Merge a661450633 into 89b5229ce9

.github/workflows/docker-build.yaml

@@ -8,12 +8,15 @@ on:
     types: [published]
 
 jobs:
-  build-amd64:
+  build-and-push:
     runs-on: ubuntu-latest
     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:
@@ -30,109 +33,24 @@ jobs:
         id: version
         run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
 
-      - name: Build and push AMD64 Docker image
+      - name: Build and push Docker image (latest)
         if: github.ref == 'refs/heads/master' && github.event_name == 'push'
         run: |
-          DOCKERFILE=app.dockerfile
-          IMAGE_NAME=perplexica
-          docker buildx build --platform linux/amd64 \
-            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:amd64 \
+          docker buildx create --use
+          docker buildx build --platform linux/amd64,linux/arm64,linux/arm/v7 \
+            --cache-from=type=registry,ref=itzcrazykns1337/perplexica:latest \
             --cache-to=type=inline \
-            --provenance false \
-            -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:amd64 \
+            -f docker/Dockerfile \
+            -t itzcrazykns1337/perplexica:latest \
             --push .
 
-      - name: Build and push AMD64 release Docker image
+      - name: Build and push Docker image (release)
         if: github.event_name == 'release'
         run: |
-          DOCKERFILE=app.dockerfile
-          IMAGE_NAME=perplexica
-          docker buildx build --platform linux/amd64 \
-            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
+          docker buildx create --use
+          docker buildx build --platform linux/amd64,linux/arm64,linux/arm/v7 \
+            --cache-from=type=registry,ref=itzcrazykns1337/perplexica:${{ env.RELEASE_VERSION }} \
             --cache-to=type=inline \
-            --provenance false \
-            -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
+            -f docker/Dockerfile \
+            -t itzcrazykns1337/perplexica:${{ env.RELEASE_VERSION }} \
             --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: ${{ secrets.DOCKER_PASSWORD }}
-
-      - name: Extract version from release tag
-        if: github.event_name == 'release'
-        id: version
-        run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
-
-      - name: Create and push multi-arch manifest for main
-        if: github.ref == 'refs/heads/master' && github.event_name == 'push'
-        run: |
-          IMAGE_NAME=perplexica
-          docker manifest create itzcrazykns1337/${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/
-out/
-dist/
+/.next/
+/out/
+/dist/
 
 # IDE/Editor specific
 .vscode/

.prettierrc.js

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

CONTRIBUTING.md

@@ -1,43 +1,32 @@
 # How to Contribute to Perplexica
 
-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.
+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.
 
 ## Project Structure
 
-Perplexica's codebase is organized as follows:
+Perplexica's design consists of two main domains:
 
-- **UI Components and Pages**:
-  - **Components (`src/components`)**: Reusable UI components.
-  - **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`.
+- **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.
+- **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.
+  - 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.
 
 ## 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.
-3. Run `npm install` to install all dependencies.
-4. Run `npm run db:push` to set up the local sqlite database.
-5. Use `npm run dev` to start the application in development mode.
+2. Rename it to `config.toml` and fill in the necessary configuration fields specific to the backend.
+3. Run `npm install` to install dependencies.
+4. Run `npm run db:push` to set up the local sqlite.
+5. Use `npm run dev` to start the backend 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

@@ -26,12 +26,13 @@
 - [Preview](#preview)
 - [Features](#features)
 - [Installation](#installation)
-  - [Getting Started with Docker (Recommended)](#getting-started-with-docker-recommended)
+  - [Docker Installation (Recommended)](#docker-installation-recommended)
   - [Non-Docker Installation](#non-docker-installation)
+  - [Nginx Reverse Proxy](#nginx-reverse-proxy)
   - [Ollama Connection Errors](#ollama-connection-errors)
 - [Using as a Search Engine](#using-as-a-search-engine)
 - [Using Perplexica's API](#using-perplexicas-api)
-- [Expose Perplexica to a network](#expose-perplexica-to-network)
+- [Expose Perplexica to a Network](#expose-perplexica-to-a-network)
 - [One-Click Deployment](#one-click-deployment)
 - [Upcoming Features](#upcoming-features)
 - [Support Us](#support-us)
@@ -71,9 +72,9 @@ It has many more features like image and video search. Some of the planned featu
 
 ## Installation
 
-There are mainly 2 ways of installing Perplexica - With Docker, Without Docker. Using Docker is highly recommended.
+Perplexica can be installed using Docker (recommended) or directly on your system.
 
-### Getting Started with Docker (Recommended)
+### Docker Installation (Recommended)
 
 1. Ensure Docker is installed and running on your system.
 2. Clone the Perplexica repository:
@@ -101,21 +102,38 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
    docker compose up -d
    ```
 
-6. Wait a few minutes for the setup to complete. You can access Perplexica at http://localhost:3000 in your web browser.
+6. Wait a few minutes for the setup to complete. You can access Perplexica at http://localhost:8080 in your web browser.
 
 **Note**: After the containers are built, you can start Perplexica directly from Docker without having to open a terminal.
 
+The Docker configuration is located in the `docker/` directory, containing:
+- Dockerfile with multi-stage build for efficient images
+- Service configurations for the integrated process manager
+- Nginx reverse proxy configuration
+
 ### Non-Docker Installation
 
 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. After populating the configuration run `npm i`.
-4. Install the dependencies and then execute `npm run build`.
-5. Finally, start the app by running `npm rum start`
+3. Rename the `.env.example` file to `.env` in the `ui` folder and fill in all necessary fields.
+4. After populating the configuration and environment files, run `npm i` in both the `ui` folder and the root directory.
+5. Install the dependencies and then 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.
 
 **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 updating, etc.
+See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like exposing it your network, etc.
+
+### Nginx Reverse Proxy
+
+Perplexica includes an Nginx reverse proxy that provides several key benefits:
+
+- **Single Port Access**: Access both frontend and backend through a single port (8080)
+- **Dynamic Configuration**: Works with any domain or IP without rebuilding
+- **WebSocket Support**: Automatic WebSocket URL configuration based on the current domain
+- **Security Headers**: Enhanced security with proper HTTP headers
+
+When using Docker, the reverse proxy is automatically configured. Access Perplexica at `http://localhost:8080` or `http://your-ip:8080` after starting the containers.
 
 ### Ollama Connection Errors
 
@@ -142,7 +160,7 @@ If you wish to use Perplexica as an alternative to traditional search engines li
 
 1. Open your browser's settings.
 2. Navigate to the 'Search Engines' section.
-3. Add a new site search with the following URL: `http://localhost:3000/?q=%s`. Replace `localhost` with your IP address or domain name, and `3000` with the port number if Perplexica is not hosted locally.
+3. Add a new site search with the following URL: `http://localhost:8080/?q=%s`. Replace `localhost` with your IP address or domain name if needed.
 4. Click the add button. Now, you can use Perplexica directly from your browser's search bar.
 
 ## Using Perplexica's API
@@ -151,15 +169,20 @@ Perplexica also provides an API for developers looking to integrate its powerful
 
 For more details, check out the full documentation [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/API/SEARCH.md).
 
-## Expose Perplexica to network
+## Expose Perplexica to a Network
 
-Perplexica runs on Next.js and handles all API requests. It works right away on the same network and stays accessible even with port forwarding.
+Perplexica can be easily accessed over your home network or exposed to the internet through the Nginx reverse proxy. With this setup:
+
+1. **Local Network Access**: Access Perplexica from any device on your network using `http://server-ip:8080`
+2. **Domain Configuration**: If you have a domain name, point it to your server and access Perplexica with `http://your-domain.com:8080`
+3. **SSL Support**: Configure SSL certificates in Nginx for secure `https://` access
+
+For more network configuration details, see our [networking guide](https://github.com/ItzCrazyKns/Perplexica/blob/master/docs/installation/NETWORKING.md).
 
 ## One-Click Deployment
 
 [![Deploy to Sealos](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://usw.sealos.io/?openapp=system-template%3FtemplateName%3Dperplexica)
 [![Deploy to RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploylobe.svg)](https://repocloud.io/details/?app_id=267)
-[![Run on ClawCloud](https://raw.githubusercontent.com/ClawCloud/Run-Template/refs/heads/main/Run-on-ClawCloud.svg)](https://template.run.claw.cloud/?referralCode=U11MRQ8U9RM4&openapp=system-fastdeploy%3FtemplateName%3Dperplexica)
 
 ## Upcoming Features
 

app.dockerfile

@@ -1,27 +0,0 @@
-FROM node:20.18.0-slim AS builder
-
-WORKDIR /home/perplexica
-
-COPY package.json yarn.lock ./
-RUN yarn install --frozen-lockfile --network-timeout 600000
-
-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
-
-FROM node:20.18.0-slim
-
-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"]

docker-compose.yaml

@@ -2,29 +2,43 @@ services:
   searxng:
     image: docker.io/searxng/searxng:latest
     volumes:
-      - ./searxng:/etc/searxng:rw
-    ports:
-      - 4000:8080
+      - ./searxng:/etc/searxng
     networks:
       - perplexica-network
     restart: unless-stopped
 
-  app:
-    image: itzcrazykns1337/perplexica:main
-    build:
-      context: .
-      dockerfile: app.dockerfile
-    environment:
-      - SEARXNG_API_URL=http://searxng:8080
+  perplexica:
+    image: itzcrazykns1337/perplexica:latest
     ports:
-      - 3000:3000
+      - "8080:8080"
+    environment:
+      - SEARXNG_API_URL=http://searxng:4000
+      - SIMILARITY_MEASURE=cosine
+      - KEEP_ALIVE=5m
+      - OPENAI_API_KEY=${OPENAI_API_KEY:-}
+      - GROQ_API_KEY=${GROQ_API_KEY:-}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
+      - GEMINI_API_KEY=${GEMINI_API_KEY:-}
+      - OLLAMA_API_URL=${OLLAMA_API_URL:-}
+      - CUSTOM_OPENAI_API_KEY=${CUSTOM_OPENAI_API_KEY:-}
+      - CUSTOM_OPENAI_API_URL=${CUSTOM_OPENAI_API_URL:-}
+      - CUSTOM_OPENAI_MODEL_NAME=${CUSTOM_OPENAI_MODEL_NAME:-}
+    volumes:
+      - backend-dbstore:/app/backend/data
+      - uploads:/app/backend/uploads
+    extra_hosts:
+      - 'host.docker.internal:host-gateway'
+    depends_on:
+      - searxng
     networks:
       - perplexica-network
-    volumes:
-      - backend-dbstore:/home/perplexica/data
-      - uploads:/home/perplexica/uploads
-      - ./config.toml:/home/perplexica/config.toml
     restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8080/"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 5s
 
 networks:
   perplexica-network:

docker/Dockerfile

@@ -0,0 +1,93 @@
+# Multi-stage build for Perplexica
+# Stage 1: Build the backend
+FROM node:lts-alpine as backend-builder
+
+WORKDIR /app
+
+COPY src ./src
+COPY tsconfig.json drizzle.config.ts package.json yarn.lock ./
+
+RUN yarn install --frozen-lockfile --network-timeout 600000 && \
+    yarn build
+
+# Stage 2: Build the frontend
+FROM node:lts-alpine as frontend-builder
+
+WORKDIR /app
+
+COPY ui ./
+ARG NEXT_PUBLIC_API_URL=/api
+ARG NEXT_PUBLIC_WS_URL=auto
+ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
+ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
+
+RUN yarn install --frozen-lockfile && \
+    yarn build
+
+# Stage 3: Final image
+FROM node:lts-alpine
+
+# Install curl and jq for GitHub API access
+RUN apk add --no-cache curl jq
+
+# Determine latest S6 overlay version at build time
+RUN S6_OVERLAY_VERSION=$(curl -s https://api.github.com/repos/just-containers/s6-overlay/releases/latest | jq -r .tag_name | sed 's/^v//') && \
+    echo "Using S6 overlay version: $S6_OVERLAY_VERSION" && \
+    echo "$S6_OVERLAY_VERSION" > /tmp/s6-version
+
+# Use Docker's TARGETARCH for automatic architecture detection
+ARG TARGETARCH
+
+# Install additional required packages and create directory structure in one layer
+RUN apk add --no-cache \
+    nginx \
+    tzdata \
+    bash && \
+    mkdir -p /app/backend /app/frontend /app/data /app/uploads
+
+# Map Docker's architecture names to s6-overlay architecture names and download/install
+RUN S6_OVERLAY_VERSION=$(cat /tmp/s6-version) && \
+    case "${TARGETARCH}" in \
+      "amd64")  S6_OVERLAY_ARCH="x86_64"  ;; \
+      "arm64")  S6_OVERLAY_ARCH="aarch64" ;; \
+      "arm")    S6_OVERLAY_ARCH="arm"     ;; \
+      *) echo "Unsupported architecture: ${TARGETARCH}. Only amd64, arm64, and arm are supported." && exit 1 ;; \
+    esac && \
+    echo "Target architecture: ${TARGETARCH} -> S6 architecture: ${S6_OVERLAY_ARCH}" && \
+    echo "Downloading s6-overlay v${S6_OVERLAY_VERSION} for architecture: ${S6_OVERLAY_ARCH}" && \
+    curl -L -s -o /tmp/s6-overlay-noarch.tar.xz "https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-noarch.tar.xz" && \
+    tar -C / -Jxpf /tmp/s6-overlay-noarch.tar.xz && \
+    curl -L -s -o /tmp/s6-overlay-arch.tar.xz "https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-${S6_OVERLAY_ARCH}.tar.xz" && \
+    tar -C / -Jxpf /tmp/s6-overlay-arch.tar.xz && \
+    curl -L -s -o /tmp/s6-overlay-symlinks-noarch.tar.xz "https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-symlinks-noarch.tar.xz" && \
+    tar -C / -Jxpf /tmp/s6-overlay-symlinks-noarch.tar.xz && \
+    rm -f /tmp/s6-overlay-*.tar.xz /tmp/s6-version
+
+# Copy configuration files
+COPY docker/etc/s6-overlay/services /etc/services.d/
+COPY docker/etc/nginx/nginx.conf /etc/nginx/nginx.conf
+
+# Make service scripts executable
+RUN chmod +x /etc/services.d/*/run /etc/services.d/*/finish
+
+# Copy application files from builders
+COPY --from=backend-builder /app/dist /app/backend/dist
+COPY --from=backend-builder /app/node_modules /app/backend/node_modules
+COPY --from=backend-builder /app/package.json /app/backend/package.json
+COPY --from=backend-builder /app/drizzle.config.ts /app/backend/drizzle.config.ts
+# Copy only the schema file for Drizzle migrations
+COPY --from=backend-builder /app/src/db/schema.ts /app/backend/src/db/schema.ts
+COPY --from=frontend-builder /app/.next /app/frontend/.next
+COPY --from=frontend-builder /app/node_modules /app/frontend/node_modules
+COPY --from=frontend-builder /app/package.json /app/frontend/package.json
+COPY --from=frontend-builder /app/public /app/frontend/public
+
+# Configure volumes and ports
+VOLUME ["/app/backend/data", "/app/backend/uploads"]
+EXPOSE 8080
+
+# Set up healthcheck
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8080/ || exit 1
+
+ENTRYPOINT ["/init"]

docker/etc/nginx/nginx.conf

@@ -0,0 +1,55 @@
+events {
+    worker_connections 1024;
+}
+
+http {
+    port_in_redirect on;
+    absolute_redirect off;
+
+    server {
+        listen 8080;
+        server_name localhost;
+
+        # Global timeout settings for all locations
+        proxy_read_timeout 86400s;    # 24 hours
+        proxy_send_timeout 86400s;    # 24 hours
+        proxy_connect_timeout 60s;    # Connection establishment timeout
+
+        # API requests
+        location /api {
+            proxy_pass http://localhost:3001;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+        }
+
+        # WebSocket requests
+        location /ws {
+            proxy_pass http://localhost:3001;
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+        }
+
+        # Frontend requests
+        location / {
+            proxy_pass http://localhost:3000;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+        }
+
+        # Security headers
+        add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always;
+        add_header X-XSS-Protection "1; mode=block" always;
+        add_header X-Content-Type-Options "nosniff" always;
+        add_header X-Frame-Options "SAMEORIGIN" always;
+        server_tokens off;
+    }
+}

docker/etc/s6-overlay/services/backend/finish

@@ -0,0 +1,3 @@
+#!/usr/bin/with-contenv bash
+s6-svc -d /var/run/s6/services/frontend
+s6-svc -d /var/run/s6/services/nginx

docker/etc/s6-overlay/services/backend/run

@@ -0,0 +1,8 @@
+#!/usr/bin/with-contenv bash
+cd /app/backend
+
+# Run database migrations before starting the app
+yarn db:push
+
+# Start the application
+exec node dist/app.js

docker/etc/s6-overlay/services/frontend/finish

@@ -0,0 +1,2 @@
+#!/usr/bin/with-contenv bash
+s6-svc -d /var/run/s6/services/nginx

docker/etc/s6-overlay/services/frontend/run

@@ -0,0 +1,3 @@
+#!/usr/bin/with-contenv bash
+cd /app/frontend
+exec node_modules/.bin/next start

docker/etc/s6-overlay/services/nginx/run

@@ -0,0 +1,2 @@
+#!/usr/bin/with-contenv bash
+exec nginx -g "daemon off;"

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:3000/api/search`
+### **POST** `http://localhost:3001/api/search`
 
-**Note**: Replace `3000` with any other port if you've changed the default PORT
+**Note**: Replace `3001` 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",
-    "name": "gpt-4o-mini"
+    "model": "gpt-4o-mini"
   },
   "embeddingModel": {
     "provider": "openai",
-    "name": "text-embedding-3-large"
+    "model": "text-embedding-3-large"
   },
   "optimizationMode": "speed",
   "focusMode": "webSearch",
@@ -32,26 +32,24 @@ The API accepts a JSON object in the request body, where you define the focus mo
   "history": [
     ["human", "Hi, how are you?"],
     ["assistant", "I am doing well, how can I help you today?"]
-  ],
-  "systemInstructions": "Focus on providing technical details about Perplexica's architecture.",
-  "stream": false
+  ]
 }
 ```
 
 ### 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:3000/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:3001/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`).
-  - `name`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
+  - `model`: 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: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").
+- **`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").
 
   - `provider`: The provider for the embedding model (e.g., `openai`).
-  - `name`: The specific embedding model (e.g., `text-embedding-3-large`).
+  - `model`: The specific embedding model (e.g., `text-embedding-3-large`).
 
 - **`focusMode`** (string, required): Specifies which focus mode to use. Available modes:
 
@@ -64,8 +62,6 @@ The API accepts a JSON object in the request body, where you define the focus mo
 
 - **`query`** (string, required): The search query or question.
 
-- **`systemInstructions`** (string, optional): Custom instructions provided by the user to guide the AI's response. These instructions are treated as user preferences and have lower priority than the system's core instructions. For example, you can specify a particular writing style, format, or focus area.
-
 - **`history`** (array, optional): An array of message pairs representing the conversation history. Each pair consists of a role (either 'human' or 'assistant') and the message content. This allows the system to use the context of the conversation to refine results. Example:
 
   ```json
@@ -75,13 +71,11 @@ The API accepts a JSON object in the request body, where you define the focus mo
   ]
   ```
 
-- **`stream`** (boolean, optional): When set to `true`, enables streaming responses. Default is `false`.
-
 ### Response
 
 The response from the API includes both the final message and the sources used to generate that message.
 
-#### Standard Response (stream: false)
+#### Example Response
 
 ```json
 {
@@ -106,28 +100,6 @@ The response from the API includes both the final message and the sources used t
 }
 ```
 
-#### Streaming Response (stream: true)
-
-When streaming is enabled, the API returns a stream of newline-delimited JSON objects. Each line contains a complete, valid JSON object. The response has Content-Type: application/json.
-
-Example of streamed response objects:
-
-```
-{"type":"init","data":"Stream connected"}
-{"type":"sources","data":[{"pageContent":"...","metadata":{"title":"...","url":"..."}},...]}
-{"type":"response","data":"Perplexica is an "}
-{"type":"response","data":"innovative, open-source "}
-{"type":"response","data":"AI-powered search engine..."}
-{"type":"done"}
-```
-
-Clients should process each line as a separate JSON object. The different message types include:
-
-- **`init`**: Initial connection message
-- **`sources`**: All sources used for the response
-- **`response`**: Chunks of the generated answer text
-- **`done`**: Indicates the stream is complete
-
 ### Fields in the Response
 
 - **`message`** (string): The search result, generated based on the query and focus mode.

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 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.
+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.
 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

@@ -0,0 +1,46 @@
+# Accessing Perplexica over a Network
+
+This guide explains how to access Perplexica over your network using the nginx reverse proxy included in the Docker setup.
+
+## Basic Network Access
+
+Perplexica is automatically accessible from any device on your network:
+
+1. Start Perplexica using Docker Compose:
+   ```bash
+   docker compose up -d
+   ```
+
+2. Find your server's IP address:
+   - **Windows**: `ipconfig` in Command Prompt
+   - **macOS**: `ifconfig | grep "inet "` in Terminal
+   - **Linux**: `ip addr show | grep "inet "` in Terminal
+
+3. Access Perplexica from any device on your network:
+   ```
+   http://YOUR_SERVER_IP:8080
+   ```
+
+## Custom Port Configuration
+
+If you need to use a different port instead of the default 8080:
+
+1. Modify the `docker-compose.yaml` file:
+   ```yaml
+   perplexica:
+     ports:
+       - "YOUR_CUSTOM_PORT:8080"
+   ```
+
+2. Restart the containers:
+   ```bash
+   docker compose down && docker compose up -d
+   ```
+
+## Troubleshooting
+
+If you encounter issues accessing Perplexica over your network:
+
+1. **Firewall Settings**: Ensure port 8080 (or your custom port) is allowed in your firewall
+2. **Docker Logs**: Check for any connection issues with `docker logs perplexica`
+3. **Network Access**: Make sure your devices are on the same network and can reach the server

docs/installation/UPDATING.md

@@ -39,8 +39,11 @@ 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`.
-5. Install the dependencies and then execute `npm run build`.
-6. Finally, start the app by running `npm rum start`
+
+4. Execute `npm i` in both the `ui` folder and the root directory.
+
+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/lib/db/schema.ts',
+  schema: './src/db/schema.ts',
   out: './drizzle',
   dbCredentials: {
     url: './data/db.sqlite',

next-env.d.ts

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

package.json

@@ -1,65 +1,53 @@
 {
-  "name": "perplexica-frontend",
-  "version": "1.10.2",
+  "name": "perplexica-backend",
+  "version": "1.10.0-rc3",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
-    "dev": "next dev",
-    "build": "npm run db:push && next build",
-    "start": "next start",
-    "lint": "next lint",
-    "format:write": "prettier . --write",
-    "db:push": "drizzle-kit push"
-  },
-  "dependencies": {
-    "@headlessui/react": "^2.2.0",
-    "@iarna/toml": "^2.2.5",
-    "@icons-pack/react-simple-icons": "^12.3.0",
-    "@langchain/anthropic": "^0.3.15",
-    "@langchain/community": "^0.3.36",
-    "@langchain/core": "^0.3.42",
-    "@langchain/google-genai": "^0.1.12",
-    "@langchain/openai": "^0.0.25",
-    "@langchain/textsplitters": "^0.1.0",
-    "@tailwindcss/typography": "^0.5.12",
-    "@xenova/transformers": "^2.17.2",
-    "axios": "^1.8.3",
-    "better-sqlite3": "^11.9.1",
-    "clsx": "^2.1.0",
-    "compute-cosine-similarity": "^1.1.0",
-    "compute-dot": "^1.1.0",
-    "drizzle-orm": "^0.40.1",
-    "html-to-text": "^9.0.5",
-    "langchain": "^0.1.30",
-    "lucide-react": "^0.363.0",
-    "markdown-to-jsx": "^7.7.2",
-    "next": "^15.2.2",
-    "next-themes": "^0.3.0",
-    "pdf-parse": "^1.1.1",
-    "react": "^18",
-    "react-dom": "^18",
-    "react-text-to-speech": "^0.14.5",
-    "react-textarea-autosize": "^8.5.3",
-    "sonner": "^1.4.41",
-    "tailwind-merge": "^2.2.2",
-    "winston": "^3.17.0",
-    "yet-another-react-lightbox": "^3.17.2",
-    "zod": "^3.22.4"
+    "start": "npm run db:push && node dist/app.js",
+    "build": "tsc",
+    "dev": "nodemon --ignore uploads/ src/app.ts ",
+    "db:push": "drizzle-kit push sqlite",
+    "format": "prettier . --check",
+    "format:write": "prettier . --write"
   },
   "devDependencies": {
-    "@types/better-sqlite3": "^7.6.12",
+    "@types/better-sqlite3": "^7.6.10",
+    "@types/cors": "^2.8.17",
+    "@types/express": "^4.17.21",
     "@types/html-to-text": "^9.0.4",
-    "@types/node": "^20",
+    "@types/multer": "^1.4.12",
     "@types/pdf-parse": "^1.1.4",
-    "@types/react": "^18",
-    "@types/react-dom": "^18",
-    "autoprefixer": "^10.0.1",
-    "drizzle-kit": "^0.30.5",
-    "eslint": "^8",
-    "eslint-config-next": "14.1.4",
-    "postcss": "^8",
+    "@types/readable-stream": "^4.0.11",
+    "@types/ws": "^8.5.12",
+    "drizzle-kit": "^0.22.7",
+    "nodemon": "^3.1.0",
     "prettier": "^3.2.5",
-    "tailwindcss": "^3.3.0",
-    "typescript": "^5"
+    "ts-node": "^10.9.2",
+    "typescript": "^5.4.3"
+  },
+  "dependencies": {
+    "@iarna/toml": "^2.2.5",
+    "@langchain/anthropic": "^0.2.3",
+    "@langchain/community": "^0.2.16",
+    "@langchain/openai": "^0.0.25",
+    "@langchain/google-genai": "^0.0.23",
+    "@xenova/transformers": "^2.17.1",
+    "axios": "^1.6.8",
+    "better-sqlite3": "^11.0.0",
+    "compute-cosine-similarity": "^1.1.0",
+    "compute-dot": "^1.1.0",
+    "cors": "^2.8.5",
+    "dotenv": "^16.4.5",
+    "drizzle-orm": "^0.31.2",
+    "express": "^4.19.2",
+    "html-to-text": "^9.0.5",
+    "langchain": "^0.1.30",
+    "mammoth": "^1.8.0",
+    "multer": "^1.4.5-lts.1",
+    "pdf-parse": "^1.1.1",
+    "winston": "^3.13.0",
+    "ws": "^8.17.1",
+    "zod": "^3.22.4"
   }
 }

sample.config.toml

@@ -1,4 +1,5 @@
 [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")
 
@@ -17,16 +18,9 @@ API_KEY = ""
 [MODELS.CUSTOM_OPENAI]
 API_KEY = ""
 API_URL = ""
-MODEL_NAME = ""
 
 [MODELS.OLLAMA]
 API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
-[MODELS.DEEPSEEK]
-API_KEY = ""
-
-[MODELS.LM_STUDIO]
-API_URL = "" # LM Studio API URL - http://host.docker.internal:1234
-
 [API_ENDPOINTS]
-SEARXNG = "" # SearxNG API URL - http://localhost:32768
+SEARXNG = "http://localhost:32768" # SearxNG API URL

src/app.ts

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

src/app/api/chat/route.ts

@@ -1,306 +0,0 @@
-import prompts from '@/lib/prompts';
-import MetaSearchAgent from '@/lib/search/metaSearchAgent';
-import crypto from 'crypto';
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { EventEmitter } from 'stream';
-import {
-  chatModelProviders,
-  embeddingModelProviders,
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '@/lib/providers';
-import db from '@/lib/db';
-import { chats, messages as messagesSchema } from '@/lib/db/schema';
-import { and, eq, gt } from 'drizzle-orm';
-import { getFileDetails } from '@/lib/utils/files';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { ChatOpenAI } from '@langchain/openai';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '@/lib/config';
-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;
-  systemInstructions: string;
-};
-
-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,
-      body.systemInstructions,
-    );
-
-    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 occurred while processing chat request:', err);
-    return Response.json(
-      { message: 'An error occurred while processing chat request' },
-      { status: 500 },
-    );
-  }
-};

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

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

src/app/api/chats/route.ts

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

src/app/api/config/route.ts

@@ -1,119 +0,0 @@
-import {
-  getAnthropicApiKey,
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-  getGeminiApiKey,
-  getGroqApiKey,
-  getOllamaApiEndpoint,
-  getOpenaiApiKey,
-  getDeepseekApiKey,
-  getLMStudioApiEndpoint,
-  updateConfig,
-} from '@/lib/config';
-import {
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '@/lib/providers';
-
-export const GET = async (req: Request) => {
-  try {
-    const config: Record<string, any> = {};
-
-    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
-      getAvailableChatModelProviders(),
-      getAvailableEmbeddingModelProviders(),
-    ]);
-
-    config['chatModelProviders'] = {};
-    config['embeddingModelProviders'] = {};
-
-    for (const provider in chatModelProviders) {
-      config['chatModelProviders'][provider] = Object.keys(
-        chatModelProviders[provider],
-      ).map((model) => {
-        return {
-          name: model,
-          displayName: chatModelProviders[provider][model].displayName,
-        };
-      });
-    }
-
-    for (const provider in embeddingModelProviders) {
-      config['embeddingModelProviders'][provider] = Object.keys(
-        embeddingModelProviders[provider],
-      ).map((model) => {
-        return {
-          name: model,
-          displayName: embeddingModelProviders[provider][model].displayName,
-        };
-      });
-    }
-
-    config['openaiApiKey'] = getOpenaiApiKey();
-    config['ollamaApiUrl'] = getOllamaApiEndpoint();
-    config['lmStudioApiUrl'] = getLMStudioApiEndpoint();
-    config['anthropicApiKey'] = getAnthropicApiKey();
-    config['groqApiKey'] = getGroqApiKey();
-    config['geminiApiKey'] = getGeminiApiKey();
-    config['deepseekApiKey'] = getDeepseekApiKey();
-    config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
-    config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
-    config['customOpenaiModelName'] = getCustomOpenaiModelName();
-
-    return Response.json({ ...config }, { status: 200 });
-  } catch (err) {
-    console.error('An error occurred while getting config:', err);
-    return Response.json(
-      { message: 'An error occurred while getting config' },
-      { status: 500 },
-    );
-  }
-};
-
-export const POST = async (req: Request) => {
-  try {
-    const config = await req.json();
-
-    const updatedConfig = {
-      MODELS: {
-        OPENAI: {
-          API_KEY: config.openaiApiKey,
-        },
-        GROQ: {
-          API_KEY: config.groqApiKey,
-        },
-        ANTHROPIC: {
-          API_KEY: config.anthropicApiKey,
-        },
-        GEMINI: {
-          API_KEY: config.geminiApiKey,
-        },
-        OLLAMA: {
-          API_URL: config.ollamaApiUrl,
-        },
-        DEEPSEEK: {
-          API_KEY: config.deepseekApiKey,
-        },
-        LM_STUDIO: {
-          API_URL: config.lmStudioApiUrl,
-        },
-        CUSTOM_OPENAI: {
-          API_URL: config.customOpenaiApiUrl,
-          API_KEY: config.customOpenaiApiKey,
-          MODEL_NAME: config.customOpenaiModelName,
-        },
-      },
-    };
-
-    updateConfig(updatedConfig);
-
-    return Response.json({ message: 'Config updated' }, { status: 200 });
-  } catch (err) {
-    console.error('An error occurred while updating config:', err);
-    return Response.json(
-      { message: 'An error occurred while updating config' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/discover/route.ts

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

src/app/api/images/route.ts

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

src/app/api/models/route.ts

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

src/app/api/search/route.ts

@@ -1,270 +0,0 @@
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import { ChatOpenAI } from '@langchain/openai';
-import {
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '@/lib/providers';
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { MetaSearchAgentType } from '@/lib/search/metaSearchAgent';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '@/lib/config';
-import { searchHandlers } from '@/lib/search';
-
-interface chatModel {
-  provider: string;
-  name: string;
-  customOpenAIKey?: string;
-  customOpenAIBaseURL?: string;
-}
-
-interface embeddingModel {
-  provider: string;
-  name: string;
-}
-
-interface ChatRequestBody {
-  optimizationMode: 'speed' | 'balanced';
-  focusMode: string;
-  chatModel?: chatModel;
-  embeddingModel?: embeddingModel;
-  query: string;
-  history: Array<[string, string]>;
-  stream?: boolean;
-  systemInstructions?: string;
-}
-
-export const POST = async (req: Request) => {
-  try {
-    const body: ChatRequestBody = await req.json();
-
-    if (!body.focusMode || !body.query) {
-      return Response.json(
-        { message: 'Missing focus mode or query' },
-        { status: 400 },
-      );
-    }
-
-    body.history = body.history || [];
-    body.optimizationMode = body.optimizationMode || 'balanced';
-    body.stream = body.stream || false;
-
-    const history: BaseMessage[] = body.history.map((msg) => {
-      return msg[0] === 'human'
-        ? new HumanMessage({ content: msg[1] })
-        : new AIMessage({ content: msg[1] });
-    });
-
-    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
-      getAvailableChatModelProviders(),
-      getAvailableEmbeddingModelProviders(),
-    ]);
-
-    const chatModelProvider =
-      body.chatModel?.provider || Object.keys(chatModelProviders)[0];
-    const chatModel =
-      body.chatModel?.name ||
-      Object.keys(chatModelProviders[chatModelProvider])[0];
-
-    const embeddingModelProvider =
-      body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0];
-    const embeddingModel =
-      body.embeddingModel?.name ||
-      Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
-
-    let llm: BaseChatModel | undefined;
-    let embeddings: Embeddings | undefined;
-
-    if (body.chatModel?.provider === 'custom_openai') {
-      llm = new ChatOpenAI({
-        modelName: body.chatModel?.name || getCustomOpenaiModelName(),
-        openAIApiKey:
-          body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
-        temperature: 0.7,
-        configuration: {
-          baseURL:
-            body.chatModel?.customOpenAIBaseURL || getCustomOpenaiApiUrl(),
-        },
-      }) as unknown as BaseChatModel;
-    } else if (
-      chatModelProviders[chatModelProvider] &&
-      chatModelProviders[chatModelProvider][chatModel]
-    ) {
-      llm = chatModelProviders[chatModelProvider][chatModel]
-        .model as unknown as BaseChatModel | undefined;
-    }
-
-    if (
-      embeddingModelProviders[embeddingModelProvider] &&
-      embeddingModelProviders[embeddingModelProvider][embeddingModel]
-    ) {
-      embeddings = embeddingModelProviders[embeddingModelProvider][
-        embeddingModel
-      ].model as Embeddings | undefined;
-    }
-
-    if (!llm || !embeddings) {
-      return Response.json(
-        { message: 'Invalid model selected' },
-        { status: 400 },
-      );
-    }
-
-    const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
-
-    if (!searchHandler) {
-      return Response.json({ message: 'Invalid focus mode' }, { status: 400 });
-    }
-
-    const emitter = await searchHandler.searchAndAnswer(
-      body.query,
-      history,
-      llm,
-      embeddings,
-      body.optimizationMode,
-      [],
-      body.systemInstructions || '',
-    );
-
-    if (!body.stream) {
-      return new Promise(
-        (
-          resolve: (value: Response) => void,
-          reject: (value: Response) => void,
-        ) => {
-          let message = '';
-          let sources: any[] = [];
-
-          emitter.on('data', (data: string) => {
-            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', () => {
-            resolve(Response.json({ message, sources }, { status: 200 }));
-          });
-
-          emitter.on('error', (error: any) => {
-            reject(
-              Response.json(
-                { message: 'Search error', error },
-                { status: 500 },
-              ),
-            );
-          });
-        },
-      );
-    }
-
-    const encoder = new TextEncoder();
-
-    const abortController = new AbortController();
-    const { signal } = abortController;
-
-    const stream = new ReadableStream({
-      start(controller) {
-        let sources: any[] = [];
-
-        controller.enqueue(
-          encoder.encode(
-            JSON.stringify({
-              type: 'init',
-              data: 'Stream connected',
-            }) + '\n',
-          ),
-        );
-
-        signal.addEventListener('abort', () => {
-          emitter.removeAllListeners();
-
-          try {
-            controller.close();
-          } catch (error) {}
-        });
-
-        emitter.on('data', (data: string) => {
-          if (signal.aborted) return;
-
-          try {
-            const parsedData = JSON.parse(data);
-
-            if (parsedData.type === 'response') {
-              controller.enqueue(
-                encoder.encode(
-                  JSON.stringify({
-                    type: 'response',
-                    data: parsedData.data,
-                  }) + '\n',
-                ),
-              );
-            } else if (parsedData.type === 'sources') {
-              sources = parsedData.data;
-              controller.enqueue(
-                encoder.encode(
-                  JSON.stringify({
-                    type: 'sources',
-                    data: sources,
-                  }) + '\n',
-                ),
-              );
-            }
-          } catch (error) {
-            controller.error(error);
-          }
-        });
-
-        emitter.on('end', () => {
-          if (signal.aborted) return;
-
-          controller.enqueue(
-            encoder.encode(
-              JSON.stringify({
-                type: 'done',
-              }) + '\n',
-            ),
-          );
-          controller.close();
-        });
-
-        emitter.on('error', (error: any) => {
-          if (signal.aborted) return;
-
-          controller.error(error);
-        });
-      },
-      cancel() {
-        abortController.abort();
-      },
-    });
-
-    return new Response(stream, {
-      headers: {
-        'Content-Type': 'text/event-stream',
-        'Cache-Control': 'no-cache, no-transform',
-        Connection: 'keep-alive',
-      },
-    });
-  } catch (err: any) {
-    console.error(`Error in getting search results: ${err.message}`);
-    return Response.json(
-      { message: 'An error has occurred.' },
-      { status: 500 },
-    );
-  }
-};

src/app/api/suggestions/route.ts

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

src/app/api/uploads/route.ts

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

src/app/api/videos/route.ts

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

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

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

src/chains/imageSearchAgent.ts

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

src/chains/suggestionGeneratorAgent.ts

@@ -1,5 +1,5 @@
 import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
-import ListLineOutputParser from '../outputParsers/listLineOutputParser';
+import ListLineOutputParser from '../lib/outputParsers/listLineOutputParser';
 import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
@@ -8,7 +8,7 @@ import { ChatOpenAI } from '@langchain/openai';
 
 const suggestionGeneratorPrompt = `
 You are an AI suggestion generator for an AI powered search engine. You will be given a conversation below. You need to generate 4-5 suggestions based on the conversation. The suggestion should be relevant to the conversation that can be used by the user to ask the chat model for more information.
-You need to make sure the suggestions are relevant to the conversation and are helpful to the user. Keep a note that the user might use these suggestions to ask a chat model for more information. 
+You need to make sure the suggestions are relevant to the conversation and are helpful to the user. Keep a note that the user might use these suggestions to ask a chat model for more information.
 Make sure the suggestions are medium in length and are informative and relevant to the conversation.
 
 Provide these suggestions separated by newlines between the XML tags <suggestions> and </suggestions>. For example:

src/chains/videoSearchAgent.ts

@@ -7,26 +7,26 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const VideoSearchChainPrompt = `
   You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
   You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
-  
+
   Example:
   1. Follow up question: How does a car work?
   Rephrased: How does a car work?
-  
+
   2. Follow up question: What is the theory of relativity?
   Rephrased: What is theory of relativity
-  
+
   3. Follow up question: How does an AC work?
   Rephrased: How does an AC work
-  
+
   Conversation:
   {chat_history}
-  
+
   Follow up question: {query}
   Rephrased question:
   `;
@@ -36,13 +36,6 @@ type VideoSearchChainInput = {
   query: string;
 };
 
-interface VideoSearchResult {
-  img_src: string;
-  url: string;
-  title: string;
-  iframe_src: string;
-}
-
 const strParser = new StringOutputParser();
 
 const createVideoSearchChain = (llm: BaseChatModel) => {
@@ -59,13 +52,11 @@ 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: VideoSearchResult[] = [];
+      const videos = [];
 
       res.results.forEach((result) => {
         if (

src/components/ThinkBox.tsx

@@ -1,43 +0,0 @@
-'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/config.ts

@@ -0,0 +1,161 @@
+import fs from 'fs';
+import path from 'path';
+import toml from '@iarna/toml';
+
+const configFileName = 'config.toml';
+
+interface Config {
+  GENERAL: {
+    PORT: number;
+    SIMILARITY_MEASURE: string;
+    KEEP_ALIVE: string;
+  };
+  MODELS: {
+    OPENAI: {
+      API_KEY: string;
+    };
+    GROQ: {
+      API_KEY: string;
+    };
+    ANTHROPIC: {
+      API_KEY: string;
+    };
+    GEMINI: {
+      API_KEY: string;
+    };
+    OLLAMA: {
+      API_URL: string;
+    };
+    CUSTOM_OPENAI: {
+      API_URL: string;
+      API_KEY: string;
+      MODEL_NAME: string;
+    };
+  };
+  API_ENDPOINTS: {
+    SEARXNG: string;
+  };
+}
+
+type RecursivePartial<T> = {
+  [P in keyof T]?: RecursivePartial<T[P]>;
+};
+
+const loadConfig = () => {
+  try {
+    return toml.parse(
+      fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
+    ) as any as Config;
+  } catch (error) {
+    // Return default config if file doesn't exist
+    return {
+      GENERAL: {
+        PORT: 3001,
+        SIMILARITY_MEASURE: 'cosine',
+        KEEP_ALIVE: '5m',
+      },
+      MODELS: {
+        OPENAI: {
+          API_KEY: '',
+        },
+        GROQ: {
+          API_KEY: '',
+        },
+        ANTHROPIC: {
+          API_KEY: '',
+        },
+        GEMINI: {
+          API_KEY: '',
+        },
+        OLLAMA: {
+          API_URL: '',
+        },
+        CUSTOM_OPENAI: {
+          API_URL: '',
+          API_KEY: '',
+          MODEL_NAME: '',
+        },
+      },
+      API_ENDPOINTS: {
+        SEARXNG: '',
+      },
+    };
+  }
+};
+
+export const getPort = () =>
+  process.env.PORT ? parseInt(process.env.PORT, 10) : loadConfig().GENERAL.PORT;
+
+export const getSimilarityMeasure = () =>
+  process.env.SIMILARITY_MEASURE || loadConfig().GENERAL.SIMILARITY_MEASURE;
+
+export const getKeepAlive = () =>
+  process.env.KEEP_ALIVE || loadConfig().GENERAL.KEEP_ALIVE;
+
+export const getOpenaiApiKey = () =>
+  process.env.OPENAI_API_KEY || loadConfig().MODELS.OPENAI.API_KEY;
+
+export const getGroqApiKey = () =>
+  process.env.GROQ_API_KEY || loadConfig().MODELS.GROQ.API_KEY;
+
+export const getAnthropicApiKey = () =>
+  process.env.ANTHROPIC_API_KEY || loadConfig().MODELS.ANTHROPIC.API_KEY;
+
+export const getGeminiApiKey = () =>
+  process.env.GEMINI_API_KEY || loadConfig().MODELS.GEMINI.API_KEY;
+
+export const getSearxngApiEndpoint = () =>
+  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
+
+export const getOllamaApiEndpoint = () =>
+  process.env.OLLAMA_API_URL || loadConfig().MODELS.OLLAMA.API_URL;
+
+export const getCustomOpenaiApiKey = () =>
+  process.env.CUSTOM_OPENAI_API_KEY || loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
+
+export const getCustomOpenaiApiUrl = () =>
+  process.env.CUSTOM_OPENAI_API_URL || loadConfig().MODELS.CUSTOM_OPENAI.API_URL;
+
+export const getCustomOpenaiModelName = () =>
+  process.env.CUSTOM_OPENAI_MODEL_NAME || loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
+
+const mergeConfigs = (current: any, update: any): any => {
+  if (update === null || update === undefined) {
+    return current;
+  }
+
+  if (typeof current !== 'object' || current === null) {
+    return update;
+  }
+
+  const result = { ...current };
+
+  for (const key in update) {
+    if (Object.prototype.hasOwnProperty.call(update, key)) {
+      const updateValue = update[key];
+
+      if (
+        typeof updateValue === 'object' &&
+        updateValue !== null &&
+        typeof result[key] === 'object' &&
+        result[key] !== null
+      ) {
+        result[key] = mergeConfigs(result[key], updateValue);
+      } else if (updateValue !== undefined) {
+        result[key] = updateValue;
+      }
+    }
+  }
+
+  return result;
+};
+
+export const updateConfig = (config: RecursivePartial<Config>) => {
+  const currentConfig = loadConfig();
+  const mergedConfig = mergeConfigs(currentConfig, config);
+
+  fs.writeFileSync(
+    path.join(__dirname, `../${configFileName}`),
+    toml.stringify(mergedConfig),
+  );
+};

src/db/index.ts

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

src/lib/config.ts

@@ -1,141 +0,0 @@
-import toml from '@iarna/toml';
-
-// Use dynamic imports for Node.js modules to prevent client-side errors
-let fs: any;
-let path: any;
-if (typeof window === 'undefined') {
-  // We're on the server
-  fs = require('fs');
-  path = require('path');
-}
-
-const configFileName = 'config.toml';
-
-interface Config {
-  GENERAL: {
-    SIMILARITY_MEASURE: string;
-    KEEP_ALIVE: string;
-  };
-  MODELS: {
-    OPENAI: {
-      API_KEY: string;
-    };
-    GROQ: {
-      API_KEY: string;
-    };
-    ANTHROPIC: {
-      API_KEY: string;
-    };
-    GEMINI: {
-      API_KEY: string;
-    };
-    OLLAMA: {
-      API_URL: string;
-    };
-    DEEPSEEK: {
-      API_KEY: string;
-    };
-    LM_STUDIO: {
-      API_URL: string;
-    };
-    CUSTOM_OPENAI: {
-      API_URL: string;
-      API_KEY: string;
-      MODEL_NAME: string;
-    };
-  };
-  API_ENDPOINTS: {
-    SEARXNG: string;
-  };
-}
-
-type RecursivePartial<T> = {
-  [P in keyof T]?: RecursivePartial<T[P]>;
-};
-
-const loadConfig = () => {
-  // Server-side only
-  if (typeof window === 'undefined') {
-    return toml.parse(
-      fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
-    ) as any as Config;
-  }
-
-  // Client-side fallback - settings will be loaded via API
-  return {} as Config;
-};
-
-export const getSimilarityMeasure = () =>
-  loadConfig().GENERAL.SIMILARITY_MEASURE;
-
-export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
-
-export const getOpenaiApiKey = () => loadConfig().MODELS.OPENAI.API_KEY;
-
-export const getGroqApiKey = () => loadConfig().MODELS.GROQ.API_KEY;
-
-export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
-
-export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
-
-export const getSearxngApiEndpoint = () =>
-  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
-
-export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
-
-export const getDeepseekApiKey = () => loadConfig().MODELS.DEEPSEEK.API_KEY;
-
-export const getCustomOpenaiApiKey = () =>
-  loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
-
-export const getCustomOpenaiApiUrl = () =>
-  loadConfig().MODELS.CUSTOM_OPENAI.API_URL;
-
-export const getCustomOpenaiModelName = () =>
-  loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
-
-export const getLMStudioApiEndpoint = () =>
-  loadConfig().MODELS.LM_STUDIO.API_URL;
-
-const mergeConfigs = (current: any, update: any): any => {
-  if (update === null || update === undefined) {
-    return current;
-  }
-
-  if (typeof current !== 'object' || current === null) {
-    return update;
-  }
-
-  const result = { ...current };
-
-  for (const key in update) {
-    if (Object.prototype.hasOwnProperty.call(update, key)) {
-      const updateValue = update[key];
-
-      if (
-        typeof updateValue === 'object' &&
-        updateValue !== null &&
-        typeof result[key] === 'object' &&
-        result[key] !== null
-      ) {
-        result[key] = mergeConfigs(result[key], updateValue);
-      } else if (updateValue !== undefined) {
-        result[key] = updateValue;
-      }
-    }
-  }
-
-  return result;
-};
-
-export const updateConfig = (config: RecursivePartial<Config>) => {
-  // Server-side only
-  if (typeof window === 'undefined') {
-    const currentConfig = loadConfig();
-    const mergedConfig = mergeConfigs(currentConfig, config);
-    fs.writeFileSync(
-      path.join(path.join(process.cwd(), `${configFileName}`)),
-      toml.stringify(mergedConfig),
-    );
-  }
-};

src/lib/huggingfaceTransformer.ts

@@ -28,7 +28,7 @@ export class HuggingFaceTransformersEmbeddings
 
   timeout?: number;
 
-  private pipelinePromise: Promise<any> | undefined;
+  private pipelinePromise: Promise<any>;
 
   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

@@ -1,43 +1,6 @@
 import { ChatAnthropic } from '@langchain/anthropic';
-import { ChatModel } from '.';
-import { getAnthropicApiKey } from '../config';
-
-export const PROVIDER_INFO = {
-  key: 'anthropic',
-  displayName: 'Anthropic',
-};
-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',
-  },
-];
+import { getAnthropicApiKey } from '../../config';
+import logger from '../../utils/logger';
 
 export const loadAnthropicChatModels = async () => {
   const anthropicApiKey = getAnthropicApiKey();
@@ -45,22 +8,52 @@ export const loadAnthropicChatModels = async () => {
   if (!anthropicApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
-
-    anthropicChatModels.forEach((model) => {
-      chatModels[model.key] = {
-        displayName: model.displayName,
+    const chatModels = {
+      'claude-3-5-sonnet-20241022': {
+        displayName: 'Claude 3.5 Sonnet',
         model: new ChatAnthropic({
-          apiKey: anthropicApiKey,
-          modelName: model.key,
           temperature: 0.7,
-        }) as unknown as BaseChatModel,
-      };
-    });
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-5-sonnet-20241022',
+        }),
+      },
+      'claude-3-5-haiku-20241022': {
+        displayName: 'Claude 3.5 Haiku',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-5-haiku-20241022',
+        }),
+      },
+      'claude-3-opus-20240229': {
+        displayName: 'Claude 3 Opus',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-opus-20240229',
+        }),
+      },
+      'claude-3-sonnet-20240229': {
+        displayName: 'Claude 3 Sonnet',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-sonnet-20240229',
+        }),
+      },
+      'claude-3-haiku-20240307': {
+        displayName: 'Claude 3 Haiku',
+        model: new ChatAnthropic({
+          temperature: 0.7,
+          anthropicApiKey: anthropicApiKey,
+          model: 'claude-3-haiku-20240307',
+        }),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Anthropic models: ${err}`);
+    logger.error(`Error loading Anthropic models: ${err}`);
     return {};
   }
 };

src/lib/providers/deepseek.ts

@@ -1,49 +0,0 @@
-import { ChatOpenAI } from '@langchain/openai';
-import { getDeepseekApiKey } from '../config';
-import { ChatModel } from '.';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-export const PROVIDER_INFO = {
-  key: 'deepseek',
-  displayName: 'Deepseek AI',
-};
-
-const deepseekChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Deepseek Chat (Deepseek V3)',
-    key: 'deepseek-chat',
-  },
-  {
-    displayName: 'Deepseek Reasoner (Deepseek R1)',
-    key: 'deepseek-reasoner',
-  },
-];
-
-export const loadDeepseekChatModels = async () => {
-  const deepseekApiKey = getDeepseekApiKey();
-
-  if (!deepseekApiKey) return {};
-
-  try {
-    const chatModels: Record<string, ChatModel> = {};
-
-    deepseekChatModels.forEach((model) => {
-      chatModels[model.key] = {
-        displayName: model.displayName,
-        model: new ChatOpenAI({
-          openAIApiKey: deepseekApiKey,
-          modelName: model.key,
-          temperature: 0.7,
-          configuration: {
-            baseURL: 'https://api.deepseek.com',
-          },
-        }) as unknown as BaseChatModel,
-      };
-    });
-
-    return chatModels;
-  } catch (err) {
-    console.error(`Error loading Deepseek models: ${err}`);
-    return {};
-  }
-};

src/lib/providers/gemini.ts

@@ -2,57 +2,8 @@ import {
   ChatGoogleGenerativeAI,
   GoogleGenerativeAIEmbeddings,
 } from '@langchain/google-genai';
-import { getGeminiApiKey } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-
-export const PROVIDER_INFO = {
-  key: 'gemini',
-  displayName: 'Google Gemini',
-};
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Embeddings } from '@langchain/core/embeddings';
-
-const geminiChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemini 2.5 Pro Experimental',
-    key: 'gemini-2.5-pro-exp-03-25',
-  },
-  {
-    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 Flash Thinking Experimental',
-    key: 'gemini-2.0-flash-thinking-exp-01-21',
-  },
-  {
-    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: 'Text Embedding 004',
-    key: 'models/text-embedding-004',
-  },
-  {
-    displayName: 'Embedding 001',
-    key: 'models/embedding-001',
-  },
-];
+import { getGeminiApiKey } from '../../config';
+import logger from '../../utils/logger';
 
 export const loadGeminiChatModels = async () => {
   const geminiApiKey = getGeminiApiKey();
@@ -60,47 +11,75 @@ export const loadGeminiChatModels = async () => {
   if (!geminiApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
-
-    geminiChatModels.forEach((model) => {
-      chatModels[model.key] = {
-        displayName: model.displayName,
+    const chatModels = {
+      'gemini-1.5-flash': {
+        displayName: 'Gemini 1.5 Flash',
         model: new ChatGoogleGenerativeAI({
-          apiKey: geminiApiKey,
-          modelName: model.key,
+          modelName: 'gemini-1.5-flash',
           temperature: 0.7,
-        }) as unknown as BaseChatModel,
-      };
-    });
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-1.5-flash-8b': {
+        displayName: 'Gemini 1.5 Flash 8B',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-1.5-flash-8b',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-1.5-pro': {
+        displayName: 'Gemini 1.5 Pro',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-1.5-pro',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-2.0-flash-exp': {
+        displayName: 'Gemini 2.0 Flash Exp',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-2.0-flash-exp',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-2.0-flash-thinking-exp-01-21': {
+        displayName: 'Gemini 2.0 Flash Thinking Exp 01-21',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-2.0-flash-thinking-exp-01-21',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Gemini models: ${err}`);
+    logger.error(`Error loading Gemini models: ${err}`);
     return {};
   }
 };
 
-export const loadGeminiEmbeddingModels = async () => {
+export const loadGeminiEmbeddingsModels = async () => {
   const geminiApiKey = getGeminiApiKey();
 
   if (!geminiApiKey) return {};
 
   try {
-    const embeddingModels: Record<string, EmbeddingModel> = {};
-
-    geminiEmbeddingModels.forEach((model) => {
-      embeddingModels[model.key] = {
-        displayName: model.displayName,
+    const embeddingModels = {
+      'text-embedding-004': {
+        displayName: 'Text Embedding',
         model: new GoogleGenerativeAIEmbeddings({
           apiKey: geminiApiKey,
-          modelName: model.key,
-        }) as unknown as Embeddings,
-      };
-    });
+          modelName: 'text-embedding-004',
+        }),
+      },
+    };
 
     return embeddingModels;
   } catch (err) {
-    console.error(`Error loading OpenAI embeddings models: ${err}`);
+    logger.error(`Error loading Gemini embeddings model: ${err}`);
     return {};
   }
 };

src/lib/providers/groq.ts

@@ -1,91 +1,6 @@
 import { ChatOpenAI } from '@langchain/openai';
-import { getGroqApiKey } from '../config';
-import { ChatModel } from '.';
-
-export const PROVIDER_INFO = {
-  key: 'groq',
-  displayName: 'Groq',
-};
-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',
-  },
-  /* {
-    displayName: 'Llama 4 Maverick 17B 128E Instruct (Preview)',
-    key: 'meta-llama/llama-4-maverick-17b-128e-instruct',
-  }, */
-  {
-    displayName: 'Llama 4 Scout 17B 16E Instruct (Preview)',
-    key: 'meta-llama/llama-4-scout-17b-16e-instruct',
-  },
-];
+import { getGroqApiKey } from '../../config';
+import logger from '../../utils/logger';
 
 export const loadGroqChatModels = async () => {
   const groqApiKey = getGroqApiKey();
@@ -93,25 +8,129 @@ export const loadGroqChatModels = async () => {
   if (!groqApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
-
-    groqChatModels.forEach((model) => {
-      chatModels[model.key] = {
-        displayName: model.displayName,
-        model: new ChatOpenAI({
-          openAIApiKey: groqApiKey,
-          modelName: model.key,
-          temperature: 0.7,
-          configuration: {
+    const chatModels = {
+      'llama-3.3-70b-versatile': {
+        displayName: 'Llama 3.3 70B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.3-70b-versatile',
+            temperature: 0.7,
+          },
+          {
             baseURL: 'https://api.groq.com/openai/v1',
           },
-        }) as unknown as BaseChatModel,
-      };
-    });
+        ),
+      },
+      'llama-3.2-3b-preview': {
+        displayName: 'Llama 3.2 3B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.2-3b-preview',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama-3.2-11b-vision-preview': {
+        displayName: 'Llama 3.2 11B Vision',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.2-11b-vision-preview',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama-3.2-90b-vision-preview': {
+        displayName: 'Llama 3.2 90B Vision',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.2-90b-vision-preview',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama-3.1-8b-instant': {
+        displayName: 'Llama 3.1 8B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama-3.1-8b-instant',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama3-8b-8192': {
+        displayName: 'LLaMA3 8B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama3-8b-8192',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'llama3-70b-8192': {
+        displayName: 'LLaMA3 70B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'llama3-70b-8192',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'mixtral-8x7b-32768': {
+        displayName: 'Mixtral 8x7B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'mixtral-8x7b-32768',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+      'gemma2-9b-it': {
+        displayName: 'Gemma2 9B',
+        model: new ChatOpenAI(
+          {
+            openAIApiKey: groqApiKey,
+            modelName: 'gemma2-9b-it',
+            temperature: 0.7,
+          },
+          {
+            baseURL: 'https://api.groq.com/openai/v1',
+          },
+        ),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Groq models: ${err}`);
+    logger.error(`Error loading Groq models: ${err}`);
     return {};
   }
 };

src/lib/providers/index.ts

@@ -1,97 +1,33 @@
-import { Embeddings } from '@langchain/core/embeddings';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import {
-  loadOpenAIChatModels,
-  loadOpenAIEmbeddingModels,
-  PROVIDER_INFO as OpenAIInfo,
-  PROVIDER_INFO,
-} from './openai';
+import { loadGroqChatModels } from './groq';
+import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
+import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
+import { loadAnthropicChatModels } from './anthropic';
+import { loadTransformersEmbeddingsModels } from './transformers';
+import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
 import {
   getCustomOpenaiApiKey,
   getCustomOpenaiApiUrl,
   getCustomOpenaiModelName,
-} from '../config';
+} from '../../config';
 import { ChatOpenAI } from '@langchain/openai';
-import {
-  loadOllamaChatModels,
-  loadOllamaEmbeddingModels,
-  PROVIDER_INFO as OllamaInfo,
-} from './ollama';
-import { loadGroqChatModels, PROVIDER_INFO as GroqInfo } from './groq';
-import {
-  loadAnthropicChatModels,
-  PROVIDER_INFO as AnthropicInfo,
-} from './anthropic';
-import {
-  loadGeminiChatModels,
-  loadGeminiEmbeddingModels,
-  PROVIDER_INFO as GeminiInfo,
-} from './gemini';
-import {
-  loadTransformersEmbeddingsModels,
-  PROVIDER_INFO as TransformersInfo,
-} from './transformers';
-import {
-  loadDeepseekChatModels,
-  PROVIDER_INFO as DeepseekInfo,
-} from './deepseek';
-import {
-  loadLMStudioChatModels,
-  loadLMStudioEmbeddingsModels,
-  PROVIDER_INFO as LMStudioInfo,
-} from './lmstudio';
 
-export const PROVIDER_METADATA = {
-  openai: OpenAIInfo,
-  ollama: OllamaInfo,
-  groq: GroqInfo,
-  anthropic: AnthropicInfo,
-  gemini: GeminiInfo,
-  transformers: TransformersInfo,
-  deepseek: DeepseekInfo,
-  lmstudio: LMStudioInfo,
-  custom_openai: {
-    key: 'custom_openai',
-    displayName: 'Custom OpenAI',
-  },
-};
-
-export interface ChatModel {
-  displayName: string;
-  model: BaseChatModel;
-}
-
-export interface EmbeddingModel {
-  displayName: string;
-  model: Embeddings;
-}
-
-export const chatModelProviders: Record<
-  string,
-  () => Promise<Record<string, ChatModel>>
-> = {
+const chatModelProviders = {
   openai: loadOpenAIChatModels,
-  ollama: loadOllamaChatModels,
   groq: loadGroqChatModels,
+  ollama: loadOllamaChatModels,
   anthropic: loadAnthropicChatModels,
   gemini: loadGeminiChatModels,
-  deepseek: loadDeepseekChatModels,
-  lmstudio: loadLMStudioChatModels,
 };
 
-export const embeddingModelProviders: Record<
-  string,
-  () => Promise<Record<string, EmbeddingModel>>
-> = {
-  openai: loadOpenAIEmbeddingModels,
-  ollama: loadOllamaEmbeddingModels,
-  gemini: loadGeminiEmbeddingModels,
-  transformers: loadTransformersEmbeddingsModels,
-  lmstudio: loadLMStudioEmbeddingsModels,
+const embeddingModelProviders = {
+  openai: loadOpenAIEmbeddingsModels,
+  local: loadTransformersEmbeddingsModels,
+  ollama: loadOllamaEmbeddingsModels,
+  gemini: loadGeminiEmbeddingsModels,
 };
 
 export const getAvailableChatModelProviders = async () => {
-  const models: Record<string, Record<string, ChatModel>> = {};
+  const models = {};
 
   for (const provider in chatModelProviders) {
     const providerModels = await chatModelProviders[provider]();
@@ -116,7 +52,7 @@ export const getAvailableChatModelProviders = async () => {
               configuration: {
                 baseURL: customOpenAiApiUrl,
               },
-            }) as unknown as BaseChatModel,
+            }),
           },
         }
       : {}),
@@ -126,7 +62,7 @@ export const getAvailableChatModelProviders = async () => {
 };
 
 export const getAvailableEmbeddingModelProviders = async () => {
-  const models: Record<string, Record<string, EmbeddingModel>> = {};
+  const models = {};
 
   for (const provider in embeddingModelProviders) {
     const providerModels = await embeddingModelProviders[provider]();

src/lib/providers/lmstudio.ts

@@ -1,100 +0,0 @@
-import { getKeepAlive, getLMStudioApiEndpoint } from '../config';
-import axios from 'axios';
-import { ChatModel, EmbeddingModel } from '.';
-
-export const PROVIDER_INFO = {
-  key: 'lmstudio',
-  displayName: 'LM Studio',
-};
-import { ChatOpenAI } from '@langchain/openai';
-import { OpenAIEmbeddings } from '@langchain/openai';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Embeddings } from '@langchain/core/embeddings';
-
-interface LMStudioModel {
-  id: string;
-  name?: string;
-}
-
-const ensureV1Endpoint = (endpoint: string): string =>
-  endpoint.endsWith('/v1') ? endpoint : `${endpoint}/v1`;
-
-const checkServerAvailability = async (endpoint: string): Promise<boolean> => {
-  try {
-    await axios.get(`${ensureV1Endpoint(endpoint)}/models`, {
-      headers: { 'Content-Type': 'application/json' },
-    });
-    return true;
-  } catch {
-    return false;
-  }
-};
-
-export const loadLMStudioChatModels = async () => {
-  const endpoint = getLMStudioApiEndpoint();
-
-  if (!endpoint) return {};
-  if (!(await checkServerAvailability(endpoint))) return {};
-
-  try {
-    const response = await axios.get(`${ensureV1Endpoint(endpoint)}/models`, {
-      headers: { 'Content-Type': 'application/json' },
-    });
-
-    const chatModels: Record<string, ChatModel> = {};
-
-    response.data.data.forEach((model: LMStudioModel) => {
-      chatModels[model.id] = {
-        displayName: model.name || model.id,
-        model: new ChatOpenAI({
-          openAIApiKey: 'lm-studio',
-          configuration: {
-            baseURL: ensureV1Endpoint(endpoint),
-          },
-          modelName: model.id,
-          temperature: 0.7,
-          streaming: true,
-          maxRetries: 3,
-        }) as unknown as BaseChatModel,
-      };
-    });
-
-    return chatModels;
-  } catch (err) {
-    console.error(`Error loading LM Studio models: ${err}`);
-    return {};
-  }
-};
-
-export const loadLMStudioEmbeddingsModels = async () => {
-  const endpoint = getLMStudioApiEndpoint();
-
-  if (!endpoint) return {};
-  if (!(await checkServerAvailability(endpoint))) return {};
-
-  try {
-    const response = await axios.get(`${ensureV1Endpoint(endpoint)}/models`, {
-      headers: { 'Content-Type': 'application/json' },
-    });
-
-    const embeddingsModels: Record<string, EmbeddingModel> = {};
-
-    response.data.data.forEach((model: LMStudioModel) => {
-      embeddingsModels[model.id] = {
-        displayName: model.name || model.id,
-        model: new OpenAIEmbeddings({
-          openAIApiKey: 'lm-studio',
-          configuration: {
-            baseURL: ensureV1Endpoint(endpoint),
-          },
-          modelName: model.id,
-        }) as unknown as Embeddings,
-      };
-    });
-
-    return embeddingsModels;
-  } catch (err) {
-    console.error(`Error loading LM Studio embeddings model: ${err}`);
-    return {};
-  }
-};

src/lib/providers/ollama.ts

@@ -1,78 +1,74 @@
-import axios from 'axios';
-import { getKeepAlive, getOllamaApiEndpoint } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-
-export const PROVIDER_INFO = {
-  key: 'ollama',
-  displayName: 'Ollama',
-};
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
 import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
+import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
+import logger from '../../utils/logger';
+import { ChatOllama } from '@langchain/community/chat_models/ollama';
+import axios from 'axios';
 
 export const loadOllamaChatModels = async () => {
-  const ollamaApiEndpoint = getOllamaApiEndpoint();
+  const ollamaEndpoint = getOllamaApiEndpoint();
+  const keepAlive = getKeepAlive();
 
-  if (!ollamaApiEndpoint) return {};
+  if (!ollamaEndpoint) return {};
 
   try {
-    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
+    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models } = res.data;
+    const { models: ollamaModels } = response.data;
 
-    const chatModels: Record<string, ChatModel> = {};
-
-    models.forEach((model: any) => {
-      chatModels[model.model] = {
+    const chatModels = ollamaModels.reduce((acc, model) => {
+      acc[model.model] = {
         displayName: model.name,
         model: new ChatOllama({
-          baseUrl: ollamaApiEndpoint,
+          baseUrl: ollamaEndpoint,
           model: model.model,
           temperature: 0.7,
-          keepAlive: getKeepAlive(),
+          keepAlive: keepAlive,
         }),
       };
-    });
+
+      return acc;
+    }, {});
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading Ollama models: ${err}`);
+    logger.error(`Error loading Ollama models: ${err}`);
     return {};
   }
 };
 
-export const loadOllamaEmbeddingModels = async () => {
-  const ollamaApiEndpoint = getOllamaApiEndpoint();
+export const loadOllamaEmbeddingsModels = async () => {
+  const ollamaEndpoint = getOllamaApiEndpoint();
 
-  if (!ollamaApiEndpoint) return {};
+  if (!ollamaEndpoint) return {};
 
   try {
-    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
+    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models } = res.data;
+    const { models: ollamaModels } = response.data;
 
-    const embeddingModels: Record<string, EmbeddingModel> = {};
-
-    models.forEach((model: any) => {
-      embeddingModels[model.model] = {
+    const embeddingsModels = ollamaModels.reduce((acc, model) => {
+      acc[model.model] = {
         displayName: model.name,
         model: new OllamaEmbeddings({
-          baseUrl: ollamaApiEndpoint,
+          baseUrl: ollamaEndpoint,
           model: model.model,
         }),
       };
-    });
 
-    return embeddingModels;
+      return acc;
+    }, {});
+
+    return embeddingsModels;
   } catch (err) {
-    console.error(`Error loading Ollama embeddings models: ${err}`);
+    logger.error(`Error loading Ollama embeddings model: ${err}`);
     return {};
   }
 };

src/lib/providers/openai.ts

@@ -1,107 +1,89 @@
 import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { getOpenaiApiKey } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-
-export const PROVIDER_INFO = {
-  key: 'openai',
-  displayName: 'OpenAI',
-};
-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',
-  },
-  {
-    displayName: 'GPT 4.1 nano',
-    key: 'gpt-4.1-nano',
-  },
-  {
-    displayName: 'GPT 4.1 mini',
-    key: 'gpt-4.1-mini',
-  },
-  {
-    displayName: 'GPT 4.1',
-    key: 'gpt-4.1',
-  },
-];
-
-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',
-  },
-];
+import { getOpenaiApiKey } from '../../config';
+import logger from '../../utils/logger';
 
 export const loadOpenAIChatModels = async () => {
-  const openaiApiKey = getOpenaiApiKey();
+  const openAIApiKey = getOpenaiApiKey();
 
-  if (!openaiApiKey) return {};
+  if (!openAIApiKey) return {};
 
   try {
-    const chatModels: Record<string, ChatModel> = {};
-
-    openaiChatModels.forEach((model) => {
-      chatModels[model.key] = {
-        displayName: model.displayName,
+    const chatModels = {
+      'gpt-3.5-turbo': {
+        displayName: 'GPT-3.5 Turbo',
         model: new ChatOpenAI({
-          openAIApiKey: openaiApiKey,
-          modelName: model.key,
+          openAIApiKey,
+          modelName: 'gpt-3.5-turbo',
           temperature: 0.7,
-        }) as unknown as BaseChatModel,
-      };
-    });
+        }),
+      },
+      'gpt-4': {
+        displayName: 'GPT-4',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4',
+          temperature: 0.7,
+        }),
+      },
+      'gpt-4-turbo': {
+        displayName: 'GPT-4 turbo',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4-turbo',
+          temperature: 0.7,
+        }),
+      },
+      'gpt-4o': {
+        displayName: 'GPT-4 omni',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4o',
+          temperature: 0.7,
+        }),
+      },
+      'gpt-4o-mini': {
+        displayName: 'GPT-4 omni mini',
+        model: new ChatOpenAI({
+          openAIApiKey,
+          modelName: 'gpt-4o-mini',
+          temperature: 0.7,
+        }),
+      },
+    };
 
     return chatModels;
   } catch (err) {
-    console.error(`Error loading OpenAI models: ${err}`);
+    logger.error(`Error loading OpenAI models: ${err}`);
     return {};
   }
 };
 
-export const loadOpenAIEmbeddingModels = async () => {
-  const openaiApiKey = getOpenaiApiKey();
+export const loadOpenAIEmbeddingsModels = async () => {
+  const openAIApiKey = getOpenaiApiKey();
 
-  if (!openaiApiKey) return {};
+  if (!openAIApiKey) return {};
 
   try {
-    const embeddingModels: Record<string, EmbeddingModel> = {};
-
-    openaiEmbeddingModels.forEach((model) => {
-      embeddingModels[model.key] = {
-        displayName: model.displayName,
+    const embeddingModels = {
+      'text-embedding-3-small': {
+        displayName: 'Text Embedding 3 Small',
         model: new OpenAIEmbeddings({
-          openAIApiKey: openaiApiKey,
-          modelName: model.key,
-        }) as unknown as Embeddings,
-      };
-    });
+          openAIApiKey,
+          modelName: 'text-embedding-3-small',
+        }),
+      },
+      'text-embedding-3-large': {
+        displayName: 'Text Embedding 3 Large',
+        model: new OpenAIEmbeddings({
+          openAIApiKey,
+          modelName: 'text-embedding-3-large',
+        }),
+      },
+    };
 
     return embeddingModels;
   } catch (err) {
-    console.error(`Error loading OpenAI embeddings models: ${err}`);
+    logger.error(`Error loading OpenAI embeddings model: ${err}`);
     return {};
   }
 };

src/lib/providers/transformers.ts

@@ -1,10 +1,6 @@
+import logger from '../../utils/logger';
 import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
 
-export const PROVIDER_INFO = {
-  key: 'transformers',
-  displayName: 'Hugging Face',
-};
-
 export const loadTransformersEmbeddingsModels = async () => {
   try {
     const embeddingModels = {
@@ -30,7 +26,7 @@ export const loadTransformersEmbeddingsModels = async () => {
 
     return embeddingModels;
   } catch (err) {
-    console.error(`Error loading Transformers embeddings model: ${err}`);
+    logger.error(`Error loading Transformers embeddings model: ${err}`);
     return {};
   }
 };

src/lib/search/index.ts

@@ -1,59 +0,0 @@
-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/searxng.ts

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

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

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

src/prompts/academicSearch.ts

@@ -50,10 +50,6 @@ export const academicSearchResponsePrompt = `
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
-    
-    ### User instructions
-    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
-    {systemInstructions}
 
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.

src/prompts/redditSearch.ts

@@ -50,10 +50,6 @@ export const redditSearchResponsePrompt = `
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
-    
-    ### User instructions
-    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
-    {systemInstructions}
 
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.

src/prompts/webSearch.ts

@@ -1,6 +1,6 @@
 export const webSearchRetrieverPrompt = `
 You are an AI question rephraser. You will be given a conversation and a follow-up question,  you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
-If it is a simple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
+If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
 If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
 You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
 
@@ -92,10 +92,6 @@ export const webSearchResponsePrompt = `
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
 
-    ### User instructions
-    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
-    {systemInstructions}
-
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/prompts/wolframAlpha.ts

@@ -50,10 +50,6 @@ export const wolframAlphaSearchResponsePrompt = `
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
-    
-    ### User instructions
-    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
-    {systemInstructions}
 
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.

src/prompts/writingAssistant.ts

@@ -1,5 +1,5 @@
 export const writingAssistantPrompt = `
-You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query. 
+You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query.
 Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
 You will be shared a context that can contain information from files user has uploaded to get answers from. You will have to generate answers upon that.
 
@@ -7,10 +7,6 @@ You have to cite the answer using [number] notation. You must cite the sentences
 Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
 However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
 
-### User instructions
-These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
-{systemInstructions}
-
 <context>
 {context}
 </context>

src/prompts/youtubeSearch.ts

@@ -50,10 +50,6 @@ export const youtubeSearchResponsePrompt = `
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcrip
-    
-    ### User instructions
-    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
-    {systemInstructions}
 
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.

src/routes/chats.ts

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

src/routes/config.ts

@@ -0,0 +1,104 @@
+import express from 'express';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+import {
+  getGroqApiKey,
+  getOllamaApiEndpoint,
+  getAnthropicApiKey,
+  getGeminiApiKey,
+  getOpenaiApiKey,
+  updateConfig,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiApiKey,
+  getCustomOpenaiModelName,
+} from '../config';
+import logger from '../utils/logger';
+
+const router = express.Router();
+
+router.get('/', async (_, res) => {
+  try {
+    const config = {};
+
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    config['chatModelProviders'] = {};
+    config['embeddingModelProviders'] = {};
+
+    for (const provider in chatModelProviders) {
+      config['chatModelProviders'][provider] = Object.keys(
+        chatModelProviders[provider],
+      ).map((model) => {
+        return {
+          name: model,
+          displayName: chatModelProviders[provider][model].displayName,
+        };
+      });
+    }
+
+    for (const provider in embeddingModelProviders) {
+      config['embeddingModelProviders'][provider] = Object.keys(
+        embeddingModelProviders[provider],
+      ).map((model) => {
+        return {
+          name: model,
+          displayName: embeddingModelProviders[provider][model].displayName,
+        };
+      });
+    }
+
+    config['openaiApiKey'] = getOpenaiApiKey();
+    config['ollamaApiUrl'] = getOllamaApiEndpoint();
+    config['anthropicApiKey'] = getAnthropicApiKey();
+    config['groqApiKey'] = getGroqApiKey();
+    config['geminiApiKey'] = getGeminiApiKey();
+    config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
+    config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
+    config['customOpenaiModelName'] = getCustomOpenaiModelName();
+
+    res.status(200).json(config);
+  } catch (err: any) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(`Error getting config: ${err.message}`);
+  }
+});
+
+router.post('/', async (req, res) => {
+  const config = req.body;
+
+  const updatedConfig = {
+    MODELS: {
+      OPENAI: {
+        API_KEY: config.openaiApiKey,
+      },
+      GROQ: {
+        API_KEY: config.groqApiKey,
+      },
+      ANTHROPIC: {
+        API_KEY: config.anthropicApiKey,
+      },
+      GEMINI: {
+        API_KEY: config.geminiApiKey,
+      },
+      OLLAMA: {
+        API_URL: config.ollamaApiUrl,
+      },
+      CUSTOM_OPENAI: {
+        API_URL: config.customOpenaiApiUrl,
+        API_KEY: config.customOpenaiApiKey,
+        MODEL_NAME: config.customOpenaiModelName,
+      },
+    },
+  };
+
+  updateConfig(updatedConfig);
+
+  res.status(200).json({ message: 'Config updated' });
+});
+
+export default router;

src/routes/discover.ts

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

src/routes/images.ts

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

src/routes/index.ts

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

src/routes/models.ts

@@ -0,0 +1,36 @@
+import express from 'express';
+import logger from '../utils/logger';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+
+const router = express.Router();
+
+router.get('/', async (req, res) => {
+  try {
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    Object.keys(chatModelProviders).forEach((provider) => {
+      Object.keys(chatModelProviders[provider]).forEach((model) => {
+        delete chatModelProviders[provider][model].model;
+      });
+    });
+
+    Object.keys(embeddingModelProviders).forEach((provider) => {
+      Object.keys(embeddingModelProviders[provider]).forEach((model) => {
+        delete embeddingModelProviders[provider][model].model;
+      });
+    });
+
+    res.status(200).json({ chatModelProviders, embeddingModelProviders });
+  } catch (err) {
+    res.status(500).json({ message: 'An error has occurred.' });
+    logger.error(err.message);
+  }
+});
+
+export default router;

src/routes/search.ts

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

src/routes/suggestions.ts

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

src/routes/uploads.ts

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

src/routes/videos.ts

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

src/search/metaSearchAgent.ts

@@ -13,17 +13,18 @@ import {
 } from '@langchain/core/runnables';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import LineListOutputParser from '../outputParsers/listLineOutputParser';
-import LineOutputParser from '../outputParsers/lineOutputParser';
+import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
+import LineOutputParser from '../lib/outputParsers/lineOutputParser';
 import { getDocumentsFromLinks } from '../utils/documents';
 import { Document } from 'langchain/document';
-import { searchSearxng } from '../searxng';
-import path from 'node:path';
-import fs from 'node:fs';
+import { searchSearxng } from '../lib/searxng';
+import path from 'path';
+import fs from 'fs';
 import computeSimilarity from '../utils/computeSimilarity';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import { StreamEvent } from '@langchain/core/tracers/log_stream';
+import { IterableReadableStream } from '@langchain/core/utils/stream';
 
 export interface MetaSearchAgentType {
   searchAndAnswer: (
@@ -33,7 +34,6 @@ export interface MetaSearchAgentType {
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
     fileIds: string[],
-    systemInstructions: string,
   ) => Promise<eventEmitter>;
 }
 
@@ -90,7 +90,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
             question = 'summarize';
           }
 
-          let docs: Document[] = [];
+          let docs = [];
 
           const linkDocs = await getDocumentsFromLinks({ links });
 
@@ -129,10 +129,10 @@ class MetaSearchAgent implements MetaSearchAgentType {
           await Promise.all(
             docGroups.map(async (doc) => {
               const res = await llm.invoke(`
-            You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the 
+            You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the
             text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
             If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
-            
+
             - **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
             - **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
             - **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
@@ -141,8 +141,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
             <example>
             1. \`<text>
-            Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. 
-            It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications 
+            Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers.
+            It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications
             by using containers.
             </text>
 
@@ -151,8 +151,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
             </query>
 
             Response:
-            Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application 
-            deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in 
+            Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application
+            deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in
             any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
             \`
             2. \`<text>
@@ -203,8 +203,6 @@ 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,
@@ -237,11 +235,9 @@ class MetaSearchAgent implements MetaSearchAgentType {
     fileIds: string[],
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
-    systemInstructions: string,
   ) {
     return RunnableSequence.from([
       RunnableMap.from({
-        systemInstructions: () => systemInstructions,
         query: (input: BasicChainInput) => input.query,
         chat_history: (input: BasicChainInput) => input.chat_history,
         date: () => new Date().toISOString(),
@@ -315,7 +311,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
         const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
 
         const fileSimilaritySearchObject = content.contents.map(
-          (c: string, i: number) => {
+          (c: string, i) => {
             return {
               fileName: content.title,
               content: c,
@@ -418,8 +414,6 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
       return sortedDocs;
     }
-
-    return [];
   }
 
   private processDocs(docs: Document[]) {
@@ -432,7 +426,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
   }
 
   private async handleStream(
-    stream: AsyncGenerator<StreamEvent, any, any>,
+    stream: IterableReadableStream<StreamEvent>,
     emitter: eventEmitter,
   ) {
     for await (const event of stream) {
@@ -471,7 +465,6 @@ class MetaSearchAgent implements MetaSearchAgentType {
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
     fileIds: string[],
-    systemInstructions: string,
   ) {
     const emitter = new eventEmitter();
 
@@ -480,7 +473,6 @@ class MetaSearchAgent implements MetaSearchAgentType {
       fileIds,
       embeddings,
       optimizationMode,
-      systemInstructions,
     );
 
     const stream = answeringChain.streamEvents(

src/utils/computeSimilarity.ts

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

src/utils/documents.ts

@@ -3,6 +3,7 @@ import { htmlToText } from 'html-to-text';
 import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
 import { Document } from '@langchain/core/documents';
 import pdfParse from 'pdf-parse';
+import logger from './logger';
 
 export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
   const splitter = new RecursiveCharacterTextSplitter();
@@ -64,7 +65,7 @@ export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
         const splittedText = await splitter.splitText(parsedText);
         const title = res.data
           .toString('utf8')
-          .match(/<title.*>(.*?)<\/title>/)?.[1];
+          .match(/<title>(.*?)<\/title>/)?.[1];
 
         const linkDocs = splittedText.map((text) => {
           return new Document({
@@ -78,13 +79,12 @@ export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
 
         docs.push(...linkDocs);
       } catch (err) {
-        console.error(
-          'An error occurred while getting documents from links: ',
-          err,
+        logger.error(
+          `Error at generating documents from links: ${err.message}`,
         );
         docs.push(
           new Document({
-            pageContent: `Failed to retrieve content from the link: ${err}`,
+            pageContent: `Failed to retrieve content from the link: ${err.message}`,
             metadata: {
               title: 'Failed to retrieve content',
               url: link,

src/utils/logger.ts

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

src/websocket/connectionManager.ts

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

src/websocket/index.ts

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

src/websocket/messageHandler.ts

@@ -0,0 +1,272 @@
+import { EventEmitter, WebSocket } from 'ws';
+import { BaseMessage, AIMessage, HumanMessage } from '@langchain/core/messages';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
+import logger from '../utils/logger';
+import db from '../db';
+import { chats, messages as messagesSchema } from '../db/schema';
+import { eq, asc, gt, and } from 'drizzle-orm';
+import crypto from 'crypto';
+import { getFileDetails } from '../utils/files';
+import MetaSearchAgent, {
+  MetaSearchAgentType,
+} from '../search/metaSearchAgent';
+import prompts from '../prompts';
+
+type Message = {
+  messageId: string;
+  chatId: string;
+  content: string;
+};
+
+type WSMessage = {
+  message: Message;
+  optimizationMode: 'speed' | 'balanced' | 'quality';
+  type: string;
+  focusMode: string;
+  history: Array<[string, string]>;
+  files: Array<string>;
+};
+
+export const searchHandlers = {
+  webSearch: new MetaSearchAgent({
+    activeEngines: [],
+    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
+    responsePrompt: prompts.webSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: true,
+  }),
+  academicSearch: new MetaSearchAgent({
+    activeEngines: ['arxiv', 'google scholar', 'pubmed'],
+    queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
+    responsePrompt: prompts.academicSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  writingAssistant: new MetaSearchAgent({
+    activeEngines: [],
+    queryGeneratorPrompt: '',
+    responsePrompt: prompts.writingAssistantPrompt,
+    rerank: true,
+    rerankThreshold: 0,
+    searchWeb: false,
+    summarizer: false,
+  }),
+  wolframAlphaSearch: new MetaSearchAgent({
+    activeEngines: ['wolframalpha'],
+    queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
+    responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
+    rerank: false,
+    rerankThreshold: 0,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  youtubeSearch: new MetaSearchAgent({
+    activeEngines: ['youtube'],
+    queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
+    responsePrompt: prompts.youtubeSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  redditSearch: new MetaSearchAgent({
+    activeEngines: ['reddit'],
+    queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
+    responsePrompt: prompts.redditSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: false,
+  }),
+};
+
+const handleEmitterEvents = (
+  emitter: EventEmitter,
+  ws: WebSocket,
+  messageId: string,
+  chatId: string,
+) => {
+  let recievedMessage = '';
+  let sources = [];
+
+  emitter.on('data', (data) => {
+    const parsedData = JSON.parse(data);
+    if (parsedData.type === 'response') {
+      ws.send(
+        JSON.stringify({
+          type: 'message',
+          data: parsedData.data,
+          messageId: messageId,
+        }),
+      );
+      recievedMessage += parsedData.data;
+    } else if (parsedData.type === 'sources') {
+      ws.send(
+        JSON.stringify({
+          type: 'sources',
+          data: parsedData.data,
+          messageId: messageId,
+        }),
+      );
+      sources = parsedData.data;
+    }
+  });
+  emitter.on('end', () => {
+    ws.send(JSON.stringify({ type: 'messageEnd', messageId: messageId }));
+
+    db.insert(messagesSchema)
+      .values({
+        content: recievedMessage,
+        chatId: chatId,
+        messageId: messageId,
+        role: 'assistant',
+        metadata: JSON.stringify({
+          createdAt: new Date(),
+          ...(sources && sources.length > 0 && { sources }),
+        }),
+      })
+      .execute();
+  });
+  emitter.on('error', (data) => {
+    const parsedData = JSON.parse(data);
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: parsedData.data,
+        key: 'CHAIN_ERROR',
+      }),
+    );
+  });
+};
+
+export const handleMessage = async (
+  message: string,
+  ws: WebSocket,
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  try {
+    const parsedWSMessage = JSON.parse(message) as WSMessage;
+    const parsedMessage = parsedWSMessage.message;
+
+    if (parsedWSMessage.files.length > 0) {
+      /* TODO: Implement uploads in other classes/single meta class system*/
+      parsedWSMessage.focusMode = 'webSearch';
+    }
+
+    const humanMessageId =
+      parsedMessage.messageId ?? crypto.randomBytes(7).toString('hex');
+    const aiMessageId = crypto.randomBytes(7).toString('hex');
+
+    if (!parsedMessage.content)
+      return ws.send(
+        JSON.stringify({
+          type: 'error',
+          data: 'Invalid message format',
+          key: 'INVALID_FORMAT',
+        }),
+      );
+
+    const history: BaseMessage[] = parsedWSMessage.history.map((msg) => {
+      if (msg[0] === 'human') {
+        return new HumanMessage({
+          content: msg[1],
+        });
+      } else {
+        return new AIMessage({
+          content: msg[1],
+        });
+      }
+    });
+
+    if (parsedWSMessage.type === 'message') {
+      const handler: MetaSearchAgentType =
+        searchHandlers[parsedWSMessage.focusMode];
+
+      if (handler) {
+        try {
+          const emitter = await handler.searchAndAnswer(
+            parsedMessage.content,
+            history,
+            llm,
+            embeddings,
+            parsedWSMessage.optimizationMode,
+            parsedWSMessage.files,
+          );
+
+          handleEmitterEvents(emitter, ws, aiMessageId, parsedMessage.chatId);
+
+          const chat = await db.query.chats.findFirst({
+            where: eq(chats.id, parsedMessage.chatId),
+          });
+
+          if (!chat) {
+            await db
+              .insert(chats)
+              .values({
+                id: parsedMessage.chatId,
+                title: parsedMessage.content,
+                createdAt: new Date().toString(),
+                focusMode: parsedWSMessage.focusMode,
+                files: parsedWSMessage.files.map(getFileDetails),
+              })
+              .execute();
+          }
+
+          const messageExists = await db.query.messages.findFirst({
+            where: eq(messagesSchema.messageId, humanMessageId),
+          });
+
+          if (!messageExists) {
+            await db
+              .insert(messagesSchema)
+              .values({
+                content: parsedMessage.content,
+                chatId: parsedMessage.chatId,
+                messageId: humanMessageId,
+                role: 'user',
+                metadata: JSON.stringify({
+                  createdAt: new Date(),
+                }),
+              })
+              .execute();
+          } else {
+            await db
+              .delete(messagesSchema)
+              .where(
+                and(
+                  gt(messagesSchema.id, messageExists.id),
+                  eq(messagesSchema.chatId, parsedMessage.chatId),
+                ),
+              )
+              .execute();
+          }
+        } catch (err) {
+          console.log(err);
+        }
+      } else {
+        ws.send(
+          JSON.stringify({
+            type: 'error',
+            data: 'Invalid focus mode',
+            key: 'INVALID_FOCUS_MODE',
+          }),
+        );
+      }
+    }
+  } catch (err) {
+    ws.send(
+      JSON.stringify({
+        type: 'error',
+        data: 'Invalid message format',
+        key: 'INVALID_FORMAT',
+      }),
+    );
+    logger.error(`Failed to handle message: ${err}`);
+  }
+};

src/websocket/websocketServer.ts

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

tsconfig.json

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

ui/.env.example

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

ui/.gitignore

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

ui/.prettierrc.js

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

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

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

ui/app/discover/page.tsx

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

ui/app/library/page.tsx

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