feat(workflow): fix build errors

feat(app): fix build errors

.github/workflows/docker-build.yaml

@@ -8,18 +8,12 @@ on:
     types: [published]
 
 jobs:
-  build-and-push:
+  build-amd64:
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        service: [backend, app]
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
 
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
         with:
@@ -36,38 +30,104 @@ jobs:
         id: version
         run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
 
-      - name: Build and push Docker image for ${{ matrix.service }}
+      - name: Build and push AMD64 Docker image
         if: github.ref == 'refs/heads/master' && github.event_name == 'push'
         run: |
-          docker buildx create --use
+          DOCKERFILE=app.dockerfile
-          if [[ "${{ matrix.service }}" == "backend" ]]; then \
+          IMAGE_NAME=perplexica
-            DOCKERFILE=backend.dockerfile; \
+          docker buildx build --platform linux/amd64 \
-            IMAGE_NAME=perplexica-backend; \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:amd64 \
-          else \
-            DOCKERFILE=app.dockerfile; \
-            IMAGE_NAME=perplexica-frontend; \
-          fi
-          docker buildx build --platform linux/amd64,linux/arm64 \
-            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:main \
             --cache-to=type=inline \
+            --provenance false \
             -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:main \
+            -t itzcrazykns1337/${IMAGE_NAME}:amd64 \
             --push .
 
-      - name: Build and push release Docker image for ${{ matrix.service }}
+      - name: Build and push AMD64 release Docker image
         if: github.event_name == 'release'
         run: |
-          docker buildx create --use
+          DOCKERFILE=app.dockerfile
-          if [[ "${{ matrix.service }}" == "backend" ]]; then \
+          IMAGE_NAME=perplexica
-            DOCKERFILE=backend.dockerfile; \
+          docker buildx build --platform linux/amd64 \
-            IMAGE_NAME=perplexica-backend; \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
-          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 \
+            --provenance false \
             -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
+            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
             --push .
+
+  build-arm64:
+    runs-on: ubuntu-24.04-arm
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+        with:
+          install: true
+
+      - name: Log in to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Extract version from release tag
+        if: github.event_name == 'release'
+        id: version
+        run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
+
+      - name: Build and push ARM64 Docker image
+        if: github.ref == 'refs/heads/master' && github.event_name == 'push'
+        run: |
+          DOCKERFILE=app.dockerfile
+          IMAGE_NAME=perplexica
+          docker buildx build --platform linux/arm64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:arm64 \
+            --cache-to=type=inline \
+            --provenance false \
+            -f $DOCKERFILE \
+            -t itzcrazykns1337/${IMAGE_NAME}:arm64 \
+            --push .
+
+      - name: Build and push ARM64 release Docker image
+        if: github.event_name == 'release'
+        run: |
+          DOCKERFILE=app.dockerfile
+          IMAGE_NAME=perplexica
+          docker buildx build --platform linux/arm64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
+            --cache-to=type=inline \
+            --provenance false \
+            -f $DOCKERFILE \
+            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
+            --push .
+
+  manifest:
+    needs: [build-amd64, build-arm64]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Log in to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Create and push multi-arch manifest for main
+        if: github.ref == 'refs/heads/master' && github.event_name == 'push'
+        run: |
+          IMAGE_NAME=perplexica
+          docker manifest create itzcrazykns1337/${IMAGE_NAME}:main \
+            --amend itzcrazykns1337/${IMAGE_NAME}:amd64 \
+            --amend itzcrazykns1337/${IMAGE_NAME}:arm64
+          docker manifest push itzcrazykns1337/${IMAGE_NAME}:main
+
+      - name: Create and push multi-arch manifest for releases
+        if: github.event_name == 'release'
+        run: |
+          IMAGE_NAME=perplexica
+          docker manifest create itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
+            --amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
+            --amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64
+          docker manifest push itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}

.gitignore

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

.prettierrc.js

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

CONTRIBUTING.md

@@ -1,32 +1,43 @@
 # How to Contribute to Perplexica
 
-Hey there, thanks for deciding to contribute to Perplexica. Anything you help with will support the development of Perplexica and will make it better. Let's walk you through the key aspects to ensure your contributions are effective and in harmony with the project's setup.
+Thanks for your interest in contributing to Perplexica! Your help makes this project better. This guide explains how to contribute effectively.
+
+Perplexica is a modern AI chat application with advanced search capabilities.
 
 ## Project Structure
 
-Perplexica's design consists of two main domains:
+Perplexica's codebase is organized as follows:
 
-- **Frontend (`ui` directory)**: This is a Next.js application holding all user interface components. It's a self-contained environment that manages everything the user interacts with.
+- **UI Components and Pages**:
-- **Backend (root and `src` directory)**: The backend logic is situated in the `src` folder, but the root directory holds the main `package.json` for backend dependency management.
+  - **Components (`src/components`)**: Reusable UI components.
-  - All of the focus modes are created using the Meta Search Agent class present in `src/search/metaSearchAgent.ts`. The main logic behind Perplexica lies there.
+  - **Pages and Routes (`src/app`)**: Next.js app directory structure with page components.
+    - Main app routes include: home (`/`), chat (`/c`), discover (`/discover`), library (`/library`), and settings (`/settings`).
+  - **API Routes (`src/app/api`)**: API endpoints implemented with Next.js API routes.
+    - `/api/chat`: Handles chat interactions.
+    - `/api/search`: Provides direct access to Perplexica's search capabilities.
+    - Other endpoints for models, files, and suggestions.
+- **Backend Logic (`src/lib`)**: Contains all the backend functionality including search, database, and API logic.
+  - The search functionality is present inside `src/lib/search` directory.
+  - All of the focus modes are implemented using the Meta Search Agent class in `src/lib/search/metaSearchAgent.ts`.
+  - Database functionality is in `src/lib/db`.
+  - Chat model and embedding model providers are managed in `src/lib/providers`.
+  - Prompt templates and LLM chain definitions are in `src/lib/prompts` and `src/lib/chains` respectively.
+
+## API Documentation
+
+Perplexica exposes several API endpoints for programmatic access, including:
+
+- **Search API**: Access Perplexica's advanced search capabilities directly via the `/api/search` endpoint. For detailed documentation, see `docs/api/search.md`.
 
 ## Setting Up Your Environment
 
 Before diving into coding, setting up your local environment is key. Here's what you need to do:
 
-### Backend
-
 1. In the root directory, locate the `sample.config.toml` file.
-2. Rename it to `config.toml` and fill in the necessary configuration fields specific to the backend.
+2. Rename it to `config.toml` and fill in the necessary configuration fields.
-3. Run `npm install` to install dependencies.
+3. Run `npm install` to install all dependencies.
-4. Run `npm run db:push` to set up the local sqlite.
+4. Run `npm run db:push` to set up the local sqlite database.
-5. Use `npm run dev` to start the backend in development mode.
+5. Use `npm run dev` to start the application in development mode.
-
-### Frontend
-
-1. Navigate to the `ui` folder and repeat the process of renaming `.env.example` to `.env`, making sure to provide the frontend-specific variables.
-2. Execute `npm install` within the `ui` directory to get the frontend dependencies ready.
-3. Launch the frontend development server with `npm run dev`.
 
 **Please note**: Docker configurations are present for setting up production environments, whereas `npm run dev` is used for development purposes.
 

README.md

@@ -109,14 +109,13 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
 
 1. Install SearXNG and allow `JSON` format in the SearXNG settings.
 2. Clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. Ensure you complete all required fields in this file.
-3. Rename the `.env.example` file to `.env` in the `ui` folder and fill in all necessary fields.
+3. After populating the configuration run `npm i`.
-4. After populating the configuration and environment files, run `npm i` in both the `ui` folder and the root directory.
+4. Install the dependencies and then execute `npm run build`.
-5. Install the dependencies and then execute `npm run build` in both the `ui` folder and the root directory.
+5. Finally, start the app by running `npm rum start`
-6. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
 
 **Note**: Using Docker is recommended as it simplifies the setup process, especially for managing environment variables and dependencies.
 
-See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like exposing it your network, etc.
+See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like updating, etc.
 
 ### Ollama Connection Errors
 

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 --network-timeout 600000
 
-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

@@ -9,41 +9,21 @@ services:
       - perplexica-network
     restart: unless-stopped
 
-  perplexica-backend:
+  app:
-    build:
+    image: itzcrazykns1337/perplexica:main
-      context: .
-      dockerfile: backend.dockerfile
-    image: itzcrazykns1337/perplexica-backend:main
-    environment:
-      - SEARXNG_API_URL=http://searxng:8080
-    depends_on:
-      - searxng
-    ports:
-      - 3001:3001
-    volumes:
-      - backend-dbstore:/home/perplexica/data
-      - uploads:/home/perplexica/uploads
-      - ./config.toml:/home/perplexica/config.toml
-    extra_hosts:
-      - 'host.docker.internal:host-gateway'
-    networks:
-      - perplexica-network
-    restart: unless-stopped
-
-  perplexica-frontend:
     build:
       context: .
       dockerfile: app.dockerfile
-      args:
+    environment:
-        - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
+      - SEARXNG_API_URL=http://searxng:8080
-        - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-    image: itzcrazykns1337/perplexica-frontend:main
-    depends_on:
-      - perplexica-backend
     ports:
       - 3000:3000
     networks:
       - perplexica-network
+    volumes:
+      - backend-dbstore:/home/perplexica/data
+      - uploads:/home/perplexica/uploads
+      - ./config.toml:/home/perplexica/config.toml
     restart: unless-stopped
 
 networks:

docs/API/SEARCH.md

@@ -6,9 +6,9 @@ Perplexica’s Search API makes it easy to use our AI-powered search engine. You
 
 ## Endpoint
 
-### **POST** `http://localhost:3001/api/search`
+### **POST** `http://localhost:3000/api/search`
 
-**Note**: Replace `3001` with any other port if you've changed the default PORT
+**Note**: Replace `3000` with any other port if you've changed the default PORT
 
 ### Request
 
@@ -20,11 +20,11 @@ The API accepts a JSON object in the request body, where you define the focus mo
 {
   "chatModel": {
     "provider": "openai",
-    "model": "gpt-4o-mini"
+    "name": "gpt-4o-mini"
   },
   "embeddingModel": {
     "provider": "openai",
-    "model": "text-embedding-3-large"
+    "name": "text-embedding-3-large"
   },
   "optimizationMode": "speed",
   "focusMode": "webSearch",
@@ -38,18 +38,18 @@ The API accepts a JSON object in the request body, where you define the focus mo
 
 ### Request Parameters
 
-- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
+- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
 
   - `provider`: Specifies the provider for the chat model (e.g., `openai`, `ollama`).
-  - `model`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
+  - `name`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
   - Optional fields for custom OpenAI configuration:
     - `customOpenAIBaseURL`: If you’re using a custom OpenAI instance, provide the base URL.
     - `customOpenAIKey`: The API key for a custom OpenAI instance.
 
-- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
+- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
 
   - `provider`: The provider for the embedding model (e.g., `openai`).
-  - `model`: The specific embedding model (e.g., `text-embedding-3-large`).
+  - `name`: The specific embedding model (e.g., `text-embedding-3-large`).
 
 - **`focusMode`** (string, required): Specifies which focus mode to use. Available modes:
 

docs/architecture/WORKING.md

@@ -4,7 +4,7 @@ Curious about how Perplexica works? Don't worry, we'll cover it here. Before we
 
 We'll understand how Perplexica works by taking an example of a scenario where a user asks: "How does an A.C. work?". We'll break down the process into steps to make it easier to understand. The steps are as follows:
 
-1. The message is sent via WS to the backend server where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
+1. The message is sent to the `/api/chat` route where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
 2. The chain is now invoked; first, the message is passed to another chain where it first predicts (using the chat history and the question) whether there is a need for sources and searching the web. If there is, it will generate a query (in accordance with the chat history) for searching the web that we'll take up later. If not, the chain will end there, and then the answer generator chain, also known as the response generator, will be started.
 3. The query returned by the first chain is passed to SearXNG to search the web for information.
 4. After the information is retrieved, it is based on keyword-based search. We then convert the information into embeddings and the query as well, then we perform a similarity search to find the most relevant sources to answer the query.

docs/installation/NETWORKING.md

@@ -1,109 +0,0 @@
-# Expose Perplexica to a network
-
-This guide will show you how to make Perplexica available over a network. Follow these steps to allow computers on the same network to interact with Perplexica. Choose the instructions that match the operating system you are using.
-
-## Windows
-
-1. Open PowerShell as Administrator
-
-2. Navigate to the directory containing the `docker-compose.yaml` file
-
-3. Stop and remove the existing Perplexica containers and images:
-
-   ```bash
-   docker compose down --rmi all
-   ```
-
-4. Open the `docker-compose.yaml` file in a text editor like Notepad++
-
-5. Replace `127.0.0.1` with the IP address of the server Perplexica is running on in these two lines:
-
-   ```bash
-   args:
-     - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
-     - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-   ```
-
-6. Save and close the `docker-compose.yaml` file
-
-7. Rebuild and restart the Perplexica container:
-
-   ```bash
-   docker compose up -d --build
-   ```
-
-## macOS
-
-1. Open the Terminal application
-
-2. Navigate to the directory with the `docker-compose.yaml` file:
-
-   ```bash
-   cd /path/to/docker-compose.yaml
-   ```
-
-3. Stop and remove existing containers and images:
-
-   ```bash
-   docker compose down --rmi all
-   ```
-
-4. Open `docker-compose.yaml` in a text editor like Sublime Text:
-
-   ```bash
-   nano docker-compose.yaml
-   ```
-
-5. Replace `127.0.0.1` with the server IP in these lines:
-
-   ```bash
-   args:
-     - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
-     - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-   ```
-
-6. Save and exit the editor
-
-7. Rebuild and restart Perplexica:
-
-   ```bash
-   docker compose up -d --build
-   ```
-
-## Linux
-
-1. Open the terminal
-
-2. Navigate to the `docker-compose.yaml` directory:
-
-   ```bash
-   cd /path/to/docker-compose.yaml
-   ```
-
-3. Stop and remove containers and images:
-
-   ```bash
-   docker compose down --rmi all
-   ```
-
-4. Edit `docker-compose.yaml`:
-
-   ```bash
-   nano docker-compose.yaml
-   ```
-
-5. Replace `127.0.0.1` with the server IP:
-
-   ```bash
-   args:
-     - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
-     - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-   ```
-
-6. Save and exit the editor
-
-7. Rebuild and restart Perplexica:
-
-   ```bash
-   docker compose up -d --build
-   ```

docs/installation/UPDATING.md

@@ -39,11 +39,8 @@ To update Perplexica to the latest version, follow these steps:
 2. Navigate to the project directory.
 
 3. Check for changes in the configuration files. If the `sample.config.toml` file contains new fields, delete your existing `config.toml` file, rename `sample.config.toml` to `config.toml`, and update the configuration accordingly.
-
+4. After populating the configuration run `npm i`.
-4. Execute `npm i` in both the `ui` folder and the root directory.
+5. Install the dependencies and then execute `npm run build`.
-
+6. Finally, start the app by running `npm rum start`
-5. Once the packages are updated, execute `npm run build` in both the `ui` folder and the root directory.
-
-6. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
 
 ---

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 +1,63 @@
 {
-  "name": "perplexica-backend",
+  "name": "perplexica-frontend",
-  "version": "1.10.0-rc3",
+  "version": "1.10.0",
   "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/core": "^0.3.42",
     "@langchain/openai": "^0.0.25",
-    "@langchain/google-genai": "^0.0.23",
+    "@langchain/textsplitters": "^0.1.0",
-    "@xenova/transformers": "^2.17.1",
+    "@tailwindcss/typography": "^0.5.12",
-    "axios": "^1.6.8",
+    "@xenova/transformers": "^2.17.2",
-    "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

@@ -1,5 +1,4 @@
 [GENERAL]
-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")
 
@@ -24,4 +23,4 @@ MODEL_NAME = ""
 API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
 [API_ENDPOINTS]
-SEARXNG = "http://localhost:32768" # SearxNG API URL
+SEARXNG = "" # SearxNG API URL - http://localhost:32768

src/app.ts

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

src/app/api/chat/route.ts

@@ -0,0 +1,304 @@
+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';
+import { searchHandlers } from '@/lib/search';
+
+export const runtime = 'nodejs';
+export const dynamic = 'force-dynamic';
+
+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

@@ -1,26 +1,22 @@
-import express from 'express';
+import {
+  getAnthropicApiKey,
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+  getGeminiApiKey,
+  getGroqApiKey,
+  getOllamaApiEndpoint,
+  getOpenaiApiKey,
+  updateConfig,
+} from '@/lib/config';
 import {
   getAvailableChatModelProviders,
   getAvailableEmbeddingModelProviders,
-} from '../lib/providers';
+} from '@/lib/providers';
-import {
-  getGroqApiKey,
-  getOllamaApiEndpoint,
-  getAnthropicApiKey,
-  getGeminiApiKey,
-  getOpenaiApiKey,
-  updateConfig,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiApiKey,
-  getCustomOpenaiModelName,
-} from '../config';
-import logger from '../utils/logger';
 
-const router = express.Router();
+export const GET = async (req: Request) => {
-
-router.get('/', async (_, res) => {
   try {
-    const config = {};
+    const config: Record<string, any> = {};
 
     const [chatModelProviders, embeddingModelProviders] = await Promise.all([
       getAvailableChatModelProviders(),
@@ -61,15 +57,19 @@ router.get('/', async (_, res) => {
     config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
     config['customOpenaiModelName'] = getCustomOpenaiModelName();
 
-    res.status(200).json(config);
+    return Response.json({ ...config }, { status: 200 });
-  } catch (err: any) {
+  } catch (err) {
-    res.status(500).json({ message: 'An error has occurred.' });
+    console.error('An error ocurred while getting config:', err);
-    logger.error(`Error getting config: ${err.message}`);
+    return Response.json(
+      { message: 'An error ocurred while getting config' },
+      { status: 500 },
+    );
   }
-});
+};
 
-router.post('/', async (req, res) => {
+export const POST = async (req: Request) => {
-  const config = req.body;
+  try {
+    const config = await req.json();
 
     const updatedConfig = {
       MODELS: {
@@ -98,7 +98,12 @@ router.post('/', async (req, res) => {
 
     updateConfig(updatedConfig);
 
-  res.status(200).json({ message: 'Config updated' });
+    return Response.json({ message: 'Config updated' }, { status: 200 });
-});
+  } catch (err) {
-
+    console.error('An error ocurred while updating config:', err);
-export default router;
+    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/search/route.ts

@@ -1,33 +1,29 @@
-import express from 'express';
-import logger from '../utils/logger';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 import type { Embeddings } from '@langchain/core/embeddings';
 import { ChatOpenAI } from '@langchain/openai';
 import {
   getAvailableChatModelProviders,
   getAvailableEmbeddingModelProviders,
-} from '../lib/providers';
+} from '@/lib/providers';
-import { searchHandlers } from '../websocket/messageHandler';
 import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { MetaSearchAgentType } from '../search/metaSearchAgent';
+import { MetaSearchAgentType } from '@/lib/search/metaSearchAgent';
 import {
   getCustomOpenaiApiKey,
   getCustomOpenaiApiUrl,
   getCustomOpenaiModelName,
-} from '../config';
+} from '@/lib/config';
-
+import { searchHandlers } from '@/lib/search';
-const router = express.Router();
 
 interface chatModel {
   provider: string;
-  model: string;
+  name: string;
   customOpenAIKey?: string;
   customOpenAIBaseURL?: string;
 }
 
 interface embeddingModel {
   provider: string;
-  model: string;
+  name: string;
 }
 
 interface ChatRequestBody {
@@ -39,27 +35,24 @@ interface ChatRequestBody {
   history: Array<[string, string]>;
 }
 
-router.post('/', async (req, res) => {
+export const POST = async (req: Request) => {
   try {
-    const body: ChatRequestBody = req.body;
+    const body: ChatRequestBody = await req.json();
 
     if (!body.focusMode || !body.query) {
-      return res.status(400).json({ message: 'Missing focus mode or query' });
+      return Response.json(
+        { message: 'Missing focus mode or query' },
+        { status: 400 },
+      );
     }
 
     body.history = body.history || [];
     body.optimizationMode = body.optimizationMode || 'balanced';
 
     const history: BaseMessage[] = body.history.map((msg) => {
-      if (msg[0] === 'human') {
+      return msg[0] === 'human'
-        return new HumanMessage({
+        ? new HumanMessage({ content: msg[1] })
-          content: msg[1],
+        : new AIMessage({ content: msg[1] });
-        });
-      } else {
-        return new AIMessage({
-          content: msg[1],
-        });
-      }
     });
 
     const [chatModelProviders, embeddingModelProviders] = await Promise.all([
@@ -70,13 +63,13 @@ router.post('/', async (req, res) => {
     const chatModelProvider =
       body.chatModel?.provider || Object.keys(chatModelProviders)[0];
     const chatModel =
-      body.chatModel?.model ||
+      body.chatModel?.name ||
       Object.keys(chatModelProviders[chatModelProvider])[0];
 
     const embeddingModelProvider =
       body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0];
     const embeddingModel =
-      body.embeddingModel?.model ||
+      body.embeddingModel?.name ||
       Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
 
     let llm: BaseChatModel | undefined;
@@ -84,7 +77,7 @@ router.post('/', async (req, res) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        modelName: body.chatModel?.model || getCustomOpenaiModelName(),
+        modelName: body.chatModel?.name || getCustomOpenaiModelName(),
         openAIApiKey:
           body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
         temperature: 0.7,
@@ -111,13 +104,16 @@ router.post('/', async (req, res) => {
     }
 
     if (!llm || !embeddings) {
-      return res.status(400).json({ message: 'Invalid model selected' });
+      return Response.json(
+        { message: 'Invalid model selected' },
+        { status: 400 },
+      );
     }
 
     const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
 
     if (!searchHandler) {
-      return res.status(400).json({ message: 'Invalid focus mode' });
+      return Response.json({ message: 'Invalid focus mode' }, { status: 400 });
     }
 
     const emitter = await searchHandler.searchAndAnswer(
@@ -129,30 +125,45 @@ router.post('/', async (req, res) => {
       [],
     );
 
+    return new Promise(
+      (
+        resolve: (value: Response) => void,
+        reject: (value: Response) => void,
+      ) => {
         let message = '';
-    let sources = [];
+        let sources: any[] = [];
 
         emitter.on('data', (data) => {
+          try {
             const parsedData = JSON.parse(data);
             if (parsedData.type === 'response') {
               message += parsedData.data;
             } else if (parsedData.type === 'sources') {
               sources = parsedData.data;
             }
+          } catch (error) {
+            reject(
+              Response.json({ message: 'Error parsing data' }, { status: 500 }),
+            );
+          }
         });
 
         emitter.on('end', () => {
-      res.status(200).json({ message, sources });
+          resolve(Response.json({ message, sources }, { status: 200 }));
         });
 
-    emitter.on('error', (data) => {
+        emitter.on('error', (error) => {
-      const parsedData = JSON.parse(data);
+          reject(
-      res.status(500).json({ message: parsedData.data });
+            Response.json({ message: 'Search error', error }, { status: 500 }),
+          );
         });
+      },
+    );
   } catch (err: any) {
-    logger.error(`Error in getting search results: ${err.message}`);
+    console.error(`Error in getting search results: ${err.message}`);
-    res.status(500).json({ message: 'An error has occurred.' });
+    return Response.json(
+      { message: 'An error has occurred.' },
+      { status: 500 },
+    );
   }
-});
+};
-
-export default router;

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

@@ -116,7 +116,7 @@ const Page = () => {
   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',
         },
@@ -187,16 +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: {
           'Content-Type': 'application/json',
         },
         body: JSON.stringify(updatedConfig),
-        },
+      });
-      );
 
       if (!response.ok) {
         throw new Error('Failed to update config');
@@ -208,7 +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',
           },

src/components/ChatWindow.tsx

@@ -29,36 +29,27 @@ export interface File {
   fileId: string;
 }
 
-const useSocket = (
+interface ChatModelProvider {
-  url: string,
+  name: string;
-  setIsWSReady: (ready: boolean) => void,
+  provider: string;
-  setError: (error: boolean) => void,
+}
+
+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);
-  const reconnectTimeoutRef = useRef<NodeJS.Timeout>();
-  const retryCountRef = useRef(0);
-  const isCleaningUpRef = useRef(false);
-  const MAX_RETRIES = 3;
-  const INITIAL_BACKOFF = 1000; // 1 second
-  const isConnectionErrorRef = useRef(false);
-
-  const getBackoffDelay = (retryCount: number) => {
-    return Math.min(INITIAL_BACKOFF * Math.pow(2, retryCount), 10000); // Cap at 10 seconds
-  };
-
-  useEffect(() => {
-    const connectWs = async () => {
-      if (wsRef.current?.readyState === WebSocket.OPEN) {
-        wsRef.current.close();
-      }
-
   try {
     let chatModel = localStorage.getItem('chatModel');
     let chatModelProvider = localStorage.getItem('chatModelProvider');
     let embeddingModel = localStorage.getItem('embeddingModel');
-        let embeddingModelProvider = localStorage.getItem(
+    let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
-          'embeddingModelProvider',
-        );
 
     const autoImageSearch = localStorage.getItem('autoImageSearch');
     const autoVideoSearch = localStorage.getItem('autoVideoSearch');
@@ -71,14 +62,11 @@ const useSocket = (
       localStorage.setItem('autoVideoSearch', 'false');
     }
 
-        const providers = await fetch(
+    const providers = await fetch(`/api/models`, {
-          `${process.env.NEXT_PUBLIC_API_URL}/models`,
-          {
       headers: {
         'Content-Type': 'application/json',
       },
-          },
+    }).then(async (res) => {
-        ).then(async (res) => {
       if (!res.ok)
         throw new Error(
           `Failed to fetch models: ${res.status} ${res.statusText}`,
@@ -100,10 +88,7 @@ const useSocket = (
 
         chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
 
-            if (
+        if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
-              !chatModelProviders ||
-              Object.keys(chatModelProviders).length === 0
-            )
           return toast.error('No chat models available');
       }
 
@@ -125,10 +110,7 @@ const useSocket = (
       localStorage.setItem('chatModel', chatModel!);
       localStorage.setItem('chatModelProvider', chatModelProvider);
       localStorage.setItem('embeddingModel', embeddingModel!);
-          localStorage.setItem(
+      localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
-            'embeddingModelProvider',
-            embeddingModelProvider,
-          );
     } else {
       const chatModelProviders = providers.chatModelProviders;
       const embeddingModelProviders = providers.embeddingModelProviders;
@@ -165,10 +147,7 @@ const useSocket = (
         !embeddingModelProviders[embeddingModelProvider]
       ) {
         embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-            localStorage.setItem(
+        localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
-              'embeddingModelProvider',
-              embeddingModelProvider,
-            );
       }
 
       if (
@@ -182,127 +161,22 @@ const useSocket = (
       }
     }
 
-        const wsURL = new URL(url);
+    setChatModelProvider({
-        const searchParams = new URLSearchParams({});
+      name: chatModel!,
-
+      provider: chatModelProvider,
-        searchParams.append('chatModel', chatModel!);
-        searchParams.append('chatModelProvider', chatModelProvider);
-
-        if (chatModelProvider === 'custom_openai') {
-          searchParams.append(
-            'openAIApiKey',
-            localStorage.getItem('openAIApiKey')!,
-          );
-          searchParams.append(
-            'openAIBaseURL',
-            localStorage.getItem('openAIBaseURL')!,
-          );
-        }
-
-        searchParams.append('embeddingModel', embeddingModel!);
-        searchParams.append('embeddingModelProvider', embeddingModelProvider);
-
-        wsURL.search = searchParams.toString();
-
-        const ws = new WebSocket(wsURL.toString());
-        wsRef.current = ws;
-
-        const timeoutId = setTimeout(() => {
-          if (ws.readyState !== 1) {
-            toast.error(
-              'Failed to connect to the server. Please try again later.',
-            );
-          }
-        }, 10000);
-
-        ws.addEventListener('message', (e) => {
-          const data = JSON.parse(e.data);
-          if (data.type === 'signal' && data.data === 'open') {
-            const interval = setInterval(() => {
-              if (ws.readyState === 1) {
-                setIsWSReady(true);
-                setError(false);
-                if (retryCountRef.current > 0) {
-                  toast.success('Connection restored.');
-                }
-                retryCountRef.current = 0;
-                clearInterval(interval);
-              }
-            }, 5);
-            clearTimeout(timeoutId);
-            console.debug(new Date(), 'ws:connected');
-          }
-          if (data.type === 'error') {
-            isConnectionErrorRef.current = true;
-            setError(true);
-            toast.error(data.data);
-          }
     });
 
-        ws.onerror = () => {
+    setEmbeddingModelProvider({
-          clearTimeout(timeoutId);
+      name: embeddingModel!,
-          setIsWSReady(false);
+      provider: embeddingModelProvider,
-          toast.error('WebSocket connection error.');
+    });
-        };
 
-        ws.onclose = () => {
+    setIsConfigReady(true);
-          clearTimeout(timeoutId);
+  } catch (err) {
-          setIsWSReady(false);
+    console.error('An error occurred while checking the configuration:', err);
-          console.debug(new Date(), 'ws:disconnected');
+    setIsConfigReady(false);
-          if (!isCleaningUpRef.current && !isConnectionErrorRef.current) {
+    setHasError(true);
-            toast.error('Connection lost. Attempting to reconnect...');
-            attemptReconnect();
   }
-        };
-      } catch (error) {
-        console.debug(new Date(), 'ws:error', error);
-        setIsWSReady(false);
-        attemptReconnect();
-      }
-    };
-
-    const attemptReconnect = () => {
-      retryCountRef.current += 1;
-
-      if (retryCountRef.current > MAX_RETRIES) {
-        console.debug(new Date(), 'ws:max_retries');
-        setError(true);
-        toast.error(
-          'Unable to connect to server after multiple attempts. Please refresh the page to try again.',
-        );
-        return;
-      }
-
-      const backoffDelay = getBackoffDelay(retryCountRef.current);
-      console.debug(
-        new Date(),
-        `ws:retry attempt=${retryCountRef.current}/${MAX_RETRIES} delay=${backoffDelay}ms`,
-      );
-
-      if (reconnectTimeoutRef.current) {
-        clearTimeout(reconnectTimeoutRef.current);
-      }
-
-      reconnectTimeoutRef.current = setTimeout(() => {
-        connectWs();
-      }, backoffDelay);
-    };
-
-    connectWs();
-
-    return () => {
-      if (reconnectTimeoutRef.current) {
-        clearTimeout(reconnectTimeoutRef.current);
-      }
-      if (wsRef.current?.readyState === WebSocket.OPEN) {
-        wsRef.current.close();
-        isCleaningUpRef.current = true;
-        console.debug(new Date(), 'ws:cleanup');
-      }
-    };
-  }, [url, setIsWSReady, setError]);
-
-  return wsRef.current;
 };
 
 const loadMessages = async (
@@ -315,15 +189,12 @@ const loadMessages = async (
   setFiles: (files: File[]) => void,
   setFileIds: (fileIds: string[]) => void,
 ) => {
-  const res = await fetch(
+  const res = await fetch(`/api/chats/${chatId}`, {
-    `${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
-    {
     method: 'GET',
     headers: {
       'Content-Type': 'application/json',
     },
-    },
+  });
-  );
 
   if (res.status === 404) {
     setNotFound(true);
@@ -373,15 +244,32 @@ const ChatWindow = ({ id }: { id?: string }) => {
   const [chatId, setChatId] = useState<string | undefined>(id);
   const [newChatCreated, setNewChatCreated] = useState(false);
 
+  const [chatModelProvider, setChatModelProvider] = useState<ChatModelProvider>(
+    {
+      name: '',
+      provider: '',
+    },
+  );
+
+  const [embeddingModelProvider, setEmbeddingModelProvider] =
+    useState<EmbeddingModelProvider>({
+      name: '',
+      provider: '',
+    });
+
+  const [isConfigReady, setIsConfigReady] = useState(false);
   const [hasError, setHasError] = useState(false);
   const [isReady, setIsReady] = useState(false);
 
-  const [isWSReady, setIsWSReady] = useState(false);
+  useEffect(() => {
-  const ws = useSocket(
+    checkConfig(
-    process.env.NEXT_PUBLIC_WS_URL!,
+      setChatModelProvider,
-    setIsWSReady,
+      setEmbeddingModelProvider,
+      setIsConfigReady,
       setHasError,
     );
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
 
   const [loading, setLoading] = useState(false);
   const [messageAppeared, setMessageAppeared] = useState(false);
@@ -399,8 +287,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
 
   const [notFound, setNotFound] = useState(false);
 
-  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
-
   useEffect(() => {
     if (
       chatId &&
@@ -426,16 +312,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, []);
 
-  useEffect(() => {
-    return () => {
-      if (ws?.readyState === 1) {
-        ws.close();
-        console.debug(new Date(), 'ws:cleanup');
-      }
-    };
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, []);
-
   const messagesRef = useRef<Message[]>([]);
 
   useEffect(() => {
@@ -443,18 +319,18 @@ const ChatWindow = ({ id }: { id?: string }) => {
   }, [messages]);
 
   useEffect(() => {
-    if (isMessagesLoaded && isWSReady) {
+    if (isMessagesLoaded && isConfigReady) {
       setIsReady(true);
       console.debug(new Date(), 'app:ready');
     } else {
       setIsReady(false);
     }
-  }, [isMessagesLoaded, isWSReady]);
+  }, [isMessagesLoaded, isConfigReady]);
 
   const sendMessage = async (message: string, messageId?: string) => {
     if (loading) return;
-    if (!ws || ws.readyState !== WebSocket.OPEN) {
+    if (!isConfigReady) {
-      toast.error('Cannot send message while disconnected');
+      toast.error('Cannot send message before the configuration is ready');
       return;
     }
 
@@ -467,21 +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: {
           'Content-Type': 'application/json',
         },
-        },
+      });
-      );
 
       if (res.status != 200) {
         throw new Error('Failed to delete chat');

src/components/MessageBox.tsx

@@ -12,13 +12,18 @@ import {
   Layers3,
   Plus,
 } from 'lucide-react';
-import Markdown from 'markdown-to-jsx';
+import Markdown, { MarkdownToJSX } from 'markdown-to-jsx';
 import Copy from './MessageActions/Copy';
 import Rewrite from './MessageActions/Rewrite';
 import MessageSources from './MessageSources';
 import SearchImages from './SearchImages';
 import SearchVideos from './SearchVideos';
 import { useSpeech } from 'react-text-to-speech';
+import ThinkBox from './ThinkBox';
+
+const ThinkTagProcessor = ({ children }: { children: React.ReactNode }) => {
+  return <ThinkBox content={children as string} />;
+};
 
 const MessageBox = ({
   message,
@@ -44,27 +49,48 @@ const MessageBox = ({
 
   useEffect(() => {
     const regex = /\[(\d+)\]/g;
+    let processedMessage = message.content;
+
+    if (message.role === 'assistant' && message.content.includes('<think>')) {
+      const openThinkTag = processedMessage.match(/<think>/g)?.length || 0;
+      const closeThinkTag = processedMessage.match(/<\/think>/g)?.length || 0;
+
+      if (openThinkTag > closeThinkTag) {
+        processedMessage += '</think> <a> </a>'; // The extra <a> </a> is to prevent the the think component from looking bad
+      }
+    }
 
     if (
       message.role === 'assistant' &&
       message?.sources &&
       message.sources.length > 0
     ) {
-      return setParsedMessage(
+      setParsedMessage(
-        message.content.replace(
+        processedMessage.replace(
           regex,
           (_, number) =>
-            `<a href="${message.sources?.[number - 1]?.metadata?.url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${number}</a>`,
+            `<a href="${
+              message.sources?.[number - 1]?.metadata?.url
+            }" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${number}</a>`,
         ),
       );
+      return;
     }
 
     setSpeechMessage(message.content.replace(regex, ''));
-    setParsedMessage(message.content);
+    setParsedMessage(processedMessage);
   }, [message.content, message.sources, message.role]);
 
   const { speechStatus, start, stop } = useSpeech({ text: speechMessage });
 
+  const markdownOverrides: MarkdownToJSX.Options = {
+    overrides: {
+      think: {
+        component: ThinkTagProcessor,
+      },
+    },
+  };
+
   return (
     <div>
       {message.role === 'user' && (
@@ -111,11 +137,13 @@ const MessageBox = ({
                   Answer
                 </h3>
               </div>
+
               <Markdown
                 className={cn(
                   'prose prose-h1:mb-3 prose-h2:mb-2 prose-h2:mt-6 prose-h2:font-[800] prose-h3:mt-4 prose-h3:mb-1.5 prose-h3:font-[600] dark:prose-invert prose-p:leading-relaxed prose-pre:p-0 font-[400]',
                   'max-w-none break-words text-black dark:text-white',
                 )}
+                options={markdownOverrides}
               >
                 {parsedMessage}
               </Markdown>
@@ -193,10 +221,12 @@ const MessageBox = ({
             <SearchImages
               query={history[messageIndex - 1].content}
               chatHistory={history.slice(0, messageIndex - 1)}
+              messageId={message.messageId}
             />
             <SearchVideos
               chatHistory={history.slice(0, messageIndex - 1)}
               query={history[messageIndex - 1].content}
+              messageId={message.messageId}
             />
           </div>
         </div>

src/components/MessageInputActions/Attach.tsx

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

src/components/MessageInputActions/AttachSmall.tsx

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

src/components/MessageInputActions/Focus.tsx

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

src/components/MessageSources.tsx

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

src/components/SearchImages.tsx

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

src/components/SearchVideos.tsx

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

src/components/Sidebar.tsx

@@ -16,8 +16,6 @@ const VerticalIconContainer = ({ children }: { children: ReactNode }) => {
 const Sidebar = ({ children }: { children: React.ReactNode }) => {
   const segments = useSelectedLayoutSegments();
 
-  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
-
   const navLinks = [
     {
       icon: Home,

src/components/ThinkBox.tsx

@@ -0,0 +1,43 @@
+'use client';
+
+import { useState } from 'react';
+import { cn } from '@/lib/utils';
+import { ChevronDown, ChevronUp, BrainCircuit } from 'lucide-react';
+
+interface ThinkBoxProps {
+  content: string;
+}
+
+const ThinkBox = ({ content }: ThinkBoxProps) => {
+  const [isExpanded, setIsExpanded] = useState(false);
+
+  return (
+    <div className="my-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200 overflow-hidden">
+      <button
+        onClick={() => setIsExpanded(!isExpanded)}
+        className="w-full flex items-center justify-between px-4 py-1 text-black/90 dark:text-white/90 hover:bg-light-200 dark:hover:bg-dark-200 transition duration-200"
+      >
+        <div className="flex items-center space-x-2">
+          <BrainCircuit
+            size={20}
+            className="text-[#9C27B0] dark:text-[#CE93D8]"
+          />
+          <p className="font-medium text-sm">Thinking Process</p>
+        </div>
+        {isExpanded ? (
+          <ChevronUp size={18} className="text-black/70 dark:text-white/70" />
+        ) : (
+          <ChevronDown size={18} className="text-black/70 dark:text-white/70" />
+        )}
+      </button>
+
+      {isExpanded && (
+        <div className="px-4 py-3 text-black/80 dark:text-white/80 text-sm border-t border-light-200 dark:border-dark-200 bg-light-100/50 dark:bg-dark-100/50 whitespace-pre-wrap">
+          {content}
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default ThinkBox;

src/lib/actions.ts

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

src/lib/chains/imageSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../lib/searxng';
+import { searchSearxng } from '../searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const imageSearchChainPrompt = `
@@ -36,6 +36,12 @@ type ImageSearchChainInput = {
   query: string;
 };
 
+interface ImageSearchResult {
+  img_src: string;
+  url: string;
+  title: string;
+}
+
 const strParser = new StringOutputParser();
 
 const createImageSearchChain = (llm: BaseChatModel) => {
@@ -52,11 +58,13 @@ const createImageSearchChain = (llm: BaseChatModel) => {
     llm,
     strParser,
     RunnableLambda.from(async (input: string) => {
+      input = input.replace(/<think>.*?<\/think>/g, '');
+
       const res = await searchSearxng(input, {
         engines: ['bing images', 'google images'],
       });
 
-      const images = [];
+      const images: ImageSearchResult[] = [];
 
       res.results.forEach((result) => {
         if (result.img_src && result.url && result.title) {

src/lib/chains/suggestionGeneratorAgent.ts

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

src/lib/chains/videoSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../lib/searxng';
+import { searchSearxng } from '../searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const VideoSearchChainPrompt = `
@@ -36,6 +36,13 @@ type VideoSearchChainInput = {
   query: string;
 };
 
+interface VideoSearchResult {
+  img_src: string;
+  url: string;
+  title: string;
+  iframe_src: string;
+}
+
 const strParser = new StringOutputParser();
 
 const createVideoSearchChain = (llm: BaseChatModel) => {
@@ -52,11 +59,13 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
     llm,
     strParser,
     RunnableLambda.from(async (input: string) => {
+      input = input.replace(/<think>.*?<\/think>/g, '');
+
       const res = await searchSearxng(input, {
         engines: ['youtube'],
       });
 
-      const videos = [];
+      const videos: VideoSearchResult[] = [];
 
       res.results.forEach((result) => {
         if (

src/lib/config.ts

@@ -6,7 +6,6 @@ const configFileName = 'config.toml';
 
 interface Config {
   GENERAL: {
-    PORT: number;
     SIMILARITY_MEASURE: string;
     KEEP_ALIVE: string;
   };
@@ -43,11 +42,9 @@ 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;
-
 export const getSimilarityMeasure = () =>
   loadConfig().GENERAL.SIMILARITY_MEASURE;
 
@@ -109,9 +106,8 @@ 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

@@ -28,7 +28,7 @@ export class HuggingFaceTransformersEmbeddings
 
   timeout?: number;
 
-  private pipelinePromise: Promise<any>;
+  private pipelinePromise: Promise<any> | undefined;
 
   constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
     super(fields ?? {});

src/lib/outputParsers/lineOutputParser.ts

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

src/lib/outputParsers/listLineOutputParser.ts

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

src/lib/providers/anthropic.ts

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

src/lib/providers/gemini.ts

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

src/lib/providers/groq.ts

@@ -1,6 +1,78 @@
 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 (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 +80,25 @@ export const loadGroqChatModels = async () => {
   if (!groqApiKey) return {};
 
   try {
-    const chatModels = {
+    const chatModels: Record<string, ChatModel> = {};
-      'llama-3.3-70b-versatile': {
+
-        displayName: 'Llama 3.3 70B',
+    groqChatModels.forEach((model) => {
-        model: new ChatOpenAI(
+      chatModels[model.key] = {
-          {
+        displayName: model.displayName,
+        model: new ChatOpenAI({
           openAIApiKey: groqApiKey,
-            modelName: 'llama-3.3-70b-versatile',
+          modelName: model.key,
           temperature: 0.7,
-          },
+          configuration: {
-          {
             baseURL: 'https://api.groq.com/openai/v1',
           },
-        ),
+        }) as unknown as BaseChatModel,
-      },
-      'llama-3.2-3b-preview': {
-        displayName: 'Llama 3.2 3B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-3b-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.2-11b-vision-preview': {
-        displayName: 'Llama 3.2 11B Vision',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-11b-vision-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.2-90b-vision-preview': {
-        displayName: 'Llama 3.2 90B Vision',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-90b-vision-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.1-8b-instant': {
-        displayName: 'Llama 3.1 8B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.1-8b-instant',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama3-8b-8192': {
-        displayName: 'LLaMA3 8B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama3-8b-8192',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama3-70b-8192': {
-        displayName: 'LLaMA3 70B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama3-70b-8192',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'mixtral-8x7b-32768': {
-        displayName: 'Mixtral 8x7B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'mixtral-8x7b-32768',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'gemma2-9b-it': {
-        displayName: 'Gemma2 9B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'gemma2-9b-it',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
       };
+    });
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Groq models: ${err}`);
+    console.error(`Error loading Groq models: ${err}`);
     return {};
   }
 };

src/lib/providers/index.ts

@@ -1,33 +1,51 @@
-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';
+import { loadTransformersEmbeddingsModels } from './transformers';
 
-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,
+  transformers: loadTransformersEmbeddingsModels,
 };
 
 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 +70,7 @@ export const getAvailableChatModelProviders = async () => {
               configuration: {
                 baseURL: customOpenAiApiUrl,
               },
-            }),
+            }) as unknown as BaseChatModel,
           },
         }
       : {}),
@@ -62,7 +80,7 @@ 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

@@ -1,4 +1,3 @@
-import logger from '../../utils/logger';
 import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
 
 export const loadTransformersEmbeddingsModels = async () => {
@@ -26,7 +25,7 @@ export const loadTransformersEmbeddingsModels = async () => {
 
     return embeddingModels;
   } catch (err) {
-    logger.error(`Error loading Transformers embeddings model: ${err}`);
+    console.error(`Error loading Transformers embeddings model: ${err}`);
     return {};
   }
 };

src/lib/search/index.ts

@@ -0,0 +1,59 @@
+import MetaSearchAgent from '@/lib/search/metaSearchAgent';
+import prompts from '../prompts';
+
+export 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,
+  }),
+};

src/lib/search/metaSearchAgent.ts

@@ -13,18 +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/searxng';
+import { searchSearxng } from '../searxng';
-import path from 'path';
+import path from 'node:path';
-import fs from 'fs';
+import fs from 'node: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: (
@@ -90,7 +89,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
             question = 'summarize';
           }
 
-          let docs = [];
+          let docs: Document[] = [];
 
           const linkDocs = await getDocumentsFromLinks({ links });
 
@@ -203,6 +202,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
           return { query: question, docs: docs };
         } else {
+          question = question.replace(/<think>.*?<\/think>/g, '');
+
           const res = await searchSearxng(question, {
             language: 'en',
             engines: this.config.activeEngines,
@@ -311,7 +312,7 @@ 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,
@@ -414,6 +415,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
       return sortedDocs;
     }
+
+    return [];
   }
 
   private processDocs(docs: Document[]) {
@@ -426,7 +429,7 @@ 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/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,