# bento-sts
Simple Terminology Service for [Bento MDB](https://github.com/CBIIT/bento-mdb).
The STS provides a web-based UI and a RESTful API for
browsing and accessing graph data models and associated controlled
vocabulary. See [https://cbiit.github.io/bento-meta/](https://cbiit.github.io/bento-meta/)
for an overview of the backend Metamodel Database (MDB).
## Install
pip install bento-sts
## Run
In the run directory, provide a `.env` file, with your appropriate
values for the environment variables given the the example file
[bento-sts.env.eg](./python/bento-sts.env.eg).
For testing:
flask --app "bento_sts.sts:create_app()" run
For production, consider using
[gunicorn](https://docs.gunicorn.org/en/latest/index.html). [./src](./src)
contains two gunicorn config file examples, for development and
production.
gunicorn -c gunicorn.conf.dev.py
will start STS on a gunicorn server at http://localhost:8000.
## Dev Install
To work on bento-sts, make sure you have [Poetry](https://python-poetry.org/):
pip install poetry
Then use it from the python working directory to install dependencies into a virtualenv:
cd bento-sts/python
poetry install
## Dev Model Database for Testing
The easiest way to provide a backend graph database for a local STS is to use Docker.
The following will get a workable model database running for STS development:
docker pull maj1/test-mdb
docker run -d -p7687:7687 --env NEO4J_AUTH=none --name test-mdb maj1/test-mdb
Then set the following in your src/.env file:
NEO4J_MDB_URI=bolt://localhost:7867
NEO4J_MDB_USER=neo4j
NEO4J_MDB_PASS=neo4j
STS_LOGFILE=./sts.log
## Run Local STS
Once the database is running and configured, start the local STS as follows:
cd bento-sts/python/src
poetry run flask --app "bento_sts.sts:create_app()" run
You should be able to see the frontend on your machine at http://localhost:5000.
## API Docs
[in progress]
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