remove retriever component and outputs (#4979)

* remove retriever component and outputs

* [autofix.ci] apply automated fixes

* ruff check fix

* [autofix.ci] apply automated fixes

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: cristhianzl <cristhian.lousa@gmail.com>
Co-authored-by: Eric Hare <ericrhare@gmail.com>

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

@@ -5,7 +5,7 @@ from typing import TYPE_CHECKING
 from loguru import logger
 
 from langflow.custom import Component
-from langflow.field_typing import Retriever, Text, VectorStore
+from langflow.field_typing import Text, VectorStore
 from langflow.helpers.data import docs_to_data
 from langflow.io import Output
 from langflow.schema import Data
@@ -54,11 +54,6 @@ class LCVectorStoreComponent(Component):
 
     trace_type = "retriever"
     outputs = [
-        Output(
-            display_name="Retriever",
-            name="base_retriever",
-            method="build_base_retriever",
-        ),
         Output(
             display_name="Search Results",
             name="search_results",
@@ -69,7 +64,6 @@ class LCVectorStoreComponent(Component):
     def _validate_outputs(self) -> None:
         # At least these three outputs must be defined
         required_output_methods = [
-            "build_base_retriever",
             "search_documents",
             "build_vector_store",
         ]
@@ -115,22 +109,6 @@ class LCVectorStoreComponent(Component):
         self.status = data
         return data
 
-    def build_base_retriever(self) -> Retriever:  # type: ignore[type-var]
-        """Builds the BaseRetriever object."""
-        if self._cached_vector_store is not None:
-            vector_store = self._cached_vector_store
-        else:
-            vector_store = self.build_vector_store()
-            self._cached_vector_store = vector_store
-
-        if hasattr(vector_store, "as_retriever"):
-            retriever = vector_store.as_retriever(**self.get_retriever_kwargs())
-            if self.status is None:
-                self.status = "Retriever built successfully."
-            return retriever
-        msg = f"Vector Store {vector_store.__class__.__name__} does not have an as_retriever method."
-        raise ValueError(msg)
-
     def search_documents(self) -> list[Data]:
         """Search for documents in the vector store."""
         search_query: str = self.search_query

src/backend/base/langflow/components/tools/__init__.py

@@ -11,7 +11,6 @@ from .google_search_api import GoogleSearchAPIComponent
 from .google_serper_api import GoogleSerperAPIComponent
 from .python_code_structured_tool import PythonCodeStructuredTool
 from .python_repl import PythonREPLToolComponent
-from .retriever import RetrieverToolComponent
 from .search_api import SearchAPIComponent
 from .searxng import SearXNGToolComponent
 from .serp_api import SerpAPIComponent
@@ -39,7 +38,6 @@ __all__ = [
     "GoogleSerperAPIComponent",
     "PythonCodeStructuredTool",
     "PythonREPLToolComponent",
-    "RetrieverToolComponent",
     "SearXNGToolComponent",
     "SearchAPIComponent",
     "SerpAPIComponent",

src/backend/base/langflow/components/tools/retriever.py

@@ -1,30 +0,0 @@
-from langchain_core.tools import create_retriever_tool
-
-from langflow.custom import CustomComponent
-from langflow.field_typing import BaseRetriever, Tool
-
-
-class RetrieverToolComponent(CustomComponent):
-    display_name = "RetrieverTool"
-    description = "Tool for interacting with retriever"
-    name = "RetrieverTool"
-
-    def build_config(self):
-        return {
-            "retriever": {
-                "display_name": "Retriever",
-                "info": "Retriever to interact with",
-                "type": BaseRetriever,
-                "input_types": ["Retriever"],
-            },
-            "name": {"display_name": "Name", "info": "Name of the tool"},
-            "description": {"display_name": "Description", "info": "Description of the tool"},
-        }
-
-    def build(self, retriever: BaseRetriever, name: str, description: str, **kwargs) -> Tool:
-        _ = kwargs
-        return create_retriever_tool(
-            retriever=retriever,
-            name=name,
-            description=description,
-        )

src/backend/tests/integration/components/astra/test_astra_component.py

@@ -55,9 +55,7 @@ async def test_base(astradb_client: AstraDB):
             ),
         },
     )
-    from langchain_core.vectorstores import VectorStoreRetriever
 
-    assert isinstance(results["base_retriever"], VectorStoreRetriever)
     assert results["vector_store"] is not None
     assert results["search_results"] == []
     assert astradb_client.collection(BASIC_COLLECTION)

src/backend/tests/unit/initial_setup/starter_projects/test_vector_store_rag.py

@@ -36,7 +36,6 @@ def ingestion_graph():
         api_endpoint="https://astra.example.com",
         token="token",  # noqa: S106
     )
-    vector_store.set_on_output(name="base_retriever", value="mock_retriever", cache=True)
     vector_store.set_on_output(name="search_results", value=[Data(text="This is a test file.")], cache=True)
 
     return Graph(file_component, vector_store)
@@ -64,7 +63,6 @@ def rag_graph():
         ],
         cache=True,
     )
-    rag_vector_store.set_on_output(name="base_retriever", value="mock_retriever", cache=True)
     parse_data = ParseDataComponent(_id="parse-data-123")
     parse_data.set(data=rag_vector_store.search_documents)
     prompt_component = PromptComponent(_id="prompt-123")