File size: 2,847 Bytes
362a075 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
import json
from haystack import Document, Pipeline
from haystack.components.readers import ExtractiveReader
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
def test_extractive_qa_pipeline(tmp_path):
# Create the pipeline
qa_pipeline = Pipeline()
qa_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=InMemoryDocumentStore()), name="retriever")
qa_pipeline.add_component(instance=ExtractiveReader(model="deepset/tinyroberta-squad2"), name="reader")
qa_pipeline.connect("retriever", "reader")
# Draw the pipeline
qa_pipeline.draw(tmp_path / "test_extractive_qa_pipeline.png")
# Serialize the pipeline to YAML
with open(tmp_path / "test_bm25_rag_pipeline.yaml", "w") as f:
qa_pipeline.dump(f)
# Load the pipeline back
with open(tmp_path / "test_bm25_rag_pipeline.yaml", "r") as f:
qa_pipeline = Pipeline.load(f)
# Populate the document store
documents = [
Document(content="My name is Jean and I live in Paris."),
Document(content="My name is Mark and I live in Berlin."),
Document(content="My name is Giorgio and I live in Rome."),
]
qa_pipeline.get_component("retriever").document_store.write_documents(documents)
# Query and assert
questions = ["Who lives in Paris?", "Who lives in Berlin?", "Who lives in Rome?"]
answers_spywords = ["Jean", "Mark", "Giorgio"]
for question, spyword, doc in zip(questions, answers_spywords, documents):
result = qa_pipeline.run({"retriever": {"query": question}, "reader": {"query": question}})
extracted_answers = result["reader"]["answers"]
# we expect at least one real answer and no_answer
assert len(extracted_answers) > 1
# the best answer should contain the spyword
assert spyword in extracted_answers[0].data
# no_answer
assert extracted_answers[-1].data is None
# since these questions are easily answerable, the best answer should have higher score than no_answer
assert extracted_answers[0].score >= extracted_answers[-1].score
for answer in extracted_answers:
assert answer.query == question
assert hasattr(answer, "score")
assert hasattr(answer, "document_offset")
assert hasattr(answer, "document")
# the top answer is extracted from the correct document
top_answer = extracted_answers[0]
if top_answer.document is not None:
if top_answer.document.id != doc.id:
print(top_answer.document.id, doc.id)
assert top_answer.document.id == doc.id
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