Add validation for types that are not installed

.github/workflows/main.yaml

@@ -291,17 +291,17 @@ jobs:
           $WEAVIATE_126
         ]
     steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
       - name: Download build artifact to append to release
         uses: actions/download-artifact@v4
         with:
           name: weaviate-python-client-wheel
       - run: |
           pip install weaviate_client-*.whl
-          pip install pytest pytest-benchmark pytest-profiling grpcio grpcio-tools pytest-xdist
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
+          pip install -r requirements-devel.txt  # install test dependencies
       - name: free space
         run: sudo rm -rf /usr/local/lib/android
       - run: rm -r weaviate

integration/test_collection.py

@@ -7,6 +7,7 @@
 
 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
@@ -51,8 +52,6 @@
 )
 from weaviate.types import UUID, UUIDS
 
-import weaviate.classes as wvc
-
 UUID1 = uuid.UUID("806827e0-2b31-43ca-9269-24fa95a221f9")
 UUID2 = uuid.UUID("8ad0d33c-8db1-4437-87f3-72161ca2a51a")
 UUID3 = uuid.UUID("83d99755-9deb-4b16-8431-d1dff4ab0a75")
@@ -863,107 +862,6 @@ def test_query_properties(collection_factory: CollectionFactory) -> None:
     assert len(objects) == 0
 
 
-def test_near_vector(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
-        ),
-    )
-    uuid_banana = collection.data.insert({"Name": "Banana"})
-    collection.data.insert({"Name": "Fruit"})
-    collection.data.insert({"Name": "car"})
-    collection.data.insert({"Name": "Mountain"})
-
-    banana = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)
-
-    full_objects = collection.query.near_vector(
-        banana.vector["default"], return_metadata=MetadataQuery(distance=True, certainty=True)
-    ).objects
-    assert len(full_objects) == 4
-
-    objects_distance = collection.query.near_vector(
-        banana.vector["default"], distance=full_objects[2].metadata.distance
-    ).objects
-    assert len(objects_distance) == 3
-
-    objects_distance = collection.query.near_vector(
-        banana.vector["default"], certainty=full_objects[2].metadata.certainty
-    ).objects
-    assert len(objects_distance) == 3
-
-
-def test_near_vector_limit(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
-        ),
-    )
-    uuid_banana = collection.data.insert({"Name": "Banana"})
-    collection.data.insert({"Name": "Fruit"})
-    collection.data.insert({"Name": "car"})
-    collection.data.insert({"Name": "Mountain"})
-
-    banana = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)
-
-    objs = collection.query.near_vector(banana.vector["default"], limit=2).objects
-    assert len(objs) == 2
-
-
-def test_near_vector_offset(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
-        ),
-    )
-    uuid_banana = collection.data.insert({"Name": "Banana"})
-    uuid_fruit = collection.data.insert({"Name": "Fruit"})
-    collection.data.insert({"Name": "car"})
-    collection.data.insert({"Name": "Mountain"})
-
-    banana = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)
-
-    objs = collection.query.near_vector(banana.vector["default"], offset=1).objects
-    assert len(objs) == 3
-    assert objs[0].uuid == uuid_fruit
-
-
-def test_near_vector_group_by_argument(collection_factory: CollectionFactory) -> None:
-    collection = collection_factory(
-        properties=[
-            Property(name="Name", data_type=DataType.TEXT),
-            Property(name="Count", data_type=DataType.INT),
-        ],
-        vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
-            vectorize_collection_name=False
-        ),
-    )
-    uuid_banana1 = collection.data.insert({"Name": "Banana", "Count": 51})
-    collection.data.insert({"Name": "Banana", "Count": 72})
-    collection.data.insert({"Name": "car", "Count": 12})
-    collection.data.insert({"Name": "Mountain", "Count": 1})
-
-    banana1 = collection.query.fetch_object_by_id(uuid_banana1, include_vector=True)
-
-    ret = collection.query.near_vector(
-        banana1.vector["default"],
-        group_by=GroupBy(
-            prop="name",
-            number_of_groups=4,
-            objects_per_group=10,
-        ),
-        return_metadata=MetadataQuery(distance=True, certainty=True),
-    )
-
-    assert len(ret.objects) == 4
-    assert ret.objects[0].belongs_to_group == "Banana"
-    assert ret.objects[1].belongs_to_group == "Banana"
-    assert ret.objects[2].belongs_to_group == "car"
-    assert ret.objects[3].belongs_to_group == "Mountain"
-
-
 def test_near_object(collection_factory: CollectionFactory) -> None:
     collection = collection_factory(
         properties=[Property(name="Name", data_type=DataType.TEXT)],

integration/test_collection_near_vector.py

@@ -0,0 +1,170 @@
+import uuid
+from typing import Any
+
+import numpy as np
+import pytest
+
+from integration.conftest import CollectionFactory
+from weaviate.collections.classes.config import (
+    Configure,
+    DataType,
+    Property,
+)
+from weaviate.collections.classes.grpc import (
+    GroupBy,
+    MetadataQuery,
+)
+
+UUID1 = uuid.UUID("806827e0-2b31-43ca-9269-24fa95a221f9")
+UUID2 = uuid.UUID("8ad0d33c-8db1-4437-87f3-72161ca2a51a")
+UUID3 = uuid.UUID("83d99755-9deb-4b16-8431-d1dff4ab0a75")
+
+
+def test_near_vector(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
+        ),
+    )
+    uuid_banana = collection.data.insert({"Name": "Banana"})
+    collection.data.insert({"Name": "Fruit"})
+    collection.data.insert({"Name": "car"})
+    collection.data.insert({"Name": "Mountain"})
+
+    banana = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)
+
+    full_objects = collection.query.near_vector(
+        banana.vector["default"], return_metadata=MetadataQuery(distance=True, certainty=True)
+    ).objects
+    assert len(full_objects) == 4
+
+    objects_distance = collection.query.near_vector(
+        banana.vector["default"], distance=full_objects[2].metadata.distance
+    ).objects
+    assert len(objects_distance) == 3
+
+    objects_distance = collection.query.near_vector(
+        banana.vector["default"], certainty=full_objects[2].metadata.certainty
+    ).objects
+    assert len(objects_distance) == 3
+
+
+def test_near_vector_limit(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
+        ),
+    )
+    uuid_banana = collection.data.insert({"Name": "Banana"})
+    collection.data.insert({"Name": "Fruit"})
+    collection.data.insert({"Name": "car"})
+    collection.data.insert({"Name": "Mountain"})
+
+    banana = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)
+
+    objs = collection.query.near_vector(banana.vector["default"], limit=2).objects
+    assert len(objs) == 2
+
+
+def test_near_vector_offset(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
+        ),
+    )
+    uuid_banana = collection.data.insert({"Name": "Banana"})
+    uuid_fruit = collection.data.insert({"Name": "Fruit"})
+    collection.data.insert({"Name": "car"})
+    collection.data.insert({"Name": "Mountain"})
+
+    banana = collection.query.fetch_object_by_id(uuid_banana, include_vector=True)
+
+    objs = collection.query.near_vector(banana.vector["default"], offset=1).objects
+    assert len(objs) == 3
+    assert objs[0].uuid == uuid_fruit
+
+
+def test_near_vector_group_by_argument(collection_factory: CollectionFactory) -> None:
+    collection = collection_factory(
+        properties=[
+            Property(name="Name", data_type=DataType.TEXT),
+            Property(name="Count", data_type=DataType.INT),
+        ],
+        vectorizer_config=Configure.Vectorizer.text2vec_contextionary(
+            vectorize_collection_name=False
+        ),
+    )
+    uuid_banana1 = collection.data.insert({"Name": "Banana", "Count": 51})
+    collection.data.insert({"Name": "Banana", "Count": 72})
+    collection.data.insert({"Name": "car", "Count": 12})
+    collection.data.insert({"Name": "Mountain", "Count": 1})
+
+    banana1 = collection.query.fetch_object_by_id(uuid_banana1, include_vector=True)
+
+    ret = collection.query.near_vector(
+        banana1.vector["default"],
+        group_by=GroupBy(
+            prop="name",
+            number_of_groups=4,
+            objects_per_group=10,
+        ),
+        return_metadata=MetadataQuery(distance=True, certainty=True),
+    )
+
+    assert len(ret.objects) == 4
+    assert ret.objects[0].belongs_to_group == "Banana"
+    assert ret.objects[1].belongs_to_group == "Banana"
+    assert ret.objects[2].belongs_to_group == "car"
+    assert ret.objects[3].belongs_to_group == "Mountain"
+
+
+@pytest.mark.parametrize("near_vector", [[1, 0], [1.0, 0.0], np.array([1, 0])])
+def test_near_vector_with_other_input(
+    collection_factory: CollectionFactory, near_vector: Any
+) -> None:
+    collection = collection_factory(vectorizer_config=Configure.Vectorizer.none())
+
+    uuid1 = collection.data.insert({}, vector=[1, 0])
+    collection.data.insert({}, vector=[0, 1])
+
+    ret = collection.query.near_vector(
+        near_vector,
+        distance=0.1,
+    )
+    assert len(ret.objects) == 1
+    assert ret.objects[0].uuid == uuid1
+
+
+@pytest.mark.parametrize(
+    "near_vector",
+    [
+        {"first": [1, 0], "second": [1, 0, 0]},
+        {"first": np.array([1, 0]), "second": [1, 0, 0]},
+        {"first": np.array([1, 0]), "second": [1, 0, 0]},
+        [np.array([1, 0]), [1, 0, 0]],
+        {"first": [1.0, 0.0], "second": [1.0, 0.0, 0.0]},
+    ],
+)
+def test_near_vector_with_named_vector_other_input(
+    collection_factory: CollectionFactory, near_vector: Any
+) -> None:
+    dummy = collection_factory("dummy")
+    if dummy._connection._weaviate_version.is_lower_than(1, 26, 0):
+        pytest.skip("Named vectors are supported in versions higher than 1.26.0")
+
+    collection = collection_factory(
+        vectorizer_config=[
+            Configure.NamedVectors.none("first"),
+            Configure.NamedVectors.none("second"),
+        ]
+    )
+
+    uuid1 = collection.data.insert({}, vector={"first": [1, 0], "second": [1, 0, 0]})
+    collection.data.insert({}, vector={"first": [0, 1], "second": [0, 0, 1]})
+
+    ret = collection.query.near_vector(near_vector, distance=0.1, target_vector=["first", "second"])
+    assert len(ret.objects) == 1
+    assert ret.objects[0].uuid == uuid1

test/collection/test_validator.py

@@ -0,0 +1,38 @@
+from typing import Any, List
+
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+
+from weaviate.exceptions import WeaviateInvalidInputError
+from weaviate.validator import _validate_input, _ValidateArgument, _ExtraTypes
+
+
+@pytest.mark.parametrize(
+    "inputs,expected,error",
+    [
+        (1, [int], False),
+        (1.0, [int], True),
+        ([1, 1], [List], False),
+        (np.array([1, 2, 3]), [_ExtraTypes.NUMPY], False),
+        (np.array([1, 2, 3]), [_ExtraTypes.NUMPY, List], False),
+        (np.array([1, 2, 3]), [List], True),
+        ([1, 1], [List, _ExtraTypes.NUMPY], False),
+        (pd.array([1, 1]), [_ExtraTypes.PANDAS, List], False),
+        (pd.Series([1, 1]), [_ExtraTypes.PANDAS, List], False),
+        (pl.Series([1, 1]), [_ExtraTypes.POLARS, List], False),
+        (
+            pl.Series([1, 1]),
+            [_ExtraTypes.POLARS, _ExtraTypes.PANDAS, _ExtraTypes.NUMPY, List],
+            False,
+        ),
+        (pl.Series([1, 1]), [_ExtraTypes.PANDAS, _ExtraTypes.NUMPY, List], True),
+    ],
+)
+def test_validator(inputs: Any, expected: List[Any], error: bool) -> None:
+    if error:
+        with pytest.raises(WeaviateInvalidInputError):
+            _validate_input(_ValidateArgument(expected=expected, name="test", value=inputs))
+    else:
+        _validate_input(_ValidateArgument(expected=expected, name="test", value=inputs))

weaviate/collections/classes/grpc.py

@@ -6,8 +6,8 @@
 
 from weaviate.collections.classes.types import _WeaviateInput
 from weaviate.proto.v1 import search_get_pb2
+from weaviate.str_enum import BaseEnum
 from weaviate.types import INCLUDE_VECTOR, UUID
-from weaviate.util import BaseEnum
 
 
 class HybridFusion(str, BaseEnum):