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Hybrid search vector per target #1160

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merged 6 commits into from
Jul 5, 2024
Merged

Hybrid search vector per target #1160

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58126ed
Add support for vector per target for hybrid
dirkkul Jul 4, 2024
7f226c3
Fix argument for vector
dirkkul Jul 4, 2024
cae600f
Fix hybrid near nector search
dirkkul Jul 4, 2024
ac8429c
Add support for vector per target for vector argument in hybrid
dirkkul Jul 4, 2024
8d95d60
Add fast path for near vector search with list
dirkkul Jul 4, 2024
0cc1b9a
Allow none as vector input again
dirkkul Jul 4, 2024
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7 changes: 4 additions & 3 deletions integration/conftest.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,11 +19,10 @@
_VectorIndexConfigCreate,
_RerankerConfigCreate,
)
from weaviate.collections.classes.config_named_vectors import _NamedVectorConfigCreate
from weaviate.collections.classes.types import Properties
from weaviate.config import AdditionalConfig

from weaviate.collections.classes.config_named_vectors import _NamedVectorConfigCreate


class CollectionFactory(Protocol):
"""Typing for fixture."""
Expand Down Expand Up @@ -77,7 +76,9 @@ def _factory(
reranker_config: Optional[_RerankerConfigCreate] = None,
) -> Collection[Any, Any]:
nonlocal client_fixture, name_fixture
name_fixture = _sanitize_collection_name(request.node.name) + name
name_fixture = (
_sanitize_collection_name(request.node.fspath.basename + "_" + request.node.name) + name
)
client_fixture = weaviate.connect_to_local(
headers=headers,
grpc_port=ports[1],
Expand Down
356 changes: 0 additions & 356 deletions integration/test_collection.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@

import pytest

import weaviate.classes as wvc
from integration.conftest import CollectionFactory, CollectionFactoryGet, _sanitize_collection_name
from integration.constants import WEAVIATE_LOGO_OLD_ENCODED, WEAVIATE_LOGO_NEW_ENCODED
from weaviate.collections.classes.batch import ErrorObject
Expand Down Expand Up @@ -541,144 +540,6 @@ def test_types(collection_factory: CollectionFactory, data_type: DataType, value
assert object_get_from_batch is not None and object_get_from_batch.properties[name] == value


@pytest.mark.parametrize("fusion_type", [HybridFusion.RANKED, HybridFusion.RELATIVE_SCORE])
def test_search_hybrid(collection_factory: CollectionFactory, fusion_type: HybridFusion) -> None:
collection = collection_factory(
properties=[Property(name="Name", data_type=DataType.TEXT)],
vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
vectorize_collection_name=False
),
)
collection.data.insert({"Name": "some name"}, uuid=uuid.uuid4())
collection.data.insert({"Name": "other word"}, uuid=uuid.uuid4())
objs = collection.query.hybrid(
alpha=0, query="name", fusion_type=fusion_type, include_vector=True
).objects
assert len(objs) == 1

objs = collection.query.hybrid(
alpha=1, query="name", fusion_type=fusion_type, vector=objs[0].vector["default"]
).objects
assert len(objs) == 2


def test_search_hybrid_group_by(collection_factory: CollectionFactory) -> None:
collection = collection_factory(
properties=[Property(name="Name", data_type=DataType.TEXT)],
vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
vectorize_collection_name=False
),
)
collection.data.insert({"Name": "some name"}, uuid=uuid.uuid4())
collection.data.insert({"Name": "other word"}, uuid=uuid.uuid4())
if collection._connection.supports_groupby_in_bm25_and_hybrid():
objs = collection.query.hybrid(
alpha=0,
query="name",
include_vector=True,
group_by=GroupBy(prop="name", objects_per_group=1, number_of_groups=2),
).objects
assert len(objs) == 1
assert objs[0].belongs_to_group == "some name"
else:
with pytest.raises(WeaviateUnsupportedFeatureError):
collection.query.hybrid(
alpha=0,
query="name",
include_vector=True,
group_by=GroupBy(prop="name", objects_per_group=1, number_of_groups=2),
)


@pytest.mark.parametrize("query", [None, ""])
def test_search_hybrid_only_vector(
collection_factory: CollectionFactory, query: Optional[str]
) -> None:
collection = collection_factory(
properties=[Property(name="Name", data_type=DataType.TEXT)],
vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
vectorize_collection_name=False
),
)
uuid_ = collection.data.insert({"Name": "some name"}, uuid=uuid.uuid4())
vec = collection.query.fetch_object_by_id(uuid_, include_vector=True).vector
assert vec is not None

collection.data.insert({"Name": "other word"}, uuid=uuid.uuid4())

objs = collection.query.hybrid(alpha=1, query=query, vector=vec["default"]).objects
assert len(objs) == 2


@pytest.mark.parametrize("limit", [1, 2])
def test_hybrid_limit(collection_factory: CollectionFactory, limit: int) -> None:
collection = collection_factory(
properties=[Property(name="Name", data_type=DataType.TEXT)],
vectorizer_config=Configure.Vectorizer.none(),
)

res = collection.data.insert_many(
[
{"Name": "test"},
{"Name": "another"},
{"Name": "test"},
]
)
assert res.has_errors is False
assert len(collection.query.hybrid(query="test", alpha=0, limit=limit).objects) == limit


@pytest.mark.parametrize("offset,expected", [(0, 2), (1, 1), (2, 0)])
def test_hybrid_offset(collection_factory: CollectionFactory, offset: int, expected: int) -> None:
collection = collection_factory(
properties=[Property(name="Name", data_type=DataType.TEXT)],
vectorizer_config=Configure.Vectorizer.none(),
)

res = collection.data.insert_many(
[
{"Name": "test"},
{"Name": "another"},
{"Name": "test"},
]
)
assert res.has_errors is False

assert len(collection.query.hybrid(query="test", alpha=0, offset=offset).objects) == expected


def test_hybrid_alpha(collection_factory: CollectionFactory) -> None:
collection = collection_factory(
properties=[Property(name="name", data_type=DataType.TEXT)],
vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
vectorize_collection_name=False
),
)

res = collection.data.insert_many(
[
{"name": "banana"},
{"name": "fruit"},
{"name": "car"},
]
)
assert res.has_errors is False

hybrid_res = collection.query.hybrid(query="fruit", alpha=0)
bm25_res = collection.query.bm25(query="fruit")
assert all(
bm25_res.objects[i].uuid == hybrid_res.objects[i].uuid
for i in range(len(hybrid_res.objects))
)

hybrid_res = collection.query.hybrid(query="fruit", alpha=1)
text_res = collection.query.near_text(query="fruit")
assert all(
text_res.objects[i].uuid == hybrid_res.objects[i].uuid
for i in range(len(hybrid_res.objects))
)


def test_bm25(collection_factory: CollectionFactory) -> None:
collection = collection_factory(
properties=[Property(name="Name", data_type=DataType.TEXT)],
Expand Down Expand Up @@ -1844,220 +1705,3 @@ def test_none_query_hybrid_bm25(collection_factory: CollectionFactory) -> None:
bm25_objs = collection.query.bm25(query=None, return_metadata=MetadataQuery.full()).objects
assert len(bm25_objs) == 3
assert all(obj.metadata.score is not None and obj.metadata.score == 0.0 for obj in bm25_objs)


def test_hybrid_near_vector_search(collection_factory: CollectionFactory) -> None:
collection = collection_factory(
properties=[
Property(name="text", data_type=DataType.TEXT),
],
vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
vectorize_collection_name=False
),
)
uuid_banana = collection.data.insert({"text": "banana"})
obj = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)

if collection._connection._weaviate_version.is_lower_than(1, 25, 0):
with pytest.raises(WeaviateUnsupportedFeatureError):
collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_vector(vector=obj.vector["default"]),
).objects
return

collection.data.insert({"text": "dog"})
collection.data.insert({"text": "different concept"})

hybrid_objs: List[Object[Any, Any]] = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_vector(vector=obj.vector["default"]),
).objects

assert hybrid_objs[0].uuid == uuid_banana
assert len(hybrid_objs) == 3

# make a near vector search to get the distance
near_vec = collection.query.near_vector(
near_vector=obj.vector["default"], return_metadata=["distance"]
).objects
assert near_vec[0].metadata.distance is not None

hybrid_objs2 = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_vector(
vector=obj.vector["default"], distance=near_vec[0].metadata.distance + 0.001
),
return_metadata=MetadataQuery.full(),
).objects

assert hybrid_objs2[0].uuid == uuid_banana
assert len(hybrid_objs2) == 1


def test_hybrid_near_vector_search_named_vectors(collection_factory: CollectionFactory) -> None:
dummy = collection_factory("dummy")
collection_maker = lambda: collection_factory(
properties=[
Property(name="text", data_type=DataType.TEXT),
Property(name="int", data_type=DataType.INT),
],
vectorizer_config=[
Configure.NamedVectors.text2vec_contextionary(
name="text", vectorize_collection_name=False
),
Configure.NamedVectors.text2vec_contextionary(
name="int", vectorize_collection_name=False
),
],
)

if dummy._connection._weaviate_version.is_lower_than(1, 24, 0):
with pytest.raises(WeaviateInvalidInputError):
collection_maker()
return

collection = collection_maker()
uuid_banana = collection.data.insert({"text": "banana"})
collection.data.insert({"text": "dog"})
collection.data.insert({"text": "different concept"})

obj = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)

if collection._connection._weaviate_version.is_lower_than(1, 25, 0):
with pytest.raises(WeaviateUnsupportedFeatureError):
hybrid_objs: List[Object[Any, Any]] = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_vector(vector=obj.vector["text"]),
target_vector="text",
).objects
return

hybrid_objs = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_vector(vector=obj.vector["text"]),
target_vector="text",
).objects

assert hybrid_objs[0].uuid == uuid_banana
assert len(hybrid_objs) == 3

# make a near vector search to get the distance
near_vec = collection.query.near_vector(
near_vector=obj.vector["text"], return_metadata=["distance"], target_vector="text"
).objects
assert near_vec[0].metadata.distance is not None

hybrid_objs2 = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_vector(
vector=obj.vector["text"],
distance=near_vec[0].metadata.distance + 0.001,
),
target_vector="text",
return_metadata=MetadataQuery.full(),
).objects

assert hybrid_objs2[0].uuid == uuid_banana
assert len(hybrid_objs2) == 1


def test_hybrid_near_text_search(collection_factory: CollectionFactory) -> None:
collection = collection_factory(
properties=[
Property(name="text", data_type=DataType.TEXT),
],
vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
vectorize_collection_name=False
),
)

if collection._connection._weaviate_version.is_lower_than(1, 25, 0):
with pytest.raises(WeaviateUnsupportedFeatureError):
collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_text(query="banana pudding"),
).objects
return

uuid_banana_pudding = collection.data.insert({"text": "banana pudding"})
collection.data.insert({"text": "banana smoothie"})
collection.data.insert({"text": "different concept"})

hybrid_objs: List[Object[Any, Any]] = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_text(query="banana pudding"),
).objects

assert hybrid_objs[0].uuid == uuid_banana_pudding
assert len(hybrid_objs) == 3

hybrid_objs2 = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_text(
query="banana",
move_to=wvc.query.Move(concepts="pudding", force=0.1),
move_away=wvc.query.Move(concepts="smoothie", force=0.1),
),
return_metadata=MetadataQuery.full(),
).objects

assert hybrid_objs2[0].uuid == uuid_banana_pudding


def test_hybrid_near_text_search_named_vectors(collection_factory: CollectionFactory) -> None:
dummy = collection_factory("dummy")
collection_maker = lambda: collection_factory(
properties=[
Property(name="text", data_type=DataType.TEXT),
Property(name="int", data_type=DataType.INT),
],
vectorizer_config=[
Configure.NamedVectors.text2vec_contextionary(
name="text", vectorize_collection_name=False
),
Configure.NamedVectors.text2vec_contextionary(
name="int", vectorize_collection_name=False
),
],
)
if dummy._connection._weaviate_version.is_lower_than(1, 24, 0):
with pytest.raises(WeaviateInvalidInputError):
collection_maker()
return

collection = collection_maker()
uuid_banana_pudding = collection.data.insert({"text": "banana pudding"})
collection.data.insert({"text": "banana smoothie"})
collection.data.insert({"text": "different concept"})

if collection._connection._weaviate_version.is_lower_than(1, 25, 0):
with pytest.raises(WeaviateUnsupportedFeatureError):
hybrid_objs: List[Object[Any, Any]] = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_text(query="banana pudding"),
target_vector="text",
).objects
return

hybrid_objs = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_text(query="banana pudding"),
target_vector="text",
).objects

assert hybrid_objs[0].uuid == uuid_banana_pudding
assert len(hybrid_objs) == 3

hybrid_objs2 = collection.query.hybrid(
query=None,
vector=wvc.query.HybridVector.near_text(
query="banana",
move_to=wvc.query.Move(concepts="pudding", force=0.1),
move_away=wvc.query.Move(concepts="smoothie", force=0.1),
),
target_vector="text",
return_metadata=MetadataQuery.full(),
).objects

assert hybrid_objs2[0].uuid == uuid_banana_pudding
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