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https://github.com/deepset-ai/haystack.git
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ec72dd73fc
* add e2e tests * move tests to their own module * add e2e workflow * pylint * remove from job * fix index field name * skip test on sql * removed unused code * fix embedding tests * adjust test for pinecone * adjust assertions to the new documents * bad copypasta * test * fix tests * fix tests * fix test * fix tests * pylint * update milvus version * remove debug * move graphdb tests under e2e
108 lines
4.8 KiB
Python
108 lines
4.8 KiB
Python
from pathlib import Path
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from haystack.nodes import Text2SparqlRetriever
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from haystack.document_stores import GraphDBKnowledgeGraph, InMemoryKnowledgeGraph
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from haystack.utils import fetch_archive_from_http
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def test_graph_retrieval():
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# we use a timeout double the default in the CI to account for slow runners
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timeout = 20
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# TODO rename doc_dir
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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 natural language questions 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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kg = GraphDBKnowledgeGraph(index="tutorial_10_index")
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kg.delete_index(timeout=timeout)
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kg.create_index(config_path=Path(graph_dir + "repo-config.ttl"), timeout=timeout)
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kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir + "triples.ttl"), timeout=timeout)
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triple = {
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"p": {"type": "uri", "value": "https://deepset.ai/harry_potter/_paternalgrandfather"},
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"s": {"type": "uri", "value": "https://deepset.ai/harry_potter/Melody_fawley"},
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"o": {"type": "uri", "value": "https://deepset.ai/harry_potter/Marshall_fawley"},
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}
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triples = kg.get_all_triples()
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assert len(triples) > 0
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assert triple in triples
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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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kgqa_retriever = Text2SparqlRetriever(knowledge_graph=kg, model_name_or_path=model_dir + "hp_v3.4")
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result = kgqa_retriever.retrieve(query="In which house is Harry Potter?")
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assert result[0] == {
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"answer": ["https://deepset.ai/harry_potter/Gryffindor"],
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"prediction_meta": {
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"model": "Text2SparqlRetriever",
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"sparql_query": "select ?a { hp:Harry_potter hp:house ?a . }",
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},
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}
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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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assert result[0][0] == "https://deepset.ai/harry_potter/Rubeus_hagrid"
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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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assert result[0][0] == "https://deepset.ai/harry_potter/Otter"
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def test_inmemory_graph_retrieval():
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# TODO rename doc_dir
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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 natural language questions 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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kg = InMemoryKnowledgeGraph(index="tutorial_10_index")
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kg.delete_index()
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kg.create_index()
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kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir + "triples.ttl"))
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triple = {
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"p": {"type": "uri", "value": "https://deepset.ai/harry_potter/_paternalgrandfather"},
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"s": {"type": "uri", "value": "https://deepset.ai/harry_potter/Melody_fawley"},
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"o": {"type": "uri", "value": "https://deepset.ai/harry_potter/Marshall_fawley"},
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}
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triples = kg.get_all_triples()
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assert len(triples) > 0
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assert triple in triples
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kgqa_retriever = Text2SparqlRetriever(knowledge_graph=kg, model_name_or_path=model_dir + "hp_v3.4")
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result = kgqa_retriever.retrieve(query="In which house is Harry Potter?")
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assert result[0] == {
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"answer": ["https://deepset.ai/harry_potter/Gryffindor"],
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"prediction_meta": {
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"model": "Text2SparqlRetriever",
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"sparql_query": "select ?a { hp:Harry_potter hp:house ?a . }",
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},
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}
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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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assert result[0][0] == "https://deepset.ai/harry_potter/Rubeus_hagrid"
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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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assert result[0][0] == "https://deepset.ai/harry_potter/Otter"
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