liuzhenghua 9d5a89eab6 feat: add log date timezone (#4623) 10 місяців тому
..
.vscode f62f71a81a build: initial support for poetry build tool (#4513) 10 місяців тому
configs 9d5a89eab6 feat: add log date timezone (#4623) 10 місяців тому
constants 6ccde0452a feat: Added hindi translation i18n (#5240) 10 місяців тому
controllers 3cc6093e4b feat: introduce pydantic-settings for config definition and validation (#5202) 10 місяців тому
core bb33ffc332 feat: initial support for Milvus 2.4.x (#3795) 10 місяців тому
docker 5f0ce5811a feat: add `flask upgrade-db` command for running db upgrade with redis lock (#5333) 10 місяців тому
events d160d1ed02 feat: support opensearch approximate k-NN (#5322) 10 місяців тому
extensions 3cc6093e4b feat: introduce pydantic-settings for config definition and validation (#5202) 10 місяців тому
fields 43c19007e0 fix: workspace member's last_active should be last_active_time, but not last_login_time (#4906) 10 місяців тому
libs 7305713b97 fix: allow special characters in email (#5327) 10 місяців тому
migrations 3cc6093e4b feat: introduce pydantic-settings for config definition and validation (#5202) 10 місяців тому
models e785cbb81d Fix: multi image preview sign (#5376) 10 місяців тому
schedule 6c4e6bf1d6 Feat/dify rag (#2528) 1 рік тому
services 07387e9586 add the filename length limit (#5326) 10 місяців тому
tasks ba5f8afaa8 Feat/firecrawl data source (#5232) 10 місяців тому
templates 3d92784bd4 fix: email template style (#1914) 1 рік тому
tests 3cc6093e4b feat: introduce pydantic-settings for config definition and validation (#5202) 10 місяців тому
.dockerignore 220f7c81e9 build: fix .dockerignore file (#800) 1 рік тому
.env.example 147a39b984 feat: support tencent cos storage (#5297) 10 місяців тому
Dockerfile 55fc46c707 improvement: speed up dependency installation in docker image rebuilds by mounting cache layer (#3218) 1 рік тому
README.md bdf3ea4369 docs(api/README): Remove unnecessary `=` (#5380) 10 місяців тому
app.py 9d5a89eab6 feat: add log date timezone (#4623) 10 місяців тому
commands.py d160d1ed02 feat: support opensearch approximate k-NN (#5322) 10 місяців тому
config.py 3cc6093e4b feat: introduce pydantic-settings for config definition and validation (#5202) 10 місяців тому
poetry.lock bb33ffc332 feat: initial support for Milvus 2.4.x (#3795) 10 місяців тому
poetry.toml f62f71a81a build: initial support for poetry build tool (#4513) 10 місяців тому
pyproject.toml bb33ffc332 feat: initial support for Milvus 2.4.x (#3795) 10 місяців тому
requirements-dev.txt 23498883d4 chore: skip explicit installing jinja2 as testing dependency (#4845) 10 місяців тому
requirements.txt bb33ffc332 feat: initial support for Milvus 2.4.x (#3795) 10 місяців тому

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 -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.

Using pip can be found below.

  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

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

Usage with pip

[!NOTE]
In the next version, we will deprecate pip as the primary package management tool for dify api service, currently Poetry and pip coexist.

  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
  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
  1. Create environment.

If you use Anaconda, 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
  1. Run migrate

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

   flask db upgrade
  1. Start backend:
   flask run --host 0.0.0.0 --port=5001 --debug
  1. Setup your application by visiting http://localhost:5001/console/api/setup or other apis...
  2. If you need to debug local async processing, please start the worker service.
   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.