Merge 32ce39437f into 46541e6c0c

.github/workflows/docker-build.yaml

@@ -8,12 +8,18 @@ on:
     types: [published]
 
 jobs:
-  build-amd64:
+  build-and-push:
     runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        service: [backend, app]
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
 
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
         with:
@@ -30,109 +36,38 @@ 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 for ${{ matrix.service }}
         if: github.ref == 'refs/heads/master' && github.event_name == 'push'
         run: |
-          DOCKERFILE=app.dockerfile
-          IMAGE_NAME=perplexica
-          docker buildx build --platform linux/amd64 \
-            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:amd64 \
+          docker buildx create --use
+          if [[ "${{ matrix.service }}" == "backend" ]]; then \
+            DOCKERFILE=backend.dockerfile; \
+            IMAGE_NAME=perplexica-backend; \
+          else \
+            DOCKERFILE=app.dockerfile; \
+            IMAGE_NAME=perplexica-frontend; \
+          fi
+          docker buildx build --platform linux/amd64,linux/arm64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:main \
             --cache-to=type=inline \
-            --provenance false \
             -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:amd64 \
+            -t itzcrazykns1337/${IMAGE_NAME}:main \
             --push .
 
-      - name: Build and push AMD64 release Docker image
+      - name: Build and push release Docker image for ${{ matrix.service }}
         if: github.event_name == 'release'
         run: |
-          DOCKERFILE=app.dockerfile
-          IMAGE_NAME=perplexica
-          docker buildx build --platform linux/amd64 \
-            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
+          docker buildx create --use
+          if [[ "${{ matrix.service }}" == "backend" ]]; then \
+            DOCKERFILE=backend.dockerfile; \
+            IMAGE_NAME=perplexica-backend; \
+          else \
+            DOCKERFILE=app.dockerfile; \
+            IMAGE_NAME=perplexica-frontend; \
+          fi
+          docker buildx build --platform linux/amd64,linux/arm64 \
+            --cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
             --cache-to=type=inline \
-            --provenance false \
             -f $DOCKERFILE \
-            -t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
+            -t itzcrazykns1337/${IMAGE_NAME}:${{ 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

@@ -1,23 +1,8 @@
 # 🚀 Perplexica - An AI-powered search engine 🔎 <!-- omit in toc -->
 
-<div align="center" markdown="1">
-   <sup>Special thanks to:</sup>
-   <br>
-   <br>
-   <a href="https://www.warp.dev/perplexica">
-      <img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/775dd593-9b5f-40f1-bf48-479faff4c27b">
-   </a>
-
-### [Warp, the AI Devtool that lives in your terminal](https://www.warp.dev/perplexica)
-
-[Available for MacOS, Linux, & Windows](https://www.warp.dev/perplexica)
-
-</div>
-
-<hr/>
-
 [![Discord](https://dcbadge.vercel.app/api/server/26aArMy8tT?style=flat&compact=true)](https://discord.gg/26aArMy8tT)
 
+
 ![preview](.assets/perplexica-screenshot.png?)
 
 ## Table of Contents <!-- omit in toc -->
@@ -59,7 +44,7 @@ Want to know more about its architecture and how it works? You can read it [here
   - **Normal Mode:** Processes your query and performs a web search.
 - **Focus Modes:** Special modes to better answer specific types of questions. Perplexica currently has 6 focus modes:
   - **All Mode:** Searches the entire web to find the best results.
-  - **Writing Assistant Mode:** Helpful for writing tasks that do not require searching the web.
+  - **Writing Assistant Mode:** Helpful for writing tasks that does not require searching the web.
   - **Academic Search Mode:** Finds articles and papers, ideal for academic research.
   - **YouTube Search Mode:** Finds YouTube videos based on the search query.
   - **Wolfram Alpha Search Mode:** Answers queries that need calculations or data analysis using Wolfram Alpha.
@@ -109,13 +94,14 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
 
 1. Install SearXNG and allow `JSON` format in the SearXNG settings.
 2. Clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. Ensure you complete all required fields in this file.
-3. 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.
 
 ### Ollama Connection Errors
 
@@ -153,11 +139,10 @@ For more details, check out the full documentation [here](https://github.com/Itz
 
 ## Expose Perplexica to 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.
+You can access Perplexica over your home network by following our networking guide [here](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)
 
 ## Upcoming Features

app.dockerfile

@@ -1,27 +1,15 @@
-FROM node:20.18.0-slim AS builder
+FROM node:20.18.0-alpine
+
+ARG NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
+ARG NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
+ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
+ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
 
 WORKDIR /home/perplexica
 
-COPY package.json yarn.lock ./
-RUN yarn install --frozen-lockfile --network-timeout 600000
+COPY ui /home/perplexica/
 
-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 install --frozen-lockfile
 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"]
+CMD ["yarn", "start"]

backend.dockerfile

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

docker-compose.yaml

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

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

docs/installation/UPDATING.md

@@ -7,40 +7,34 @@ To update Perplexica to the latest version, follow these steps:
 1. Clone the latest version of Perplexica from GitHub:
 
    ```bash
-   git clone https://github.com/ItzCrazyKns/Perplexica.git
+      git clone https://github.com/ItzCrazyKns/Perplexica.git
    ```
 
-2. Navigate to the project directory.
+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. Pull the latest images from the registry.
+3. Pull latest images from registry.
 
    ```bash
    docker compose pull
    ```
 
-5. Update and recreate the containers.
+4. Update and Recreate containers.
 
    ```bash
    docker compose up -d
    ```
 
-6. Once the command completes, go to http://localhost:3000 and verify the latest changes.
+5. Once the command completes running go to http://localhost:3000 and verify the latest changes.
 
-## For non-Docker users
+## For non Docker users
 
 1. Clone the latest version of Perplexica from GitHub:
 
    ```bash
-   git clone https://github.com/ItzCrazyKns/Perplexica.git
+      git clone https://github.com/ItzCrazyKns/Perplexica.git
    ```
 
-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`
-
----
+2. Navigate to the Project Directory
+3. Execute `npm i` in both the `ui` folder and the root directory.
+4. Once packages are updated, execute `npm run build` in both the `ui` folder and the root directory.
+5. 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": "1.10.1",
+  "name": "perplexica-backend",
+  "version": "1.10.0-rc2",
   "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

@@ -1,29 +1,14 @@
 [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")
 
-[MODELS.OPENAI]
-API_KEY = ""
-
-[MODELS.GROQ]
-API_KEY = ""
-
-[MODELS.ANTHROPIC]
-API_KEY = ""
-
-[MODELS.GEMINI]
-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 = ""
+[API_KEYS]
+OPENAI = "" # OpenAI API key - sk-1234567890abcdef1234567890abcdef
+GROQ = "" # Groq API key - gsk_1234567890abcdef1234567890abcdef
+ANTHROPIC = "" # Anthropic API key - sk-ant-1234567890abcdef1234567890abcdef
+GEMINI = "" # Gemini API key - sk-1234567890abcdef1234567890abcdef
 
 [API_ENDPOINTS]
-SEARXNG = "" # SearxNG API URL - http://localhost:32768
+SEARXNG = "http://localhost:32768" # SearxNG API URL
+OLLAMA = "" # Ollama API URL - http://host.docker.internal:11434

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

@@ -1,114 +0,0 @@
-import {
-  getAnthropicApiKey,
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-  getGeminiApiKey,
-  getGroqApiKey,
-  getOllamaApiEndpoint,
-  getOpenaiApiKey,
-  getDeepseekApiKey,
-  updateConfig,
-} from '@/lib/config';
-import {
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '@/lib/providers';
-
-export const GET = async (req: Request) => {
-  try {
-    const config: Record<string, any> = {};
-
-    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
-      getAvailableChatModelProviders(),
-      getAvailableEmbeddingModelProviders(),
-    ]);
-
-    config['chatModelProviders'] = {};
-    config['embeddingModelProviders'] = {};
-
-    for (const provider in chatModelProviders) {
-      config['chatModelProviders'][provider] = Object.keys(
-        chatModelProviders[provider],
-      ).map((model) => {
-        return {
-          name: model,
-          displayName: chatModelProviders[provider][model].displayName,
-        };
-      });
-    }
-
-    for (const provider in embeddingModelProviders) {
-      config['embeddingModelProviders'][provider] = Object.keys(
-        embeddingModelProviders[provider],
-      ).map((model) => {
-        return {
-          name: model,
-          displayName: embeddingModelProviders[provider][model].displayName,
-        };
-      });
-    }
-
-    config['openaiApiKey'] = getOpenaiApiKey();
-    config['ollamaApiUrl'] = getOllamaApiEndpoint();
-    config['anthropicApiKey'] = getAnthropicApiKey();
-    config['groqApiKey'] = getGroqApiKey();
-    config['geminiApiKey'] = getGeminiApiKey();
-    config['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,
-        },
-        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/app/settings/page.tsx

@@ -1,870 +0,0 @@
-'use client';
-
-import { Settings as SettingsIcon, ArrowLeft, Loader2 } from 'lucide-react';
-import { useEffect, useState } from 'react';
-import { cn } from '@/lib/utils';
-import { Switch } from '@headlessui/react';
-import ThemeSwitcher from '@/components/theme/Switcher';
-import { ImagesIcon, VideoIcon } from 'lucide-react';
-import Link from 'next/link';
-
-interface SettingsType {
-  chatModelProviders: {
-    [key: string]: [Record<string, any>];
-  };
-  embeddingModelProviders: {
-    [key: string]: [Record<string, any>];
-  };
-  openaiApiKey: string;
-  groqApiKey: string;
-  anthropicApiKey: string;
-  geminiApiKey: string;
-  ollamaApiUrl: string;
-  deepseekApiKey: string;
-  customOpenaiApiKey: string;
-  customOpenaiApiUrl: string;
-  customOpenaiModelName: string;
-}
-
-interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
-  isSaving?: boolean;
-  onSave?: (value: string) => void;
-}
-
-const Input = ({ className, isSaving, onSave, ...restProps }: InputProps) => {
-  return (
-    <div className="relative">
-      <input
-        {...restProps}
-        className={cn(
-          'bg-light-secondary dark:bg-dark-secondary w-full px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
-          isSaving && 'pr-10',
-          className,
-        )}
-        onBlur={(e) => onSave?.(e.target.value)}
-      />
-      {isSaving && (
-        <div className="absolute right-3 top-1/2 -translate-y-1/2">
-          <Loader2
-            size={16}
-            className="animate-spin text-black/70 dark:text-white/70"
-          />
-        </div>
-      )}
-    </div>
-  );
-};
-
-interface TextareaProps extends React.InputHTMLAttributes<HTMLTextAreaElement> {
-  isSaving?: boolean;
-  onSave?: (value: string) => void;
-}
-
-const Textarea = ({
-  className,
-  isSaving,
-  onSave,
-  ...restProps
-}: TextareaProps) => {
-  return (
-    <div className="relative">
-      <textarea
-        placeholder="Any special instructions for the LLM"
-        className="placeholder:text-sm text-sm w-full flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors"
-        rows={4}
-        onBlur={(e) => onSave?.(e.target.value)}
-        {...restProps}
-      />
-      {isSaving && (
-        <div className="absolute right-3 top-3">
-          <Loader2
-            size={16}
-            className="animate-spin text-black/70 dark:text-white/70"
-          />
-        </div>
-      )}
-    </div>
-  );
-};
-
-const Select = ({
-  className,
-  options,
-  ...restProps
-}: React.SelectHTMLAttributes<HTMLSelectElement> & {
-  options: { value: string; label: string; disabled?: boolean }[];
-}) => {
-  return (
-    <select
-      {...restProps}
-      className={cn(
-        'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
-        className,
-      )}
-    >
-      {options.map(({ label, value, disabled }) => (
-        <option key={value} value={value} disabled={disabled}>
-          {label}
-        </option>
-      ))}
-    </select>
-  );
-};
-
-const SettingsSection = ({
-  title,
-  children,
-}: {
-  title: string;
-  children: React.ReactNode;
-}) => (
-  <div className="flex flex-col space-y-4 p-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200">
-    <h2 className="text-black/90 dark:text-white/90 font-medium">{title}</h2>
-    {children}
-  </div>
-);
-
-const Page = () => {
-  const [config, setConfig] = useState<SettingsType | null>(null);
-  const [chatModels, setChatModels] = useState<Record<string, any>>({});
-  const [embeddingModels, setEmbeddingModels] = useState<Record<string, any>>(
-    {},
-  );
-  const [selectedChatModelProvider, setSelectedChatModelProvider] = useState<
-    string | null
-  >(null);
-  const [selectedChatModel, setSelectedChatModel] = useState<string | null>(
-    null,
-  );
-  const [selectedEmbeddingModelProvider, setSelectedEmbeddingModelProvider] =
-    useState<string | null>(null);
-  const [selectedEmbeddingModel, setSelectedEmbeddingModel] = useState<
-    string | null
-  >(null);
-  const [isLoading, setIsLoading] = useState(false);
-  const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
-  const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
-  const [systemInstructions, setSystemInstructions] = useState<string>('');
-  const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
-
-  useEffect(() => {
-    const fetchConfig = async () => {
-      setIsLoading(true);
-      const res = await fetch(`/api/config`, {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-      });
-
-      const data = (await res.json()) as SettingsType;
-      setConfig(data);
-
-      const chatModelProvidersKeys = Object.keys(data.chatModelProviders || {});
-      const embeddingModelProvidersKeys = Object.keys(
-        data.embeddingModelProviders || {},
-      );
-
-      const defaultChatModelProvider =
-        chatModelProvidersKeys.length > 0 ? chatModelProvidersKeys[0] : '';
-      const defaultEmbeddingModelProvider =
-        embeddingModelProvidersKeys.length > 0
-          ? embeddingModelProvidersKeys[0]
-          : '';
-
-      const chatModelProvider =
-        localStorage.getItem('chatModelProvider') ||
-        defaultChatModelProvider ||
-        '';
-      const chatModel =
-        localStorage.getItem('chatModel') ||
-        (data.chatModelProviders &&
-        data.chatModelProviders[chatModelProvider]?.length > 0
-          ? data.chatModelProviders[chatModelProvider][0].name
-          : undefined) ||
-        '';
-      const embeddingModelProvider =
-        localStorage.getItem('embeddingModelProvider') ||
-        defaultEmbeddingModelProvider ||
-        '';
-      const embeddingModel =
-        localStorage.getItem('embeddingModel') ||
-        (data.embeddingModelProviders &&
-          data.embeddingModelProviders[embeddingModelProvider]?.[0].name) ||
-        '';
-
-      setSelectedChatModelProvider(chatModelProvider);
-      setSelectedChatModel(chatModel);
-      setSelectedEmbeddingModelProvider(embeddingModelProvider);
-      setSelectedEmbeddingModel(embeddingModel);
-      setChatModels(data.chatModelProviders || {});
-      setEmbeddingModels(data.embeddingModelProviders || {});
-
-      setAutomaticImageSearch(
-        localStorage.getItem('autoImageSearch') === 'true',
-      );
-      setAutomaticVideoSearch(
-        localStorage.getItem('autoVideoSearch') === 'true',
-      );
-
-      setSystemInstructions(localStorage.getItem('systemInstructions')!);
-
-      setIsLoading(false);
-    };
-
-    fetchConfig();
-  }, []);
-
-  const saveConfig = async (key: string, value: any) => {
-    setSavingStates((prev) => ({ ...prev, [key]: true }));
-
-    try {
-      const updatedConfig = {
-        ...config,
-        [key]: value,
-      } as SettingsType;
-
-      const response = await fetch(`/api/config`, {
-        method: 'POST',
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        body: JSON.stringify(updatedConfig),
-      });
-
-      if (!response.ok) {
-        throw new Error('Failed to update config');
-      }
-
-      setConfig(updatedConfig);
-
-      if (
-        key.toLowerCase().includes('api') ||
-        key.toLowerCase().includes('url')
-      ) {
-        const res = await fetch(`/api/config`, {
-          headers: {
-            'Content-Type': 'application/json',
-          },
-        });
-
-        if (!res.ok) {
-          throw new Error('Failed to fetch updated config');
-        }
-
-        const data = await res.json();
-
-        setChatModels(data.chatModelProviders || {});
-        setEmbeddingModels(data.embeddingModelProviders || {});
-
-        const currentChatProvider = selectedChatModelProvider;
-        const newChatProviders = Object.keys(data.chatModelProviders || {});
-
-        if (!currentChatProvider && newChatProviders.length > 0) {
-          const firstProvider = newChatProviders[0];
-          const firstModel = data.chatModelProviders[firstProvider]?.[0]?.name;
-
-          if (firstModel) {
-            setSelectedChatModelProvider(firstProvider);
-            setSelectedChatModel(firstModel);
-            localStorage.setItem('chatModelProvider', firstProvider);
-            localStorage.setItem('chatModel', firstModel);
-          }
-        } else if (
-          currentChatProvider &&
-          (!data.chatModelProviders ||
-            !data.chatModelProviders[currentChatProvider] ||
-            !Array.isArray(data.chatModelProviders[currentChatProvider]) ||
-            data.chatModelProviders[currentChatProvider].length === 0)
-        ) {
-          const firstValidProvider = Object.entries(
-            data.chatModelProviders || {},
-          ).find(
-            ([_, models]) => Array.isArray(models) && models.length > 0,
-          )?.[0];
-
-          if (firstValidProvider) {
-            setSelectedChatModelProvider(firstValidProvider);
-            setSelectedChatModel(
-              data.chatModelProviders[firstValidProvider][0].name,
-            );
-            localStorage.setItem('chatModelProvider', firstValidProvider);
-            localStorage.setItem(
-              'chatModel',
-              data.chatModelProviders[firstValidProvider][0].name,
-            );
-          } else {
-            setSelectedChatModelProvider(null);
-            setSelectedChatModel(null);
-            localStorage.removeItem('chatModelProvider');
-            localStorage.removeItem('chatModel');
-          }
-        }
-
-        const currentEmbeddingProvider = selectedEmbeddingModelProvider;
-        const newEmbeddingProviders = Object.keys(
-          data.embeddingModelProviders || {},
-        );
-
-        if (!currentEmbeddingProvider && newEmbeddingProviders.length > 0) {
-          const firstProvider = newEmbeddingProviders[0];
-          const firstModel =
-            data.embeddingModelProviders[firstProvider]?.[0]?.name;
-
-          if (firstModel) {
-            setSelectedEmbeddingModelProvider(firstProvider);
-            setSelectedEmbeddingModel(firstModel);
-            localStorage.setItem('embeddingModelProvider', firstProvider);
-            localStorage.setItem('embeddingModel', firstModel);
-          }
-        } else if (
-          currentEmbeddingProvider &&
-          (!data.embeddingModelProviders ||
-            !data.embeddingModelProviders[currentEmbeddingProvider] ||
-            !Array.isArray(
-              data.embeddingModelProviders[currentEmbeddingProvider],
-            ) ||
-            data.embeddingModelProviders[currentEmbeddingProvider].length === 0)
-        ) {
-          const firstValidProvider = Object.entries(
-            data.embeddingModelProviders || {},
-          ).find(
-            ([_, models]) => Array.isArray(models) && models.length > 0,
-          )?.[0];
-
-          if (firstValidProvider) {
-            setSelectedEmbeddingModelProvider(firstValidProvider);
-            setSelectedEmbeddingModel(
-              data.embeddingModelProviders[firstValidProvider][0].name,
-            );
-            localStorage.setItem('embeddingModelProvider', firstValidProvider);
-            localStorage.setItem(
-              'embeddingModel',
-              data.embeddingModelProviders[firstValidProvider][0].name,
-            );
-          } else {
-            setSelectedEmbeddingModelProvider(null);
-            setSelectedEmbeddingModel(null);
-            localStorage.removeItem('embeddingModelProvider');
-            localStorage.removeItem('embeddingModel');
-          }
-        }
-
-        setConfig(data);
-      }
-
-      if (key === 'automaticImageSearch') {
-        localStorage.setItem('autoImageSearch', value.toString());
-      } else if (key === 'automaticVideoSearch') {
-        localStorage.setItem('autoVideoSearch', value.toString());
-      } else if (key === 'chatModelProvider') {
-        localStorage.setItem('chatModelProvider', value);
-      } else if (key === 'chatModel') {
-        localStorage.setItem('chatModel', value);
-      } else if (key === 'embeddingModelProvider') {
-        localStorage.setItem('embeddingModelProvider', value);
-      } else if (key === 'embeddingModel') {
-        localStorage.setItem('embeddingModel', value);
-      } else if (key === 'systemInstructions') {
-        localStorage.setItem('systemInstructions', value);
-      }
-    } catch (err) {
-      console.error('Failed to save:', err);
-      setConfig((prev) => ({ ...prev! }));
-    } finally {
-      setTimeout(() => {
-        setSavingStates((prev) => ({ ...prev, [key]: false }));
-      }, 500);
-    }
-  };
-
-  return (
-    <div className="max-w-3xl mx-auto">
-      <div className="flex flex-col pt-4">
-        <div className="flex items-center space-x-2">
-          <Link href="/" className="lg:hidden">
-            <ArrowLeft className="text-black/70 dark:text-white/70" />
-          </Link>
-          <div className="flex flex-row space-x-0.5 items-center">
-            <SettingsIcon size={23} />
-            <h1 className="text-3xl font-medium p-2">Settings</h1>
-          </div>
-        </div>
-        <hr className="border-t border-[#2B2C2C] my-4 w-full" />
-      </div>
-
-      {isLoading ? (
-        <div className="flex flex-row items-center justify-center min-h-[50vh]">
-          <svg
-            aria-hidden="true"
-            className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
-            viewBox="0 0 100 101"
-            fill="none"
-            xmlns="http://www.w3.org/2000/svg"
-          >
-            <path
-              d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
-              fill="currentColor"
-            />
-            <path
-              d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
-              fill="currentFill"
-            />
-          </svg>
-        </div>
-      ) : (
-        config && (
-          <div className="flex flex-col space-y-6 pb-28 lg:pb-8">
-            <SettingsSection title="Appearance">
-              <div className="flex flex-col space-y-1">
-                <p className="text-black/70 dark:text-white/70 text-sm">
-                  Theme
-                </p>
-                <ThemeSwitcher />
-              </div>
-            </SettingsSection>
-
-            <SettingsSection title="Automatic Search">
-              <div className="flex flex-col space-y-4">
-                <div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
-                  <div className="flex items-center space-x-3">
-                    <div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
-                      <ImagesIcon
-                        size={18}
-                        className="text-black/70 dark:text-white/70"
-                      />
-                    </div>
-                    <div>
-                      <p className="text-sm text-black/90 dark:text-white/90 font-medium">
-                        Automatic Image Search
-                      </p>
-                      <p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
-                        Automatically search for relevant images in chat
-                        responses
-                      </p>
-                    </div>
-                  </div>
-                  <Switch
-                    checked={automaticImageSearch}
-                    onChange={(checked) => {
-                      setAutomaticImageSearch(checked);
-                      saveConfig('automaticImageSearch', checked);
-                    }}
-                    className={cn(
-                      automaticImageSearch
-                        ? 'bg-[#24A0ED]'
-                        : 'bg-light-200 dark:bg-dark-200',
-                      'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
-                    )}
-                  >
-                    <span
-                      className={cn(
-                        automaticImageSearch
-                          ? 'translate-x-6'
-                          : 'translate-x-1',
-                        'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
-                      )}
-                    />
-                  </Switch>
-                </div>
-
-                <div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
-                  <div className="flex items-center space-x-3">
-                    <div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
-                      <VideoIcon
-                        size={18}
-                        className="text-black/70 dark:text-white/70"
-                      />
-                    </div>
-                    <div>
-                      <p className="text-sm text-black/90 dark:text-white/90 font-medium">
-                        Automatic Video Search
-                      </p>
-                      <p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
-                        Automatically search for relevant videos in chat
-                        responses
-                      </p>
-                    </div>
-                  </div>
-                  <Switch
-                    checked={automaticVideoSearch}
-                    onChange={(checked) => {
-                      setAutomaticVideoSearch(checked);
-                      saveConfig('automaticVideoSearch', checked);
-                    }}
-                    className={cn(
-                      automaticVideoSearch
-                        ? 'bg-[#24A0ED]'
-                        : 'bg-light-200 dark:bg-dark-200',
-                      'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
-                    )}
-                  >
-                    <span
-                      className={cn(
-                        automaticVideoSearch
-                          ? 'translate-x-6'
-                          : 'translate-x-1',
-                        'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
-                      )}
-                    />
-                  </Switch>
-                </div>
-              </div>
-            </SettingsSection>
-
-            <SettingsSection title="System Instructions">
-              <div className="flex flex-col space-y-4">
-                <Textarea
-                  value={systemInstructions}
-                  isSaving={savingStates['systemInstructions']}
-                  onChange={(e) => {
-                    setSystemInstructions(e.target.value);
-                  }}
-                  onSave={(value) => saveConfig('systemInstructions', value)}
-                />
-              </div>
-            </SettingsSection>
-
-            <SettingsSection title="Model Settings">
-              {config.chatModelProviders && (
-                <div className="flex flex-col space-y-4">
-                  <div className="flex flex-col space-y-1">
-                    <p className="text-black/70 dark:text-white/70 text-sm">
-                      Chat Model Provider
-                    </p>
-                    <Select
-                      value={selectedChatModelProvider ?? undefined}
-                      onChange={(e) => {
-                        const value = e.target.value;
-                        setSelectedChatModelProvider(value);
-                        saveConfig('chatModelProvider', value);
-                        const firstModel =
-                          config.chatModelProviders[value]?.[0]?.name;
-                        if (firstModel) {
-                          setSelectedChatModel(firstModel);
-                          saveConfig('chatModel', firstModel);
-                        }
-                      }}
-                      options={Object.keys(config.chatModelProviders).map(
-                        (provider) => ({
-                          value: provider,
-                          label:
-                            provider.charAt(0).toUpperCase() +
-                            provider.slice(1),
-                        }),
-                      )}
-                    />
-                  </div>
-
-                  {selectedChatModelProvider &&
-                    selectedChatModelProvider != 'custom_openai' && (
-                      <div className="flex flex-col space-y-1">
-                        <p className="text-black/70 dark:text-white/70 text-sm">
-                          Chat Model
-                        </p>
-                        <Select
-                          value={selectedChatModel ?? undefined}
-                          onChange={(e) => {
-                            const value = e.target.value;
-                            setSelectedChatModel(value);
-                            saveConfig('chatModel', value);
-                          }}
-                          options={(() => {
-                            const chatModelProvider =
-                              config.chatModelProviders[
-                                selectedChatModelProvider
-                              ];
-                            return chatModelProvider
-                              ? chatModelProvider.length > 0
-                                ? chatModelProvider.map((model) => ({
-                                    value: model.name,
-                                    label: model.displayName,
-                                  }))
-                                : [
-                                    {
-                                      value: '',
-                                      label: 'No models available',
-                                      disabled: true,
-                                    },
-                                  ]
-                              : [
-                                  {
-                                    value: '',
-                                    label:
-                                      'Invalid provider, please check backend logs',
-                                    disabled: true,
-                                  },
-                                ];
-                          })()}
-                        />
-                      </div>
-                    )}
-                </div>
-              )}
-
-              {selectedChatModelProvider &&
-                selectedChatModelProvider === 'custom_openai' && (
-                  <div className="flex flex-col space-y-4">
-                    <div className="flex flex-col space-y-1">
-                      <p className="text-black/70 dark:text-white/70 text-sm">
-                        Model Name
-                      </p>
-                      <Input
-                        type="text"
-                        placeholder="Model name"
-                        value={config.customOpenaiModelName}
-                        isSaving={savingStates['customOpenaiModelName']}
-                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
-                          setConfig((prev) => ({
-                            ...prev!,
-                            customOpenaiModelName: e.target.value,
-                          }));
-                        }}
-                        onSave={(value) =>
-                          saveConfig('customOpenaiModelName', value)
-                        }
-                      />
-                    </div>
-                    <div className="flex flex-col space-y-1">
-                      <p className="text-black/70 dark:text-white/70 text-sm">
-                        Custom OpenAI API Key
-                      </p>
-                      <Input
-                        type="text"
-                        placeholder="Custom OpenAI API Key"
-                        value={config.customOpenaiApiKey}
-                        isSaving={savingStates['customOpenaiApiKey']}
-                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
-                          setConfig((prev) => ({
-                            ...prev!,
-                            customOpenaiApiKey: e.target.value,
-                          }));
-                        }}
-                        onSave={(value) =>
-                          saveConfig('customOpenaiApiKey', value)
-                        }
-                      />
-                    </div>
-                    <div className="flex flex-col space-y-1">
-                      <p className="text-black/70 dark:text-white/70 text-sm">
-                        Custom OpenAI Base URL
-                      </p>
-                      <Input
-                        type="text"
-                        placeholder="Custom OpenAI Base URL"
-                        value={config.customOpenaiApiUrl}
-                        isSaving={savingStates['customOpenaiApiUrl']}
-                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
-                          setConfig((prev) => ({
-                            ...prev!,
-                            customOpenaiApiUrl: e.target.value,
-                          }));
-                        }}
-                        onSave={(value) =>
-                          saveConfig('customOpenaiApiUrl', value)
-                        }
-                      />
-                    </div>
-                  </div>
-                )}
-
-              {config.embeddingModelProviders && (
-                <div className="flex flex-col space-y-4 mt-4 pt-4 border-t border-light-200 dark:border-dark-200">
-                  <div className="flex flex-col space-y-1">
-                    <p className="text-black/70 dark:text-white/70 text-sm">
-                      Embedding Model Provider
-                    </p>
-                    <Select
-                      value={selectedEmbeddingModelProvider ?? undefined}
-                      onChange={(e) => {
-                        const value = e.target.value;
-                        setSelectedEmbeddingModelProvider(value);
-                        saveConfig('embeddingModelProvider', value);
-                        const firstModel =
-                          config.embeddingModelProviders[value]?.[0]?.name;
-                        if (firstModel) {
-                          setSelectedEmbeddingModel(firstModel);
-                          saveConfig('embeddingModel', firstModel);
-                        }
-                      }}
-                      options={Object.keys(config.embeddingModelProviders).map(
-                        (provider) => ({
-                          value: provider,
-                          label:
-                            provider.charAt(0).toUpperCase() +
-                            provider.slice(1),
-                        }),
-                      )}
-                    />
-                  </div>
-
-                  {selectedEmbeddingModelProvider && (
-                    <div className="flex flex-col space-y-1">
-                      <p className="text-black/70 dark:text-white/70 text-sm">
-                        Embedding Model
-                      </p>
-                      <Select
-                        value={selectedEmbeddingModel ?? undefined}
-                        onChange={(e) => {
-                          const value = e.target.value;
-                          setSelectedEmbeddingModel(value);
-                          saveConfig('embeddingModel', value);
-                        }}
-                        options={(() => {
-                          const embeddingModelProvider =
-                            config.embeddingModelProviders[
-                              selectedEmbeddingModelProvider
-                            ];
-                          return embeddingModelProvider
-                            ? embeddingModelProvider.length > 0
-                              ? embeddingModelProvider.map((model) => ({
-                                  value: model.name,
-                                  label: model.displayName,
-                                }))
-                              : [
-                                  {
-                                    value: '',
-                                    label: 'No models available',
-                                    disabled: true,
-                                  },
-                                ]
-                            : [
-                                {
-                                  value: '',
-                                  label:
-                                    'Invalid provider, please check backend logs',
-                                  disabled: true,
-                                },
-                              ];
-                        })()}
-                      />
-                    </div>
-                  )}
-                </div>
-              )}
-            </SettingsSection>
-
-            <SettingsSection title="API Keys">
-              <div className="flex flex-col space-y-4">
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    OpenAI API Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="OpenAI API Key"
-                    value={config.openaiApiKey}
-                    isSaving={savingStates['openaiApiKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        openaiApiKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('openaiApiKey', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Ollama API URL
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Ollama API URL"
-                    value={config.ollamaApiUrl}
-                    isSaving={savingStates['ollamaApiUrl']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        ollamaApiUrl: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('ollamaApiUrl', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    GROQ API Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="GROQ API Key"
-                    value={config.groqApiKey}
-                    isSaving={savingStates['groqApiKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        groqApiKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('groqApiKey', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Anthropic API Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Anthropic API key"
-                    value={config.anthropicApiKey}
-                    isSaving={savingStates['anthropicApiKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        anthropicApiKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('anthropicApiKey', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Gemini API Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Gemini API key"
-                    value={config.geminiApiKey}
-                    isSaving={savingStates['geminiApiKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        geminiApiKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('geminiApiKey', value)}
-                  />
-                </div>
-
-                <div className="flex flex-col space-y-1">
-                  <p className="text-black/70 dark:text-white/70 text-sm">
-                    Deepseek API Key
-                  </p>
-                  <Input
-                    type="text"
-                    placeholder="Deepseek API Key"
-                    value={config.deepseekApiKey}
-                    isSaving={savingStates['deepseekApiKey']}
-                    onChange={(e) => {
-                      setConfig((prev) => ({
-                        ...prev!,
-                        deepseekApiKey: e.target.value,
-                      }));
-                    }}
-                    onSave={(value) => saveConfig('deepseekApiKey', value)}
-                  />
-                </div>
-              </div>
-            </SettingsSection>
-          </div>
-        )
-      )}
-    </div>
-  );
-};
-
-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';

src/chains/videoSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../searxng';
+import { searchSearxng } from '../lib/searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const VideoSearchChainPrompt = `
@@ -36,13 +36,6 @@ type VideoSearchChainInput = {
   query: string;
 };
 
-interface VideoSearchResult {
-  img_src: string;
-  url: string;
-  title: string;
-  iframe_src: string;
-}
-
 const strParser = new StringOutputParser();
 
 const createVideoSearchChain = (llm: BaseChatModel) => {
@@ -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/ChatWindow.tsx

@@ -1,612 +0,0 @@
-'use client';
-
-import { useEffect, useRef, useState } from 'react';
-import { Document } from '@langchain/core/documents';
-import Navbar from './Navbar';
-import Chat from './Chat';
-import EmptyChat from './EmptyChat';
-import crypto from 'crypto';
-import { toast } from 'sonner';
-import { useSearchParams } from 'next/navigation';
-import { getSuggestions } from '@/lib/actions';
-import { Settings } from 'lucide-react';
-import Link from 'next/link';
-import NextError from 'next/error';
-
-export type Message = {
-  messageId: string;
-  chatId: string;
-  createdAt: Date;
-  content: string;
-  role: 'user' | 'assistant';
-  suggestions?: string[];
-  sources?: Document[];
-};
-
-export interface File {
-  fileName: string;
-  fileExtension: string;
-  fileId: string;
-}
-
-interface ChatModelProvider {
-  name: string;
-  provider: string;
-}
-
-interface EmbeddingModelProvider {
-  name: string;
-  provider: string;
-}
-
-const checkConfig = async (
-  setChatModelProvider: (provider: ChatModelProvider) => void,
-  setEmbeddingModelProvider: (provider: EmbeddingModelProvider) => void,
-  setIsConfigReady: (ready: boolean) => void,
-  setHasError: (hasError: boolean) => void,
-) => {
-  try {
-    let chatModel = localStorage.getItem('chatModel');
-    let chatModelProvider = localStorage.getItem('chatModelProvider');
-    let embeddingModel = localStorage.getItem('embeddingModel');
-    let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
-
-    const autoImageSearch = localStorage.getItem('autoImageSearch');
-    const autoVideoSearch = localStorage.getItem('autoVideoSearch');
-
-    if (!autoImageSearch) {
-      localStorage.setItem('autoImageSearch', 'true');
-    }
-
-    if (!autoVideoSearch) {
-      localStorage.setItem('autoVideoSearch', 'false');
-    }
-
-    const providers = await fetch(`/api/models`, {
-      headers: {
-        'Content-Type': 'application/json',
-      },
-    }).then(async (res) => {
-      if (!res.ok)
-        throw new Error(
-          `Failed to fetch models: ${res.status} ${res.statusText}`,
-        );
-      return res.json();
-    });
-
-    if (
-      !chatModel ||
-      !chatModelProvider ||
-      !embeddingModel ||
-      !embeddingModelProvider
-    ) {
-      if (!chatModel || !chatModelProvider) {
-        const chatModelProviders = providers.chatModelProviders;
-
-        chatModelProvider =
-          chatModelProvider || Object.keys(chatModelProviders)[0];
-
-        chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
-
-        if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
-          return toast.error('No chat models available');
-      }
-
-      if (!embeddingModel || !embeddingModelProvider) {
-        const embeddingModelProviders = providers.embeddingModelProviders;
-
-        if (
-          !embeddingModelProviders ||
-          Object.keys(embeddingModelProviders).length === 0
-        )
-          return toast.error('No embedding models available');
-
-        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-        embeddingModel = Object.keys(
-          embeddingModelProviders[embeddingModelProvider],
-        )[0];
-      }
-
-      localStorage.setItem('chatModel', chatModel!);
-      localStorage.setItem('chatModelProvider', chatModelProvider);
-      localStorage.setItem('embeddingModel', embeddingModel!);
-      localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
-    } else {
-      const chatModelProviders = providers.chatModelProviders;
-      const embeddingModelProviders = providers.embeddingModelProviders;
-
-      if (
-        Object.keys(chatModelProviders).length > 0 &&
-        !chatModelProviders[chatModelProvider]
-      ) {
-        const chatModelProvidersKeys = Object.keys(chatModelProviders);
-        chatModelProvider =
-          chatModelProvidersKeys.find(
-            (key) => Object.keys(chatModelProviders[key]).length > 0,
-          ) || chatModelProvidersKeys[0];
-
-        localStorage.setItem('chatModelProvider', chatModelProvider);
-      }
-
-      if (
-        chatModelProvider &&
-        !chatModelProviders[chatModelProvider][chatModel]
-      ) {
-        chatModel = Object.keys(
-          chatModelProviders[
-            Object.keys(chatModelProviders[chatModelProvider]).length > 0
-              ? chatModelProvider
-              : Object.keys(chatModelProviders)[0]
-          ],
-        )[0];
-        localStorage.setItem('chatModel', chatModel);
-      }
-
-      if (
-        Object.keys(embeddingModelProviders).length > 0 &&
-        !embeddingModelProviders[embeddingModelProvider]
-      ) {
-        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-        localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
-      }
-
-      if (
-        embeddingModelProvider &&
-        !embeddingModelProviders[embeddingModelProvider][embeddingModel]
-      ) {
-        embeddingModel = Object.keys(
-          embeddingModelProviders[embeddingModelProvider],
-        )[0];
-        localStorage.setItem('embeddingModel', embeddingModel);
-      }
-    }
-
-    setChatModelProvider({
-      name: chatModel!,
-      provider: chatModelProvider,
-    });
-
-    setEmbeddingModelProvider({
-      name: embeddingModel!,
-      provider: embeddingModelProvider,
-    });
-
-    setIsConfigReady(true);
-  } catch (err) {
-    console.error('An error occurred while checking the configuration:', err);
-    setIsConfigReady(false);
-    setHasError(true);
-  }
-};
-
-const loadMessages = async (
-  chatId: string,
-  setMessages: (messages: Message[]) => void,
-  setIsMessagesLoaded: (loaded: boolean) => void,
-  setChatHistory: (history: [string, string][]) => void,
-  setFocusMode: (mode: string) => void,
-  setNotFound: (notFound: boolean) => void,
-  setFiles: (files: File[]) => void,
-  setFileIds: (fileIds: string[]) => void,
-) => {
-  const res = await fetch(`/api/chats/${chatId}`, {
-    method: 'GET',
-    headers: {
-      'Content-Type': 'application/json',
-    },
-  });
-
-  if (res.status === 404) {
-    setNotFound(true);
-    setIsMessagesLoaded(true);
-    return;
-  }
-
-  const data = await res.json();
-
-  const messages = data.messages.map((msg: any) => {
-    return {
-      ...msg,
-      ...JSON.parse(msg.metadata),
-    };
-  }) as Message[];
-
-  setMessages(messages);
-
-  const history = messages.map((msg) => {
-    return [msg.role, msg.content];
-  }) as [string, string][];
-
-  console.debug(new Date(), 'app:messages_loaded');
-
-  document.title = messages[0].content;
-
-  const files = data.chat.files.map((file: any) => {
-    return {
-      fileName: file.name,
-      fileExtension: file.name.split('.').pop(),
-      fileId: file.fileId,
-    };
-  });
-
-  setFiles(files);
-  setFileIds(files.map((file: File) => file.fileId));
-
-  setChatHistory(history);
-  setFocusMode(data.chat.focusMode);
-  setIsMessagesLoaded(true);
-};
-
-const ChatWindow = ({ id }: { id?: string }) => {
-  const searchParams = useSearchParams();
-  const initialMessage = searchParams.get('q');
-
-  const [chatId, setChatId] = useState<string | undefined>(id);
-  const [newChatCreated, setNewChatCreated] = useState(false);
-
-  const [chatModelProvider, setChatModelProvider] = useState<ChatModelProvider>(
-    {
-      name: '',
-      provider: '',
-    },
-  );
-
-  const [embeddingModelProvider, setEmbeddingModelProvider] =
-    useState<EmbeddingModelProvider>({
-      name: '',
-      provider: '',
-    });
-
-  const [isConfigReady, setIsConfigReady] = useState(false);
-  const [hasError, setHasError] = useState(false);
-  const [isReady, setIsReady] = useState(false);
-
-  useEffect(() => {
-    checkConfig(
-      setChatModelProvider,
-      setEmbeddingModelProvider,
-      setIsConfigReady,
-      setHasError,
-    );
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, []);
-
-  const [loading, setLoading] = useState(false);
-  const [messageAppeared, setMessageAppeared] = useState(false);
-
-  const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
-  const [messages, setMessages] = useState<Message[]>([]);
-
-  const [files, setFiles] = useState<File[]>([]);
-  const [fileIds, setFileIds] = useState<string[]>([]);
-
-  const [focusMode, setFocusMode] = useState('webSearch');
-  const [optimizationMode, setOptimizationMode] = useState('speed');
-
-  const [isMessagesLoaded, setIsMessagesLoaded] = useState(false);
-
-  const [notFound, setNotFound] = useState(false);
-
-  useEffect(() => {
-    if (
-      chatId &&
-      !newChatCreated &&
-      !isMessagesLoaded &&
-      messages.length === 0
-    ) {
-      loadMessages(
-        chatId,
-        setMessages,
-        setIsMessagesLoaded,
-        setChatHistory,
-        setFocusMode,
-        setNotFound,
-        setFiles,
-        setFileIds,
-      );
-    } else if (!chatId) {
-      setNewChatCreated(true);
-      setIsMessagesLoaded(true);
-      setChatId(crypto.randomBytes(20).toString('hex'));
-    }
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, []);
-
-  const messagesRef = useRef<Message[]>([]);
-
-  useEffect(() => {
-    messagesRef.current = messages;
-  }, [messages]);
-
-  useEffect(() => {
-    if (isMessagesLoaded && isConfigReady) {
-      setIsReady(true);
-      console.debug(new Date(), 'app:ready');
-    } else {
-      setIsReady(false);
-    }
-  }, [isMessagesLoaded, isConfigReady]);
-
-  const sendMessage = async (message: string, messageId?: string) => {
-    if (loading) return;
-    if (!isConfigReady) {
-      toast.error('Cannot send message before the configuration is ready');
-      return;
-    }
-
-    setLoading(true);
-    setMessageAppeared(false);
-
-    let sources: Document[] | undefined = undefined;
-    let recievedMessage = '';
-    let added = false;
-
-    messageId = messageId ?? crypto.randomBytes(7).toString('hex');
-
-    setMessages((prevMessages) => [
-      ...prevMessages,
-      {
-        content: message,
-        messageId: messageId,
-        chatId: chatId!,
-        role: 'user',
-        createdAt: new Date(),
-      },
-    ]);
-
-    const messageHandler = async (data: any) => {
-      if (data.type === 'error') {
-        toast.error(data.data);
-        setLoading(false);
-        return;
-      }
-
-      if (data.type === 'sources') {
-        sources = data.data;
-        if (!added) {
-          setMessages((prevMessages) => [
-            ...prevMessages,
-            {
-              content: '',
-              messageId: data.messageId,
-              chatId: chatId!,
-              role: 'assistant',
-              sources: sources,
-              createdAt: new Date(),
-            },
-          ]);
-          added = true;
-        }
-        setMessageAppeared(true);
-      }
-
-      if (data.type === 'message') {
-        if (!added) {
-          setMessages((prevMessages) => [
-            ...prevMessages,
-            {
-              content: data.data,
-              messageId: data.messageId,
-              chatId: chatId!,
-              role: 'assistant',
-              sources: sources,
-              createdAt: new Date(),
-            },
-          ]);
-          added = true;
-        }
-
-        setMessages((prev) =>
-          prev.map((message) => {
-            if (message.messageId === data.messageId) {
-              return { ...message, content: message.content + data.data };
-            }
-
-            return message;
-          }),
-        );
-
-        recievedMessage += data.data;
-        setMessageAppeared(true);
-      }
-
-      if (data.type === 'messageEnd') {
-        setChatHistory((prevHistory) => [
-          ...prevHistory,
-          ['human', message],
-          ['assistant', recievedMessage],
-        ]);
-
-        setLoading(false);
-
-        const lastMsg = messagesRef.current[messagesRef.current.length - 1];
-
-        const autoImageSearch = localStorage.getItem('autoImageSearch');
-        const autoVideoSearch = localStorage.getItem('autoVideoSearch');
-
-        if (autoImageSearch === 'true') {
-          document
-            .getElementById(`search-images-${lastMsg.messageId}`)
-            ?.click();
-        }
-
-        if (autoVideoSearch === 'true') {
-          document
-            .getElementById(`search-videos-${lastMsg.messageId}`)
-            ?.click();
-        }
-
-        if (
-          lastMsg.role === 'assistant' &&
-          lastMsg.sources &&
-          lastMsg.sources.length > 0 &&
-          !lastMsg.suggestions
-        ) {
-          const suggestions = await getSuggestions(messagesRef.current);
-          setMessages((prev) =>
-            prev.map((msg) => {
-              if (msg.messageId === lastMsg.messageId) {
-                return { ...msg, suggestions: suggestions };
-              }
-              return msg;
-            }),
-          );
-        }
-      }
-    };
-
-    const res = await fetch('/api/chat', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      body: JSON.stringify({
-        content: message,
-        message: {
-          messageId: messageId,
-          chatId: chatId!,
-          content: message,
-        },
-        chatId: chatId!,
-        files: fileIds,
-        focusMode: focusMode,
-        optimizationMode: optimizationMode,
-        history: chatHistory,
-        chatModel: {
-          name: chatModelProvider.name,
-          provider: chatModelProvider.provider,
-        },
-        embeddingModel: {
-          name: embeddingModelProvider.name,
-          provider: embeddingModelProvider.provider,
-        },
-        systemInstructions: localStorage.getItem('systemInstructions'),
-      }),
-    });
-
-    if (!res.body) throw new Error('No response body');
-
-    const reader = res.body?.getReader();
-    const decoder = new TextDecoder('utf-8');
-
-    let partialChunk = '';
-
-    while (true) {
-      const { value, done } = await reader.read();
-      if (done) break;
-
-      partialChunk += decoder.decode(value, { stream: true });
-
-      try {
-        const messages = partialChunk.split('\n');
-        for (const msg of messages) {
-          if (!msg.trim()) continue;
-          const json = JSON.parse(msg);
-          messageHandler(json);
-        }
-        partialChunk = '';
-      } catch (error) {
-        console.warn('Incomplete JSON, waiting for next chunk...');
-      }
-    }
-  };
-
-  const rewrite = (messageId: string) => {
-    const index = messages.findIndex((msg) => msg.messageId === messageId);
-
-    if (index === -1) return;
-
-    const message = messages[index - 1];
-
-    setMessages((prev) => {
-      return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
-    });
-    setChatHistory((prev) => {
-      return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
-    });
-
-    sendMessage(message.content, message.messageId);
-  };
-
-  useEffect(() => {
-    if (isReady && initialMessage && isConfigReady) {
-      sendMessage(initialMessage);
-    }
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [isConfigReady, isReady, initialMessage]);
-
-  if (hasError) {
-    return (
-      <div className="relative">
-        <div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
-          <Link href="/settings">
-            <Settings className="cursor-pointer lg:hidden" />
-          </Link>
-        </div>
-        <div className="flex flex-col items-center justify-center min-h-screen">
-          <p className="dark:text-white/70 text-black/70 text-sm">
-            Failed to connect to the server. Please try again later.
-          </p>
-        </div>
-      </div>
-    );
-  }
-
-  return isReady ? (
-    notFound ? (
-      <NextError statusCode={404} />
-    ) : (
-      <div>
-        {messages.length > 0 ? (
-          <>
-            <Navbar chatId={chatId!} messages={messages} />
-            <Chat
-              loading={loading}
-              messages={messages}
-              sendMessage={sendMessage}
-              messageAppeared={messageAppeared}
-              rewrite={rewrite}
-              fileIds={fileIds}
-              setFileIds={setFileIds}
-              files={files}
-              setFiles={setFiles}
-            />
-          </>
-        ) : (
-          <EmptyChat
-            sendMessage={sendMessage}
-            focusMode={focusMode}
-            setFocusMode={setFocusMode}
-            optimizationMode={optimizationMode}
-            setOptimizationMode={setOptimizationMode}
-            fileIds={fileIds}
-            setFileIds={setFileIds}
-            files={files}
-            setFiles={setFiles}
-          />
-        )}
-      </div>
-    )
-  ) : (
-    <div className="flex flex-row items-center justify-center min-h-screen">
-      <svg
-        aria-hidden="true"
-        className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
-        viewBox="0 0 100 101"
-        fill="none"
-        xmlns="http://www.w3.org/2000/svg"
-      >
-        <path
-          d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
-          fill="currentColor"
-        />
-        <path
-          d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
-          fill="currentFill"
-        />
-      </svg>
-    </div>
-  );
-};
-
-export default ChatWindow;

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/components/ui/Select.tsx

@@ -1,28 +0,0 @@
-import { cn } from '@/lib/utils';
-import { SelectHTMLAttributes } from 'react';
-
-interface SelectProps extends SelectHTMLAttributes<HTMLSelectElement> {
-  options: { value: string; label: string; disabled?: boolean }[];
-}
-
-export const Select = ({ className, options, ...restProps }: SelectProps) => {
-  return (
-    <select
-      {...restProps}
-      className={cn(
-        'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
-        className,
-      )}
-    >
-      {options.map(({ label, value, disabled }) => {
-        return (
-          <option key={value} value={value} disabled={disabled}>
-            {label}
-          </option>
-        );
-      })}
-    </select>
-  );
-};
-
-export default Select;

src/config.ts

@@ -0,0 +1,79 @@
+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;
+  };
+  API_KEYS: {
+    OPENAI: string;
+    GROQ: string;
+    ANTHROPIC: string;
+    GEMINI: string;
+  };
+  API_ENDPOINTS: {
+    SEARXNG: string;
+    OLLAMA: string;
+  };
+}
+
+type RecursivePartial<T> = {
+  [P in keyof T]?: RecursivePartial<T[P]>;
+};
+
+const loadConfig = () =>
+  toml.parse(
+    fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
+  ) as any as Config;
+
+export const getPort = () => loadConfig().GENERAL.PORT;
+
+export const getSimilarityMeasure = () =>
+  loadConfig().GENERAL.SIMILARITY_MEASURE;
+
+export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
+
+export const getOpenaiApiKey = () => loadConfig().API_KEYS.OPENAI;
+
+export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
+
+export const getAnthropicApiKey = () => loadConfig().API_KEYS.ANTHROPIC;
+
+export const getGeminiApiKey = () => loadConfig().API_KEYS.GEMINI;
+
+export const getSearxngApiEndpoint = () =>
+  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
+
+export const getOllamaApiEndpoint = () => loadConfig().API_ENDPOINTS.OLLAMA;
+
+export const updateConfig = (config: RecursivePartial<Config>) => {
+  const currentConfig = loadConfig();
+
+  for (const key in currentConfig) {
+    if (!config[key]) config[key] = {};
+
+    if (typeof currentConfig[key] === 'object' && currentConfig[key] !== null) {
+      for (const nestedKey in currentConfig[key]) {
+        if (
+          !config[key][nestedKey] &&
+          currentConfig[key][nestedKey] &&
+          config[key][nestedKey] !== ''
+        ) {
+          config[key][nestedKey] = currentConfig[key][nestedKey];
+        }
+      }
+    } else if (currentConfig[key] && config[key] !== '') {
+      config[key] = currentConfig[key];
+    }
+  }
+
+  fs.writeFileSync(
+    path.join(__dirname, `../${configFileName}`),
+    toml.stringify(config),
+  );
+};

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

@@ -1,118 +0,0 @@
-import fs from 'fs';
-import path from 'path';
-import toml from '@iarna/toml';
-
-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;
-    };
-    CUSTOM_OPENAI: {
-      API_URL: string;
-      API_KEY: string;
-      MODEL_NAME: string;
-    };
-  };
-  API_ENDPOINTS: {
-    SEARXNG: string;
-  };
-}
-
-type RecursivePartial<T> = {
-  [P in keyof T]?: RecursivePartial<T[P]>;
-};
-
-const loadConfig = () =>
-  toml.parse(
-    fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
-  ) as any 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;
-
-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(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

@@ -1,38 +1,6 @@
 import { ChatAnthropic } from '@langchain/anthropic';
-import { ChatModel } from '.';
-import { getAnthropicApiKey } from '../config';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-const anthropicChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Claude 3.7 Sonnet',
-    key: 'claude-3-7-sonnet-20250219',
-  },
-  {
-    displayName: 'Claude 3.5 Haiku',
-    key: 'claude-3-5-haiku-20241022',
-  },
-  {
-    displayName: 'Claude 3.5 Sonnet v2',
-    key: 'claude-3-5-sonnet-20241022',
-  },
-  {
-    displayName: 'Claude 3.5 Sonnet',
-    key: 'claude-3-5-sonnet-20240620',
-  },
-  {
-    displayName: 'Claude 3 Opus',
-    key: 'claude-3-opus-20240229',
-  },
-  {
-    displayName: 'Claude 3 Sonnet',
-    key: 'claude-3-sonnet-20240229',
-  },
-  {
-    displayName: 'Claude 3 Haiku',
-    key: 'claude-3-haiku-20240307',
-  },
-];
+import { getAnthropicApiKey } from '../../config';
+import logger from '../../utils/logger';
 
 export const loadAnthropicChatModels = async () => {
   const anthropicApiKey = getAnthropicApiKey();
@@ -40,22 +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

@@ -1,44 +0,0 @@
-import { ChatOpenAI } from '@langchain/openai';
-import { getDeepseekApiKey } from '../config';
-import { ChatModel } from '.';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-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

@@ -2,48 +2,8 @@ import {
   ChatGoogleGenerativeAI,
   GoogleGenerativeAIEmbeddings,
 } from '@langchain/google-genai';
-import { getGeminiApiKey } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-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: 'Gemini Embedding',
-    key: 'gemini-embedding-exp',
-  },
-];
+import { getGeminiApiKey } from '../../config';
+import logger from '../../utils/logger';
 
 export const loadGeminiChatModels = async () => {
   const geminiApiKey = getGeminiApiKey();
@@ -51,47 +11,59 @@ 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,
+        }),
+      },
+    };
 
     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

@@ -1,78 +1,6 @@
 import { ChatOpenAI } from '@langchain/openai';
-import { getGroqApiKey } from '../config';
-import { ChatModel } from '.';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-
-const groqChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemma2 9B IT',
-    key: 'gemma2-9b-it',
-  },
-  {
-    displayName: 'Llama 3.3 70B Versatile',
-    key: 'llama-3.3-70b-versatile',
-  },
-  {
-    displayName: 'Llama 3.1 8B Instant',
-    key: 'llama-3.1-8b-instant',
-  },
-  {
-    displayName: 'Llama3 70B 8192',
-    key: 'llama3-70b-8192',
-  },
-  {
-    displayName: 'Llama3 8B 8192',
-    key: 'llama3-8b-8192',
-  },
-  {
-    displayName: 'Mixtral 8x7B 32768',
-    key: 'mixtral-8x7b-32768',
-  },
-  {
-    displayName: 'Qwen QWQ 32B (Preview)',
-    key: 'qwen-qwq-32b',
-  },
-  {
-    displayName: 'Mistral Saba 24B (Preview)',
-    key: 'mistral-saba-24b',
-  },
-  {
-    displayName: 'Qwen 2.5 Coder 32B (Preview)',
-    key: 'qwen-2.5-coder-32b',
-  },
-  {
-    displayName: 'Qwen 2.5 32B (Preview)',
-    key: 'qwen-2.5-32b',
-  },
-  {
-    displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
-    key: 'deepseek-r1-distill-qwen-32b',
-  },
-  {
-    displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
-    key: 'deepseek-r1-distill-llama-70b',
-  },
-  {
-    displayName: 'Llama 3.3 70B SpecDec (Preview)',
-    key: 'llama-3.3-70b-specdec',
-  },
-  {
-    displayName: 'Llama 3.2 1B Preview (Preview)',
-    key: 'llama-3.2-1b-preview',
-  },
-  {
-    displayName: 'Llama 3.2 3B Preview (Preview)',
-    key: 'llama-3.2-3b-preview',
-  },
-  {
-    displayName: 'Llama 3.2 11B Vision Preview (Preview)',
-    key: 'llama-3.2-11b-vision-preview',
-  },
-  {
-    displayName: 'Llama 3.2 90B Vision Preview (Preview)',
-    key: 'llama-3.2-90b-vision-preview',
-  },
-];
+import { getGroqApiKey } from '../../config';
+import logger from '../../utils/logger';
 
 export const loadGroqChatModels = async () => {
   const groqApiKey = getGroqApiKey();
@@ -80,25 +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

@@ -1,53 +1,27 @@
-import { Embeddings } from '@langchain/core/embeddings';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '../config';
-import { ChatOpenAI } from '@langchain/openai';
-import { loadOllamaChatModels, loadOllamaEmbeddingModels } from './ollama';
 import { loadGroqChatModels } from './groq';
+import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
+import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
 import { loadAnthropicChatModels } from './anthropic';
-import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini';
 import { loadTransformersEmbeddingsModels } from './transformers';
-import { loadDeepseekChatModels } from './deepseek';
+import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
 
-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,
 };
 
-export const embeddingModelProviders: Record<
-  string,
-  () => Promise<Record<string, EmbeddingModel>>
-> = {
-  openai: loadOpenAIEmbeddingModels,
-  ollama: loadOllamaEmbeddingModels,
-  gemini: loadGeminiEmbeddingModels,
-  transformers: loadTransformersEmbeddingsModels,
+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]();
@@ -56,33 +30,13 @@ export const getAvailableChatModelProviders = async () => {
     }
   }
 
-  const customOpenAiApiKey = getCustomOpenaiApiKey();
-  const customOpenAiApiUrl = getCustomOpenaiApiUrl();
-  const customOpenAiModelName = getCustomOpenaiModelName();
-
-  models['custom_openai'] = {
-    ...(customOpenAiApiKey && customOpenAiApiUrl && customOpenAiModelName
-      ? {
-          [customOpenAiModelName]: {
-            displayName: customOpenAiModelName,
-            model: new ChatOpenAI({
-              openAIApiKey: customOpenAiApiKey,
-              modelName: customOpenAiModelName,
-              temperature: 0.7,
-              configuration: {
-                baseURL: customOpenAiApiUrl,
-              },
-            }) as unknown as BaseChatModel,
-          },
-        }
-      : {}),
-  };
+  models['custom_openai'] = {};
 
   return models;
 };
 
 export const getAvailableEmbeddingModelProviders = async () => {
-  const models: Record<string, Record<string, EmbeddingModel>> = {};
+  const models = {};
 
   for (const provider in embeddingModelProviders) {
     const providerModels = await embeddingModelProviders[provider]();

src/lib/providers/ollama.ts

@@ -1,73 +1,74 @@
-import axios from 'axios';
-import { getKeepAlive, getOllamaApiEndpoint } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
 import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
+import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
+import logger from '../../utils/logger';
+import { ChatOllama } from '@langchain/community/chat_models/ollama';
+import axios from 'axios';
 
 export const loadOllamaChatModels = async () => {
-  const ollamaApiEndpoint = getOllamaApiEndpoint();
+  const ollamaEndpoint = getOllamaApiEndpoint();
+  const keepAlive = getKeepAlive();
 
-  if (!ollamaApiEndpoint) return {};
+  if (!ollamaEndpoint) return {};
 
   try {
-    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
+    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models } = res.data;
+    const { models: ollamaModels } = response.data;
 
-    const chatModels: Record<string, ChatModel> = {};
-
-    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

@@ -1,90 +1,89 @@
 import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { getOpenaiApiKey } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Embeddings } from '@langchain/core/embeddings';
-
-const openaiChatModels: Record<string, string>[] = [
-  {
-    displayName: 'GPT-3.5 Turbo',
-    key: 'gpt-3.5-turbo',
-  },
-  {
-    displayName: 'GPT-4',
-    key: 'gpt-4',
-  },
-  {
-    displayName: 'GPT-4 turbo',
-    key: 'gpt-4-turbo',
-  },
-  {
-    displayName: 'GPT-4 omni',
-    key: 'gpt-4o',
-  },
-  {
-    displayName: 'GPT-4 omni mini',
-    key: 'gpt-4o-mini',
-  },
-];
-
-const openaiEmbeddingModels: Record<string, string>[] = [
-  {
-    displayName: 'Text Embedding 3 Small',
-    key: 'text-embedding-3-small',
-  },
-  {
-    displayName: 'Text Embedding 3 Large',
-    key: 'text-embedding-3-large',
-  },
-];
+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

@@ -1,3 +1,4 @@
+import logger from '../../utils/logger';
 import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
 
 export const loadTransformersEmbeddingsModels = async () => {
@@ -25,7 +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

@@ -51,10 +51,6 @@ export const academicSearchResponsePrompt = `
     - 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.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/prompts/redditSearch.ts

@@ -51,10 +51,6 @@ export const redditSearchResponsePrompt = `
     - 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.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

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

@@ -51,10 +51,6 @@ export const wolframAlphaSearchResponsePrompt = `
     - 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.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/prompts/writingAssistant.ts

@@ -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

@@ -51,10 +51,6 @@ export const youtubeSearchResponsePrompt = `
     - 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.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

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 +1,85 @@
+import express from 'express';
+import {
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '../lib/providers';
+import {
+  getGroqApiKey,
+  getOllamaApiEndpoint,
+  getAnthropicApiKey,
+  getGeminiApiKey,
+  getOpenaiApiKey,
+  updateConfig,
+} 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();
+
+    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 = {
+    API_KEYS: {
+      OPENAI: config.openaiApiKey,
+      GROQ: config.groqApiKey,
+      ANTHROPIC: config.anthropicApiKey,
+      GEMINI: config.geminiApiKey,
+    },
+    API_ENDPOINTS: {
+      OLLAMA: config.ollamaApiUrl,
+    },
+  };
+
+  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 +1,88 @@
+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';
+
+const router = express.Router();
+
+interface ChatModel {
+  provider: string;
+  model: string;
+  customOpenAIBaseURL?: string;
+  customOpenAIKey?: 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') {
+      if (
+        !body.chatModel?.customOpenAIBaseURL ||
+        !body.chatModel?.customOpenAIKey
+      ) {
+        return res
+          .status(400)
+          .json({ message: 'Missing custom OpenAI base URL or key' });
+      }
+
+      llm = new ChatOpenAI({
+        modelName: body.chatModel.model,
+        openAIApiKey: body.chatModel.customOpenAIKey,
+        temperature: 0.7,
+        configuration: {
+          baseURL: body.chatModel.customOpenAIBaseURL,
+        },
+      }) 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 +1,160 @@
+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';
+
+const router = express.Router();
+
+interface chatModel {
+  provider: string;
+  model: string;
+  customOpenAIBaseURL?: string;
+  customOpenAIKey?: 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') {
+      if (
+        !body.chatModel?.customOpenAIBaseURL ||
+        !body.chatModel?.customOpenAIKey
+      ) {
+        return res
+          .status(400)
+          .json({ message: 'Missing custom OpenAI base URL or key' });
+      }
+
+      llm = new ChatOpenAI({
+        modelName: body.chatModel.model,
+        openAIApiKey: body.chatModel.customOpenAIKey,
+        temperature: 0.7,
+        configuration: {
+          baseURL: body.chatModel.customOpenAIBaseURL,
+        },
+      }) 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 +1,87 @@
+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';
+
+const router = express.Router();
+
+interface ChatModel {
+  provider: string;
+  model: string;
+  customOpenAIBaseURL?: string;
+  customOpenAIKey?: 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') {
+      if (
+        !body.chatModel?.customOpenAIBaseURL ||
+        !body.chatModel?.customOpenAIKey
+      ) {
+        return res
+          .status(400)
+          .json({ message: 'Missing custom OpenAI base URL or key' });
+      }
+
+      llm = new ChatOpenAI({
+        modelName: body.chatModel.model,
+        openAIApiKey: body.chatModel.customOpenAIKey,
+        temperature: 0.7,
+        configuration: {
+          baseURL: body.chatModel.customOpenAIBaseURL,
+        },
+      }) 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 +1,88 @@
+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';
+
+const router = express.Router();
+
+interface ChatModel {
+  provider: string;
+  model: string;
+  customOpenAIBaseURL?: string;
+  customOpenAIKey?: 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') {
+      if (
+        !body.chatModel?.customOpenAIBaseURL ||
+        !body.chatModel?.customOpenAIKey
+      ) {
+        return res
+          .status(400)
+          .json({ message: 'Missing custom OpenAI base URL or key' });
+      }
+
+      llm = new ChatOpenAI({
+        modelName: body.chatModel.model,
+        openAIApiKey: body.chatModel.customOpenAIKey,
+        temperature: 0.7,
+        configuration: {
+          baseURL: body.chatModel.customOpenAIBaseURL,
+        },
+      }) 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 });
 
@@ -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();
@@ -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 +1,111 @@
+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';
+
+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') {
+      llm = new ChatOpenAI({
+        modelName: chatModel,
+        openAIApiKey: searchParams.get('openAIApiKey'),
+        temperature: 0.7,
+        configuration: {
+          baseURL: searchParams.get('openAIBaseURL'),
+        },
+      }) 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

@@ -1,10 +1,10 @@
 'use client';
 
 import DeleteChat from '@/components/DeleteChat';
-import { cn, formatTimeDifference } from '@/lib/utils';
-import { BookOpenText, ClockIcon, Delete, ScanEye } from 'lucide-react';
+import {cn, formatTimeDifference} from '@/lib/utils';
+import {BookOpenText, ClockIcon, Delete, ScanEye} from 'lucide-react';
 import Link from 'next/link';
-import { useEffect, useState } from 'react';
+import {useEffect, useState} from 'react';
 
 export interface Chat {
   id: string;
@@ -20,8 +20,8 @@ const Page = () => {
   useEffect(() => {
     const fetchChats = async () => {
       setLoading(true);
-
-      const res = await fetch(`/api/chats`, {
+      let userId = localStorage.getItem("userId");
+      const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chats?userId=` + userId, {
         method: 'GET',
         headers: {
           'Content-Type': 'application/json',
@@ -60,19 +60,19 @@ const Page = () => {
     <div>
       <div className="flex flex-col pt-4">
         <div className="flex items-center">
-          <BookOpenText />
+          <BookOpenText/>
           <h1 className="text-3xl font-medium p-2">Library</h1>
         </div>
-        <hr className="border-t border-[#2B2C2C] my-4 w-full" />
+        <hr className="border-t border-[#2B2C2C] my-4 w-full"/>
       </div>
-      {chats.length === 0 && (
+      {chats && chats.length === 0 && (
         <div className="flex flex-row items-center justify-center min-h-screen">
           <p className="text-black/70 dark:text-white/70 text-sm">
             No chats found.
           </p>
         </div>
       )}
-      {chats.length > 0 && (
+      {chats && chats.length > 0 && (
         <div className="flex flex-col pb-20 lg:pb-2">
           {chats.map((chat, i) => (
             <div
@@ -92,7 +92,7 @@ const Page = () => {
               </Link>
               <div className="flex flex-row items-center justify-between w-full">
                 <div className="flex flex-row items-center space-x-1 lg:space-x-1.5 text-black/70 dark:text-white/70">
-                  <ClockIcon size={15} />
+                  <ClockIcon size={15}/>
                   <p className="text-xs">
                     {formatTimeDifference(new Date(), chat.createdAt)} Ago
                   </p>

ui/app/page.tsx

@@ -3,8 +3,8 @@ import { Metadata } from 'next';
 import { Suspense } from 'react';
 
 export const metadata: Metadata = {
-  title: 'Chat - Perplexica',
-  description: 'Chat with the internet, chat with Perplexica.',
+  title: 'MyCounsellor Ai Serach Eengine - Searching Thking with Gemini',
+  description: 'Chat with the internet, chat with MyCounsellor.',
 };
 
 const Home = () => {

ui/assets/DeepSeekIcon.tsx

@@ -0,0 +1,20 @@
+import React from 'react';
+
+const DeepSeekIcon = (props: React.JSX.IntrinsicAttributes & React.SVGProps<SVGSVGElement>) => (
+  <svg
+    viewBox="0 0 30 30"
+    fill="none"
+    xmlns="http://www.w3.org/2000/svg"
+    xmlnsXlink="http://www.w3.org/1999/xlink"
+    {...props}
+  >
+    <path
+      id="path"
+      d="M27.501 8.46875C27.249 8.3457 27.1406 8.58008 26.9932 8.69922C26.9434 8.73828 26.9004 8.78906 26.8584 8.83398C26.4902 9.22852 26.0605 9.48633 25.5 9.45508C24.6787 9.41016 23.9785 9.66797 23.3594 10.2969C23.2275 9.52148 22.79 9.05859 22.125 8.76172C21.7764 8.60742 21.4238 8.45312 21.1807 8.11719C21.0098 7.87891 20.9639 7.61328 20.8779 7.35156C20.8242 7.19336 20.7695 7.03125 20.5879 7.00391C20.3906 6.97266 20.3135 7.13867 20.2363 7.27734C19.9258 7.84375 19.8066 8.46875 19.8174 9.10156C19.8447 10.5234 20.4453 11.6562 21.6367 12.4629C21.7725 12.5547 21.8076 12.6484 21.7646 12.7832C21.6836 13.0605 21.5869 13.3301 21.501 13.6074C21.4473 13.7852 21.3662 13.8242 21.1768 13.7461C20.5225 13.4727 19.957 13.0684 19.458 12.5781C18.6104 11.7578 17.8438 10.8516 16.8877 10.1426C16.6631 9.97656 16.4395 9.82227 16.207 9.67578C15.2314 8.72656 16.335 7.94727 16.5898 7.85547C16.8574 7.75977 16.6826 7.42773 15.8193 7.43164C14.957 7.43555 14.167 7.72461 13.1611 8.10938C13.0137 8.16797 12.8594 8.21094 12.7002 8.24414C11.7871 8.07227 10.8389 8.0332 9.84766 8.14453C7.98242 8.35352 6.49219 9.23633 5.39648 10.7441C4.08105 12.5547 3.77148 14.6133 4.15039 16.7617C4.54883 19.0234 5.70215 20.8984 7.47559 22.3633C9.31348 23.8809 11.4307 24.625 13.8457 24.4824C15.3125 24.3984 16.9463 24.2012 18.7881 22.6406C19.2529 22.8711 19.7402 22.9629 20.5498 23.0332C21.1729 23.0918 21.7725 23.002 22.2373 22.9062C22.9648 22.752 22.9141 22.0781 22.6514 21.9531C20.5186 20.959 20.9863 21.3633 20.5605 21.0371C21.6445 19.752 23.2783 18.418 23.917 14.0977C23.9668 13.7539 23.9238 13.5391 23.917 13.2598C23.9131 13.0918 23.9512 13.0254 24.1445 13.0059C24.6787 12.9453 25.1973 12.7988 25.6738 12.5352C27.0557 11.7793 27.6123 10.5391 27.7441 9.05078C27.7637 8.82422 27.7402 8.58789 27.501 8.46875ZM15.46 21.8613C13.3926 20.2344 12.3906 19.6992 11.9766 19.7227C11.5898 19.7441 11.6592 20.1875 11.7441 20.4766C11.833 20.7617 11.9492 20.959 12.1123 21.209C12.2246 21.375 12.3018 21.623 12 21.8066C11.334 22.2207 10.1768 21.668 10.1221 21.6406C8.77539 20.8477 7.64941 19.7988 6.85547 18.3652C6.08984 16.9844 5.64453 15.5039 5.57129 13.9238C5.55176 13.541 5.66406 13.4062 6.04297 13.3379C6.54199 13.2461 7.05762 13.2266 7.55664 13.2988C9.66602 13.6074 11.4619 14.5527 12.9668 16.0469C13.8262 16.9004 14.4766 17.918 15.1465 18.9121C15.8584 19.9688 16.625 20.9746 17.6006 21.7988C17.9443 22.0879 18.2197 22.3086 18.4824 22.4707C17.6895 22.5586 16.3652 22.5781 15.46 21.8613ZM16.4502 15.4805C16.4502 15.3105 16.5859 15.1758 16.7568 15.1758C16.7949 15.1758 16.8301 15.1836 16.8613 15.1953C16.9033 15.2109 16.9424 15.2344 16.9727 15.2695C17.0273 15.3223 17.0586 15.4004 17.0586 15.4805C17.0586 15.6504 16.9229 15.7852 16.7529 15.7852C16.582 15.7852 16.4502 15.6504 16.4502 15.4805ZM19.5273 17.0625C19.3301 17.1426 19.1328 17.2129 18.9434 17.2207C18.6494 17.2344 18.3281 17.1152 18.1533 16.9688C17.8828 16.7422 17.6895 16.6152 17.6074 16.2168C17.5732 16.0469 17.5928 15.7852 17.623 15.6348C17.6934 15.3105 17.6152 15.1035 17.3877 14.9141C17.2012 14.7598 16.9658 14.7188 16.7061 14.7188C16.6094 14.7188 16.5205 14.6758 16.4541 14.6406C16.3457 14.5859 16.2568 14.4512 16.3418 14.2852C16.3691 14.2324 16.501 14.1016 16.5322 14.0781C16.8838 13.877 17.29 13.9434 17.666 14.0938C18.0146 14.2363 18.2773 14.498 18.6562 14.8672C19.0439 15.3145 19.1133 15.4395 19.334 15.7734C19.5078 16.0371 19.667 16.3066 19.7754 16.6152C19.8408 16.8066 19.7559 16.9648 19.5273 17.0625Z"
+      fillRule="nonzero"
+      fill="#4D6BFE"
+    />
+  </svg>
+);
+
+export default DeepSeekIcon;

ui/assets/deepseek.svg

@@ -0,0 +1 @@
+<svg viewBox="0 0 30 30" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><path id="path" d="M27.501 8.46875C27.249 8.3457 27.1406 8.58008 26.9932 8.69922C26.9434 8.73828 26.9004 8.78906 26.8584 8.83398C26.4902 9.22852 26.0605 9.48633 25.5 9.45508C24.6787 9.41016 23.9785 9.66797 23.3594 10.2969C23.2275 9.52148 22.79 9.05859 22.125 8.76172C21.7764 8.60742 21.4238 8.45312 21.1807 8.11719C21.0098 7.87891 20.9639 7.61328 20.8779 7.35156C20.8242 7.19336 20.7695 7.03125 20.5879 7.00391C20.3906 6.97266 20.3135 7.13867 20.2363 7.27734C19.9258 7.84375 19.8066 8.46875 19.8174 9.10156C19.8447 10.5234 20.4453 11.6562 21.6367 12.4629C21.7725 12.5547 21.8076 12.6484 21.7646 12.7832C21.6836 13.0605 21.5869 13.3301 21.501 13.6074C21.4473 13.7852 21.3662 13.8242 21.1768 13.7461C20.5225 13.4727 19.957 13.0684 19.458 12.5781C18.6104 11.7578 17.8438 10.8516 16.8877 10.1426C16.6631 9.97656 16.4395 9.82227 16.207 9.67578C15.2314 8.72656 16.335 7.94727 16.5898 7.85547C16.8574 7.75977 16.6826 7.42773 15.8193 7.43164C14.957 7.43555 14.167 7.72461 13.1611 8.10938C13.0137 8.16797 12.8594 8.21094 12.7002 8.24414C11.7871 8.07227 10.8389 8.0332 9.84766 8.14453C7.98242 8.35352 6.49219 9.23633 5.39648 10.7441C4.08105 12.5547 3.77148 14.6133 4.15039 16.7617C4.54883 19.0234 5.70215 20.8984 7.47559 22.3633C9.31348 23.8809 11.4307 24.625 13.8457 24.4824C15.3125 24.3984 16.9463 24.2012 18.7881 22.6406C19.2529 22.8711 19.7402 22.9629 20.5498 23.0332C21.1729 23.0918 21.7725 23.002 22.2373 22.9062C22.9648 22.752 22.9141 22.0781 22.6514 21.9531C20.5186 20.959 20.9863 21.3633 20.5605 21.0371C21.6445 19.752 23.2783 18.418 23.917 14.0977C23.9668 13.7539 23.9238 13.5391 23.917 13.2598C23.9131 13.0918 23.9512 13.0254 24.1445 13.0059C24.6787 12.9453 25.1973 12.7988 25.6738 12.5352C27.0557 11.7793 27.6123 10.5391 27.7441 9.05078C27.7637 8.82422 27.7402 8.58789 27.501 8.46875ZM15.46 21.8613C13.3926 20.2344 12.3906 19.6992 11.9766 19.7227C11.5898 19.7441 11.6592 20.1875 11.7441 20.4766C11.833 20.7617 11.9492 20.959 12.1123 21.209C12.2246 21.375 12.3018 21.623 12 21.8066C11.334 22.2207 10.1768 21.668 10.1221 21.6406C8.77539 20.8477 7.64941 19.7988 6.85547 18.3652C6.08984 16.9844 5.64453 15.5039 5.57129 13.9238C5.55176 13.541 5.66406 13.4062 6.04297 13.3379C6.54199 13.2461 7.05762 13.2266 7.55664 13.2988C9.66602 13.6074 11.4619 14.5527 12.9668 16.0469C13.8262 16.9004 14.4766 17.918 15.1465 18.9121C15.8584 19.9688 16.625 20.9746 17.6006 21.7988C17.9443 22.0879 18.2197 22.3086 18.4824 22.4707C17.6895 22.5586 16.3652 22.5781 15.46 21.8613ZM16.4502 15.4805C16.4502 15.3105 16.5859 15.1758 16.7568 15.1758C16.7949 15.1758 16.8301 15.1836 16.8613 15.1953C16.9033 15.2109 16.9424 15.2344 16.9727 15.2695C17.0273 15.3223 17.0586 15.4004 17.0586 15.4805C17.0586 15.6504 16.9229 15.7852 16.7529 15.7852C16.582 15.7852 16.4502 15.6504 16.4502 15.4805ZM19.5273 17.0625C19.3301 17.1426 19.1328 17.2129 18.9434 17.2207C18.6494 17.2344 18.3281 17.1152 18.1533 16.9688C17.8828 16.7422 17.6895 16.6152 17.6074 16.2168C17.5732 16.0469 17.5928 15.7852 17.623 15.6348C17.6934 15.3105 17.6152 15.1035 17.3877 14.9141C17.2012 14.7598 16.9658 14.7188 16.7061 14.7188C16.6094 14.7188 16.5205 14.6758 16.4541 14.6406C16.3457 14.5859 16.2568 14.4512 16.3418 14.2852C16.3691 14.2324 16.501 14.1016 16.5322 14.0781C16.8838 13.877 17.29 13.9434 17.666 14.0938C18.0146 14.2363 18.2773 14.498 18.6562 14.8672C19.0439 15.3145 19.1133 15.4395 19.334 15.7734C19.5078 16.0371 19.667 16.3066 19.7754 16.6152C19.8408 16.8066 19.7559 16.9648 19.5273 17.0625Z" fill-rule="nonzero" fill="#4D6BFE"></path></svg>

ui/components/Chat.tsx

@@ -16,6 +16,8 @@ const Chat = ({
   setFileIds,
   files,
   setFiles,
+  copilotEnabled,
+  setCopilotEnabled,
 }: {
   messages: Message[];
   sendMessage: (message: string) => void;
@@ -26,6 +28,8 @@ const Chat = ({
   setFileIds: (fileIds: string[]) => void;
   files: File[];
   setFiles: (files: File[]) => void;
+  copilotEnabled:boolean
+  setCopilotEnabled:(mode: boolean) => void;
 }) => {
   const [dividerWidth, setDividerWidth] = useState(0);
   const dividerRef = useRef<HTMLDivElement | null>(null);
@@ -48,17 +52,11 @@ const Chat = ({
   });
 
   useEffect(() => {
-    const scroll = () => {
-      messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
-    };
+    messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
 
     if (messages.length === 1) {
       document.title = `${messages[0].content.substring(0, 30)} - Perplexica`;
     }
-
-    if (messages[messages.length - 1]?.role == 'user') {
-      scroll();
-    }
   }, [messages]);
 
   return (
@@ -99,6 +97,8 @@ const Chat = ({
             setFileIds={setFileIds}
             files={files}
             setFiles={setFiles}
+            copilotEnabled={copilotEnabled}
+            setCopilotEnabled={setCopilotEnabled}
           />
         </div>
       )}

ui/components/ChatWindow.tsx

@@ -0,0 +1,762 @@
+'use client';
+
+import {useEffect, useRef, useState} from 'react';
+import {Document} from '@langchain/core/documents';
+import Navbar from './Navbar';
+import Chat from './Chat';
+import EmptyChat from './EmptyChat';
+import {toast} from 'sonner';
+import {useSearchParams} from 'next/navigation';
+import {getSuggestions} from '@/lib/actions';
+import {Settings} from 'lucide-react';
+import SettingsDialog from './SettingsDialog';
+import NextError from 'next/error';
+import {Mcid} from "@/lib/mcid";
+
+export type Message = {
+  messageId: string;
+  chatId: string;
+  createdAt: Date;
+  content: string;
+  role: 'user' | 'assistant';
+  suggestions?: string[];
+  sources?: Document[];
+};
+
+export interface File {
+  fileName: string;
+  fileExtension: string;
+  fileId: string;
+}
+
+const useSocket = (
+  url: string,
+  setIsWSReady: (ready: boolean) => void,
+  setError: (error: boolean) => void,
+) => {
+  const wsRef = useRef<WebSocket | null>(null);
+  const reconnectTimeoutRef = useRef<NodeJS.Timeout>();
+  const retryCountRef = useRef(0);
+  const isCleaningUpRef = useRef(false);
+  const MAX_RETRIES = 3;
+  const INITIAL_BACKOFF = 1000; // 1 second
+
+  const getBackoffDelay = (retryCount: number) => {
+    return Math.min(INITIAL_BACKOFF * Math.pow(2, retryCount), 10000); // Cap at 10 seconds
+  };
+
+  useEffect(() => {
+    const connectWs = async () => {
+      if (wsRef.current?.readyState === WebSocket.OPEN) {
+        wsRef.current.close();
+      }
+
+      try {
+        let chatModel = localStorage.getItem('chatModel');
+        let chatModelProvider = localStorage.getItem('chatModelProvider');
+        let embeddingModel = localStorage.getItem('embeddingModel');
+        let embeddingModelProvider = localStorage.getItem(
+          'embeddingModelProvider',
+        );
+        let openAIBaseURL =
+          chatModelProvider === 'custom_openai'
+            ? localStorage.getItem('openAIBaseURL')
+            : null;
+        let openAIPIKey =
+          chatModelProvider === 'custom_openai'
+            ? localStorage.getItem('openAIApiKey')
+            : null;
+
+        const providers = await fetch(
+          `${process.env.NEXT_PUBLIC_API_URL}/models`,
+          {
+            headers: {
+              'Content-Type': 'application/json',
+            },
+          },
+        ).then(async (res) => {
+          if (!res.ok)
+            throw new Error(
+              `Failed to fetch models: ${res.status} ${res.statusText}`,
+            );
+          return res.json();
+        });
+
+        if (
+          !chatModel ||
+          !chatModelProvider ||
+          !embeddingModel ||
+          !embeddingModelProvider
+        ) {
+          if (!chatModel || !chatModelProvider) {
+            const chatModelProviders = providers.chatModelProviders;
+
+            chatModelProvider =
+              chatModelProvider || Object.keys(chatModelProviders)[0];
+
+            if (chatModelProvider === 'custom_openai') {
+              toast.error(
+                'Seems like you are using the custom OpenAI provider, please open the settings and enter a model name to use.',
+              );
+              setError(true);
+              return;
+            } else {
+              chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+
+              if (
+                !chatModelProviders ||
+                Object.keys(chatModelProviders).length === 0
+              )
+                return toast.error('No chat models available');
+            }
+          }
+
+          if (!embeddingModel || !embeddingModelProvider) {
+            const embeddingModelProviders = providers.embeddingModelProviders;
+
+            if (
+              !embeddingModelProviders ||
+              Object.keys(embeddingModelProviders).length === 0
+            )
+              return toast.error('No embedding models available');
+
+            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+            embeddingModel = Object.keys(
+              embeddingModelProviders[embeddingModelProvider],
+            )[0];
+          }
+
+          localStorage.setItem('chatModel', chatModel!);
+          localStorage.setItem('chatModelProvider', chatModelProvider);
+          localStorage.setItem('embeddingModel', embeddingModel!);
+          localStorage.setItem(
+            'embeddingModelProvider',
+            embeddingModelProvider,
+          );
+        } else {
+          const chatModelProviders = providers.chatModelProviders;
+          const embeddingModelProviders = providers.embeddingModelProviders;
+
+          if (
+            Object.keys(chatModelProviders).length > 0 &&
+            (((!openAIBaseURL || !openAIPIKey) &&
+                chatModelProvider === 'custom_openai') ||
+              !chatModelProviders[chatModelProvider])
+          ) {
+            const chatModelProvidersKeys = Object.keys(chatModelProviders);
+            chatModelProvider =
+              chatModelProvidersKeys.find(
+                (key) => Object.keys(chatModelProviders[key]).length > 0,
+              ) || chatModelProvidersKeys[0];
+
+            if (
+              chatModelProvider === 'custom_openai' &&
+              (!openAIBaseURL || !openAIPIKey)
+            ) {
+              toast.error(
+                'Seems like you are using the custom OpenAI provider, please open the settings and configure the API key and base URL',
+              );
+              setError(true);
+              return;
+            }
+
+            localStorage.setItem('chatModelProvider', chatModelProvider);
+          }
+
+          if (
+            chatModelProvider &&
+            (!openAIBaseURL || !openAIPIKey) &&
+            !chatModelProviders[chatModelProvider][chatModel]
+          ) {
+            chatModel = Object.keys(
+              chatModelProviders[
+                Object.keys(chatModelProviders[chatModelProvider]).length > 0
+                  ? chatModelProvider
+                  : Object.keys(chatModelProviders)[0]
+                ],
+            )[0];
+            localStorage.setItem('chatModel', chatModel);
+          }
+
+          if (
+            Object.keys(embeddingModelProviders).length > 0 &&
+            !embeddingModelProviders[embeddingModelProvider]
+          ) {
+            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+            localStorage.setItem(
+              'embeddingModelProvider',
+              embeddingModelProvider,
+            );
+          }
+
+          if (
+            embeddingModelProvider &&
+            !embeddingModelProviders[embeddingModelProvider][embeddingModel]
+          ) {
+            embeddingModel = Object.keys(
+              embeddingModelProviders[embeddingModelProvider],
+            )[0];
+            localStorage.setItem('embeddingModel', embeddingModel);
+          }
+        }
+
+        const wsURL = new URL(url);
+        const searchParams = new URLSearchParams({});
+
+        searchParams.append('chatModel', chatModel!);
+        searchParams.append('chatModelProvider', chatModelProvider);
+
+        if (chatModelProvider === 'custom_openai') {
+          searchParams.append(
+            'openAIApiKey',
+            localStorage.getItem('openAIApiKey')!,
+          );
+          searchParams.append(
+            'openAIBaseURL',
+            localStorage.getItem('openAIBaseURL')!,
+          );
+        }
+
+        searchParams.append('embeddingModel', embeddingModel!);
+        searchParams.append('embeddingModelProvider', embeddingModelProvider);
+
+        wsURL.search = searchParams.toString();
+
+        const ws = new WebSocket(wsURL.toString());
+        wsRef.current = ws;
+
+        const timeoutId = setTimeout(() => {
+          if (ws.readyState !== 1) {
+            toast.error(
+              'Failed to connect to the server. Please try again later.',
+            );
+          }
+        }, 10000);
+
+        ws.addEventListener('message', (e) => {
+          const data = JSON.parse(e.data);
+          if (data.type === 'signal' && data.data === 'open') {
+            const interval = setInterval(() => {
+              if (ws.readyState === 1) {
+                setIsWSReady(true);
+                setError(false);
+                if (retryCountRef.current > 0) {
+                  toast.success('Connection restored.');
+                }
+                retryCountRef.current = 0;
+                clearInterval(interval);
+              }
+            }, 5);
+            clearTimeout(timeoutId);
+            console.debug(new Date(), 'ws:connected');
+          }
+          if (data.type === 'error') {
+            toast.error(data.data);
+          }
+        });
+
+        ws.onerror = () => {
+          clearTimeout(timeoutId);
+          setIsWSReady(false);
+          toast.error('WebSocket connection error.');
+        };
+
+        ws.onclose = () => {
+          clearTimeout(timeoutId);
+          setIsWSReady(false);
+          console.debug(new Date(), 'ws:disconnected');
+          if (!isCleaningUpRef.current) {
+            toast.error('Connection lost. Attempting to reconnect...');
+            attemptReconnect();
+          }
+        };
+      } catch (error) {
+        console.debug(new Date(), 'ws:error', error);
+        setIsWSReady(false);
+        attemptReconnect();
+      }
+    };
+
+    const attemptReconnect = () => {
+      retryCountRef.current += 1;
+
+      if (retryCountRef.current > MAX_RETRIES) {
+        console.debug(new Date(), 'ws:max_retries');
+        setError(true);
+        toast.error(
+          'Unable to connect to server after multiple attempts. Please refresh the page to try again.',
+        );
+        return;
+      }
+
+      const backoffDelay = getBackoffDelay(retryCountRef.current);
+      console.debug(
+        new Date(),
+        `ws:retry attempt=${retryCountRef.current}/${MAX_RETRIES} delay=${backoffDelay}ms`,
+      );
+
+      if (reconnectTimeoutRef.current) {
+        clearTimeout(reconnectTimeoutRef.current);
+      }
+
+      reconnectTimeoutRef.current = setTimeout(() => {
+        connectWs();
+      }, backoffDelay);
+    };
+
+    connectWs();
+
+    return () => {
+      if (reconnectTimeoutRef.current) {
+        clearTimeout(reconnectTimeoutRef.current);
+      }
+      if (wsRef.current?.readyState === WebSocket.OPEN) {
+        wsRef.current.close();
+        isCleaningUpRef.current = true;
+        console.debug(new Date(), 'ws:cleanup');
+      }
+    };
+  }, [url, setIsWSReady, setError]);
+
+  return wsRef.current;
+};
+
+const loadMessages = async (
+  chatId: string,
+  setMessages: (messages: Message[]) => void,
+  setIsMessagesLoaded: (loaded: boolean) => void,
+  setChatHistory: (history: [string, string][]) => void,
+  setFocusMode: (mode: string) => void,
+  setNotFound: (notFound: boolean) => void,
+  setFiles: (files: File[]) => void,
+  setFileIds: (fileIds: string[]) => void,
+) => {
+  const res = await fetch(
+    `${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
+    {
+      method: 'GET',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    },
+  );
+
+  if (res.status === 404) {
+    setNotFound(true);
+    setIsMessagesLoaded(true);
+    return;
+  }
+
+  const data = await res.json();
+
+  const messages = data.messages.map((msg: any) => {
+    return {
+      ...msg,
+      // ...JSON.parse(msg.metadata),
+    };
+  }) as Message[];
+
+  setMessages(messages);
+
+  const history = messages.map((msg) => {
+    return [msg.role, msg.content];
+  }) as [string, string][];
+
+  console.debug(new Date(), 'app:messages_loaded');
+
+  document.title = messages[0].content;
+
+  const files = data.chat.files && data.chat.files.map((file: any) => {
+    return {
+      fileName: file.name,
+      fileExtension: file.name.split('.').pop(),
+      fileId: file.fileId,
+    };
+  });
+
+  setFiles(files);
+  setFileIds(files && files.map((file: File) => file.fileId));
+
+  setChatHistory(history);
+  setFocusMode(data.chat.focusMode);
+  setIsMessagesLoaded(true);
+};
+
+const ChatWindow = ({id}: { id?: string }) => {
+  const searchParams = useSearchParams();
+  const initialMessage = searchParams.get('q');
+  const [userId, setUserId] = useState<string | undefined>();
+  const [chatId, setChatId] = useState<string | undefined>(id);
+  const [newChatCreated, setNewChatCreated] = useState(false);
+
+  const [hasError, setHasError] = useState(false);
+  const [isReady, setIsReady] = useState(false);
+
+  const [isWSReady, setIsWSReady] = useState(false);
+  const ws = useSocket(
+    process.env.NEXT_PUBLIC_WS_URL!,
+    setIsWSReady,
+    setHasError,
+  );
+
+  const [loading, setLoading] = useState(false);
+  const [messageAppeared, setMessageAppeared] = useState(false);
+
+  const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
+  const [messages, setMessages] = useState<Message[]>([]);
+
+  const [files, setFiles] = useState<File[]>([]);
+  const [fileIds, setFileIds] = useState<string[]>([]);
+
+  const [focusMode, setFocusMode] = useState('webSearch');
+  const [optimizationMode, setOptimizationMode] = useState('speed');
+  const [copilotEnabled, setCopilotEnabled] = useState(true);
+
+  const [isMessagesLoaded, setIsMessagesLoaded] = useState(false);
+
+  const [notFound, setNotFound] = useState(false);
+
+  const [isSettingsOpen, setIsSettingsOpen] = useState(false);
+
+  useEffect(() => {
+    const initializeUserId = () => {
+      try {
+        // 从 localStorage 读取现有用户 ID
+        const storedUserId = localStorage.getItem('userId');
+
+        if (storedUserId) {
+          setUserId(storedUserId);
+          console.debug('Using existing user ID:', storedUserId);
+        } else {
+          const newUserId = new Mcid().generate().toString();
+
+          localStorage.setItem('userId', newUserId);
+          setUserId(newUserId);
+          console.debug('Generated new user ID:', newUserId);
+        }
+      } catch (error) {
+        console.error('Error initializing user ID:', error);
+        const fallbackId = "1234567890";
+        localStorage.setItem('userId', fallbackId);
+        setUserId(fallbackId);
+      }
+    };
+
+    initializeUserId();
+  }, []);
+
+  useEffect(() => {
+    if (
+      chatId &&
+      !newChatCreated &&
+      !isMessagesLoaded &&
+      messages.length === 0
+    ) {
+      loadMessages(
+        chatId,
+        setMessages,
+        setIsMessagesLoaded,
+        setChatHistory,
+        setFocusMode,
+        setNotFound,
+        setFiles,
+        setFileIds,
+      );
+    } else if (!chatId) {
+      setNewChatCreated(true);
+      setIsMessagesLoaded(true);
+      setChatId(new Mcid().generate().toString());
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
+  useEffect(() => {
+    return () => {
+      if (ws?.readyState === 1) {
+        ws.close();
+        console.debug(new Date(), 'ws:cleanup');
+      }
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
+  useEffect(() => {
+    const savedFocusMode = localStorage.getItem('focusMode');
+    if (savedFocusMode) {
+      setFocusMode(savedFocusMode);
+    }
+  }, [setFocusMode]);
+
+  const handleFocusModeChange = (mode: string) => {
+    localStorage.setItem('focusMode', mode);
+    setFocusMode(mode);
+  };
+
+  useEffect(() => {
+    const mode = localStorage.getItem('optimizationMode');
+    if (mode) {
+      setOptimizationMode(mode);
+    }
+  }, [setOptimizationMode]);
+
+  const handleOptimizationModeChange = (mode: string) => {
+    localStorage.setItem('optimizationMode', mode);
+    setOptimizationMode(mode);
+  };
+
+  const messagesRef = useRef<Message[]>([]);
+
+  useEffect(() => {
+    messagesRef.current = messages;
+  }, [messages]);
+
+  useEffect(() => {
+    if (isMessagesLoaded && isWSReady) {
+      setIsReady(true);
+      console.debug(new Date(), 'app:ready');
+    } else {
+      setIsReady(false);
+    }
+  }, [isMessagesLoaded, isWSReady, userId]);
+
+  const sendMessage = async (message: string, messageId?: string) => {
+    if (loading) return;
+    if (!ws || ws.readyState !== WebSocket.OPEN) {
+      toast.error('Cannot send message while disconnected');
+      return;
+    }
+
+    setLoading(true);
+    setMessageAppeared(false);
+
+    let sources: Document[] | undefined = undefined;
+    let recievedMessage = '';
+    let added = false;
+
+    messageId = messageId ?? new Mcid().generate().toString();
+
+    ws.send(
+      JSON.stringify({
+        type: 'message',
+        userId: userId,
+        message: {
+          messageId: messageId,
+          chatId: chatId!,
+          content: message,
+        },
+        files: fileIds,
+        focusMode: focusMode,
+        copilotEnabled: copilotEnabled,
+        optimizationMode: optimizationMode,
+        history: [],
+      }),
+    );
+
+    setMessages((prevMessages) => [
+      ...prevMessages,
+      {
+        content: message,
+        messageId: messageId,
+        chatId: chatId!,
+        role: 'user',
+        createdAt: new Date(),
+      },
+    ]);
+
+    const messageHandler = async (e: MessageEvent) => {
+      const data = JSON.parse(e.data);
+
+      if (data.type === 'error') {
+        toast.error(data.data);
+        setLoading(false);
+        return;
+      }
+
+      if (data.type === 'sources') {
+        sources = data.data;
+        if (!added) {
+          setMessages((prevMessages) => [
+            ...prevMessages,
+            {
+              content: '',
+              messageId: data.messageId,
+              chatId: chatId!,
+              role: 'assistant',
+              sources: sources,
+              createdAt: new Date(),
+            },
+          ]);
+          added = true;
+        }
+        setMessageAppeared(true);
+      }
+
+      if (data.type === 'message') {
+        if (!added) {
+          setMessages((prevMessages) => [
+            ...prevMessages,
+            {
+              content: data.data,
+              messageId: data.messageId,
+              chatId: chatId!,
+              role: 'assistant',
+              sources: sources,
+              createdAt: new Date(),
+            },
+          ]);
+          added = true;
+        }
+
+        setMessages((prev) =>
+          prev.map((message) => {
+            if (message.messageId === data.messageId) {
+              return {...message, content: message.content + data.data};
+            }
+
+            return message;
+          }),
+        );
+
+        recievedMessage += data.data;
+        setMessageAppeared(true);
+      }
+
+      if (data.type === 'messageEnd') {
+        setChatHistory((prevHistory) => [
+          ...prevHistory,
+          ['human', message],
+          ['assistant', recievedMessage],
+        ]);
+
+        ws?.removeEventListener('message', messageHandler);
+        setLoading(false);
+
+        const lastMsg = messagesRef.current[messagesRef.current.length - 1];
+
+        if (
+          lastMsg.role === 'assistant' &&
+          lastMsg.sources &&
+          lastMsg.sources.length > 0 &&
+          !lastMsg.suggestions
+        ) {
+          const suggestions = await getSuggestions(messagesRef.current);
+          setMessages((prev) =>
+            prev.map((msg) => {
+              if (msg.messageId === lastMsg.messageId) {
+                return {...msg, suggestions: suggestions};
+              }
+              return msg;
+            }),
+          );
+        }
+      }
+    };
+
+    ws?.addEventListener('message', messageHandler);
+  };
+
+  const rewrite = (messageId: string) => {
+    const index = messages.findIndex((msg) => msg.messageId === messageId);
+
+    if (index === -1) return;
+
+    const message = messages[index - 1];
+
+    setMessages((prev) => {
+      return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
+    });
+    setChatHistory((prev) => {
+      return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
+    });
+
+    sendMessage(message.content, message.messageId);
+  };
+
+  useEffect(() => {
+    if (isReady && initialMessage && ws?.readyState === 1) {
+      sendMessage(initialMessage);
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [ws?.readyState, isReady, initialMessage, isWSReady]);
+
+  if (hasError) {
+    return (
+      <div className="relative">
+        <div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
+          <Settings
+            className="cursor-pointer lg:hidden"
+            onClick={() => setIsSettingsOpen(true)}
+          />
+        </div>
+        <div className="flex flex-col items-center justify-center min-h-screen">
+          <p className="dark:text-white/70 text-black/70 text-sm">
+            Failed to connect to the server. Please try again later.
+          </p>
+        </div>
+        <SettingsDialog isOpen={isSettingsOpen} setIsOpen={setIsSettingsOpen}/>
+      </div>
+    );
+  }
+
+  return isReady ? (
+    notFound ? (
+      <NextError statusCode={404}/>
+    ) : (
+      <div>
+        {messages.length > 0 ? (
+          <>
+            <Navbar chatId={chatId!} messages={messages}/>
+            <Chat
+              loading={loading}
+              messages={messages}
+              sendMessage={sendMessage}
+              messageAppeared={messageAppeared}
+              rewrite={rewrite}
+              fileIds={fileIds}
+              setFileIds={setFileIds}
+              files={files}
+              setFiles={setFiles}
+              copilotEnabled={copilotEnabled}
+              setCopilotEnabled={setCopilotEnabled}
+            />
+          </>
+        ) : (
+          <EmptyChat
+            sendMessage={sendMessage}
+            focusMode={focusMode}
+            copilotEnabled={copilotEnabled}
+            setCopilotEnabled={setCopilotEnabled}
+            setFocusMode={handleFocusModeChange}
+            optimizationMode={optimizationMode}
+            setOptimizationMode={handleOptimizationModeChange}
+            fileIds={fileIds}
+            setFileIds={setFileIds}
+            files={files}
+            setFiles={setFiles}
+          />
+        )}
+      </div>
+    )
+  ) : (
+    <div className="flex flex-row items-center justify-center min-h-screen">
+      <svg
+        aria-hidden="true"
+        className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
+        viewBox="0 0 100 101"
+        fill="none"
+        xmlns="http://www.w3.org/2000/svg"
+      >
+        <path
+          d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
+          fill="currentColor"
+        />
+        <path
+          d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
+          fill="currentFill"
+        />
+      </svg>
+    </div>
+  );
+};
+
+export default ChatWindow;

ui/components/DeleteChat.tsx

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

ui/components/EmptyChat.tsx

@@ -1,13 +1,15 @@
 import { Settings } from 'lucide-react';
 import EmptyChatMessageInput from './EmptyChatMessageInput';
+import SettingsDialog from './SettingsDialog';
 import { useState } from 'react';
 import { File } from './ChatWindow';
-import Link from 'next/link';
 
 const EmptyChat = ({
   sendMessage,
   focusMode,
   setFocusMode,
+  copilotEnabled,
+  setCopilotEnabled,
   optimizationMode,
   setOptimizationMode,
   fileIds,
@@ -18,6 +20,8 @@ const EmptyChat = ({
   sendMessage: (message: string) => void;
   focusMode: string;
   setFocusMode: (mode: string) => void;
+  copilotEnabled: boolean;
+  setCopilotEnabled: (enabled: boolean) => void;
   optimizationMode: string;
   setOptimizationMode: (mode: string) => void;
   fileIds: string[];
@@ -29,19 +33,23 @@ const EmptyChat = ({
 
   return (
     <div className="relative">
+      <SettingsDialog isOpen={isSettingsOpen} setIsOpen={setIsSettingsOpen} />
       <div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
-        <Link href="/settings">
-          <Settings className="cursor-pointer lg:hidden" />
-        </Link>
+        <Settings
+          className="cursor-pointer lg:hidden"
+          onClick={() => setIsSettingsOpen(true)}
+        />
       </div>
-      <div className="flex flex-col items-center justify-center min-h-screen max-w-screen-sm mx-auto p-2 space-y-8">
-        <h2 className="text-black/70 dark:text-white/70 text-3xl font-medium -mt-8">
+      <div className="flex flex-col items-center max-w-screen-sm mx-auto p-2 pt-16 mt-16 space-y-8">
+        <h2 className="text-black/70 dark:text-white/70 text-5xl font-medium -mt-8">
           Research begins here.
         </h2>
         <EmptyChatMessageInput
           sendMessage={sendMessage}
           focusMode={focusMode}
           setFocusMode={setFocusMode}
+          copilotEnabled={copilotEnabled}
+          setCopilotEnabled={setCopilotEnabled}
           optimizationMode={optimizationMode}
           setOptimizationMode={setOptimizationMode}
           fileIds={fileIds}