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.

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

   Using pip can be found below.

   poetry env use 3.10
   

5. Install dependencies

   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.

   poetry run python -m flask db upgrade
   

7. Start backend

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

8. Start Dify web service.

9. Setup your application by visiting http://localhost:3000...

10. 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

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
   

2. 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
   

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.

   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.

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

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

5. Install dependencies

   pip install -r requirements.txt
   

6. Run migrate

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

   flask db upgrade
   

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.

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