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88 lines
2.6 KiB
Markdown
88 lines
2.6 KiB
Markdown
# Dify Backend API
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## Usage
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> [!IMPORTANT]
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> In the v0.6.12 release, we deprecated `pip` as the package management tool for Dify API Backend service and replaced it with `poetry`.
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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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cp middleware.env.example middleware.env
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# change the profile to other vector database if you are not using weaviate
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docker compose -f docker-compose.middleware.yaml --profile weaviate -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 for Linux
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sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
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```
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```bash for Mac
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secret_key=$(openssl rand -base64 42)
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sed -i '' "/^SECRET_KEY=/c\\
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SECRET_KEY=${secret_key}" .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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5. Install dependencies
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```bash
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poetry env use 3.10
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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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6. 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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7. 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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8. Start Dify [web](../web) service.
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9. Setup your application by visiting `http://localhost:3000`...
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10. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), 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,ops_trace,app_deletion
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```
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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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