From 8be42bb0d411cb446814b7d01947c8bd9f2ec1dd Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Fri, 12 Jul 2024 17:06:37 -0400
Subject: [PATCH] x

---
 libs/core/langchain_core/vectorstores/base.py | 50 ++++++++++++++++++-
 .../example_selectors/test_similarity.py      | 13 +----
 2 files changed, 50 insertions(+), 13 deletions(-)

diff --git a/libs/core/langchain_core/vectorstores/base.py b/libs/core/langchain_core/vectorstores/base.py
index cad6ffeedc3..5476747190d 100644
--- a/libs/core/langchain_core/vectorstores/base.py
+++ b/libs/core/langchain_core/vectorstores/base.py
@@ -54,7 +54,7 @@ if TYPE_CHECKING:
         AsyncCallbackManagerForRetrieverRun,
         CallbackManagerForRetrieverRun,
     )
-    from langchain_core.indexing.base import DeleteResponse, UpsertResponse
+    from langchain_core.indexing.base import DeleteResponse, Sort, T, UpsertResponse
 
 from langchain_core.documents.base import Document
 from langchain_core.indexing import BaseIndex
@@ -516,6 +516,54 @@ class VectorStore(BaseIndex[Document]):
                 "search_type to be 'similarity', 'similarity_score_threshold' or 'mmr'."
             )
 
+    def delete_by_filter(
+        self,
+        filter: Union[Dict[str, Any], List[Dict[str, Any]]],
+        /,
+        **kwargs: Any,
+    ) -> DeleteResponse:
+        """Delete documents by filter.
+
+        Args:
+            filter: Filter to apply to documents.
+            **kwargs: Other keyword arguments that subclasses might use.
+
+        Returns:
+            DeleteResponse: A response object that contains the list of IDs that were
+            successfully deleted from the vectorstore and the list of IDs that failed
+            to be deleted.
+
+        .. versionadded:: 0.2.15
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not yet support delete_by_filter."
+        )
+
+    def get_by_filter(
+        self,
+        *,
+        filter: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
+        limit: Optional[int] = None,
+        sort: Optional[Sort] = None,
+        **kwargs: Any,
+    ) -> Iterable[T]:
+        """Get documents by filter.
+
+        Args:
+            filter: Filter to apply to documents.
+            limit: Maximum number of documents to return.
+            sort: Sort order for the returned documents.
+            **kwargs: Other keyword arguments that subclasses might use.
+
+        Returns:
+            Iterable of documents that match the filter.
+
+        .. versionadded:: 0.2.15
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not yet support get_by_filter."
+        )
+
     @abstractmethod
     def similarity_search(
         self, query: str, k: int = 4, **kwargs: Any
diff --git a/libs/core/tests/unit_tests/example_selectors/test_similarity.py b/libs/core/tests/unit_tests/example_selectors/test_similarity.py
index d9009f7bf1b..2cd50ca8dd2 100644
--- a/libs/core/tests/unit_tests/example_selectors/test_similarity.py
+++ b/libs/core/tests/unit_tests/example_selectors/test_similarity.py
@@ -1,4 +1,4 @@
-from typing import Any, Iterable, List, Optional, Sequence, cast
+from typing import Any, Iterable, List, Optional, cast
 
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings, FakeEmbeddings
@@ -6,7 +6,6 @@ from langchain_core.example_selectors import (
     MaxMarginalRelevanceExampleSelector,
     SemanticSimilarityExampleSelector,
 )
-from langchain_core.indexing.base import DeleteResponse
 from langchain_core.vectorstores import VectorStore
 
 
@@ -32,16 +31,6 @@ class DummyVectorStore(VectorStore):
             self.metadatas.extend(metadatas)
         return ["dummy_id"]
 
-    def get_by_ids(self, ids: Sequence[str], /) -> List[Document]:
-        raise NotImplementedError()
-
-    def delete_by_ids(
-        self,
-        ids: Sequence[str],
-        /,
-    ) -> DeleteResponse:
-        raise NotImplementedError()
-
     def similarity_search(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]: