gcgj-dify-1.7.0/api

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.

   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.

   sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
   

4. Create environment.

   • Anaconda
     If you use Anaconda, create a new environment and activate it

   conda create --name dify python=3.10
   conda activate dify
   

   • Poetry
     If you use Poetry, you don't need to manually create the environment. You can execute poetry shell to activate the environment.

5. Install dependencies

   • Anaconda

   pip install -r requirements.txt
   

   • Poetry

   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
   

6. Run migrate

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

   flask db upgrade
   

   ⚠️ If you encounter problems with jieba, for example

   > flask db upgrade
   Error: While importing 'app', an ImportError was raised:
   

   Please run the following command instead.

   pip install -r requirements.txt --upgrade --force-reinstall
   

7. Start backend:

   flask run --host 0.0.0.0 --port=5001 --debug
   

8. Setup your application by visiting http://localhost:5001/console/api/setup or other apis...

9. If you need to debug local async processing, please start the worker service by running 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

   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

   dev/pytest/pytest_all_tests.sh