community: fixed bug in GraphVectorStoreRetriever (langchain-ai#27846)

Description:

This fixes an issue that mistakenly created in
langchain-ai#27253. The issue
currently exists only in `langchain-community==0.3.4`.

Test cases were added to prevent this issue in the future.

Co-authored-by: Erick Friis <[email protected]>

libs/community/langchain_community/graph_vectorstores/base.py

@@ -8,6 +8,7 @@
     ClassVar,
     Optional,
     Sequence,
+    cast,
 )
 
 from langchain_core._api import beta
@@ -701,7 +702,7 @@ def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
             docsearch.as_retriever(search_kwargs={'k': 1})
 
         """
-        return GraphVectorStoreRetriever(vector_store=self, **kwargs)
+        return GraphVectorStoreRetriever(vectorstore=self, **kwargs)
 
 
 @beta(message="Added in version 0.3.1 of langchain_community. API subject to change.")
@@ -837,8 +838,8 @@ class GraphVectorStoreRetriever(VectorStoreRetriever):
         retriever = graph_vectorstore.as_retriever(search_kwargs={"score_threshold": 0.5})
     """  # noqa: E501
 
-    vector_store: GraphVectorStore
-    """GraphVectorStore to use for retrieval."""
+    vectorstore: VectorStore
+    """VectorStore to use for retrieval."""
     search_type: str = "traversal"
     """Type of search to perform. Defaults to "traversal"."""
     allowed_search_types: ClassVar[Collection[str]] = (
@@ -849,14 +850,20 @@ class GraphVectorStoreRetriever(VectorStoreRetriever):
         "mmr_traversal",
     )
 
+    @property
+    def graph_vectorstore(self) -> GraphVectorStore:
+        return cast(GraphVectorStore, self.vectorstore)
+
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> list[Document]:
         if self.search_type == "traversal":
-            return list(self.vector_store.traversal_search(query, **self.search_kwargs))
+            return list(
+                self.graph_vectorstore.traversal_search(query, **self.search_kwargs)
+            )
         elif self.search_type == "mmr_traversal":
             return list(
-                self.vector_store.mmr_traversal_search(query, **self.search_kwargs)
+                self.graph_vectorstore.mmr_traversal_search(query, **self.search_kwargs)
             )
         else:
             return super()._get_relevant_documents(query, run_manager=run_manager)
@@ -867,14 +874,14 @@ async def _aget_relevant_documents(
         if self.search_type == "traversal":
             return [
                 doc
-                async for doc in self.vector_store.atraversal_search(
+                async for doc in self.graph_vectorstore.atraversal_search(
                     query, **self.search_kwargs
                 )
             ]
         elif self.search_type == "mmr_traversal":
             return [
                 doc
-                async for doc in self.vector_store.ammr_traversal_search(
+                async for doc in self.graph_vectorstore.ammr_traversal_search(
                     query, **self.search_kwargs
                 )
             ]

libs/community/tests/integration_tests/graph_vectorstores/test_cassandra.py

@@ -440,6 +440,17 @@ def test_gvs_traversal_search_sync(
         ts_labels = {doc.metadata["label"] for doc in ts_response}
         assert ts_labels == {"AR", "A0", "BR", "B0", "TR", "T0"}
 
+        # verify the same works as a retriever
+        retriever = g_store.as_retriever(
+            search_type="traversal", search_kwargs={"k": 2, "depth": 2}
+        )
+
+        ts_labels = {
+            doc.metadata["label"]
+            for doc in retriever.get_relevant_documents(query="[2, 10]")
+        }
+        assert ts_labels == {"AR", "A0", "BR", "B0", "TR", "T0"}
+
     async def test_gvs_traversal_search_async(
         self,
         populated_graph_vector_store_d2: CassandraGraphVectorStore,
@@ -453,6 +464,17 @@ async def test_gvs_traversal_search_async(
         # so ordering is not deterministic:
         assert ts_labels == {"AR", "A0", "BR", "B0", "TR", "T0"}
 
+        # verify the same works as a retriever
+        retriever = g_store.as_retriever(
+            search_type="traversal", search_kwargs={"k": 2, "depth": 2}
+        )
+
+        ts_labels = {
+            doc.metadata["label"]
+            for doc in await retriever.aget_relevant_documents(query="[2, 10]")
+        }
+        assert ts_labels == {"AR", "A0", "BR", "B0", "TR", "T0"}
+
     def test_gvs_mmr_traversal_search_sync(
         self,
         populated_graph_vector_store_d2: CassandraGraphVectorStore,