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gcgj-dify-1.7.0/api
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.idea fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) 1 year ago
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app_factory.py fix: Patch OpenTelemetry to handle None tokens (#18498) 10 months ago
commands.py feat: support Tablestore vector database (#16601) 10 months ago
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uv.lock Update Oracle db connection library and change connection pool to single connection (#18466) 10 months ago

README.md

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