Use classmethod decorator for more methods

pgvector/utils/bit.py

@@ -29,13 +29,15 @@ def to_text(self):
     def to_binary(self):
         return pack('>i', len(self._value)) + np.packbits(self._value).tobytes()
 
-    def from_text(value):
-        return Bit(np.asarray([v != '0' for v in value], dtype=bool))
+    @classmethod
+    def from_text(cls, value):
+        return cls(np.asarray([v != '0' for v in value], dtype=bool))
 
-    def from_binary(value):
+    @classmethod
+    def from_binary(cls, value):
         count = unpack_from('>i', value)[0]
         buf = np.frombuffer(value, dtype=np.uint8, offset=4)
-        return Bit(np.unpackbits(buf, count=count).astype(bool))
+        return cls(np.unpackbits(buf, count=count).astype(bool))
 
     @classmethod
     def _to_db(cls, value):

pgvector/utils/sparsevec.py

@@ -40,21 +40,23 @@ def to_binary(self):
         nnz = len(self._indices)
         return pack(f'>iii{nnz}i{nnz}f', self._dim, nnz, 0, *self._indices, *self._values)
 
-    def from_text(value):
+    @classmethod
+    def from_text(cls, value):
         elements, dim = value.split('/')
         indices = []
         values = []
         for e in elements[1:-1].split(','):
             i, v = e.split(':')
             indices.append(int(i) - 1)
             values.append(float(v))
-        return SparseVector(int(dim), indices, values)
+        return cls(int(dim), indices, values)
 
-    def from_binary(value):
+    @classmethod
+    def from_binary(cls, value):
         dim, nnz, unused = unpack_from('>iii', value)
         indices = unpack_from(f'>{nnz}i', value, 12)
         values = unpack_from(f'>{nnz}f', value, 12 + nnz * 4)
-        return SparseVector(int(dim), indices, values)
+        return cls(int(dim), indices, values)
 
     @classmethod
     def _to_db(cls, value, dim=None):

pgvector/utils/vector.py

@@ -31,12 +31,14 @@ def to_text(self):
     def to_binary(self):
         return pack('>HH', self.dim(), 0) + self._value.tobytes()
 
-    def from_text(value):
-        return Vector([float(v) for v in value[1:-1].split(',')])
+    @classmethod
+    def from_text(cls, value):
+        return cls([float(v) for v in value[1:-1].split(',')])
 
-    def from_binary(value):
+    @classmethod
+    def from_binary(cls, value):
         dim, unused = unpack_from('>HH', value)
-        return Vector(np.frombuffer(value, dtype='>f4', count=dim, offset=4))
+        return cls(np.frombuffer(value, dtype='>f4', count=dim, offset=4))
 
     @classmethod
     def _to_db(cls, value, dim=None):