📝 (model.py): Refactor LCVectorStoreComponent to use Component class instead of CustomComponent

📝 (model.py): Add outputs attribute to LCVectorStoreComponent to define available outputs and their methods
📝 (model.py): Implement _validate_outputs method in LCVectorStoreComponent to ensure required outputs are defined
📝 (model.py): Add build_vector_store and build_base_retriever methods to LCVectorStoreComponent for building Vector Store and Base Retriever objects
📝 (model.py): Update search_with_vector_store method in LCVectorStoreComponent to return data
📝 (model.py): Add NotImplementedError and ValueError handling in build_vector_store and build_base_retriever methods
📝 (component.py): Implement validate method in Component class to validate inputs and outputs
📝 (component.py): Implement _validate_outputs method in Component class to be extended by subclasses for output validation

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

@@ -4,15 +4,41 @@ from langchain_core.documents import Document
 from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
-from langflow.custom import CustomComponent
+from langflow.custom import Component
 from langflow.field_typing import Text
 from langflow.helpers.data import docs_to_data
 from langflow.schema import Data
+from langflow.template import Output
 
 
-class LCVectorStoreComponent(CustomComponent):
-    display_name: str = "LC Vector Store"
-    description: str = "Search a LC Vector Store for similar documents."
+class LCVectorStoreComponent(Component):
+    outputs = [
+        Output(
+            display_name="Vector Store",
+            name="vector_store",
+            method="build_vector_store",
+        ),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+        ),
+        Output(
+            display_name="Search Results",
+            name="search_results",
+            method="search_documents",
+        ),
+    ]
+
+    def _validate_outputs(self):
+        # At least these three outputs must be defined
+        required_output_methods = ["build_vector_store", "build_base_retriever", "search_documents"]
+        output_names = [output.name for output in self.outputs]
+        for method_name in required_output_methods:
+            if method_name not in output_names:
+                raise ValueError(f"Output with name '{method_name}' must be defined.")
+            elif not hasattr(self, method_name):
+                raise ValueError(f"Method '{method_name}' must be defined.")
 
     def search_with_vector_store(
         self,
@@ -45,3 +71,19 @@ class LCVectorStoreComponent(CustomComponent):
         data = docs_to_data(docs)
         self.status = data
         return data
+
+    def build_vector_store(self) -> VectorStore:
+        """
+        Builds the Vector Store object.
+        """
+        raise NotImplementedError("build_vector_store method must be implemented.")
+
+    def build_base_retriever(self) -> BaseRetriever:
+        """
+        Builds the BaseRetriever object.
+        """
+        vector_store = self.build_vector_store()
+        if hasattr(vector_store, "as_retriever"):
+            return vector_store.as_retriever()
+        else:
+            raise ValueError(f"Vector Store {vector_store.__class__.__name__} does not have an as_retriever method.")

src/backend/base/langflow/custom/custom_component/component.py

@@ -57,6 +57,14 @@ class Component(CustomComponent):
         for input_ in inputs:
             self._inputs[input_.name] = input_
 
+    def validate(self, params: dict):
+        self._validate_inputs(params)
+        self._validate_outputs()
+
+    def _validate_outputs(self):
+        # Raise Error if some rule isn't met
+        pass
+
     def _validate_inputs(self, params: dict):
         # Params keys are the `name` attribute of the Input objects
         for key, value in params.copy().items():