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.idea 7ae728a9a3 fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) 8 mesi fa
.vscode 0d4753785f chore: remove .idea and .vscode from root path (#7437) 8 mesi fa
configs ddf6569dc5 chore: enhance configuration descriptions (#8624) 7 mesi fa
constants 8c51d06222 feat: regenerate in `Chat`, `agent` and `Chatflow` app (#7661) 7 mesi fa
contexts 3571292fbf chore(api): Introduce Ruff Formatter. (#7291) 8 mesi fa
controllers 8c51d06222 feat: regenerate in `Chat`, `agent` and `Chatflow` app (#7661) 7 mesi fa
core c7eacd1aac chore: Optimize I18nObject class for better performance and readability (#8681) 7 mesi fa
docker 8dfdb37de3 fix: use LOG_LEVEL for celery startup (#7628) 8 mesi fa
events 40fb4d16ef chore: refurbish Python code by applying refurb linter rules (#8296) 7 mesi fa
extensions d96f5ba1ca add storage error log (#8556) 7 mesi fa
fields 8c51d06222 feat: regenerate in `Chat`, `agent` and `Chatflow` app (#7661) 7 mesi fa
libs a1104ab97e chore: refurish python code by applying Pylint linter rules (#8322) 7 mesi fa
migrations 8c51d06222 feat: regenerate in `Chat`, `agent` and `Chatflow` app (#7661) 7 mesi fa
models 8c51d06222 feat: regenerate in `Chat`, `agent` and `Chatflow` app (#7661) 7 mesi fa
schedule 3571292fbf chore(api): Introduce Ruff Formatter. (#7291) 8 mesi fa
services 03fdf5e7f8 chore: Enable Japanese descriptions for Tools (#8646) 7 mesi fa
tasks a1104ab97e chore: refurish python code by applying Pylint linter rules (#8322) 7 mesi fa
templates 00b4cc3cd4 feat: implement forgot password feature (#5534) 9 mesi fa
tests 0665268578 Add Fireworks AI as new model provider (#8428) 7 mesi fa
.dockerignore 27f0ae8416 build: support Poetry for depencencies tool in api's Dockerfile (#5105) 10 mesi fa
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Dockerfile fede54be77 fix: Version '2.6.2-2' for 'expat' was not found (#8182) 7 mesi fa
README.md e75c33a561 Enhance Readme Documentation to Clarify the Importance of Celery Service (#8558) 7 mesi fa
app.py a1104ab97e chore: refurish python code by applying Pylint linter rules (#8322) 7 mesi fa
commands.py 6e37750fbd fix: commands.py (#8483) 7 mesi fa
poetry.lock 97895ec41a chore: add Gemini newest experimental models (close #7121) (#8621) 7 mesi fa
poetry.toml f62f71a81a build: initial support for poetry build tool (#4513) 10 mesi fa
pyproject.toml 86f90fd9ff chore: skip PLR6201 linter rule (#8666) 7 mesi fa

README.md

Dify Backend API

Usage

[!IMPORTANT] In the v0.6.12 release, we deprecated pip as the package management tool for Dify API Backend service and replaced it with poetry.

  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
   cp middleware.env.example middleware.env
   # change the profile to other vector database if you are not using weaviate
   docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d
   cd ../api
  1. Copy .env.example to .env
  2. Generate a SECRET_KEY in the .env file.
   sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
   secret_key=$(openssl rand -base64 42)
   sed -i '' "/^SECRET_KEY=/c\\
   SECRET_KEY=${secret_key}" .env
  1. Create environment.

Dify API service uses Poetry to manage dependencies. You can execute poetry shell to activate the environment.

  1. Install dependencies
   poetry env use 3.10
   poetry install

In case of contributors missing to update dependencies for pyproject.toml, you can perform the following shell instead.

   poetry shell                                               # activate current environment
   poetry add $(cat requirements.txt)           # install dependencies of production and update pyproject.toml
   poetry add $(cat requirements-dev.txt) --group dev    # install dependencies of development and update pyproject.toml
  1. Run migrate

Before the first launch, migrate the database to the latest version.

   poetry run python -m flask db upgrade
  1. Start backend
   poetry run python -m flask run --host 0.0.0.0 --port=5001 --debug
  1. Start Dify web service.
  2. Setup your application by visiting http://localhost:3000...
  3. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
   poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion

Testing

  1. Install dependencies for both the backend and the test environment
   poetry install --with dev
  1. Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml
   cd ../
   poetry run -C api bash dev/pytest/pytest_all_tests.sh