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import logging
import subprocess
import time
from pathlib import Path
from haystack.graph_retriever.text_to_sparql import Text2SparqlRetriever
from haystack.knowledge_graph.graphdb import GraphDBKnowledgeGraph
from haystack.preprocessor.utils import fetch_archive_from_http
logger = logging.getLogger(__name__)
def tutorial10_knowledge_graph():
# Let's first fetch some triples that we want to store in our knowledge graph
# Here: exemplary triples from the wizarding world
graph_dir = "../data/tutorial10_knowledge_graph/"
s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/triples_and_config.zip"
fetch_archive_from_http(url=s3_url, output_dir=graph_dir)
# Fetch a pre-trained BART model that translates text queries to SPARQL queries
model_dir = "../saved_models/tutorial10_knowledge_graph/"
s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/saved_models/hp_v3.4.zip"
fetch_archive_from_http(url=s3_url, output_dir=model_dir)
LAUNCH_GRAPHDB = True
# Start a GraphDB server
if LAUNCH_GRAPHDB:
logging.info("Starting GraphDB ...")
status = subprocess.run(
['docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11'], shell=True
)
if status.returncode:
status = subprocess.run(
[
'docker start graphdb-instance-tutorial'],
shell=True
)
if status.returncode:
raise Exception("Failed to launch GraphDB. If you want to connect to an already running GraphDB instance"
"then set LAUNCH_GRAPHDB in the script to False.")
time.sleep(5)
# Initialize a knowledge graph connected to GraphDB and use "tutorial_10_index" as the name of the index
kg = GraphDBKnowledgeGraph(index="tutorial_10_index")
# Delete the index as it might have been already created in previous runs
kg.delete_index()
# Create the index based on a configuration file
kg.create_index(config_path=Path(graph_dir+"repo-config.ttl"))
# Import triples of subject, predicate, and object statements from a ttl file
kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir+"triples.ttl"))
logging.info(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
logging.info(f"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.")
# Define prefixes for names of resources so that we can use shorter resource names in queries
prefixes = """PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX hp: <https://deepset.ai/harry_potter/>
"""
kg.prefixes = prefixes
# Load a pre-trained model that translates text queries to SPARQL queries
kgqa_retriever = Text2SparqlRetriever(knowledge_graph=kg, model_name_or_path=model_dir+"hp_v3.4")
# We can now ask questions that will be answered by our knowledge graph!
# One limitation though: our pre-trained model can only generate questions about resources it has seen during training.
# Otherwise, it cannot translate the name of the resource to the identifier used in the knowledge graph.
# E.g. "Harry" -> "hp:Harry_potter"
query = "In which house is Harry Potter?"
logging.info(f"Translating the text query \"{query}\" to a SPARQL query and executing it on the knowledge graph...")
result = kgqa_retriever.retrieve(query=query)
logging.info(result)
# Correct SPARQL query: select ?a { hp:Harry_potter hp:house ?a . }
# Correct answer: Gryffindor
logging.info("Executing a SPARQL query with prefixed names of resources...")
result = kgqa_retriever._query_kg(sparql_query="select distinct ?sbj where { ?sbj hp:job hp:Keeper_of_keys_and_grounds . }")
logging.info(result)
# Paraphrased question: Who is the keeper of keys and grounds?
# Correct answer: Rubeus Hagrid
logging.info("Executing a SPARQL query with full names of resources...")
result = kgqa_retriever._query_kg(sparql_query="select distinct ?obj where { <https://deepset.ai/harry_potter/Hermione_granger> <https://deepset.ai/harry_potter/patronus> ?obj . }")
logging.info(result)
# Paraphrased question: What is the patronus of Hermione?
# Correct answer: Otter
if __name__ == "__main__":
tutorial10_knowledge_graph()