dify/api
2024-04-09 21:21:09 +08:00
..
.vscode
constants
controllers fix: empty conversation list of explore chatbot (#3235) 2024-04-09 17:04:48 +08:00
core feat: remove unregistered-llm-in-debug (#3251) 2024-04-09 20:49:52 +08:00
docker
events
extensions fix: skip Celery warning by setting broker_connection_retry_on_startup config (#3188) 2024-04-09 16:14:43 +08:00
fields
libs
migrations
models
schedule
services fix: empty conversation list of explore chatbot (#3235) 2024-04-09 17:04:48 +08:00
tasks
templates
tests feat: support setting database used in Milvus (#3003) 2024-04-09 15:39:36 +08:00
.dockerignore
.env.example
app.py
commands.py
config.py version to 0.6.1 (#3253) 2024-04-09 21:21:09 +08:00
Dockerfile
pyproject.toml
README.md
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 -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.