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|---|---|---|
| .. | ||
| .idea | ||
| .vscode | ||
| configs | ||
| constants | ||
| contexts | ||
| controllers | ||
| core | ||
| docker | ||
| events | ||
| extensions | ||
| factories | ||
| fields | ||
| libs | ||
| migrations | ||
| models | ||
| repositories | ||
| schedule | ||
| services | ||
| tasks | ||
| templates | ||
| tests | ||
| .dockerignore | ||
| .env.example | ||
| .ruff.toml | ||
| Dockerfile | ||
| README.md | ||
| app.py | ||
| app_factory.py | ||
| child_class.py | ||
| commands.py | ||
| dify_app.py | ||
| lazy_load_class.py | ||
| mypy.ini | ||
| pyproject.toml | ||
| pytest.ini | ||
| uv.lock | ||
README.md
Dify Backend API
Usage
[!IMPORTANT]
In the v1.3.0 release,
poetryhas been replaced withuvas the package manager for Dify API backend service.
-
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 -
Copy
.env.exampleto.envcp .env.example .env -
Generate a
SECRET_KEYin the.envfile.bash for Linux
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .envbash for Mac
secret_key=$(openssl rand -base64 42) sed -i '' "/^SECRET_KEY=/c\\ SECRET_KEY=${secret_key}" .env -
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 -
Install dependencies
uv sync --dev -
Run migrate
Before the first launch, migrate the database to the latest version.
uv run flask db upgrade -
Start backend
uv run flask run --host 0.0.0.0 --port=5001 --debug -
Start Dify web service.
-
Setup your application by visiting
http://localhost:3000. -
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
-
Install dependencies for both the backend and the test environment
uv sync --dev -
Run the tests locally with mocked system environment variables in
tool.pytest_envsection inpyproject.toml, more can check Claude.mduv 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