diff --git a/langchain_postgres/v2/async_vectorstore.py b/langchain_postgres/v2/async_vectorstore.py
index bae31ba..dde47f4 100644
--- a/langchain_postgres/v2/async_vectorstore.py
+++ b/langchain_postgres/v2/async_vectorstore.py
@@ -3,7 +3,6 @@
 
 import copy
 import json
-import re
 import uuid
 from typing import Any, Callable, Iterable, Optional, Sequence
 
@@ -175,7 +174,8 @@ async def create(
         stmt = "SELECT column_name, data_type FROM information_schema.columns WHERE table_name = :table_name AND table_schema = :schema_name"
         async with engine._pool.connect() as conn:
             result = await conn.execute(
-                text(stmt), {"table_name": table_name, "schema_name": schema_name}
+                text(stmt),
+                {"table_name": table_name, "schema_name": schema_name},
             )
             result_map = result.mappings()
             results = result_map.fetchall()
@@ -535,7 +535,7 @@ async def __query_collection(
         embedding: list[float],
         *,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> Sequence[RowMapping]:
         """Perform similarity search query on database."""
@@ -553,16 +553,22 @@ async def __query_collection(
 
         column_names = ", ".join(f'"{col}"' for col in columns)
 
+        safe_filter = None
+        filter_dict = None
         if filter and isinstance(filter, dict):
-            filter = self._create_filter_clause(filter)
-        filter = f"WHERE {filter}" if filter else ""
+            safe_filter, filter_dict = self._create_filter_clause(filter)
+        param_filter = f"WHERE {safe_filter}" if safe_filter else ""
         inline_embed_func = getattr(self.embedding_service, "embed_query_inline", None)
         if not embedding and callable(inline_embed_func) and "query" in kwargs:
             query_embedding = self.embedding_service.embed_query_inline(kwargs["query"])  # type: ignore
         else:
             query_embedding = f"{[float(dimension) for dimension in embedding]}"
-        stmt = f'SELECT {column_names}, {search_function}("{self.embedding_column}", :query_embedding) as distance FROM "{self.schema_name}"."{self.table_name}" {filter} ORDER BY "{self.embedding_column}" {operator} :query_embedding LIMIT :k;'
+        stmt = f"""SELECT {column_names}, {search_function}("{self.embedding_column}", :query_embedding) as distance
+        FROM "{self.schema_name}"."{self.table_name}" {param_filter} ORDER BY "{self.embedding_column}" {operator} :query_embedding LIMIT :k;
+        """
         param_dict = {"query_embedding": query_embedding, "k": k}
+        if filter_dict:
+            param_dict.update(filter_dict)
         if self.index_query_options:
             async with self.engine.connect() as conn:
                 # Set each query option individually
@@ -583,7 +589,7 @@ async def asimilarity_search(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected by similarity search on query."""
@@ -614,7 +620,7 @@ async def asimilarity_search_with_score(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected by similarity search on query."""
@@ -635,7 +641,7 @@ async def asimilarity_search_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected by vector similarity search."""
@@ -649,7 +655,7 @@ async def asimilarity_search_with_score_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected by vector similarity search."""
@@ -685,7 +691,7 @@ async def amax_marginal_relevance_search(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance."""
@@ -706,7 +712,7 @@ async def amax_marginal_relevance_search_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance."""
@@ -729,7 +735,7 @@ async def amax_marginal_relevance_search_with_score_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected using the maximal marginal relevance."""
@@ -834,7 +840,7 @@ async def is_valid_index(
     ) -> bool:
         """Check if index exists in the table."""
         index_name = index_name or self.table_name + DEFAULT_INDEX_NAME_SUFFIX
-        query = f"""
+        query = """
         SELECT tablename, indexname
         FROM pg_indexes
         WHERE tablename = :table_name AND schemaname = :schema_name AND indexname = :index_name;
@@ -898,7 +904,7 @@ def _handle_field_filter(
         *,
         field: str,
         value: Any,
-    ) -> str:
+    ) -> tuple[str, dict]:
         """Create a filter for a specific field.
 
         Args:
@@ -951,15 +957,17 @@ def _handle_field_filter(
         if operator in COMPARISONS_TO_NATIVE:
             # Then we implement an equality filter
             # native is trusted input
-            if isinstance(filter_value, str):
-                filter_value = f"'{filter_value}'"
             native = COMPARISONS_TO_NATIVE[operator]
-            return f"({field} {native} {filter_value})"
+            id = str(uuid.uuid4()).split("-")[0]
+            return f"{field} {native} :{field}_{id}", {f"{field}_{id}": filter_value}
         elif operator == "$between":
             # Use AND with two comparisons
             low, high = filter_value
 
-            return f"({field} BETWEEN {low} AND {high})"
+            return f"({field} BETWEEN :{field}_low AND :{field}_high)", {
+                f"{field}_low": low,
+                f"{field}_high": high,
+            }
         elif operator in {"$in", "$nin", "$like", "$ilike"}:
             # We'll do force coercion to text
             if operator in {"$in", "$nin"}:
@@ -975,15 +983,15 @@ def _handle_field_filter(
                         )
 
             if operator in {"$in"}:
-                values = str(tuple(val for val in filter_value))
-                return f"({field} IN {values})"
+                return f"{field} = ANY(:{field}_in)", {f"{field}_in": filter_value}
             elif operator in {"$nin"}:
-                values = str(tuple(val for val in filter_value))
-                return f"({field} NOT IN {values})"
+                return f"{field} <> ALL (:{field}_nin)", {f"{field}_nin": filter_value}
             elif operator in {"$like"}:
-                return f"({field} LIKE '{filter_value}')"
+                return f"({field} LIKE :{field}_like)", {f"{field}_like": filter_value}
             elif operator in {"$ilike"}:
-                return f"({field} ILIKE '{filter_value}')"
+                return f"({field} ILIKE :{field}_ilike)", {
+                    f"{field}_ilike": filter_value
+                }
             else:
                 raise NotImplementedError()
         elif operator == "$exists":
@@ -994,13 +1002,13 @@ def _handle_field_filter(
                 )
             else:
                 if filter_value:
-                    return f"({field} IS NOT NULL)"
+                    return f"({field} IS NOT NULL)", {}
                 else:
-                    return f"({field} IS NULL)"
+                    return f"({field} IS NULL)", {}
         else:
             raise NotImplementedError()
 
-    def _create_filter_clause(self, filters: Any) -> str:
+    def _create_filter_clause(self, filters: Any) -> tuple[str, dict]:
         """Create LangChain filter representation to matching SQL where clauses
 
         Args:
@@ -1037,7 +1045,11 @@ def _create_filter_clause(self, filters: Any) -> str:
                 op = key[1:].upper()  # Extract the operator
                 filter_clause = [self._create_filter_clause(el) for el in value]
                 if len(filter_clause) > 1:
-                    return f"({f' {op} '.join(filter_clause)})"
+                    all_clauses = [clause[0] for clause in filter_clause]
+                    params = {}
+                    for clause in filter_clause:
+                        params.update(clause[1])
+                    return f"({f' {op} '.join(all_clauses)})", params
                 elif len(filter_clause) == 1:
                     return filter_clause[0]
                 else:
@@ -1050,11 +1062,15 @@ def _create_filter_clause(self, filters: Any) -> str:
                     not_conditions = [
                         self._create_filter_clause(item) for item in value
                     ]
-                    not_stmts = [f"NOT {condition}" for condition in not_conditions]
-                    return f"({' AND '.join(not_stmts)})"
+                    all_clauses = [clause[0] for clause in not_conditions]
+                    params = {}
+                    for clause in not_conditions:
+                        params.update(clause[1])
+                    not_stmts = [f"NOT {condition}" for condition in all_clauses]
+                    return f"({' AND '.join(not_stmts)})", params
                 elif isinstance(value, dict):
-                    not_ = self._create_filter_clause(value)
-                    return f"(NOT {not_})"
+                    not_, params = self._create_filter_clause(value)
+                    return f"(NOT {not_})", params
                 else:
                     raise ValueError(
                         f"Invalid filter condition. Expected a dictionary "
@@ -1077,7 +1093,11 @@ def _create_filter_clause(self, filters: Any) -> str:
                 self._handle_field_filter(field=k, value=v) for k, v in filters.items()
             ]
             if len(and_) > 1:
-                return f"({' AND '.join(and_)})"
+                all_clauses = [clause[0] for clause in and_]
+                params = {}
+                for clause in and_:
+                    params.update(clause[1])
+                return f"({' AND '.join(all_clauses)})", params
             elif len(and_) == 1:
                 return and_[0]
             else:
@@ -1086,7 +1106,7 @@ def _create_filter_clause(self, filters: Any) -> str:
                     "but got an empty dictionary"
                 )
         else:
-            return ""
+            return "", {}
 
     def get_by_ids(self, ids: Sequence[str]) -> list[Document]:
         raise NotImplementedError(
@@ -1168,7 +1188,7 @@ def similarity_search(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         raise NotImplementedError(
@@ -1179,7 +1199,7 @@ def similarity_search_with_score(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         raise NotImplementedError(
@@ -1190,7 +1210,7 @@ def similarity_search_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         raise NotImplementedError(
@@ -1201,7 +1221,7 @@ def similarity_search_with_score_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         raise NotImplementedError(
@@ -1214,7 +1234,7 @@ def max_marginal_relevance_search(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         raise NotImplementedError(
@@ -1227,7 +1247,7 @@ def max_marginal_relevance_search_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         raise NotImplementedError(
@@ -1240,7 +1260,7 @@ def max_marginal_relevance_search_with_score_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         raise NotImplementedError(
diff --git a/langchain_postgres/v2/vectorstores.py b/langchain_postgres/v2/vectorstores.py
index 7f71d10..1dc1be9 100644
--- a/langchain_postgres/v2/vectorstores.py
+++ b/langchain_postgres/v2/vectorstores.py
@@ -567,7 +567,7 @@ def similarity_search(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected by similarity search on query."""
@@ -579,7 +579,7 @@ async def asimilarity_search(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected by similarity search on query."""
@@ -602,7 +602,7 @@ async def asimilarity_search_with_score(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected by similarity search on query."""
@@ -614,7 +614,7 @@ async def asimilarity_search_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected by vector similarity search."""
@@ -626,7 +626,7 @@ async def asimilarity_search_with_score_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected by vector similarity search."""
@@ -642,7 +642,7 @@ async def amax_marginal_relevance_search(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance."""
@@ -658,7 +658,7 @@ async def amax_marginal_relevance_search_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance."""
@@ -674,7 +674,7 @@ async def amax_marginal_relevance_search_with_score_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected using the maximal marginal relevance."""
@@ -688,7 +688,7 @@ def similarity_search_with_score(
         self,
         query: str,
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected by similarity search on query."""
@@ -700,7 +700,7 @@ def similarity_search_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected by vector similarity search."""
@@ -712,7 +712,7 @@ def similarity_search_with_score_by_vector(
         self,
         embedding: list[float],
         k: Optional[int] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected by similarity search on vector."""
@@ -728,7 +728,7 @@ def max_marginal_relevance_search(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance."""
@@ -744,7 +744,7 @@ def max_marginal_relevance_search_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance."""
@@ -760,7 +760,7 @@ def max_marginal_relevance_search_with_score_by_vector(
         k: Optional[int] = None,
         fetch_k: Optional[int] = None,
         lambda_mult: Optional[float] = None,
-        filter: Optional[dict] | Optional[str] = None,
+        filter: Optional[dict] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Return docs and distance scores selected using the maximal marginal relevance."""
diff --git a/tests/unit_tests/v2/test_async_pg_vectorstore_search.py b/tests/unit_tests/v2/test_async_pg_vectorstore_search.py
index dc3a771..8e5e371 100644
--- a/tests/unit_tests/v2/test_async_pg_vectorstore_search.py
+++ b/tests/unit_tests/v2/test_async_pg_vectorstore_search.py
@@ -149,13 +149,6 @@ async def vs_custom_filter(
         await vs_custom_filter.aadd_documents(filter_docs, ids=ids)
         yield vs_custom_filter
 
-    async def test_asimilarity_search(self, vs: AsyncPGVectorStore) -> None:
-        results = await vs.asimilarity_search("foo", k=1)
-        assert len(results) == 1
-        assert results == [Document(page_content="foo", id=ids[0])]
-        results = await vs.asimilarity_search("foo", k=1, filter="content = 'bar'")
-        assert results == [Document(page_content="bar", id=ids[1])]
-
     async def test_asimilarity_search_score(self, vs: AsyncPGVectorStore) -> None:
         results = await vs.asimilarity_search_with_score("foo")
         assert len(results) == 4
@@ -220,14 +213,6 @@ async def test_similarity_search_with_relevance_scores_threshold_euclidean(
         assert len(results) == 1
         assert results[0][0] == Document(page_content="foo", id=ids[0])
 
-    async def test_amax_marginal_relevance_search(self, vs: AsyncPGVectorStore) -> None:
-        results = await vs.amax_marginal_relevance_search("bar")
-        assert results[0] == Document(page_content="bar", id=ids[1])
-        results = await vs.amax_marginal_relevance_search(
-            "bar", filter="content = 'boo'"
-        )
-        assert results[0] == Document(page_content="boo", id=ids[3])
-
     async def test_amax_marginal_relevance_search_vector(
         self, vs: AsyncPGVectorStore
     ) -> None:
@@ -249,15 +234,6 @@ async def test_amax_marginal_relevance_search_vector_score(
         )
         assert results[0][0] == Document(page_content="bar", id=ids[1])
 
-    async def test_similarity_search(self, vs_custom: AsyncPGVectorStore) -> None:
-        results = await vs_custom.asimilarity_search("foo", k=1)
-        assert len(results) == 1
-        assert results == [Document(page_content="foo", id=ids[0])]
-        results = await vs_custom.asimilarity_search(
-            "foo", k=1, filter="mycontent = 'bar'"
-        )
-        assert results == [Document(page_content="bar", id=ids[1])]
-
     async def test_similarity_search_score(self, vs_custom: AsyncPGVectorStore) -> None:
         results = await vs_custom.asimilarity_search_with_score("foo")
         assert len(results) == 4
@@ -277,16 +253,6 @@ async def test_similarity_search_by_vector(
         assert result[0][0] == Document(page_content="foo", id=ids[0])
         assert result[0][1] == 0
 
-    async def test_max_marginal_relevance_search(
-        self, vs_custom: AsyncPGVectorStore
-    ) -> None:
-        results = await vs_custom.amax_marginal_relevance_search("bar")
-        assert results[0] == Document(page_content="bar", id=ids[1])
-        results = await vs_custom.amax_marginal_relevance_search(
-            "bar", filter="mycontent = 'boo'"
-        )
-        assert results[0] == Document(page_content="boo", id=ids[3])
-
     async def test_max_marginal_relevance_search_vector(
         self, vs_custom: AsyncPGVectorStore
     ) -> None:
diff --git a/tests/unit_tests/v2/test_pg_vectorstore_search.py b/tests/unit_tests/v2/test_pg_vectorstore_search.py
index 28d0f07..379f529 100644
--- a/tests/unit_tests/v2/test_pg_vectorstore_search.py
+++ b/tests/unit_tests/v2/test_pg_vectorstore_search.py
@@ -158,13 +158,6 @@ async def vs_custom_filter(self, engine: PGEngine) -> AsyncIterator[PGVectorStor
         await vs_custom_filter.aadd_documents(filter_docs, ids=ids)
         yield vs_custom_filter
 
-    async def test_asimilarity_search(self, vs: PGVectorStore) -> None:
-        results = await vs.asimilarity_search("foo", k=1)
-        assert len(results) == 1
-        assert results == [Document(page_content="foo", id=ids[0])]
-        results = await vs.asimilarity_search("foo", k=1, filter="content = 'bar'")
-        assert results == [Document(page_content="bar", id=ids[1])]
-
     async def test_asimilarity_search_score(self, vs: PGVectorStore) -> None:
         results = await vs.asimilarity_search_with_score("foo")
         assert len(results) == 4
@@ -222,14 +215,6 @@ async def test_similarity_search_with_relevance_scores_threshold_euclidean(
         assert len(results) == 1
         assert results[0][0] == Document(page_content="foo", id=ids[0])
 
-    async def test_amax_marginal_relevance_search(self, vs: PGVectorStore) -> None:
-        results = await vs.amax_marginal_relevance_search("bar")
-        assert results[0] == Document(page_content="bar", id=ids[1])
-        results = await vs.amax_marginal_relevance_search(
-            "bar", filter="content = 'boo'"
-        )
-        assert results[0] == Document(page_content="boo", id=ids[3])
-
     async def test_amax_marginal_relevance_search_vector(
         self, vs: PGVectorStore
     ) -> None:
@@ -354,13 +339,6 @@ async def vs_custom_filter_sync(
         vs_custom_filter_sync.add_documents(filter_docs, ids=ids)
         yield vs_custom_filter_sync
 
-    def test_similarity_search(self, vs_custom: PGVectorStore) -> None:
-        results = vs_custom.similarity_search("foo", k=1)
-        assert len(results) == 1
-        assert results == [Document(page_content="foo", id=ids[0])]
-        results = vs_custom.similarity_search("foo", k=1, filter="mycontent = 'bar'")
-        assert results == [Document(page_content="bar", id=ids[1])]
-
     def test_similarity_search_score(self, vs_custom: PGVectorStore) -> None:
         results = vs_custom.similarity_search_with_score("foo")
         assert len(results) == 4
@@ -376,14 +354,6 @@ def test_similarity_search_by_vector(self, vs_custom: PGVectorStore) -> None:
         assert result[0][0] == Document(page_content="foo", id=ids[0])
         assert result[0][1] == 0
 
-    def test_max_marginal_relevance_search(self, vs_custom: PGVectorStore) -> None:
-        results = vs_custom.max_marginal_relevance_search("bar")
-        assert results[0] == Document(page_content="bar", id=ids[1])
-        results = vs_custom.max_marginal_relevance_search(
-            "bar", filter="mycontent = 'boo'"
-        )
-        assert results[0] == Document(page_content="boo", id=ids[3])
-
     def test_max_marginal_relevance_search_vector(
         self, vs_custom: PGVectorStore
     ) -> None: