Austin/dify
1
0
Fork 0
mirror of https://github.com/langgenius/dify.git synced 2024-11-16 11:42:29 +08:00
Code Issues Actions 3 Packages Projects Releases Wiki Activity
654985177f
dify/api
History
takatost 654985177f
fix: segment resort in dataset retrieve by index_node_id_to_position (#721)
2023-08-02 21:31:54 +08:00
..
constants
controllers feat: dashboard add tps chart (#706) 2023-08-01 15:17:20 +08:00
core fix: segment resort in dataset retrieve by index_node_id_to_position (#721) 2023-08-02 21:31:54 +08:00
docker feat: add queue to celery task (#688) 2023-07-31 13:13:08 +08:00
events
extensions
libs
migrations
models
services
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
Dockerfile
README.md feat: add queue to celery task (#688) 2023-07-31 13:13:08 +08:00
requirements.txt

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 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.