fix: use init_subclass instead of metaclass to enforce decorator (#3942)

* Use init_subclass instead of metaclass to enforce decorator

* [autofix.ci] apply automated fixes

* fix imports

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

src/backend/base/langflow/base/vectorstores/model.py

@@ -1,4 +1,4 @@
-from abc import ABC, ABCMeta, abstractmethod
+from abc import abstractmethod
 from functools import wraps
 from typing import cast
 
@@ -15,6 +15,11 @@ from langflow.schema import Data
 def check_cached_vector_store(f):
     """
     Decorator to check for cached vector stores, and returns them if they exist.
+
+    Note: caching only occurs during the execution of a component - they do not persist
+    across separate invocations of the component. This method exists so that components with
+    multiple output methods share the same vector store during the same invocation of the
+    component.
     """
 
     @wraps(f)
@@ -30,30 +35,22 @@ def check_cached_vector_store(f):
     return check_cached
 
 
-class EnforceCacheDecoratorMeta(ABCMeta):
-    """
-    Enforces that abstract methods marked with @check_cached_vector_store are implemented with the decorator.
-    """
-
-    def __init__(cls, name, bases, dct):
-        for name, value in dct.items():
-            if hasattr(value, "__isabstractmethod__"):
-                cls._check_method_decorator(name, cls)
-        super().__init__(name, bases, dct)
-
-    @staticmethod
-    def _check_method_decorator(name, cls):
-        method = getattr(cls, name)
-
-        # Check if the method has been marked as decorated by `check_cached_vector_store`
-        if not getattr(method, "_is_cached_vector_store_checked", False):
-            raise TypeError(f"Concrete implementation of '{name}' must use '@check_cached_vector_store' decorator.")
-
-
-class LCVectorStoreComponent(Component, ABC, metaclass=EnforceCacheDecoratorMeta):
+class LCVectorStoreComponent(Component):
     # Used to ensure a single vector store is built for each run of the flow
     _cached_vector_store: VectorStore | None = None
 
+    def __init_subclass__(cls, **kwargs):
+        """
+        Enforces the check cached decorator on all subclasses
+        """
+        super().__init_subclass__(**kwargs)
+        if hasattr(cls, "build_vector_store"):
+            method = cls.build_vector_store
+            if not hasattr(method, "_is_cached_vector_store_checked"):
+                raise TypeError(
+                    f"The method 'build_vector_store' in class {cls.__name__} must be decorated with @check_cached_vector_store"
+                )
+
     trace_type = "retriever"
     outputs = [
         Output(

src/backend/base/langflow/components/retrievers/CohereRerank.py

@@ -3,7 +3,7 @@ from typing import cast
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain_cohere import CohereRerank
 
-from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
 from langflow.field_typing import Retriever, VectorStore
 from langflow.io import (
     DropdownInput,
@@ -80,5 +80,6 @@ class CohereRerankComponent(LCVectorStoreComponent):
         self.status = data
         return data
 
+    @check_cached_vector_store
     def build_vector_store(self) -> VectorStore:
         raise NotImplementedError("Cohere Rerank does not support vector stores.")

src/backend/base/langflow/components/retrievers/NvidiaRerank.py

@@ -2,7 +2,7 @@ from typing import Any, cast
 
 from langchain.retrievers import ContextualCompressionRetriever
 
-from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
 from langflow.field_typing import Retriever, VectorStore
 from langflow.io import DropdownInput, HandleInput, MultilineInput, SecretStrInput, StrInput
 from langflow.schema import Data
@@ -77,5 +77,6 @@ class NvidiaRerankComponent(LCVectorStoreComponent):
         self.status = data
         return data
 
+    @check_cached_vector_store
     def build_vector_store(self) -> VectorStore:
         raise NotImplementedError("NVIDIA Rerank does not support vector stores.")