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