dify/api
2023-08-12 00:57:00 +08:00
..
constants
controllers feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
core feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
docker
events feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
extensions feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
libs feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
migrations feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
models feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
services feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
tasks
tests feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
.dockerignore
.env.example feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
app.py feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
commands.py feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
config.py feat: server multi models support (#799) 2023-08-12 00:57:00 +08:00
Dockerfile
README.md
requirements.txt feat: server multi models support (#799) 2023-08-12 00:57:00 +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.

    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.