@ -19,19 +19,25 @@ Thank you for choosing to contribute to DocsGPT! We are all very grateful!
We value contributions in the form of discussions or suggestions. We recommend taking a look at existing issues and our [roadmap](https://github.com/orgs/arc53/projects/2).
If you're interested in contributing code, here are some important things to know:
Before creating issues, please check out how the latest version of our app looks and works by launching it via [Quickstart](https://github.com/arc53/DocsGPT#quickstart) the version on our live demo is slightly modified with login. Your issues should relate to the version that you can launch via [Quickstart](https://github.com/arc53/DocsGPT#quickstart).
We have a frontend built with React (Vite) and a backend in Python.
### 👨💻 If you're interested in contributing code, here are some important things to know:
### If you are looking to contribute to frontend (⚛️React, Vite):
Tech Stack Overview:
- 🌐 Frontend: Built with React (Vite) ⚛️,
- 🖥 Backend: Developed in Python 🐍
### 🌐 If you are looking to contribute to frontend (⚛️React, Vite):
- The current frontend is being migrated from [`/application`](https://github.com/arc53/DocsGPT/tree/main/application) to [`/frontend`](https://github.com/arc53/DocsGPT/tree/main/frontend) with a new design, so please contribute to the new one.
- Check out this [milestone](https://github.com/arc53/DocsGPT/milestone/1) and its issues.
- The Figma design can be found [here](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1).
- The updated Figma design can be found [here](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1).
Please try to follow the guidelines.
### If you are looking to contribute to Backend (🐍 Python):
### 🖥 If you are looking to contribute to Backend (🐍 Python):
- Review our issues and contribute to [`/application`](https://github.com/arc53/DocsGPT/tree/main/application) or [`/scripts`](https://github.com/arc53/DocsGPT/tree/main/scripts) (please disregard old [`ingest_rst.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst.py) [`ingest_rst_sphinx.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst_sphinx.py) files; they will be deprecated soon).
- All new code should be covered with unit tests ([pytest](https://github.com/pytest-dev/pytest)). Please find tests under [`/tests`](https://github.com/arc53/DocsGPT/tree/main/tests) folder.
@ -44,11 +50,62 @@ To run unit tests from the root of the repository, execute:
python -m pytest
```
### Workflow 📈 :
- Fork repository
- Make the required changes on your forked version
- Commit those changes and submit those as a pull request so that it reflects on thr main repository.
## Workflow 📈
Here's a step-by-step guide on how to contribute to DocsGPT:
1. **Fork the Repository:**
- Click the "Fork" button at the top-right of this repository to create your fork.
2. **Create and Switch to a New Branch:**
- Create a new branch for your contribution using:
```shell
git checkout -b your-branch-name
```
3. **Make Changes:**
- Make the required changes in your branch.
4. **Add Changes to the Staging Area:**
- Add your changes to the staging area using:
```shell
git add .
```
5. **Commit Your Changes:**
- Commit your changes with a descriptive commit message using:
```shell
git commit -m "Your descriptive commit message"
```
6. **Push Your Changes to the Remote Repository:**
- Push your branch with changes to your fork on GitHub using:
```shell
git push origin your-branch-name
```
7. **Submit a Pull Request (PR):**
- Create a Pull Request from your branch to the main repository. Make sure to include a detailed description of your changes and reference any related issues.
8. **Collaborate:**
- Be responsive to comments and feedback on your PR.
- Make necessary updates as suggested.
- Once your PR is approved, it will be merged into the main repository.
9. **Testing:**
- Before submitting a Pull Request, ensure your code passes all unit tests.
- To run unit tests from the root of the repository, execute:
```shell
python -m pytest
```
*Note: You should run the unit test only after making the changes to the backend code.*
10. **Questions and Collaboration:**
- Feel free to join our Discord. We're very friendly and welcoming to new contributors, so don't hesitate to reach out.
Thank you for considering contributing to DocsGPT! 🙏
## Questions/collaboration
Feel free to join our [Discord](https://discord.gg/n5BX8dh8rU). We're very friendly and welcoming to new contributors, so don't hesitate to reach out.
# Thank you so much for considering contributing to DocsGPT!🙏
# Thank you so much for considering to contribute DocsGPT!🙏
@ -32,4 +32,10 @@ Once you have created your PR and our maintainers have merged it, please fill in
Feel free to join our Discord server. We're here to help newcomers, so don't hesitate to jump in! [Join us here](https://discord.gg/n5BX8dh8rU).
Thank you very much for considering contributing to DocsGPT during Hacktoberfest! 🙏 Your contributions could earn you a stylish new t-shirt as a token of our appreciation. 🎁 Join us, and let's code together! 🚀
Thank you very much for considering contributing to DocsGPT during Hacktoberfest! 🙏 Your contributions (not just simple typo) could earn you a stylish new t-shirt as a token of our appreciation. 🎁 Join us, and let's code together! 🚀
@ -6,37 +6,30 @@ Here's a step-by-step guide on how to setup an Amazon Lightsail instance to host
(If you know how to create a Lightsail instance, you can skip to the recommended configuration part by clicking here).
### 1. Create an account or login to https://lightsail.aws.amazon.com
### 1. Create an AWS Account:
If you haven't already, create or log in to your AWS account at https://lightsail.aws.amazon.com.
### 2. Click on "Create instance"
### 2. Create an Instance:
### 3. Create your instance
a. Click "Create Instance."
The first step is to select the "Instance location". In most cases, there's no need to switch locations as the default one will work well.
b. Select the "Instance location." In most cases, the default location works fine.
After that, it is time to pick your Instance Image. We recommend using "Linux/Unix" as the image and "Ubuntu 20.04 LTS" as the Operating System.
c. Choose "Linux/Unix" as the image and "Ubuntu 20.04 LTS" as the Operating System.
As for instance plan, it'll vary depending on your unique demands, but a "1 GB, 1vCPU, 40GB SSD and 2TB transfer" setup should cover most scenarios.
d. Configure the instance plan based on your requirements. A "1 GB, 1vCPU, 40GB SSD, and 2TB transfer" setup is recommended for most scenarios.
Lastly, identify your instance by giving it a unique name and then hit "Create instance".
e. Give your instance a unique name and click "Create Instance."
PS: Once you create your instance, it'll likely take a few minutes for the setup to be completed.
PS: It may take a few minutes for the instance setup to complete.
#### The recommended configuration is as follows:
### Connecting to Your newly created Instance
- Ubuntu 20.04 LTS
- 1GB RAM
- 1vCPU
- 40GB SSD Hard Drive
- 2TB transfer
Your instance will be ready a few minutes after creation. To access it, open the instance and click "Connect using SSH."
### Connecting to your newly created instance
#### Clone the DocsGPT Repository
Your instance will be ready for use a few minutes after being created. To access it, just open it up and click on "Connect using SSH".
#### Clone the repository
A terminal window will pop up, and the first step will be to clone the DocsGPT git repository:
A terminal window will pop up, and the first step will be to clone the DocsGPT Git repository:
`git clone https://github.com/arc53/DocsGPT.git`
@ -56,15 +49,15 @@ And now install docker-compose:
`sudo apt install docker-compose`
#### Access the DocsGPT folder
#### Access the DocsGPT Folder
Enter the following command to access the folder in which DocsGPT docker-compose file is present.
Enter the following command to access the folder in which the DocsGPT docker-compose file is present.
`cd DocsGPT/`
#### Prepare the environment
#### Prepare the Environment
Inside the DocsGPT folder, create a `.env` file and copy the contents of `.env_sample` into it.
Inside the DocsGPT folder create a `.env` file and copy the contents of `.env_sample` into it.
`nano .env`
@ -78,16 +71,16 @@ SELF_HOSTED_MODEL=false
To save the file, press CTRL+X, then Y, and then ENTER.
Next, we need to set a correct IP for our Backend. To do so, open the docker-compose.yml file:
Next, set the correct IP for the Backend by opening the docker-compose.yml file:
`nano docker-compose.yml`
And change this line 7`VITE_API_HOST=http://localhost:7091`
And Change line 7 to:`VITE_API_HOST=http://localhost:7091`
to this `VITE_API_HOST=http://<your instance public IP>:7091`
This will allow the frontend to connect to the backend.
#### Running the app
#### Running the Application
You're almost there! Now that all the necessary bits and pieces have been installed, it is time to run the application. To do so, use the following command:
@ -95,18 +88,17 @@ You're almost there! Now that all the necessary bits and pieces have been instal
Launching it for the first time will take a few minutes to download all the necessary dependencies and build.
Once this is done, you can go ahead and close the terminal window.
Once this is done you can go ahead and close the terminal window.
#### Enabling ports
#### Enabling Ports
Before you are able to access your live instance, you must first enable the port that it is using.
a. Before you are able to access your live instance, you must first enable the port that it is using.
Open your Lightsail instance and head to "Networking".
b. Open your Lightsail instance and head to "Networking".
Then click on "Add rule" under "IPv4 Firewall", enter `5173` as your port, and hit "Create".
c. Then click on "Add rule" under "IPv4 Firewall", enter `5173` as your port, and hit "Create".
Repeat the process for port `7091`.
#### Access your instance
Your instance will now be available under your Public IP Address and port `5173`. Enjoy!
Your instance is now available at your Public IP Address on port 5173. Enjoy using DocsGPT!
It will install all the dependencies and give you an option to download the local model or use OpenAI
This command will install all the necessary dependencies and provide you with an option to download the local model or use OpenAI.
Otherwise, refer to this Guide:
If you prefer to follow manual steps, refer to this guide:
1. Open and download this repository with `git clone https://github.com/arc53/DocsGPT.git`.
1. Open and download this repository with
`git clone https://github.com/arc53/DocsGPT.git`.
2. Create a `.env` file in your root directory and set your `API_KEY` with your [OpenAI API key](https://platform.openai.com/account/api-keys).
3. Run `docker-compose build && docker-compose up`.
3. Run the following commands:
`docker-compose build && docker-compose up`.
4. Navigate to `http://localhost:5173/`.
To stop, just run `Ctrl + C`.
To stop, simply press Ctrl + C.
**For WINDOWS:**
To run the setup on Windows, you have two options: using the Windows Subsystem for Linux (WSL) or using Git Bash or Command Prompt.
**Option 1: Using Windows Subsystem for Linux (WSL):**
1. Install WSL if you haven't already. You can follow the official Microsoft documentation for installation: (https://learn.microsoft.com/en-us/windows/wsl/install).
2. After setting up WSL, open the WSL terminal.
3. Clone the repository and create the `.env` file:
```
git clone https://github.com/arc53/DocsGPT.git
cd DocsGPT
echo "API_KEY=Yourkey" > .env
echo "VITE_API_STREAMING=true" >> .env
```
4. Run the following command to start the setup with Docker Compose:
`./run-with-docker-compose.sh`
5. Open your web browser and navigate to (http://localhost:5173/).
6. To stop the setup, just press `Ctrl + C` in the WSL terminal
**Option 2: Using Git Bash or Command Prompt (CMD):**
1. Install Git for Windows if you haven't already. Download it from the official website: (https://gitforwindows.org/).
2. Open Git Bash or Command Prompt.
3. Clone the repository and create the `.env` file:
```
git clone https://github.com/arc53/DocsGPT.git
cd DocsGPT
echo "API_KEY=Yourkey" > .env
echo "VITE_API_STREAMING=true" >> .env
```
4.Run the following command to start the setup with Docker Compose:
`./run-with-docker-compose.sh`
5.Open your web browser and navigate to (http://localhost:5173/).
6.To stop the setup, just press Ctrl + C in the Git Bash or Command Prompt terminal.
These steps should help you set up and run the project on Windows using either WSL or Git Bash/Command Prompt.
**Important:** Ensure that Docker is installed and properly configured on your Windows system for these steps to work.
For WINDOWS:
To run the given setup on Windows, you can use the Windows Subsystem for Linux (WSL) or a Git Bash terminal to execute similar commands. Here are the steps adapted for Windows:
Option 1: Using Windows Subsystem for Linux (WSL):
1. Install WSL if you haven't already. You can follow the official Microsoft documentation for installation: (https://learn.microsoft.com/en-us/windows/wsl/install).
2. After setting up WSL, open the WSL terminal.
3. Clone the repository and create the `.env` file:
```
git clone https://github.com/arc53/DocsGPT.git
cd DocsGPT
echo "API_KEY=Yourkey" > .env
echo "VITE_API_STREAMING=true" >> .env
```
4. Run the following command to start the setup with Docker Compose:
`./run-with-docker-compose.sh`
5. Open your web browser and navigate to (http://localhost:5173/).
6. To stop the setup, just press `Ctrl + C` in the WSL terminal
Option 2: Using Git Bash or Command Prompt (CMD):
1. Install Git for Windows if you haven't already. You can download it from the official website: (https://gitforwindows.org/).
2. Open Git Bash or Command Prompt.
3. Clone the repository and create the `.env` file:
```
git clone https://github.com/arc53/DocsGPT.git
cd DocsGPT
echo "API_KEY=Yourkey" > .env
echo "VITE_API_STREAMING=true" >> .env
```
4.Run the following command to start the setup with Docker Compose:
`./run-with-docker-compose.sh`
5.Open your web browser and navigate to (http://localhost:5173/).
6.To stop the setup, just press Ctrl + C in the Git Bash or Command Prompt terminal.
These steps should help you set up and run the project on Windows using either WSL or Git Bash/Command Prompt. Make sure you have Docker installed and properly configured on your Windows system for this to work.
### Chrome Extension
To install the Chrome extension:
#### Installing the Chrome extension:
To enhance your DocsGPT experience, you can install the DocsGPT Chrome extension. Here's how:
1. In the DocsGPT GitHub repository, click on the "Code" button and select "Download ZIP".
2. Unzip the downloaded file to a location you can easily access.
Shivanand
8 months ago
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