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SPARQL endpoint for RDFLib
rdflib-endpoint is a SPARQL endpoint based on a RDFLib Graph to easily serve machine learning models, or any other logic implemented in Python via custom SPARQL functions.
It aims to enable python developers to easily deploy functions that can be queried in a federated fashion using SPARQL. For example: using a python function to resolve labels for specific identifiers, or run a classifier given entities retrieved using a SERVICE query to another SPARQL endpoint.
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How it works
The user defines and registers custom SPARQL functions using Python, and/or populate the RDFLib Graph, then the endpoint is deployed based on the FastAPI framework.
The deployed SPARQL endpoint can be used as a SERVICE in a federated SPARQL query from regular triplestores SPARQL endpoints. Tested on OpenLink Virtuoso (Jena based) and Ontotext GraphDB (rdf4j based). The endpoint is CORS enabled.
Built with RDFLib and FastAPI. Tested for Python 3.7, 3.8 and 3.9
Please create an issue, or send a pull request if you are facing issues or would like to see a feature implemented
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Install the package
Install the package from PyPI:
pip install rdflib-endpoint
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Define custom SPARQL functions
Checkout the example folder for a complete working app example to get started, with a docker deployment. The best way to create a new SPARQL endpoint is to copy this example folder and start from it.
Create a app/main.py file in your project folder with your functions and endpoint parameters:
from rdflib_endpoint import SparqlEndpoint
import rdflib
from rdflib.plugins.sparql.evalutils import _eval
def custom_concat(query_results, ctx, part, eval_part):
"""Concat 2 strings in the 2 senses and return the length as additional Length variable
"""
argument1 = str(_eval(part.expr.expr[0], eval_part.forget(ctx, _except=part.expr._vars)))
argument2 = str(_eval(part.expr.expr[1], eval_part.forget(ctx, _except=part.expr._vars)))
evaluation = []
scores = []
evaluation.append(argument1 + argument2)
evaluation.append(argument2 + argument1)
scores.append(len(argument1 + argument2))
scores.append(len(argument2 + argument1))
# Append the results for our custom function
for i, result in enumerate(evaluation):
query_results.append(eval_part.merge({
part.var: rdflib.Literal(result),
rdflib.term.Variable(part.var + 'Length'): rdflib.Literal(scores[i])
}))
return query_results, ctx, part, eval_part
# Start the SPARQL endpoint based on a RDFLib Graph and register your custom functions
g = rdflib.graph.ConjunctiveGraph()
app = SparqlEndpoint(
graph=g,
# Register the functions:
functions={
'https://w3id.org/um/sparql-functions/custom_concat': custom_concat
},
# CORS enabled by default
cors_enabled=True,
# Metadata used for the service description and Swagger UI:
title="SPARQL endpoint for RDFLib graph",
description="A SPARQL endpoint to serve machine learning models, or any other logic implemented in Python. \n[Source code](https://github.com/vemonet/rdflib-endpoint)",
version="0.1.0",
public_url='https://your-endpoint-url/sparql',
# Example queries displayed in the Swagger UI to help users try your function
example_query="""Example query:\n
```
PREFIX myfunctions: <https://w3id.org/um/sparql-functions/>
SELECT ?concat ?concatLength WHERE {
BIND("First" AS ?first)
BIND(myfunctions:custom_concat(?first, "last") AS ?concat)
}
```"""
)
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Run the SPARQL endpoint
To quickly get started you can run the FastAPI server from the example folder with uvicorn on http://localhost:8000
cd example
uvicorn main:app --reload --app-dir app
Checkout in the
example/README.mdfor more details, such as deploying it with docker.
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Development
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Install for development
Install from the latest GitHub commit to make sure you have the latest updates:
pip install rdf[email protected]+https://github.com/vemonet/[email protected]
Or clone and install locally for development:
git clone https://github.com/vemonet/rdflib-endpoint
cd rdflib-endpoint
pip install -e .
You can use a virtual environment to avoid conflicts:
# Create the virtual environment folder in your workspace
python3 -m venv .venv
# Activate it using a script in the created folder
source .venv/bin/activate
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Run the tests
Install additional dependencies:
pip install pytest requests
Run the tests locally (from the root folder):
pytest -s
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Projects using rdflib-endpoint
Here are some projects using rdflib-endpoint to deploy custom SPARQL endpoints with python:
- https://github.com/MaastrichtU-IDS/rdflib-endpoint-sparql-service
- Serve predicted biomedical entities associations (e.g. disease treated by drug) using the rdflib-endpoint classifier
- https://github.com/vemonet/translator-sparql-service
- A SPARQL endpoint to serve NCATS Translator services as SPARQL custom functions.
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