An easy way to deploy everything from Heritage Connector. The following services are included and all configured through environment variables:
- fuseki - RDF triplestore
- thor - front end for performing SPARQL queries
- thor-cors-proxy - CORS proxy to enable thor to connect to fuseki
- heritage-connector-vectors - an nearest neighbours on knowledge graph embeddings
- heritage-connector-apis - API endpoints to wrap some common SPARQL queries and the nearest neighbours API. The API documentation is hosted at
<api-endpoint>/docs.
The application also comes with a frontend, which can be accessed at <host-name>:8010/view_connections.
The following URLs should be added to .env, as well as the rest of the variables present in .env.sample.
- triples file in ntriples format -
TRIPLES_URL - entity embeddings in numpy format -
VECTORS_ENTITY_EMBEDDING_URL,VECTORS_ENTITY_MAPPING_URL,VECTORS_RELATION_EMBEDDING_URL,VECTORS_RELATION_MAPPING_URL
The contents of this repo have been tested on an EC2 t3a.large instance, which has 2vCPUs and 8GB RAM.
To setup the EC2 machine:
- Create a machine, choosing Deep Learning Base AMI (Ubuntu 18.04) Version 44.0 as your Amazon Machine Image. (This is handy because it comes installed with docker and python).
- Choose
t3a.largefor the instance type. - Choose 75GB (or larger) for the storage size.
- Click next until you reach the Configure Security Group screen.
- Open ports 80, 8080, 8020, 3030 and 8010 for the various microservices. Details of which port is served by which microservice can be found in
docker-compose.yml. - Launch the machine.
# 1. clone the repo
git clone https://github.com/TheScienceMuseum/heritage-connector-deployment
# 2. get code for submodules
git submodule update --init
# 3. fill in env variables
cp .env.sample .env
# -- Add env variables to data files as per the prerequisities above. --
# -- The value for SPARQL_ENDPOINT_PROXIED should be --
# -- "http://<IP-address-of-deployed-application>:8080" --
# 4. start containers
docker-compose up