| Name | bento-sts JSON |
| Version |
0.1.18
JSON |
| download |
| home_page | None |
| Summary | Bento Simple Terminology Server |
| upload_time | 2025-08-27 13:34:42 |
| maintainer | Mark A. Jensen |
| docs_url | None |
| author | Mark Benson |
| requires_python | >=3.10 |
| license | Apache-2.0 |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# 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
The API is documented in an [OpenAPI (Swagger) schema](/swagger/swagger.yaml).
Docker is probably the easiest way to run a local Swagger server.
This will bring up a Swagger server at port 6000:
# from the repo root, run...
docker run -p 6000:8080 -e SWAGGER_JSON=/work/swagger.json -v ${PWD}/swagger:/work docker.swagger.io/swaggerapi/swagger-ui
(see [https://swagger.io/docs/open-source-tools/swagger-ui/usage/installation/](https://swagger.io/docs/open-source-tools/swagger-ui/usage/installation/))
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"description": "# bento-sts\n\nSimple Terminology Service for [Bento MDB](https://github.com/CBIIT/bento-mdb).\n\nThe STS provides a web-based UI and a RESTful API for\nbrowsing and accessing graph data models and associated controlled\nvocabulary. See [https://cbiit.github.io/bento-meta/](https://cbiit.github.io/bento-meta/)\nfor an overview of the backend Metamodel Database (MDB). \n\n## Install\n\n pip install bento-sts\n\n## Run\n\nIn the run directory, provide a `.env` file, with your appropriate\nvalues for the environment variables given the the example file\n[bento-sts.env.eg](./python/bento-sts.env.eg).\n\nFor testing:\n\n flask --app \"bento_sts.sts:create_app()\" run\n\nFor production, consider using\n[gunicorn](https://docs.gunicorn.org/en/latest/index.html). [./src](./src)\ncontains two gunicorn config file examples, for development and production.\n\n gunicorn -c gunicorn.conf.dev.py\n\nwill start STS on a gunicorn server at http://localhost:8000.\n\n\n## Dev Install\n\nTo work on bento-sts, make sure you have [Poetry](https://python-poetry.org/):\n\n pip install poetry\n\nThen use it from the python working directory to install dependencies into a virtualenv:\n\n cd bento-sts/python\n poetry install\n\n## Dev Model Database for Testing\n\nThe easiest way to provide a backend graph database for a local STS is to use Docker.\nThe following will get a workable model database running for STS development:\n\n docker pull maj1/test-mdb\n docker run -d -p7687:7687 --env NEO4J_AUTH=none --name test-mdb maj1/test-mdb\n\nThen set the following in your src/.env file:\n\n NEO4J_MDB_URI=bolt://localhost:7867\n NEO4J_MDB_USER=neo4j\n NEO4J_MDB_PASS=neo4j\n STS_LOGFILE=./sts.log\n\n## Run Local STS\n\nOnce the database is running and configured, start the local STS as follows:\n\n cd bento-sts/python/src\n poetry run flask --app \"bento_sts.sts:create_app()\" run\n\nYou should be able to see the frontend on your machine at http://localhost:5000.\n\n## API Docs\n\nThe API is documented in an [OpenAPI (Swagger) schema](/swagger/swagger.yaml). \n\nDocker is probably the easiest way to run a local Swagger server. \nThis will bring up a Swagger server at port 6000:\n\n # from the repo root, run...\n docker run -p 6000:8080 -e SWAGGER_JSON=/work/swagger.json -v ${PWD}/swagger:/work docker.swagger.io/swaggerapi/swagger-ui\n\n(see [https://swagger.io/docs/open-source-tools/swagger-ui/usage/installation/](https://swagger.io/docs/open-source-tools/swagger-ui/usage/installation/))\n\n",
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