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