tjdify/api

Dify Backend API

Usage

[!IMPORTANT] In the v0.6.12 release, we deprecated pip as the package management tool for Dify API Backend service and replaced it with poetry.

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

1. Copy .env.example to .env
2. 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

1. Create environment.

Dify API service uses Poetry to manage dependencies. You can execute poetry shell to activate the environment.

1. Install dependencies

   poetry env use 3.10
   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

1. Run migrate

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

   poetry run python -m flask db upgrade

1. Start backend

   poetry run python -m flask run --host 0.0.0.0 --port=5001 --debug

1. Start Dify web service.
2. Setup your application by visiting http://localhost:3000...
3. If you need to debug local async processing, 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

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

   poetry install --with dev

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

   cd ../
   poetry run -C api bash dev/pytest/pytest_all_tests.sh