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deepset-ai--haystack/test/components/retrievers/test_sentence_window_retriever_async.py
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chore: import upstream snapshot with attribution
2026-07-13 13:22:28 +08:00

227 lines
9.9 KiB
Python

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
import random
import re
import pytest
from haystack import Document, Pipeline
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.components.retrievers.sentence_window_retriever import SentenceWindowRetriever
class TestSentenceWindowRetrieverAsync:
async def test_document_without_split_id(self, in_memory_doc_store):
docs = [
Document(content="This is a text with some words. There is a ", meta={"id": "doc_0"}),
Document(content="some words. There is a second sentence. And there is ", meta={"id": "doc_1"}),
]
with pytest.raises(ValueError, match="The retrieved documents must have 'split_id_test' in their metadata."):
retriever = SentenceWindowRetriever(
document_store=in_memory_doc_store, window_size=3, split_id_meta_field="split_id_test"
)
await retriever.run_async(retrieved_documents=docs)
@pytest.mark.asyncio
async def test_document_without_source_id(self, in_memory_doc_store):
docs = [
Document(content="This is a text with some words. There is a ", meta={"id": "doc_0", "split_id": 0}),
Document(
content="some words. There is a second sentence. And there is ",
meta={"id": "doc_1", "split_id": 1, "source_id_test": "source1"},
),
]
with pytest.raises(ValueError, match="The retrieved documents must have 'source_id_test' in their metadata."):
retriever = SentenceWindowRetriever(
document_store=in_memory_doc_store, window_size=3, source_id_meta_field="source_id_test"
)
await retriever.run_async(retrieved_documents=docs)
@pytest.mark.asyncio
async def test_document_without_all_source_ids(self, in_memory_doc_store):
docs = [
Document(
content="These are words from the first section",
meta={"id": "doc_1", "split_id": 0, "section_id": "section1"},
),
Document(
content="These are words from the second section, but missing section_id",
meta={"id": "doc_0", "split_id": 0},
),
]
with pytest.raises(
ValueError, match=re.escape("The retrieved documents must have '['id', 'section_id']' in their metadata.")
):
retriever = SentenceWindowRetriever(
document_store=in_memory_doc_store, window_size=3, source_id_meta_field=["id", "section_id"]
)
await retriever.run_async(retrieved_documents=docs)
@pytest.mark.asyncio
async def test_run_async_invalid_window_size(self, in_memory_doc_store):
docs = [Document(content="This is a text with some words. There is a ", meta={"id": "doc_0", "split_id": 0})]
with pytest.raises(ValueError):
retriever = SentenceWindowRetriever(document_store=in_memory_doc_store, window_size=0)
await retriever.run_async(retrieved_documents=docs)
@pytest.mark.asyncio
async def test_constructor_parameter_does_not_change(self, in_memory_doc_store):
retriever = SentenceWindowRetriever(in_memory_doc_store, window_size=5)
assert retriever.window_size == 5
doc = {
"id": "doc_0",
"content": "This is a text with some words. There is a ",
"source_id": "c5d7c632affc486d0cfe7b3c0f4dc1d3896ea720da2b538d6d10b104a3df5f99",
"page_number": 1,
"split_id": 0,
"split_idx_start": 0,
"_split_overlap": [{"doc_id": "doc_1", "range": (0, 23)}],
}
await retriever.run_async(retrieved_documents=[Document.from_dict(doc)], window_size=1)
assert retriever.window_size == 5
@pytest.mark.asyncio
async def test_context_documents_returned_are_ordered_by_split_idx_start(self, in_memory_doc_store):
docs = []
accumulated_length = 0
for sent in range(10):
content = f"Sentence {sent}."
docs.append(
Document(
content=content,
meta={
"id": f"doc_{sent}",
"split_idx_start": accumulated_length,
"source_id": "source1",
"split_id": sent,
},
)
)
accumulated_length += len(content)
random.shuffle(docs)
in_memory_doc_store.write_documents(docs)
retriever = SentenceWindowRetriever(document_store=in_memory_doc_store, window_size=3)
# run the retriever with a document whose content = "Sentence 4."
result = await retriever.run_async(retrieved_documents=[doc for doc in docs if doc.content == "Sentence 4."])
# assert that the context documents are in the correct order
assert len(result["context_documents"]) == 7
assert [doc.meta["split_idx_start"] for doc in result["context_documents"]] == [11, 22, 33, 44, 55, 66, 77]
@pytest.mark.asyncio
async def test_run_async_custom_fields(self, in_memory_doc_store):
docs = []
accumulated_length = 0
for sent in range(10):
content = f"Sentence {sent}."
docs.append(
Document(
content=content,
meta={
"id": f"doc_{sent}",
# Missing split_idx_start
"source_id_test": "source1",
"split_id_test": sent,
},
)
)
accumulated_length += len(content)
random.shuffle(docs)
in_memory_doc_store.write_documents(docs)
retriever = SentenceWindowRetriever(
document_store=in_memory_doc_store,
window_size=3,
source_id_meta_field="source_id_test",
split_id_meta_field="split_id_test",
)
# run the retriever with a document whose content = "Sentence 4."
result = await retriever.run_async(retrieved_documents=[doc for doc in docs if doc.content == "Sentence 4."])
assert len(result["context_documents"]) == 7
@pytest.mark.asyncio
async def test_run_async_with_multiple_source_ids(self, in_memory_doc_store):
docs = [
Document(content="This is the first chunk.", meta={"section": "1", "split_id": 0, "source_id": "source1"}),
Document(content="This is the second chunk.", meta={"section": "1", "split_id": 1, "source_id": "source1"}),
Document(content="This is the third chunk.", meta={"section": "1", "split_id": 2, "source_id": "source1"}),
Document(
content="This is a chunk from section 2.", meta={"section": "2", "split_id": 3, "source_id": "source1"}
),
]
in_memory_doc_store.write_documents(docs)
retriever = SentenceWindowRetriever(
document_store=in_memory_doc_store, window_size=5, source_id_meta_field=["section", "source_id"]
)
result = await retriever.run_async(
retrieved_documents=[
Document(
content="This is the second chunk.", meta={"section": "1", "split_id": 1, "source_id": "source1"}
)
]
)
assert len(result["context_windows"]) == 1
assert len(result["context_documents"]) == 3
assert all(doc.meta["section"] == "1" for doc in result["context_documents"])
@pytest.mark.asyncio
@pytest.mark.integration
async def test_run_async_with_pipeline(self, in_memory_doc_store):
splitter = DocumentSplitter(split_length=1, split_overlap=0, split_by="period")
text = (
"This is a text with some words. There is a second sentence. And there is also a third sentence. "
"It also contains a fourth sentence. And a fifth sentence. And a sixth sentence. And a seventh sentence"
)
doc = Document(content=text)
docs = splitter.run([doc])
in_memory_doc_store.write_documents(docs["documents"])
pipe = Pipeline()
pipe.add_component("bm25_retriever", InMemoryBM25Retriever(in_memory_doc_store, top_k=1))
pipe.add_component(
"sentence_window_retriever", SentenceWindowRetriever(document_store=in_memory_doc_store, window_size=2)
)
pipe.connect("bm25_retriever", "sentence_window_retriever")
result = await pipe.run_async({"bm25_retriever": {"query": "third"}})
assert result["sentence_window_retriever"]["context_windows"] == [
"This is a text with some words. There is a second sentence. And there is also a third sentence. "
"It also contains a fourth sentence. And a fifth sentence."
]
assert len(result["sentence_window_retriever"]["context_documents"]) == 5
result = await pipe.run_async(
{"bm25_retriever": {"query": "third"}, "sentence_window_retriever": {"window_size": 1}}
)
assert result["sentence_window_retriever"]["context_windows"] == [
" There is a second sentence. And there is also a third sentence. It also contains a fourth sentence."
]
assert len(result["sentence_window_retriever"]["context_documents"]) == 3
@pytest.mark.asyncio
@pytest.mark.integration
async def test_serialization_deserialization_in_pipeline(self, in_memory_doc_store):
pipe = Pipeline()
pipe.add_component("bm25_retriever", InMemoryBM25Retriever(in_memory_doc_store, top_k=1))
pipe.add_component(
"sentence_window_retriever", SentenceWindowRetriever(document_store=in_memory_doc_store, window_size=2)
)
pipe.connect("bm25_retriever", "sentence_window_retriever")
serialized = pipe.to_dict()
deserialized = Pipeline.from_dict(serialized)
assert deserialized == pipe