| .. | ||
| .idea | ||
| .vscode | ||
| configs | ||
| constants | ||
| contexts | ||
| controllers | ||
| core | ||
| docker | ||
| events | ||
| extensions | ||
| factories | ||
| fields | ||
| libs | ||
| migrations | ||
| models | ||
| schedule | ||
| services | ||
| tasks | ||
| templates | ||
| tests | ||
| .dockerignore | ||
| .env.example | ||
| .ruff.toml | ||
| app_factory.py | ||
| app.py | ||
| commands.py | ||
| dify_app.py | ||
| Dockerfile | ||
| mypy.ini | ||
| poetry.lock | ||
| poetry.toml | ||
| pyproject.toml | ||
| pytest.ini | ||
| README.md | ||
Dify Backend API
Usage
Important
In the v0.6.12 release, we deprecated
pipas the package management tool for Dify API Backend service and replaced it withpoetry.
-
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 Poetry to manage dependencies. You can execute
poetry shellto activate the environment. -
Install dependencies
poetry env use 3.12 poetry install -
Run migrate
Before the first launch, migrate the database to the latest version.
poetry run python -m flask db upgrade -
Start backend
poetry run python -m 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.
poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion
Testing
-
Install dependencies for both the backend and the test environment
poetry install -C api --with dev -
Run the tests locally with mocked system environment variables in
tool.pytest_envsection inpyproject.tomlpoetry run -P api bash dev/pytest/pytest_all_tests.sh