dify/api

Dify Backend API

Usage

1. Start the docker-compose stack

The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using docker-compose.

   cd ../docker
   docker-compose -f docker-compose.middleware.yaml -p dify up -d
   cd ../api

1. Copy .env.example to .env
2. Generate a SECRET_KEY in the .env file.

   sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env

1. Create environment.

   • Anaconda
     If you use Anaconda, create a new environment and activate it

     conda create --name dify python=3.10
     conda activate dify
     

     • Poetry
       If you use Poetry, you don't need to manually create the environment. You can execute poetry shell to activate the environment.
     • Install dependencies
     • Anaconda
       bash pip install -r requirements.txt

   • Poetry

     poetry install
     

     In case of contributors missing to update dependencies for pyproject.toml, you can perform the following shell instead.

     poetry shell                                               # activate current environment
     poetry add $(cat requirements.txt)           # install dependencies of production and update pyproject.toml
     poetry add $(cat requirements-dev.txt) --group dev    # install dependencies of development and update pyproject.toml
     

2. Run migrate

Before the first launch, migrate the database to the latest version.

   flask db upgrade

⚠️ If you encounter problems with jieba, for example

   > flask db upgrade
   Error: While importing 'app', an ImportError was raised:

Please run the following command instead.

   pip install -r requirements.txt --upgrade --force-reinstall

1. Start backend:

   flask run --host 0.0.0.0 --port=5001 --debug
   

   1. Setup your application by visiting http://localhost:5001/console/api/setup or other apis...
   2. If you need to debug local async processing, please start the worker service by running celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail. The started celery app handles the async tasks, e.g. dataset importing and documents indexing.

   Testing

   1. Install dependencies for both the backend and the test environment bash pip install -r requirements.txt -r requirements-dev.txt
      

2. Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml

   dev/pytest/pytest_all_tests.sh