qanary-helpers 0.2.2

A package that helps to build Python components for the Qanary Question Answering framework

Qanary Helpers library

Qanary Helpers implements registration and querying functionality for the Qanary framework.

This library is used within a Python Qanary Component.

Install

Via PIP

pip install qanary_helpers

Latest version from GitHub

git clone https://github.com/Perevalov/qanary_helpers.git
cd qanary_helpers
pip install .

Usage

For the "Hello world example" create a file named component.py in your working directory. Then, fill the file with the following code (pay attention to the TODO comments):

import os
from datetime import datetime
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, PlainTextResponse
import uvicorn

from qanary_helpers.registration import Registration
from qanary_helpers.registrator import Registrator
from qanary_helpers.qanary_queries import insert_into_triplestore, get_text_question_in_graph
from qanary_helpers.logging import MLFlowLogger

if not os.getenv("PRODUCTION"):
    from dotenv import load_dotenv
    load_dotenv() # required for debugging outside Docker

SPRING_BOOT_ADMIN_URL = os.environ['SPRING_BOOT_ADMIN_URL']    
SPRING_BOOT_ADMIN_USERNAME = os.environ['SPRING_BOOT_ADMIN_USERNAME']
SPRING_BOOT_ADMIN_PASSWORD = os.environ['SPRING_BOOT_ADMIN_PASSWORD']
SERVICE_HOST = os.environ['SERVICE_HOST']
SERVICE_PORT = os.environ['SERVICE_PORT']
SERVICE_NAME_COMPONENT = os.environ['SERVICE_NAME_COMPONENT']
SERVICE_DESCRIPTION_COMPONENT = os.environ['SERVICE_DESCRIPTION_COMPONENT']
URL_COMPONENT = f"{SERVICE_HOST}" # While using server with permanent external IP address: URL_COMPONENT = f"http://{SERVICE_HOST}:{SERVICE_PORT}"

app = FastAPI()


@app.post("/annotatequestion")
async def qanary_service(request: Request):
    request_json = await request.json()
    triplestore_endpoint_url = request_json["values"]["urn:qanary#endpoint"]
    triplestore_ingraph_uuid = request_json["values"]["urn:qanary#inGraph"]

    # get question text from triplestore
    question_text = get_text_question_in_graph(triplestore_endpoint_url, triplestore_ingraph_uuid)[0]['text']

    # Start TODO: configure your business logic here and adjust the sparql query

    # here we simulate that our component created this sparql query:
    sparql_query = """
        PREFIX dbr: <http://dbpedia.org/resource/>
        PREFIX dbo: <http://dbpedia.org/ontology/>
        SELECT * WHERE {
        dbr:Angela_Merkel dbo:birthPlace ?uri .
        }
    """
    # and this "generated" query is stored in the triplestore with this INSERT query:
    SPARQLquery = """
                    PREFIX dbr: <http://dbpedia.org/resource/>
                    PREFIX dbo: <http://dbpedia.org/ontology/>
                    PREFIX qa: <http://www.wdaqua.eu/qa#>
                    PREFIX oa: <http://www.w3.org/ns/openannotation/core/>
                    PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
                    PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
                    INSERT {{
                    GRAPH <{uuid}> {{
                        ?newAnnotation rdf:type qa:AnnotationOfAnswerSPARQL .
                        ?newAnnotation oa:hasTarget <{question_uri}> .
                        ?newAnnotation oa:hasBody \"{sparql_query}\"^^xsd:string .
                        ?newAnnotation qa:score \"1.0\"^^xsd:float .
                        ?newAnnotation oa:annotatedAt ?time .
                        ?newAnnotation oa:annotatedBy <urn:qanary:{component}> .
                        }}
                    }}
                    WHERE {{
                        BIND (IRI(str(RAND())) AS ?newAnnotation) .
                        BIND (now() as ?time) 
                    }}
                """.format(
                    uuid=triplestore_ingraph_uuid,
                    question_uri=triplestore_endpoint_url,
                    component=SERVICE_NAME_COMPONENT.replace(" ", "-"),
                    sparql_query=sparql_query.replace("\n", "\\n").replace("\"", "\\\""))

    insert_into_triplestore(triplestore_endpoint_url,
                            SPARQLquery)  # inserting new data to the triplestore

    # Initializing logging with MLFlow
    # TODO: Update connection settings, if necessary
    logger = MLFlowLogger()

    # logging the annotation of the component
    # TODO: replace "sparql_query" with your annotation data
    logger.log_annotation(SERVICE_NAME_COMPONENT, question_text, sparql_query, triplestore_ingraph_uuid)

    # End TODO

    return JSONResponse(content=request_json)


@app.get("/health")
def health():
    return PlainTextResponse(content="alive") 


metadata = {
    "start": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    "description": SERVICE_DESCRIPTION_COMPONENT,
    "written in": "Python"
}

print(metadata)

registration = Registration(
    name=SERVICE_NAME_COMPONENT,
    serviceUrl=f"{URL_COMPONENT}",
    healthUrl=f"{URL_COMPONENT}/health",
    metadata=metadata
)

reg_thread = Registrator(SPRING_BOOT_ADMIN_URL, SPRING_BOOT_ADMIN_USERNAME,
                        SPRING_BOOT_ADMIN_PASSWORD, registration)
reg_thread.setDaemon(True)
reg_thread.start()

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=int(SERVICE_PORT))

As you may see, several environment variables has to be set before the script execution:

• SPRING_BOOT_ADMIN_URL -- URL of the Qanary pipeline (see Step 1 and Step 2 of the tutorial)
• SPRING_BOOT_ADMIN_USERNAME -- the admin username of the Qanary pipeline
• SPRING_BOOT_ADMIN_PASSWORD -- the admin password of the Qanary pipeline
• SERVICE_HOST -- the host of your component without protocol prefix (e.g. http://). It has to be visible to the Qanary pipeline
• SERVICE_PORT -- the port of your component (has to be visible to the Qanary pipeline)
• SERVICE_NAME_COMPONENT -- the name of your component
• SERVICE_DESCRIPTION_COMPONENT -- the description of your component

You may also change the configuration via environment variables to any configuration that you want (e.g. via a json file).

To run the component, simply execute python component.py in your terminal. If the component registration was successful, a corresponding message will appear in the output.