feat: Add Boost API

integration/test_collection_boost.py

@@ -0,0 +1,291 @@
+import pytest
+
+from integration.conftest import CollectionFactory
+from weaviate.classes.query import Boost, Filter, MetadataQuery
+from weaviate.collections.classes.config import Configure, DataType, Property
+from weaviate.exceptions import WeaviateInvalidInputError
+from weaviate.collections.classes.data import DataObject
+
+
+def _create_collection(collection_factory: CollectionFactory):
+    """Create a collection with numeric and date properties for boost testing."""
+    collection = collection_factory(
+        properties=[
+            Property(name="text", data_type=DataType.TEXT),
+            Property(name="price", data_type=DataType.NUMBER),
+            Property(name="rating", data_type=DataType.NUMBER),
+            Property(name="count", data_type=DataType.INT),
+            Property(name="created", data_type=DataType.DATE),
+        ],
+        vectorizer_config=Configure.Vectorizer.none(),
+        vector_index_config=Configure.VectorIndex.flat(),
+    )
+    if collection._connection._weaviate_version.is_lower_than(1, 38, 0):
+        pytest.skip("Boost requires Weaviate >= 1.38.0")
+    collection.data.insert_many(
+        [
+            DataObject(
+                properties={
+                    "text": "cheap good",
+                    "price": 10.0,
+                    "rating": 4.9,
+                    "count": 1000,
+                    "created": "2024-01-01T00:00:00Z",
+                },
+                vector=[1.0, 0.0, 0.0],
+            ),
+            DataObject(
+                properties={
+                    "text": "cheap bad",
+                    "price": 10.0,
+                    "rating": 2.0,
+                    "count": 5,
+                    "created": "2020-01-01T00:00:00Z",
+                },
+                vector=[0.9, 0.1, 0.0],
+            ),
+            DataObject(
+                properties={
+                    "text": "expensive good",
+                    "price": 500.0,
+                    "rating": 4.8,
+                    "count": 500,
+                    "created": "2023-06-01T00:00:00Z",
+                },
+                vector=[0.0, 1.0, 0.0],
+            ),
+            DataObject(
+                properties={
+                    "text": "expensive bad",
+                    "price": 500.0,
+                    "rating": 1.5,
+                    "count": 2,
+                    "created": "2019-01-01T00:00:00Z",
+                },
+                vector=[0.0, 0.9, 0.1],
+            ),
+            DataObject(
+                properties={
+                    "text": "mid range",
+                    "price": 50.0,
+                    "rating": 3.5,
+                    "count": 100,
+                    "created": "2022-01-01T00:00:00Z",
+                },
+                vector=[0.0, 0.0, 1.0],
+            ),
+        ]
+    )
+    return collection
+
+
+def test_boost_filter(collection_factory: CollectionFactory) -> None:
+    """Boost results matching a filter — boosted items should score higher."""
+    collection = _create_collection(collection_factory)
+
+    baseline = collection.query.near_vector(
+        near_vector=[1.0, 0.0, 0.0],
+        limit=5,
+        return_metadata=MetadataQuery(distance=True),
+    ).objects
+
+    boosted = collection.query.near_vector(
+        near_vector=[1.0, 0.0, 0.0],
+        limit=5,
+        boost=Boost.filter(
+            Filter.by_property("rating").greater_or_equal(4.0),
+            weight=1.0,
+        ),
+        return_metadata=MetadataQuery(distance=True),
+    ).objects
+
+    assert len(boosted) == 5
+    # The boost should change the ordering compared to baseline
+    assert [o.uuid for o in baseline] != [o.uuid for o in boosted]
+
+
+def test_boost_numeric_decay(collection_factory: CollectionFactory) -> None:
+    """Numeric decay: prefer items with price near the origin."""
+    collection = _create_collection(collection_factory)
+
+    result = collection.query.near_vector(
+        near_vector=[1.0, 0.0, 0.0],
+        limit=5,
+        boost=Boost.numeric_decay(
+            "price",
+            origin=50.0,
+            scale=20.0,
+            curve=Boost.Curve.LINEAR,
+            decay=0.5,
+            weight=1.0,
+        ),
+        return_metadata=MetadataQuery(distance=True),
+    ).objects
+
+    assert len(result) == 5
+
+
+def test_boost_time_decay(collection_factory: CollectionFactory) -> None:
+    """Time decay: prefer items with dates closer to origin."""
+    collection = _create_collection(collection_factory)
+
+    result = collection.query.near_vector(
+        near_vector=[1.0, 0.0, 0.0],
+        limit=5,
+        boost=Boost.time_decay(
+            "created",
+            origin="2024-01-01T00:00:00Z",
+            scale="365d",
+            curve=Boost.Curve.EXPONENTIAL,
+            decay=0.3,
+            weight=1.0,
+        ),
+        return_metadata=MetadataQuery(distance=True),
+    ).objects
+
+    assert len(result) == 5
+
+
+def test_boost_property_value(collection_factory: CollectionFactory) -> None:
+    """Property value boost: rank by a numeric property directly."""
+    collection = _create_collection(collection_factory)
+
+    result = collection.query.near_vector(
+        near_vector=[1.0, 0.0, 0.0],
+        limit=5,
+        boost=Boost.property(
+            "count",
+            modifier=Boost.Modifier.LOG1P,
+            weight=1.0,
+        ),
+        return_metadata=MetadataQuery(distance=True),
+    ).objects
+
+    assert len(result) == 5
+
+
+def test_boost_blend(collection_factory: CollectionFactory) -> None:
+    """Blend multiple boost conditions together."""
+    collection = _create_collection(collection_factory)
+
+    result = collection.query.near_vector(
+        near_vector=[1.0, 0.0, 0.0],
+        limit=5,
+        boost=Boost.blend(
+            Boost.filter(
+                Filter.by_property("rating").greater_or_equal(4.0),
+                weight=2.0,
+            ),
+            Boost.numeric_decay(
+                "price",
+                origin=30.0,
+                scale=100.0,
+                curve=Boost.Curve.EXPONENTIAL,
+            ),
+            weight=0.8,
+        ),
+        return_metadata=MetadataQuery(distance=True),
+    ).objects
+
+    assert len(result) == 5
+
+
+def test_boost_with_depth(collection_factory: CollectionFactory) -> None:
+    """Boost with explicit depth parameter."""
+    collection = _create_collection(collection_factory)
+
+    result = collection.query.near_vector(
+        near_vector=[1.0, 0.0, 0.0],
+        limit=5,
+        boost=Boost.filter(
+            Filter.by_property("rating").greater_or_equal(4.0),
+            weight=1.0,
+            depth=100,
+        ),
+        return_metadata=MetadataQuery(distance=True),
+    ).objects
+
+    assert len(result) == 5
+
+
+def test_boost_bm25(collection_factory: CollectionFactory) -> None:
+    """Boost works with BM25 keyword search."""
+    collection = _create_collection(collection_factory)
+
+    result = collection.query.bm25(
+        query="cheap",
+        limit=5,
+        boost=Boost.filter(
+            Filter.by_property("rating").greater_or_equal(4.0),
+            weight=1.0,
+        ),
+        return_metadata=MetadataQuery(score=True),
+    ).objects
+
+    assert len(result) >= 1
+
+
+def test_boost_hybrid(collection_factory: CollectionFactory) -> None:
+    """Boost works with hybrid search."""
+    collection = _create_collection(collection_factory)
+
+    result = collection.query.hybrid(
+        query="cheap",
+        vector=[1.0, 0.0, 0.0],
+        limit=5,
+        boost=Boost.filter(
+            Filter.by_property("price").less_than(100.0),
+            weight=0.6,
+        ),
+        return_metadata=MetadataQuery(score=True),
+    ).objects
+
+    assert len(result) >= 1
+
+
+def test_boost_api_surface() -> None:
+    """Test the public API surface: factory guard + static methods."""
+    with pytest.raises(TypeError):
+        Boost()
+
+    # Static methods produce _Boost instances
+    b = Boost.filter(
+        Filter.by_property("x").equal("y"),
+        weight=0.5,
+    )
+    assert len(b.conditions) == 1
+    assert b.weight == 0.5
+
+    b = Boost.blend(
+        Boost.filter(Filter.by_property("x").equal("y"), weight=1.0),
+        Boost.property("z", modifier=Boost.Modifier.LOG1P),
+        weight=0.8,
+        depth=200,
+    )
+    assert len(b.conditions) == 2
+    assert b.weight == 0.8
+    assert b.depth == 200
+
+
+def test_boost_blend_rejects_sub_boost_depth() -> None:
+    """blend() raises if any sub-boost has depth set."""
+    with pytest.raises(WeaviateInvalidInputError):
+        Boost.blend(
+            Boost.property("count", depth=500),
+            depth=100,
+        )
+
+
+def test_boost_default_curve_is_unspecified() -> None:
+    """Omitting curve defaults to None (sent as UNSPECIFIED on the wire)."""
+    b = Boost.numeric_decay("price", origin=50.0, scale=20.0)
+    assert b.conditions[0].numeric_decay.curve is None
+
+    b = Boost.time_decay("created", scale="7d")
+    assert b.conditions[0].time_decay.curve is None
+
+
+def test_boost_default_modifier_is_unspecified() -> None:
+    """Omitting modifier defaults to None (sent as UNSPECIFIED on the wire)."""
+    b = Boost.property("count")
+    assert b.conditions[0].property_value.modifier is None

weaviate/classes/query.py

@@ -5,6 +5,8 @@
     BM25OperatorFactory as BM25Operator,
 )
 from weaviate.collections.classes.grpc import (
+    Boost,
+    BoostReturn,
     Diversity,
     GroupBy,
     HybridFusion,
@@ -38,6 +40,8 @@
     "QueryNested",
     "QueryReference",
     "NearVector",
+    "Boost",
+    "BoostReturn",
     "Rerank",
     "Sort",
     "TargetVectors",