dify/api
1102 59cba930e5
bedrock llm Model file name change (#3714)
Co-authored-by: heshunchang <shuncanghe@clouditera.com>
Co-authored-by: crazywoola <427733928@qq.com>
2024-04-23 18:57:34 +08:00
..
.vscode
constants
controllers refactor: tool parameter cache (#3703) 2024-04-23 15:22:42 +08:00
core bedrock llm Model file name change (#3714) 2024-04-23 18:57:34 +08:00
docker
events refactor: tool parameter cache (#3703) 2024-04-23 15:22:42 +08:00
extensions
fields
libs
migrations
models
schedule
services refactor: tool parameter cache (#3703) 2024-04-23 15:22:42 +08:00
tasks
templates
tests
.dockerignore
.env.example
app.py
commands.py
config.py version to 0.6.4 (#3670) 2024-04-22 12:13:31 +08:00
Dockerfile
pyproject.toml
README.md
requirements-dev.txt
requirements.txt python 3.12 support (#3652) 2024-04-22 11:41:13 +08:00

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. 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
    

    ⚠️ 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