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#
# SPDX-License-Identifier: Apache-2.0
import pandas as pd
import pytest
from haystack import Document
from haystack.dataclasses.byte_stream import ByteStream
from haystack.dataclasses.sparse_embedding import SparseEmbedding
@pytest.mark.parametrize(
"doc,doc_str",
[
(Document(content="test text"), "content: 'test text'"),
(
Document(dataframe=pd.DataFrame([["John", 25], ["Martha", 34]], columns=["name", "age"])),
"dataframe: (2, 2)",
),
(Document(blob=ByteStream(b"hello, test string")), "blob: 18 bytes"),
(
Document(
content="test text",
dataframe=pd.DataFrame([["John", 25], ["Martha", 34]], columns=["name", "age"]),
blob=ByteStream(b"hello, test string"),
),
"content: 'test text', dataframe: (2, 2), blob: 18 bytes",
),
],
)
def test_document_str(doc, doc_str):
assert f"Document(id={doc.id}, {doc_str})" == str(doc)
def test_init():
doc = Document()
assert doc.id == "d4675c57fcfe114db0b95f1da46eea3c5d6f5729c17d01fb5251ae19830a3455"
assert doc.content == None
assert doc.dataframe == None
assert doc.blob == None
assert doc.meta == {}
assert doc.score == None
assert doc.embedding == None
assert doc.sparse_embedding == None
def test_init_with_wrong_parameters():
with pytest.raises(TypeError):
Document(text="")
def test_init_with_parameters():
blob_data = b"some bytes"
sparse_embedding = SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3])
doc = Document(
content="test text",
dataframe=pd.DataFrame([0]),
blob=ByteStream(data=blob_data, mime_type="text/markdown"),
meta={"text": "test text"},
score=0.812,
embedding=[0.1, 0.2, 0.3],
sparse_embedding=sparse_embedding,
)
assert doc.id == "967b7bd4a21861ad9e863f638cefcbdd6bf6306bebdd30aa3fedf0c26bc636ed"
assert doc.content == "test text"
assert doc.dataframe is not None
assert doc.dataframe.equals(pd.DataFrame([0]))
assert doc.blob.data == blob_data
assert doc.blob.mime_type == "text/markdown"
assert doc.meta == {"text": "test text"}
assert doc.score == 0.812
assert doc.embedding == [0.1, 0.2, 0.3]
assert doc.sparse_embedding == sparse_embedding
def test_init_with_legacy_fields():
doc = Document(
content="test text",
content_type="text",
id_hash_keys=["content"],
score=0.812,
embedding=[0.1, 0.2, 0.3], # type: ignore
)
assert doc.id == "18fc2c114825872321cf5009827ca162f54d3be50ab9e9ffa027824b6ec223af"
assert doc.content == "test text"
assert doc.dataframe == None
assert doc.blob == None
assert doc.meta == {}
assert doc.score == 0.812
assert doc.embedding == [0.1, 0.2, 0.3]
assert doc.sparse_embedding == None
def test_init_with_legacy_field():
doc = Document(
content="test text",
content_type="text", # type: ignore
id_hash_keys=["content"], # type: ignore
score=0.812,
embedding=[0.1, 0.2, 0.3],
meta={"date": "10-10-2023", "type": "article"},
)
assert doc.id == "a2c0321b34430cc675294611e55529fceb56140ca3202f1c59a43a8cecac1f43"
assert doc.content == "test text"
assert doc.dataframe == None
assert doc.meta == {"date": "10-10-2023", "type": "article"}
assert doc.score == 0.812
assert doc.embedding == [0.1, 0.2, 0.3]
assert doc.sparse_embedding == None
def test_basic_equality_type_mismatch():
doc = Document(content="test text")
assert doc != "test text"
def test_basic_equality_id():
doc1 = Document(content="test text")
doc2 = Document(content="test text")
assert doc1 == doc2
doc1.id = "1234"
doc2.id = "5678"
assert doc1 != doc2
def test_to_dict():
doc = Document()
assert doc.to_dict() == {
"id": doc._create_id(),
"content": None,
"dataframe": None,
"blob": None,
"score": None,
"embedding": None,
"sparse_embedding": None,
}
def test_to_dict_without_flattening():
doc = Document()
assert doc.to_dict(flatten=False) == {
"id": doc._create_id(),
"content": None,
"dataframe": None,
"blob": None,
"meta": {},
"score": None,
"embedding": None,
"sparse_embedding": None,
}
def test_to_dict_with_custom_parameters():
doc = Document(
content="test text",
dataframe=pd.DataFrame([10, 20, 30]),
blob=ByteStream(b"some bytes", mime_type="application/pdf"),
meta={"some": "values", "test": 10},
score=0.99,
embedding=[10.0, 10.0],
sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
)
assert doc.to_dict() == {
"id": doc.id,
"content": "test text",
"dataframe": pd.DataFrame([10, 20, 30]).to_json(),
"blob": {"data": list(b"some bytes"), "mime_type": "application/pdf"},
"some": "values",
"test": 10,
"score": 0.99,
"embedding": [10.0, 10.0],
"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
}
def test_to_dict_with_custom_parameters_without_flattening():
doc = Document(
content="test text",
dataframe=pd.DataFrame([10, 20, 30]),
blob=ByteStream(b"some bytes", mime_type="application/pdf"),
meta={"some": "values", "test": 10},
score=0.99,
embedding=[10.0, 10.0],
sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
)
assert doc.to_dict(flatten=False) == {
"id": doc.id,
"content": "test text",
"dataframe": pd.DataFrame([10, 20, 30]).to_json(),
"blob": {"data": list(b"some bytes"), "mime_type": "application/pdf"},
"meta": {"some": "values", "test": 10},
"score": 0.99,
"embedding": [10, 10],
"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
}
def test_from_dict():
assert Document.from_dict({}) == Document()
def from_from_dict_with_parameters():
blob_data = b"some bytes"
assert Document.from_dict(
{
"content": "test text",
"dataframe": pd.DataFrame([0]).to_json(),
"blob": {"data": list(blob_data), "mime_type": "text/markdown"},
"meta": {"text": "test text"},
"score": 0.812,
"embedding": [0.1, 0.2, 0.3],
"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
}
) == Document(
content="test text",
dataframe=pd.DataFrame([0]),
blob=ByteStream(blob_data, mime_type="text/markdown"),
meta={"text": "test text"},
score=0.812,
embedding=[0.1, 0.2, 0.3],
sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
)
def test_from_dict_with_legacy_fields():
assert Document.from_dict(
{
"content": "test text",
"content_type": "text",
"id_hash_keys": ["content"],
"score": 0.812,
"embedding": [0.1, 0.2, 0.3],
}
) == Document(
content="test text",
content_type="text",
id_hash_keys=["content"],
score=0.812,
embedding=[0.1, 0.2, 0.3], # type: ignore
)
def test_from_dict_with_legacy_field_and_flat_meta():
assert Document.from_dict(
{
"content": "test text",
"content_type": "text",
"id_hash_keys": ["content"],
"score": 0.812,
"embedding": [0.1, 0.2, 0.3],
"date": "10-10-2023",
"type": "article",
}
) == Document(
content="test text",
content_type="text", # type: ignore
id_hash_keys=["content"], # type: ignore
score=0.812,
embedding=[0.1, 0.2, 0.3],
meta={"date": "10-10-2023", "type": "article"},
)
def test_from_dict_with_flat_meta():
blob_data = b"some bytes"
assert Document.from_dict(
{
"content": "test text",
"dataframe": pd.DataFrame([0]).to_json(),
"blob": {"data": list(blob_data), "mime_type": "text/markdown"},
"score": 0.812,
"embedding": [0.1, 0.2, 0.3],
"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
"date": "10-10-2023",
"type": "article",
}
) == Document(
content="test text",
dataframe=pd.DataFrame([0]),
blob=ByteStream(blob_data, mime_type="text/markdown"),
score=0.812,
embedding=[0.1, 0.2, 0.3],
sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
meta={"date": "10-10-2023", "type": "article"},
)
def test_from_dict_with_flat_and_non_flat_meta():
with pytest.raises(ValueError, match="Pass either the 'meta' parameter or flattened metadata keys"):
Document.from_dict(
{
"content": "test text",
"dataframe": pd.DataFrame([0]).to_json(),
"blob": {"data": list(b"some bytes"), "mime_type": "text/markdown"},
"score": 0.812,
"meta": {"test": 10},
"embedding": [0.1, 0.2, 0.3],
"date": "10-10-2023",
"type": "article",
}
)
def test_content_type():
assert Document(content="text").content_type == "text"
assert Document(dataframe=pd.DataFrame([0])).content_type == "table"
with pytest.raises(ValueError):
_ = Document().content_type
with pytest.raises(ValueError):
_ = Document(content="text", dataframe=pd.DataFrame([0])).content_type
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