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.vscode f62f71a81a build: initial support for poetry build tool (#4513) 10 months ago
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core 8d1386df0f feat(Tools): Add Feishu multi-dimensional table operation function (#5213) 10 months ago
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schedule 6c4e6bf1d6 Feat/dify rag (#2528) 1 year ago
services 7f98c2ea3f refactor: Delete the dataset to verify whether it is in use (#5112) 10 months ago
tasks d1dbbc1e33 feat: backend model load balancing support (#4927) 10 months ago
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app.py 8bca908f15 refactor: config file (#3852) 11 months ago
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config.py 4080f7b8ad feat: support tencent vector db (#3568) 10 months ago
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pyproject.toml 4080f7b8ad feat: support tencent vector db (#3568) 10 months ago
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requirements.txt 4080f7b8ad feat: support tencent vector db (#3568) 10 months ago

README.md

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
  1. Copy .env.example to .env
  2. Generate a SECRET_KEY in the .env file.

    sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
    
  3. 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.

  1. 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
  1. Run migrate

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

   poetry run python -m flask db upgrade
  1. Start backend

    poetry run python -m flask run --host 0.0.0.0 --port=5001 --debug
    
  2. Start Dify web service.

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

  4. 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
  1. Copy .env.example to .env
  2. Generate a SECRET_KEY in the .env file.

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

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

   conda create --name dify python=3.10
   conda activate dify
  1. Install dependencies

    pip install -r requirements.txt
    
  2. Run migrate

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

   flask db upgrade
  1. Start backend:

    flask run --host 0.0.0.0 --port=5001 --debug
    
  2. Setup your application by visiting http://localhost:5001/console/api/setup or other apis...

  3. 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