mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-28 03:12:54 +00:00
a59bca3661
* Testing black on ui/ * Applying black on docstores * Add latest docstring and tutorial changes * Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too * Remove comments * Relax constraints on pydoc-markdown * Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade * Fix a couple of bugs * Add a type: ignore that was missing somehow * Give path to black * Apply Black * Apply Black * Relocate a couple of type: ignore * Update documentation * Make Linux CI run after applying Black * Triggering Black * Apply Black * Remove dependency, does not work well * Remove manually double trailing commas * Update documentation Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
import pytest
|
|
|
|
from haystack.nodes.retriever.sparse import ElasticsearchRetriever
|
|
from haystack.nodes.reader import FARMReader
|
|
from haystack.pipelines import Pipeline
|
|
|
|
from haystack.nodes.extractor import EntityExtractor, simplify_ner_for_qa
|
|
|
|
|
|
@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
|
|
def test_extractor(document_store_with_docs):
|
|
|
|
es_retriever = ElasticsearchRetriever(document_store=document_store_with_docs)
|
|
ner = EntityExtractor()
|
|
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", num_processes=0)
|
|
|
|
pipeline = Pipeline()
|
|
pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
|
|
pipeline.add_node(component=ner, name="NER", inputs=["ESRetriever"])
|
|
pipeline.add_node(component=reader, name="Reader", inputs=["NER"])
|
|
|
|
prediction = pipeline.run(
|
|
query="Who lives in Berlin?",
|
|
params={
|
|
"ESRetriever": {"top_k": 1},
|
|
"Reader": {"top_k": 1},
|
|
},
|
|
)
|
|
entities = [entity["word"] for entity in prediction["answers"][0].meta["entities"]]
|
|
assert "Carla" in entities
|
|
assert "Berlin" in entities
|
|
|
|
|
|
@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
|
|
def test_extractor_output_simplifier(document_store_with_docs):
|
|
|
|
es_retriever = ElasticsearchRetriever(document_store=document_store_with_docs)
|
|
ner = EntityExtractor()
|
|
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", num_processes=0)
|
|
|
|
pipeline = Pipeline()
|
|
pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
|
|
pipeline.add_node(component=ner, name="NER", inputs=["ESRetriever"])
|
|
pipeline.add_node(component=reader, name="Reader", inputs=["NER"])
|
|
|
|
prediction = pipeline.run(
|
|
query="Who lives in Berlin?",
|
|
params={
|
|
"ESRetriever": {"top_k": 1},
|
|
"Reader": {"top_k": 1},
|
|
},
|
|
)
|
|
simplified = simplify_ner_for_qa(prediction)
|
|
assert simplified[0] == {"answer": "Carla", "entities": ["Carla"]}
|