guogeer 4fdcb30ff8 fix: custom tool input number fail (#6200) 2 vuotta sitten
..
configs 63e34e5227 feat: support MyScale vector database (#6092) 2 vuotta sitten
constants 6ef401a9f0 feat:add tts-streaming config and future (#5492) 2 vuotta sitten
controllers 1df71ec64d refactor(api): switch to dify_config with Pydantic in controllers and schedule (#6237) 2 vuotta sitten
core 4fdcb30ff8 fix: custom tool input number fail (#6200) 2 vuotta sitten
docker cb09dbef66 feat: correctly delete applications using Celery workers (#5787) 2 vuotta sitten
events cb09dbef66 feat: correctly delete applications using Celery workers (#5787) 2 vuotta sitten
extensions 678ad6b7eb Fix/file stream azure blob (#6196) 2 vuotta sitten
fields 9622fbb62f feat: app rate limit (#5844) 2 vuotta sitten
libs 9622fbb62f feat: app rate limit (#5844) 2 vuotta sitten
migrations 9622fbb62f feat: app rate limit (#5844) 2 vuotta sitten
models 9622fbb62f feat: app rate limit (#5844) 2 vuotta sitten
schedule 1df71ec64d refactor(api): switch to dify_config with Pydantic in controllers and schedule (#6237) 2 vuotta sitten
services 7b225a5ab0 refactor(services/tasks): Swtich to dify_config witch Pydantic (#6203) 2 vuotta sitten
tasks 7b225a5ab0 refactor(services/tasks): Swtich to dify_config witch Pydantic (#6203) 2 vuotta sitten
templates 00b4cc3cd4 feat: implement forgot password feature (#5534) 2 vuotta sitten
tests 63e34e5227 feat: support MyScale vector database (#6092) 2 vuotta sitten
.dockerignore 27f0ae8416 build: support Poetry for depencencies tool in api's Dockerfile (#5105) 2 vuotta sitten
.env.example 63e34e5227 feat: support MyScale vector database (#6092) 2 vuotta sitten
Dockerfile 9b7c74a5d9 chore: skip pip upgrade preparation in api dockerfile (#5999) 2 vuotta sitten
README.md 2d6624cf9e typo: Update README.md (#5987) 2 vuotta sitten
app.py d7f75d17cc Chore/remove-unused-code (#5917) 2 vuotta sitten
commands.py 7c70eb87bc feat: support AnalyticDB vector store (#5586) 2 vuotta sitten
poetry.lock 63e34e5227 feat: support MyScale vector database (#6092) 2 vuotta sitten
poetry.toml f62f71a81a build: initial support for poetry build tool (#4513) 2 vuotta sitten
pyproject.toml 63e34e5227 feat: support MyScale vector database (#6092) 2 vuotta sitten

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
   docker compose -f docker-compose.middleware.yaml -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 debug local async processing, 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

The started celery app handles the async tasks, e.g. dataset importing and documents indexing.

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