How does an AC work?
+  Rephrased: How does an AC work
+  
+  Conversation:
+  {chat_history}
+  
+  Follow up question: {query}
+  Rephrased question:
+  `;
+
+type VideoSearchChainInput = {
+  chat_history: BaseMessage[];
+  query: string;
+};
+
+interface VideoSearchResult {
+  img_src: string;
+  url: string;
+  title: string;
+  iframe_src: string;
+}
+
+const strParser = new StringOutputParser();
+
+const createVideoSearchChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    RunnableMap.from({
+      chat_history: (input: VideoSearchChainInput) => {
+        return formatChatHistoryAsString(input.chat_history);
+      },
+      query: (input: VideoSearchChainInput) => {
+        return input.query;
+      },
+    }),
+    PromptTemplate.fromTemplate(VideoSearchChainPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      const res = await searchSearxng(input, {
+        engines: ['youtube'],
+      });
+
+      const videos: VideoSearchResult[] = [];
+
+      res.results.forEach((result) => {
+        if (
+          result.thumbnail &&
+          result.url &&
+          result.title &&
+          result.iframe_src
+        ) {
+          videos.push({
+            img_src: result.thumbnail,
+            url: result.url,
+            title: result.title,
+            iframe_src: result.iframe_src,
+          });
+        }
+      });
+
+      return videos.slice(0, 10);
+    }),
+  ]);
+};
+
+const handleVideoSearch = (
+  input: VideoSearchChainInput,
+  llm: BaseChatModel,
+) => {
+  const VideoSearchChain = createVideoSearchChain(llm);
+  return VideoSearchChain.invoke(input);
+};
+
+export default handleVideoSearch;

src/lib/config.ts

@@ -10,12 +10,6 @@ interface Config {
     SIMILARITY_MEASURE: string;
     KEEP_ALIVE: string;
   };
-  SEARCH_ENGINE_BACKENDS: {
-    SEARCH: string;
-    IMAGE: string;
-    VIDEO: string;
-    NEWS: string;
-  };
   MODELS: {
     OPENAI: {
       API_KEY: string;
@@ -38,23 +32,8 @@ interface Config {
       MODEL_NAME: string;
     };
   };
-  SEARCH_ENGINES: {
+  API_ENDPOINTS: {
-    GOOGLE: {
+    SEARXNG: string;
-      API_KEY: string;
-      CSE_ID: string;
-    };
-    SEARXNG: {
-      ENDPOINT: string;
-    };
-    BING: {
-      SUBSCRIPTION_KEY: string;
-    };
-    BRAVE: {
-      API_KEY: string;
-    };
-    YACY: {
-      ENDPOINT: string;
-    };
   };
 }
 
@@ -64,7 +43,7 @@ type RecursivePartial<T> = {
 
 const loadConfig = () =>
   toml.parse(
-    fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
+    fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
   ) as any as Config;
 
 export const getPort = () => loadConfig().GENERAL.PORT;
@@ -82,32 +61,8 @@ export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
 
 export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
 
-export const getSearchEngineBackend = () =>
-  loadConfig().SEARCH_ENGINE_BACKENDS.SEARCH;
-
-export const getImageSearchEngineBackend = () =>
-  loadConfig().SEARCH_ENGINE_BACKENDS.IMAGE || getSearchEngineBackend();
-
-export const getVideoSearchEngineBackend = () =>
-  loadConfig().SEARCH_ENGINE_BACKENDS.VIDEO || getSearchEngineBackend();
-
-export const getNewsSearchEngineBackend = () =>
-  loadConfig().SEARCH_ENGINE_BACKENDS.NEWS || getSearchEngineBackend();
-
-export const getGoogleApiKey = () => loadConfig().SEARCH_ENGINES.GOOGLE.API_KEY;
-
-export const getGoogleCseId = () => loadConfig().SEARCH_ENGINES.GOOGLE.CSE_ID;
-
-export const getBraveApiKey = () => loadConfig().SEARCH_ENGINES.BRAVE.API_KEY;
-
-export const getBingSubscriptionKey = () =>
-  loadConfig().SEARCH_ENGINES.BING.SUBSCRIPTION_KEY;
-
-export const getYacyJsonEndpoint = () =>
-  loadConfig().SEARCH_ENGINES.YACY.ENDPOINT;
-
 export const getSearxngApiEndpoint = () =>
-  process.env.SEARXNG_API_URL || loadConfig().SEARCH_ENGINES.SEARXNG.ENDPOINT;
+  loadConfig().API_ENDPOINTS.SEARXNG || process.env.SEARXNG_API_URL;
 
 export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
 
@@ -154,9 +109,8 @@ const mergeConfigs = (current: any, update: any): any => {
 export const updateConfig = (config: RecursivePartial<Config>) => {
   const currentConfig = loadConfig();
   const mergedConfig = mergeConfigs(currentConfig, config);
-
   fs.writeFileSync(
-    path.join(__dirname, `../${configFileName}`),
+    path.join(path.join(process.cwd(), `${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/',
-        }),
+          },
-      },
+        }) as unknown as BaseChatModel,
-      'claude-3-5-haiku-20241022': {
+      };
-        displayName: 'Claude 3.5 Haiku',
+    });
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-5-haiku-20241022',
-        }),
-      },
-      'claude-3-opus-20240229': {
-        displayName: 'Claude 3 Opus',
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-opus-20240229',
-        }),
-      },
-      'claude-3-sonnet-20240229': {
-        displayName: 'Claude 3 Sonnet',
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-sonnet-20240229',
-        }),
-      },
-      'claude-3-haiku-20240307': {
-        displayName: 'Claude 3 Haiku',
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-haiku-20240307',
-        }),
-      },
-    };
 
     return chatModels;
   } catch (err) {
-    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': {
+        }) as unknown as BaseChatModel,
-        displayName: 'Gemini 1.5 Flash 8B',
+      };
-        model: new ChatGoogleGenerativeAI({
+    });
-          modelName: 'gemini-1.5-flash-8b',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-      'gemini-1.5-pro': {
-        displayName: 'Gemini 1.5 Pro',
-        model: new ChatGoogleGenerativeAI({
-          modelName: 'gemini-1.5-pro',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-      'gemini-2.0-flash-exp': {
-        displayName: 'Gemini 2.0 Flash Exp',
-        model: new ChatGoogleGenerativeAI({
-          modelName: 'gemini-2.0-flash-exp',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-      'gemini-2.0-flash-thinking-exp-01-21': {
-        displayName: 'Gemini 2.0 Flash Thinking Exp 01-21',
-        model: new ChatGoogleGenerativeAI({
-          modelName: 'gemini-2.0-flash-thinking-exp-01-21',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-    };
 
     return chatModels;
   } catch (err) {
-    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,
-            openAIApiKey: groqApiKey,
+        model: new ChatOpenAI({
-            modelName: 'llama-3.3-70b-versatile',
+          openAIApiKey: groqApiKey,
-            temperature: 0.7,
+          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/search/metaSearchAgent.ts

@@ -13,23 +13,17 @@ import {
 } from '@langchain/core/runnables';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
+import LineListOutputParser from '../outputParsers/listLineOutputParser';
-import LineOutputParser from '../lib/outputParsers/lineOutputParser';
+import LineOutputParser from '../outputParsers/lineOutputParser';
 import { getDocumentsFromLinks } from '../utils/documents';
 import { Document } from 'langchain/document';
-import { searchSearxng } from '../lib/searchEngines/searxng';
+import { searchSearxng } from '../searxng';
-import { searchGooglePSE } from '../lib/searchEngines/google_pse';
+import path from 'node:path';
-import { searchBingAPI } from '../lib/searchEngines/bing';
+import fs from 'node:fs';
-import { searchBraveAPI } from '../lib/searchEngines/brave';
-import { searchYaCy } from '../lib/searchEngines/yacy';
-import { getSearchEngineBackend } from '../config';
-import path from 'path';
-import fs from 'fs';
 import computeSimilarity from '../utils/computeSimilarity';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import { StreamEvent } from '@langchain/core/tracers/log_stream';
-import { IterableReadableStream } from '@langchain/core/utils/stream';
 
 export interface MetaSearchAgentType {
   searchAndAnswer: (
@@ -95,7 +89,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
             question = 'summarize';
           }
 
-          let docs = [];
+          let docs: Document[] = [];
 
           const linkDocs = await getDocumentsFromLinks({ links });
 
@@ -137,7 +131,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
             You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the 
             text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
             If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
-
+            
             - **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
             - **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
             - **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
@@ -208,37 +202,10 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
           return { query: question, docs: docs };
         } else {
-          const searchEngine = getSearchEngineBackend();
+          const res = await searchSearxng(question, {
-
+            language: 'en',
-          let res;
+            engines: this.config.activeEngines,
-          switch (searchEngine) {
+          });
-            case 'searxng':
-              res = await searchSearxng(question, {
-                language: 'en',
-                engines: this.config.activeEngines,
-              });
-              break;
-            case 'google':
-              res = await searchGooglePSE(question);
-              break;
-            case 'bing':
-              res = await searchBingAPI(question);
-              break;
-            case 'brave':
-              res = await searchBraveAPI(question);
-              break;
-            case 'yacy':
-              res = await searchYaCy(question);
-              break;
-            default:
-              throw new Error(`Unknown search engine ${searchEngine}`);
-          }
-
-          if (!res?.results) {
-            throw new Error(
-              `No results found for search engine: ${searchEngine}`,
-            );
-          }
 
           const documents = res.results.map(
             (result) =>
@@ -343,7 +310,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
         const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
 
         const fileSimilaritySearchObject = content.contents.map(
-          (c: string, i) => {
+          (c: string, i: number) => {
             return {
               fileName: content.title,
               content: c,
@@ -446,6 +413,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
       return sortedDocs;
     }
+
+    return [];
   }
 
   private processDocs(docs: Document[]) {
@@ -458,7 +427,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
   }
 
   private async handleStream(
-    stream: IterableReadableStream<StreamEvent>,
+    stream: AsyncGenerator<StreamEvent, any, any>,
     emitter: eventEmitter,
   ) {
     for await (const event of stream) {

src/lib/searchEngines/bing.ts

@@ -1,105 +0,0 @@
-import axios from 'axios';
-import { getBingSubscriptionKey } from '../../config';
-
-interface BingAPISearchResult {
-  _type: string;
-  name: string;
-  url: string;
-  displayUrl: string;
-  snippet?: string;
-  dateLastCrawled?: string;
-  thumbnailUrl?: string;
-  contentUrl?: string;
-  hostPageUrl?: string;
-  width?: number;
-  height?: number;
-  accentColor?: string;
-  contentSize?: string;
-  datePublished?: string;
-  encodingFormat?: string;
-  hostPageDisplayUrl?: string;
-  id?: string;
-  isLicensed?: boolean;
-  isFamilyFriendly?: boolean;
-  language?: string;
-  mediaUrl?: string;
-  motionThumbnailUrl?: string;
-  publisher?: string;
-  viewCount?: number;
-  webSearchUrl?: string;
-  primaryImageOfPage?: {
-    thumbnailUrl?: string;
-    width?: number;
-    height?: number;
-  };
-  video?: {
-    allowHttpsEmbed?: boolean;
-    embedHtml?: string;
-    allowMobileEmbed?: boolean;
-    viewCount?: number;
-    duration?: string;
-  };
-  image?: {
-    thumbnail?: {
-      contentUrl?: string;
-      width?: number;
-      height?: number;
-    };
-    imageInsightsToken?: string;
-    imageId?: string;
-  };
-}
-
-export const searchBingAPI = async (query: string) => {
-  try {
-    const bingApiKey = await getBingSubscriptionKey();
-    const url = new URL(`https://api.cognitive.microsoft.com/bing/v7.0/search`);
-    url.searchParams.append('q', query);
-    url.searchParams.append('responseFilter', 'Webpages,Images,Videos');
-
-    const res = await axios.get(url.toString(), {
-      headers: {
-        'Ocp-Apim-Subscription-Key': bingApiKey,
-        Accept: 'application/json',
-      },
-    });
-
-    if (res.data.error) {
-      throw new Error(`Bing API Error: ${res.data.error.message}`);
-    }
-
-    const originalres = res.data;
-
-    // Extract web, image, and video results
-    const webResults = originalres.webPages?.value || [];
-    const imageResults = originalres.images?.value || [];
-    const videoResults = originalres.videos?.value || [];
-
-    const results = webResults.map((item: BingAPISearchResult) => ({
-      title: item.name,
-      url: item.url,
-      content: item.snippet,
-      img_src:
-        item.primaryImageOfPage?.thumbnailUrl ||
-        imageResults.find((img: any) => img.hostPageUrl === item.url)
-          ?.thumbnailUrl ||
-        videoResults.find((vid: any) => vid.hostPageUrl === item.url)
-          ?.thumbnailUrl,
-      ...(item.video && {
-        videoData: {
-          duration: item.video.duration,
-          embedUrl: item.video.embedHtml?.match(/src="(.*?)"/)?.[1],
-        },
-        publisher: item.publisher,
-        datePublished: item.datePublished,
-      }),
-    }));
-
-    return { results, originalres };
-  } catch (error) {
-    const errorMessage = error.response?.data
-      ? JSON.stringify(error.response.data, null, 2)
-      : error.message || 'Unknown error';
-    throw new Error(`Bing API Error: ${errorMessage}`);
-  }
-};

src/lib/searchEngines/brave.ts

@@ -1,102 +0,0 @@
-import axios from 'axios';
-import { getBraveApiKey } from '../../config';
-
-interface BraveSearchResult {
-  title: string;
-  url: string;
-  content?: string;
-  img_src?: string;
-  age?: string;
-  family_friendly?: boolean;
-  language?: string;
-  video?: {
-    embedUrl?: string;
-    duration?: string;
-  };
-  rating?: {
-    value: number;
-    scale: number;
-  };
-  products?: Array<{
-    name: string;
-    price?: string;
-  }>;
-  recipe?: {
-    ingredients?: string[];
-    cookTime?: string;
-  };
-  meta?: {
-    fetched?: string;
-    lastCrawled?: string;
-  };
-}
-
-export const searchBraveAPI = async (
-  query: string,
-  numResults: number = 20,
-): Promise<{ results: BraveSearchResult[]; originalres: any }> => {
-  try {
-    const braveApiKey = await getBraveApiKey();
-    const url = new URL(`https://api.search.brave.com/res/v1/web/search`);
-
-    url.searchParams.append('q', query);
-    url.searchParams.append('count', numResults.toString());
-
-    const res = await axios.get(url.toString(), {
-      headers: {
-        'X-Subscription-Token': braveApiKey,
-        Accept: 'application/json',
-      },
-    });
-
-    if (res.data.error) {
-      throw new Error(`Brave API Error: ${res.data.error.message}`);
-    }
-
-    const originalres = res.data;
-    const webResults = originalres.web?.results || [];
-
-    const results: BraveSearchResult[] = webResults.map((item: any) => ({
-      title: item.title,
-      url: item.url,
-      content: item.description,
-      img_src: item.thumbnail?.src || item.deep_results?.images?.[0]?.src,
-      age: item.age,
-      family_friendly: item.family_friendly,
-      language: item.language,
-      video: item.video
-        ? {
-            embedUrl: item.video.embed_url,
-            duration: item.video.duration,
-          }
-        : undefined,
-      rating: item.rating
-        ? {
-            value: item.rating.value,
-            scale: item.rating.scale_max,
-          }
-        : undefined,
-      products: item.deep_results?.product_cluster?.map((p: any) => ({
-        name: p.name,
-        price: p.price,
-      })),
-      recipe: item.recipe
-        ? {
-            ingredients: item.recipe.ingredients,
-            cookTime: item.recipe.cook_time,
-          }
-        : undefined,
-      meta: {
-        fetched: item.meta?.fetched,
-        lastCrawled: item.meta?.last_crawled,
-      },
-    }));
-
-    return { results, originalres };
-  } catch (error) {
-    const errorMessage = error.response?.data
-      ? JSON.stringify(error.response.data, null, 2)
-      : error.message || 'Unknown error';
-    throw new Error(`Brave API Error: ${errorMessage}`);
-  }
-};

src/lib/searchEngines/google_pse.ts

@@ -1,85 +0,0 @@
-import axios from 'axios';
-import { getGoogleApiKey, getGoogleCseId } from '../../config';
-
-interface GooglePSESearchResult {
-  kind: string;
-  title: string;
-  htmlTitle: string;
-  link: string;
-  displayLink: string;
-  snippet?: string;
-  htmlSnippet?: string;
-  cacheId?: string;
-  formattedUrl: string;
-  htmlFormattedUrl: string;
-  pagemap?: {
-    videoobject: any;
-    cse_thumbnail?: Array<{
-      src: string;
-      width: string;
-      height: string;
-    }>;
-    metatags?: Array<{
-      [key: string]: string;
-      author?: string;
-    }>;
-    cse_image?: Array<{
-      src: string;
-    }>;
-  };
-  fileFormat?: string;
-  image?: {
-    contextLink: string;
-    thumbnailLink: string;
-  };
-  mime?: string;
-  labels?: Array<{
-    name: string;
-    displayName: string;
-  }>;
-}
-
-export const searchGooglePSE = async (query: string) => {
-  try {
-    const [googleApiKey, googleCseID] = await Promise.all([
-      getGoogleApiKey(),
-      getGoogleCseId(),
-    ]);
-
-    const url = new URL(`https://www.googleapis.com/customsearch/v1`);
-    url.searchParams.append('q', query);
-    url.searchParams.append('cx', googleCseID);
-    url.searchParams.append('key', googleApiKey);
-
-    const res = await axios.get(url.toString());
-
-    if (res.data.error) {
-      throw new Error(`Google PSE Error: ${res.data.error.message}`);
-    }
-
-    const originalres = res.data.items;
-
-    const results = originalres.map((item: GooglePSESearchResult) => ({
-      title: item.title,
-      url: item.link,
-      content: item.snippet,
-      img_src:
-        item.pagemap?.cse_image?.[0]?.src ||
-        item.pagemap?.cse_thumbnail?.[0]?.src ||
-        item.image?.thumbnailLink,
-      ...(item.pagemap?.videoobject?.[0] && {
-        videoData: {
-          duration: item.pagemap.videoobject[0].duration,
-          embedUrl: item.pagemap.videoobject[0].embedurl,
-        },
-      }),
-    }));
-
-    return { results, originalres };
-  } catch (error) {
-    const errorMessage = error.response?.data
-      ? JSON.stringify(error.response.data, null, 2)
-      : error.message || 'Unknown error';
-    throw new Error(`Google PSE Error: ${errorMessage}`);
-  }
-};

src/lib/searchEngines/yacy.ts

@@ -1,79 +0,0 @@
-import axios from 'axios';
-import { getYacyJsonEndpoint } from '../../config';
-
-interface YaCySearchResult {
-  channels: {
-    title: string;
-    description: string;
-    link: string;
-    image: {
-      url: string;
-      title: string;
-      link: string;
-    };
-    startIndex: string;
-    itemsPerPage: string;
-    searchTerms: string;
-    items: {
-      title: string;
-      link: string;
-      code: string;
-      description: string;
-      pubDate: string;
-      image?: string;
-      size: string;
-      sizename: string;
-      guid: string;
-      faviconUrl: string;
-      host: string;
-      path: string;
-      file: string;
-      urlhash: string;
-      ranking: string;
-    }[];
-    navigation: {
-      facetname: string;
-      displayname: string;
-      type: string;
-      min: string;
-      max: string;
-      mean: string;
-      elements: {
-        name: string;
-        count: string;
-        modifier: string;
-        url: string;
-      }[];
-    }[];
-  }[];
-}
-
-export const searchYaCy = async (query: string, numResults: number = 20) => {
-  try {
-    const yacyBaseUrl = getYacyJsonEndpoint();
-
-    const url = new URL(`${yacyBaseUrl}/yacysearch.json`);
-    url.searchParams.append('query', query);
-    url.searchParams.append('count', numResults.toString());
-
-    const res = await axios.get(url.toString());
-
-    const originalres = res.data as YaCySearchResult;
-
-    const results = originalres.channels[0].items.map((item) => ({
-      title: item.title,
-      url: item.link,
-      content: item.description,
-      img_src: item.image || null,
-      pubDate: item.pubDate,
-      host: item.host,
-    }));
-
-    return { results, originalres };
-  } catch (error) {
-    const errorMessage = error.response?.data
-      ? JSON.stringify(error.response.data, null, 2)
-      : error.message || 'Unknown error';
-    throw new Error(`YaCy Error: ${errorMessage}`);
-  }
-};

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/types/compute-dot.d.ts

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

src/lib/utils/computeSimilarity.ts

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

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();
@@ -79,12 +78,13 @@ export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
 
         docs.push(...linkDocs);
       } catch (err) {
-        logger.error(
+        console.error(
-          `Error at generating documents from links: ${err.message}`,
+          'An error occurred while getting documents from links: ',
+          err,
         );
         docs.push(
           new Document({
-            pageContent: `Failed to retrieve content from the link: ${err.message}`,
+            pageContent: `Failed to retrieve content from the link: ${err}`,
             metadata: {
               title: 'Failed to retrieve content',
               url: link,

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;