Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Appearance settings

Latest commit

 

History

History
History
 
 

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Outline

Dify Backend API

Usage

Important

In the v1.3.0 release, poetry has been replaced with uv as the package manager for Dify API backend service.

  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
    cp middleware.env.example middleware.env
    # change the profile to other vector database if you are not using weaviate
    docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d
    cd ../api
  2. Copy .env.example to .env

    cp .env.example .env 
    
  3. Generate a SECRET_KEY in the .env file.

    bash for Linux

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

    bash for Mac

    secret_key=$(openssl rand -base64 42)
    sed -i '' "/^SECRET_KEY=/c\\
    SECRET_KEY=${secret_key}" .env
  4. Create environment.

    Dify API service uses UV to manage dependencies. First, you need to add the uv package manager, if you don't have it already.

    pip install uv
    # Or on macOS
    brew install uv
  5. Install dependencies

    uv sync --dev
  6. Run migrate

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

    uv run flask db upgrade
  7. Start backend

    uv run 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 handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.

uv run celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion

Testing

  1. Install dependencies for both the backend and the test environment

    uv sync --dev
  2. Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml

    uv run -P api bash dev/pytest/pytest_all_tests.sh
Morty Proxy This is a proxified and sanitized view of the page, visit original site.