diff --git a/src/sqlalchemy_cratedb/type/vector.py b/src/sqlalchemy_cratedb/type/vector.py
index 9cde7ea2..b78131cc 100644
--- a/src/sqlalchemy_cratedb/type/vector.py
+++ b/src/sqlalchemy_cratedb/type/vector.py
@@ -25,6 +25,8 @@
 - 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]]):`
 
 ## Origin
 This module is based on the corresponding pgvector implementation
@@ -44,7 +46,7 @@
 __all__ = ["FloatVector"]
 
 
-def from_db(value: t.Iterable) -> t.Optional[npt.ArrayLike]:
+def from_db(value: t.Iterable) -> t.Optional["npt.ArrayLike"]:
     import numpy as np
 
     # from `pgvector.utils`
@@ -77,8 +79,7 @@ def to_db(value: t.Any, dim: t.Optional[int] = None) -> t.Optional[t.List]:
     return value
 
 
-class FloatVector(sa.TypeDecorator[t.Sequence[float]]):
-
+class FloatVector(sa.TypeDecorator):
     """
     An improved implementation of the `FloatVector` data type for CrateDB,
     compared to the previous implementation on behalf of the LangChain adapter.
@@ -146,14 +147,14 @@ def __init__(self, dimensions: int = None):
     def as_generic(self):
         return sa.ARRAY
 
-    def bind_processor(self, dialect: sa.Dialect) -> t.Callable:
+    def bind_processor(self, dialect: sa.engine.Dialect) -> t.Callable:
         def process(value: t.Iterable) -> t.Optional[t.List]:
             return to_db(value, self.dimensions)
 
         return process
 
-    def result_processor(self, dialect: sa.Dialect, coltype: t.Any) -> t.Callable:
-        def process(value: t.Any) -> t.Optional[npt.ArrayLike]:
+    def result_processor(self, dialect: sa.engine.Dialect, coltype: t.Any) -> t.Callable:
+        def process(value: t.Any) -> t.Optional["npt.ArrayLike"]:
             return from_db(value)
 
         return process