Vector: Add wrapper for HNSW matching function KNN_MATCH

docs/working-with-types.rst

@@ -264,7 +264,7 @@ Vector type
 CrateDB's vector data type, :ref:`crate-reference:type-float_vector`,
 allows to store dense vectors of float values of fixed length.
 
-    >>> from sqlalchemy_cratedb.type.vector import FloatVector
+    >>> from sqlalchemy_cratedb import FloatVector, knn_match
 
     >>> class SearchIndex(Base):
     ...    __tablename__ = 'search'
@@ -285,6 +285,14 @@ When reading it back, the ``FLOAT_VECTOR`` value will be returned as a NumPy arr
     >>> query.all()
     [('foo', array([42.42, 43.43, 44.44], dtype=float32))]
 
+In order to apply search, i.e. to match embeddings against each other, use the
+:ref:`crate-reference:scalar_knn_match` function like this.
+
+    >>> query = session.query(SearchIndex.name) \
+    ...                .filter(knn_match(SearchIndex.embedding, [42.42, 43.43, 41.41], 3))
+    >>> query.all()
+    [('foo',)]
+
 .. hidden: Disconnect from database
 
     >>> session.close()

src/sqlalchemy_cratedb/__init__.py

@@ -27,7 +27,7 @@
 from .type.array import ObjectArray
 from .type.geo import Geopoint, Geoshape
 from .type.object import ObjectType
-from .type.vector import FloatVector
+from .type.vector import FloatVector, knn_match
 
 if SA_VERSION < SA_1_4:
     import textwrap
@@ -58,4 +58,5 @@
     ObjectArray,
     ObjectType,
     match,
+    knn_match,
 ]

src/sqlalchemy_cratedb/type/__init__.py

@@ -1,4 +1,4 @@
 from .array import ObjectArray
 from .geo import Geopoint, Geoshape
 from .object import ObjectType
-from .vector import FloatVector
+from .vector import FloatVector, knn_match

src/sqlalchemy_cratedb/type/vector.py

@@ -22,9 +22,6 @@
 <=>: cosine_distance
 
 ## Backlog
-- The type implementation might want to be accompanied by corresponding support
-  for the `KNN_MATCH` function, similar to what the dialect already offers for
-  fulltext search through its `Match` predicate.
 - After dropping support for SQLAlchemy 1.3, use
   `class FloatVector(sa.TypeDecorator[t.Sequence[float]]):`
 
@@ -42,10 +39,13 @@
     import numpy.typing as npt  # pragma: no cover
 
 import sqlalchemy as sa
+from sqlalchemy.sql.expression import ColumnElement, literal
+from sqlalchemy.ext.compiler import compiles
 
 
 __all__ = [
     "from_db",
+    "knn_match",
     "to_db",
     "FloatVector",
 ]
@@ -131,7 +131,7 @@ class KnnMatch(ColumnElement):
     inherit_cache = True
 
     def __init__(self, column, term, k=None):
-        super(KnnMatch, self).__init__()
+        super().__init__()
         self.column = column
         self.term = term
         self.k = k
@@ -150,11 +150,10 @@ def knn_match(column, term, k):
     """
     Generate a match predicate for vector search.
 
-    :param column: A reference to a column or an index, or a subcolumn, or a
-     dictionary of subcolumns with boost values.
+    :param column: A reference to a column or an index.
 
     :param term: The term to match against. This is an array of floating point
-     values, which is compared to other vectors using a HNSW index.
+     values, which is compared to other vectors using a HNSW index search.
 
     :param k: The `k` argument determines the number of nearest neighbours to
     search in the index.
@@ -165,9 +164,9 @@ def knn_match(column, term, k):
 @compiles(KnnMatch)
 def compile_knn_match(knn_match, compiler, **kwargs):
     """
-    Clause compiler for `knn_match`.
+    Clause compiler for `KNN_MATCH`.
     """
-    return "knn_match(%s, %s, %s)" % (
+    return "KNN_MATCH(%s, %s, %s)" % (
         knn_match.compile_column(compiler),
         knn_match.compile_term(compiler),
         knn_match.compile_k(compiler),

tests/vector_test.py

@@ -28,14 +28,18 @@
 
 import pytest
 import sqlalchemy as sa
-from sqlalchemy.orm import Session
+from sqlalchemy.orm import Session, sessionmaker
 from sqlalchemy.sql import select
 
-from sqlalchemy_cratedb import SA_VERSION, SA_1_4
-from sqlalchemy_cratedb.type import FloatVector
+try:
+    from sqlalchemy.orm import declarative_base
+except ImportError:
+    from sqlalchemy.ext.declarative import declarative_base
 
 from crate.client.cursor import Cursor
 
+from sqlalchemy_cratedb import SA_VERSION, SA_1_4
+from sqlalchemy_cratedb import FloatVector, knn_match
 from sqlalchemy_cratedb.type.vector import from_db, to_db
 
 fake_cursor = MagicMock(name="fake_cursor")
@@ -102,6 +106,14 @@ def test_sql_select(self):
             "SELECT testdrive.data FROM testdrive", select(self.table.c.data)
         )
 
+    def test_sql_match(self):
+        query = self.session.query(self.table.c.name) \
+                    .filter(knn_match(self.table.c.data, [42.42, 43.43], 3))
+        self.assertSQL(
+            "SELECT testdrive.name AS testdrive_name FROM testdrive WHERE KNN_MATCH(testdrive.data, ?, ?)",
+            query
+        )
+
 
 def test_from_db_success():
     """
@@ -201,3 +213,37 @@ def test_float_vector_as_generic():
     fv = FloatVector(3)
     assert isinstance(fv.as_generic(), sa.ARRAY)
     assert fv.python_type is list
+
+
+def test_float_vector_integration():
+    """
+    An integration test for `FLOAT_VECTOR` and `KNN_SEARCH`.
+    """
+    np = pytest.importorskip("numpy")
+
+    engine = sa.create_engine(f"crate://")
+    session = sessionmaker(bind=engine)()
+    Base = declarative_base()
+
+    # Define DDL.
+    class SearchIndex(Base):
+        __tablename__ = 'search'
+        name = sa.Column(sa.String, primary_key=True)
+        embedding = sa.Column(FloatVector(3))
+
+    Base.metadata.drop_all(engine, checkfirst=True)
+    Base.metadata.create_all(engine, checkfirst=True)
+
+    # Insert record.
+    foo_item = SearchIndex(name="foo", embedding=[42.42, 43.43, 44.44])
+    session.add(foo_item)
+    session.commit()
+    session.execute(sa.text("REFRESH TABLE search"))
+
+    # Query record.
+    query = session.query(SearchIndex.embedding) \
+                   .filter(knn_match(SearchIndex.embedding, [42.42, 43.43, 41.41], 3))
+    result = query.first()
+
+    # Compare outcome.
+    assert np.array_equal(result.embedding, np.array([42.42, 43.43, 44.44], np.float32))