Question Answering on a Knowledge Graph

# Install the latest release of Haystack in your own environment
#! pip install farm-haystack

# Install the latest master of Haystack
!pip install --upgrade pip
!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,graphdb]

# Here are some imports that we'll need

import subprocess
import time
from pathlib import Path

from haystack.nodes import Text2SparqlRetriever
from haystack.document_stores import GraphDBKnowledgeGraph
from haystack.utils import fetch_archive_from_http

# 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"
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)

# Unfortunately, there seems to be no good way to run GraphDB in colab environments
# In your local environment, you could start a GraphDB server with docker
# Feel free to check GraphDB's website for the free version https://www.ontotext.com/products/graphdb/graphdb-free/
print("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:
    raise Exception(
        "Failed to launch GraphDB. Maybe it is already running or you already have a container with that name that you could start?"
    )
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"))
print(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
print(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")

query = "In which house is Harry Potter?"
print(f'Translating the text query "{query}" to a SPARQL query and executing it on the knowledge graph...')
result = kgqa_retriever.retrieve(query=query)
print(result)
# Correct SPARQL query: select ?a { hp:Harry_potter hp:house ?a . }
# Correct answer: Gryffindor

print("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 . }"
)
print(result)
# Paraphrased question: Who is the keeper of keys and grounds?
# Correct answer: Rubeus Hagrid

print("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 . }"
)
print(result)
# Paraphrased question: What is the patronus of Hermione?
# Correct answer: Otter