Quick Start

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Docker Method (Recommended)

Clone the Repository Locally


git clone git@github.com:Qingyon-AI/Revornix.git
cd Revornix

Environment Variable Configuration


cp ./envs/.api.env.example ./envs/.api.env
cp ./envs/.file.env.example ./envs/.file.env
cp ./envs/.celery.env.example ./envs/.celery.env
cp ./envs/.hot.env.example ./envs/.hot.env

Go to the corresponding environment variable files for configuration, see Environment Variable Configuration Chapter for details.

Add the following file in the web subdirectory to configure frontend environment variables. Remember to modify <http/https>://<your host> to your actual service address. For example, if you are using local, then it should be http://localhost .

.env


NEXT_PUBLIC_API_PREFIX='<http/https>://<your host>/api/main-service'
NEXT_PUBLIC_NOTIFICATION_WS_API_PREFIX='<ws/wss>://<your host>/api/main-service/notification/'
NEXT_PUBLIC_FILE_API_PREFIX='<http/https>://<your host>/api/file-service'
NEXT_PUBLIC_DAILY_HOT_API_PREFIX='<http/https>://<your host>/api/daily-hot-service'

Pull Necessary Repositories with Docker and Start


docker compose up -d

After all services have started, visit http://localhost to see the frontend page. Note that because the backend services take longer to start, the frontend may need to wait for a period of time (normally about 3-5 minutes) before it can make normal API requests. You can check the core backend service startup status with docker compose logs api.

Manual Deployment Method

Unless you need to modify some source code to adapt to custom functionality, this method is not recommended. The process is indeed quite complex.

💡

It is strongly recommended to use conda to create different Python virtual environments for each service, as Python dependencies between different services may conflict. Of course, if you have other Python virtual environment management tools, you can use those as well.

Clone the Repository Locally


git clone git@github.com:Qingyon-AI/Revornix.git
cd Revornix

Environment Variable Configuration


cp ./envs/.api.env.example ./api/.env
cp ./envs/.file.env.example ./file-backend/.env
cp ./envs/.celery.env.example ./celery-worker/.env
cp ./envs/.hot.env.example ./daily-hot/.env

Please add the following content to the ./api/.env and ./file-backend/.env files:


ENV=dev

Add the following file in the web subdirectory to configure frontend environment variables:

.env


NEXT_PUBLIC_API_PREFIX='http://localhost/api/main-service'
NEXT_PUBLIC_NOTIFICATION_WS_API_PREFIX='ws://localhost/api/main-service/notification/'
NEXT_PUBLIC_FILE_API_PREFIX='http://localhost/api/file-service'
NEXT_PUBLIC_DAILY_HOT_API_PREFIX='http://localhost/api/daily-hot-service'

Go to the corresponding environment variable files for configuration, see Environment Variable Configuration Chapter for details.

Initialize Some Necessary Data


cd api
python -m script.init_vector_base_data
python -m script.init_sql_base_data

Install and Start Basic Services

💡

If you don’t have MySQL, Redis, and Milvus installed, you’ll need to manually install these services locally and modify the corresponding parameter configurations in the environment variable files. Considering that these are relatively troublesome and unimportant tasks, I’ve specifically created a docker-compose-local.yaml file that you can use directly to download and start these services.

⚠️

Note: If you have already installed some of these services locally, please disable the corresponding service configurations in the docker-compose-local.yaml file according to your actual situation, otherwise it may cause some unexpected situations.


docker compose -f ./docker-compose-local.yaml up -d

Start Core Backend Service


cd api
conda create -n api python=3.11 -y
pip install -r ./requirements.txt
fastapi run --port 8001

Start Hot Search Aggregation Service


cd daily-hot
pnpm i 
pnpm dev

Start File Backend Service


cd file-backend
conda create -n file-backend python=3.11 -y
pip install -r ./requirements.txt
fastapi run --port 8002

Start Celery Task Sequence


cd celery-worker
conda create -n celery-worker python=3.11 -y
pip install -r ./requirements.txt
celery -A common.celery.app.celery_app worker --loglevel=info --pool threads

Start Frontend Service


cd web
pnpm i
pnpm dev

After you have started all services, visit http://localhost:3000 to see the frontend page.