dify/api
2023-08-03 22:22:27 +08:00
..
constants
controllers feat: dashboard add tps chart (#706) 2023-08-01 15:17:20 +08:00
core fix: generate_more_like_this function issue (#722) 2023-08-03 11:37:09 +08:00
docker feat: add queue to celery task (#688) 2023-07-31 13:13:08 +08:00
events Feat/milvus support (#671) 2023-07-28 22:19:39 +08:00
extensions
libs
migrations
models fix: not annotation error in log (#686) 2023-07-31 11:50:35 +08:00
services Feat/milvus support (#671) 2023-07-28 22:19:39 +08:00
tasks feat: add queue to celery task (#688) 2023-07-31 13:13:08 +08:00
tests
.dockerignore
.env.example
app.py
commands.py
config.py feat: bump version to 0.3.12 (#674) 2023-07-29 17:49:35 +08:00
Dockerfile fix: wrong version tag of base docker image (#739) 2023-08-03 22:22:27 +08:00
README.md feat: add queue to celery task (#688) 2023-07-31 13:13:08 +08:00
requirements.txt

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 up -d
    cd ../api
    
  2. Copy .env.example to .env

  3. Generate a SECRET_KEY in the .env file.

    openssl rand -base64 42
    

3.5 If you use annaconda, 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
    
  3. Start backend:

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

  5. If you need to debug local async processing, you can run celery -A app.celery worker -Q dataset,generation,mail, celery can do dataset importing and other async tasks.