dify/api/README.md
2024-06-14 22:31:01 +08:00

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# Dify Backend API
## Usage
1. Start the docker-compose stack
The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
```bash
cd ../docker
docker-compose -f docker-compose.middleware.yaml -p dify up -d
cd ../api
```
2. Copy `.env.example` to `.env`
3. Generate a `SECRET_KEY` in the `.env` file.
```bash
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
```
4. Create environment.
Dify API service uses [Poetry](https://python-poetry.org/docs/) to manage dependencies. You can execute `poetry shell` to activate the environment.
> Using pip can be found [below](#usage-with-pip).
6. Install dependencies
```bash
poetry install
```
In case of contributors missing to update dependencies for `pyproject.toml`, you can perform the following shell instead.
```bash
poetry shell # activate current environment
poetry add $(cat requirements.txt) # install dependencies of production and update pyproject.toml
poetry add $(cat requirements-dev.txt) --group dev # install dependencies of development and update pyproject.toml
```
7. Run migrate
Before the first launch, migrate the database to the latest version.
```bash
poetry run python -m flask db upgrade
```
8. Start backend
```bash
poetry run python -m flask run --host 0.0.0.0 --port=5001 --debug
```
9. Start Dify [web](../web) service.
10. Setup your application by visiting `http://localhost:3000`...
11. If you need to debug local async processing, please start the worker service.
```bash
poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail
```
The started celery app handles the async tasks, e.g. dataset importing and documents indexing.
## Testing
1. Install dependencies for both the backend and the test environment
```bash
poetry install --with dev
```
2. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`
```bash
cd ../
poetry run -C api bash dev/pytest/pytest_all_tests.sh
```
## Usage with pip
> [!NOTE]
> In the next version, we will deprecate pip as the primary package management tool for dify api service, currently Poetry and pip coexist.
1. Start the docker-compose stack
The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
```bash
cd ../docker
docker-compose -f docker-compose.middleware.yaml -p dify up -d
cd ../api
```
2. Copy `.env.example` to `.env`
3. Generate a `SECRET_KEY` in the `.env` file.
```bash
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
```
4. Create environment.
If you use Anaconda, create a new environment and activate it
```bash
conda create --name dify python=3.10
conda activate dify
```
6. Install dependencies
```bash
pip install -r requirements.txt
```
7. Run migrate
Before the first launch, migrate the database to the latest version.
```bash
flask db upgrade
```
8. Start backend:
```bash
flask run --host 0.0.0.0 --port=5001 --debug
```
9. Setup your application by visiting http://localhost:5001/console/api/setup or other apis...
10. If you need to debug local async processing, please start the worker service.
```bash
celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail
```
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
```bash
pip install -r requirements.txt -r requirements-dev.txt
```
2. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`
```bash
dev/pytest/pytest_all_tests.sh
```