Files
dify/api
Harry c3ebb22a4b feat(trigger): add workflows_in_use field to TriggerProviderSubscriptionApiEntity
- Introduced a new field `workflows_in_use` to the TriggerProviderSubscriptionApiEntity to track the number of workflows utilizing each subscription.
- Enhanced the TriggerProviderService to populate this field by querying the WorkflowPluginTrigger model for usage counts associated with each subscription.

This addition improves the visibility of subscription usage within the trigger provider context.
2025-09-11 16:55:58 +08:00
..
2025-08-12 23:41:39 +08:00

Dify Backend API

Usage

Important

In the v1.3.0 release, poetry has been replaced with uv as the package manager for Dify API backend service.

  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
    cp middleware.env.example middleware.env
    # change the profile to other vector database if you are not using weaviate
    docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d
    cd ../api
    
  2. Copy .env.example to .env

    cp .env.example .env
    
  3. Generate a SECRET_KEY in the .env file.

    bash for Linux

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

    bash for Mac

    secret_key=$(openssl rand -base64 42)
    sed -i '' "/^SECRET_KEY=/c\\
    SECRET_KEY=${secret_key}" .env
    
  4. Create environment.

    Dify API service uses UV to manage dependencies. First, you need to add the uv package manager, if you don't have it already.

    pip install uv
    # Or on macOS
    brew install uv
    
  5. Install dependencies

    uv sync --dev
    
  6. Run migrate

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

    uv run flask db upgrade
    
  7. Start backend

    uv run flask run --host 0.0.0.0 --port=5001 --debug
    
  8. Start Dify web service.

  9. Setup your application by visiting http://localhost:3000.

  10. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.

uv run celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation

Addition, if you want to debug the celery scheduled tasks, you can use the following command in another terminal:

uv run celery -A app.celery beat

Testing

  1. Install dependencies for both the backend and the test environment

    uv sync --dev
    
  2. Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml, more can check Claude.md

    uv run pytest                           # Run all tests
    uv run pytest tests/unit_tests/         # Unit tests only
    uv run pytest tests/integration_tests/  # Integration tests
    
    # Code quality
    ../dev/reformat               # Run all formatters and linters
    uv run ruff check --fix ./    # Fix linting issues
    uv run ruff format ./         # Format code
    uv run mypy .                 # Type checking