docs: add Jan installation using Docker

README.md

@@ -167,6 +167,7 @@ To reset your installation:
    - Clear Application cache in `~/Library/Caches/jan`
 
 ## Requirements for running Jan
+
 - MacOS: 13 or higher
 - Windows:
   - Windows 10 or higher
@@ -194,17 +195,17 @@ Contributions are welcome! Please read the [CONTRIBUTING.md](CONTRIBUTING.md) fi
 
 1. **Clone the repository and prepare:**
 
-    ```bash
-    git clone https://github.com/janhq/jan
-    cd jan
-    git checkout -b DESIRED_BRANCH
-    ```
+   ```bash
+   git clone https://github.com/janhq/jan
+   cd jan
+   git checkout -b DESIRED_BRANCH
+   ```
 
 2. **Run development and use Jan Desktop**
 
-    ```bash
-    make dev
-    ```
+   ```bash
+   make dev
+   ```
 
 This will start the development server and open the desktop app.
 
@@ -222,14 +223,15 @@ This will build the app MacOS m1/m2 for production (with code signing already do
 
 - Supported OS: Linux, WSL2 Docker
 - Pre-requisites:
-  - `docker` and `docker compose`, follow instruction [here](https://docs.docker.com/engine/install/ubuntu/)
+
+  - Docker Engine and Docker Compose are required to run Jan in Docker mode. Follow the [instructions](https://docs.docker.com/engine/install/ubuntu/) below to get started with Docker Engine on Ubuntu.
 
     ```bash
     curl -fsSL https://get.docker.com -o get-docker.sh
     sudo sh ./get-docker.sh --dry-run
     ```
 
-  - `nvidia-driver` and `nvidia-docker2`, follow instruction [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) (If you want to run with GPU mode)
+  - If you intend to run Jan in GPU mode, you need to install `nvidia-driver` and `nvidia-docker2`. Follow the instruction [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) for installation.
 
 - Run Jan in Docker mode
 
@@ -241,7 +243,7 @@ This will build the app MacOS m1/m2 for production (with code signing already do
 
   - **Option 2**: Run Jan in GPU mode
 
-    - **Step 1**: Check cuda compatibility with your nvidia driver by running `nvidia-smi` and check the cuda version in the output
+    - **Step 1**: Check CUDA compatibility with your NVIDIA driver by running `nvidia-smi` and check the CUDA version in the output
 
       ```bash
       nvidia-smi
@@ -274,7 +276,7 @@ This will build the app MacOS m1/m2 for production (with code signing already do
       |=======================================================================================|
       ```
 
-    - **Step 2**: Go to https://catalog.ngc.nvidia.com/orgs/nvidia/containers/cuda/tags and find the smallest minor version of image tag that matches the cuda version from the output of `nvidia-smi` (e.g. 12.1 -> 12.1.0)
+    - **Step 2**: Visit [NVIDIA NGC Catalog ](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/cuda/tags) and find the smallest minor version of image tag that matches your CUDA version (e.g., 12.1 -> 12.1.0)
 
     - **Step 3**: Update the `Dockerfile.gpu` line number 5 with the latest minor version of the image tag from step 2 (e.g. change `FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 AS base` to `FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS base`)
 
@@ -286,6 +288,7 @@ This will build the app MacOS m1/m2 for production (with code signing already do
       ```
 
   This will start the web server and you can access Jan at `http://localhost:3000`.
+
   > Note: Currently, Docker mode is only work for development and localhost, production is not supported yet. RAG feature is not supported in Docker mode yet.
 
 ## Acknowledgements

docs/docs/guides/02-installation/05-docker.md

@@ -0,0 +1,102 @@
+---
+title: Docker
+slug: /install/docker
+description: Jan is a ChatGPT-alternative that runs on your own computer, with a local API server.
+keywords:
+  [
+    Jan AI,
+    Jan,
+    ChatGPT alternative,
+    local AI,
+    private AI,
+    conversational AI,
+    no-subscription fee,
+    large language model,
+    docker installation,
+  ]
+---
+
+# Installing Jan using Docker
+
+## Installation
+
+### Pre-requisites
+
+:::note
+
+**Supported OS**: Linux, WSL2 Docker
+
+:::
+
+- Docker Engine and Docker Compose are required to run Jan in Docker mode. Follow the [instructions](https://docs.docker.com/engine/install/ubuntu/) below to get started with Docker Engine on Ubuntu.
+
+```bash
+curl -fsSL https://get.docker.com -o get-docker.sh
+sudo sh ./get-docker.sh --dry-run
+```
+
+- If you intend to run Jan in GPU mode, you need to install `nvidia-driver` and `nvidia-docker2`. Follow the instruction [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) for installation.
+
+### Instructions
+
+- Run Jan in Docker mode
+
+  - **Option 1**: Run Jan in CPU mode
+
+    ```bash
+    docker compose --profile cpu up -d
+    ```
+
+  - **Option 2**: Run Jan in GPU mode
+
+    - **Step 1**: Check CUDA compatibility with your NVIDIA driver by running `nvidia-smi` and check the CUDA version in the output
+
+      ```bash
+      nvidia-smi
+
+      # Output
+      +---------------------------------------------------------------------------------------+
+      | NVIDIA-SMI 531.18                 Driver Version: 531.18       CUDA Version: 12.1     |
+      |-----------------------------------------+----------------------+----------------------+
+      | GPU  Name                      TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
+      | Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+      |                                         |                      |               MIG M. |
+      |=========================================+======================+======================|
+      |   0  NVIDIA GeForce RTX 4070 Ti    WDDM | 00000000:01:00.0  On |                  N/A |
+      |  0%   44C    P8               16W / 285W|   1481MiB / 12282MiB |      2%      Default |
+      |                                         |                      |                  N/A |
+      +-----------------------------------------+----------------------+----------------------+
+      |   1  NVIDIA GeForce GTX 1660 Ti    WDDM | 00000000:02:00.0 Off |                  N/A |
+      |  0%   49C    P8               14W / 120W|      0MiB /  6144MiB |      0%      Default |
+      |                                         |                      |                  N/A |
+      +-----------------------------------------+----------------------+----------------------+
+      |   2  NVIDIA GeForce GTX 1660 Ti    WDDM | 00000000:05:00.0 Off |                  N/A |
+      | 29%   38C    P8               11W / 120W|      0MiB /  6144MiB |      0%      Default |
+      |                                         |                      |                  N/A |
+      +-----------------------------------------+----------------------+----------------------+
+
+      +---------------------------------------------------------------------------------------+
+      | Processes:                                                                            |
+      |  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
+      |        ID   ID                                                             Usage      |
+      |=======================================================================================|
+      ```
+
+    - **Step 2**: Visit [NVIDIA NGC Catalog ](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/cuda/tags) and find the smallest minor version of image tag that matches your CUDA version (e.g., 12.1 -> 12.1.0)
+
+    - **Step 3**: Update the `Dockerfile.gpu` line number 5 with the latest minor version of the image tag from step 2 (e.g. change `FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 AS base` to `FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS base`)
+
+    - **Step 4**: Run command to start Jan in GPU mode
+
+      ```bash
+      # GPU mode
+      docker compose --profile gpu up -d
+      ```
+
+  This will start the web server and you can access Jan at `http://localhost:3000`.
+
+:::warning
+
+- Docker mode is currently only suitable for development and localhost. Production is not supported yet, and the RAG feature is not available in Docker mode.
+
+:::