dify/api

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 mysql if you are not using postgres,change the profile to other vector database if you are not using weaviate
   docker compose -f docker-compose.middleware.yaml --profile postgresql --profile weaviate -p dify up -d
   cd ../api
   

2. Copy .env.example to .env

   cp .env.example .env
   

Important

When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., example.com). The frontend and backend must be under the same top-level domain in order to share authentication cookies.

1. 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
   

2. 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
   

3. Install dependencies

   uv sync --dev
   

4. Run migrate

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

   uv run flask db upgrade
   

5. Start backend

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

6. Start Dify web service.

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

8. 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 threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention

Additionally, if you want to debug the celery scheduled tasks, you can run the following command in another terminal to start the beat service:

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 basedpyright .         # Type checking