diff --git a/llama-index-core/llama_index/core/vector_stores/types.py b/llama-index-core/llama_index/core/vector_stores/types.py
index 12177296b8..29df41ad8b 100644
--- a/llama-index-core/llama_index/core/vector_stores/types.py
+++ b/llama-index-core/llama_index/core/vector_stores/types.py
@@ -51,6 +51,9 @@ class VectorStoreQueryMode(str, Enum):
     TEXT_SEARCH = "text_search"
     SEMANTIC_HYBRID = "semantic_hybrid"
 
+    # NOTE: currently only used by postgres filters search
+    FILTERS = "filters"
+
     # fit learners
     SVM = "svm"
     LOGISTIC_REGRESSION = "logistic_regression"
@@ -257,6 +260,8 @@ class VectorStoreQuery:
     sparse_top_k: Optional[int] = None
     # NOTE: return top k results from hybrid search. similarity_top_k is used for dense search top k
     hybrid_top_k: Optional[int] = None
+    # NOTE: currently only used by postgres filters search
+    filters_top_k: Optional[int] = None
 
 
 @runtime_checkable
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py
index 59e66de2c9..7a7d72ff23 100644
--- a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py
@@ -712,6 +712,28 @@ async def _aquery_with_score(
                 for item in res.all()
             ]
 
+    def _build_filters_query(
+        self,
+        limit: int,
+        metadata_filters: Optional[MetadataFilters] = None,
+    ) -> Any:
+        from sqlalchemy import select
+        from sqlalchemy.sql import text
+
+        stmt = (
+            select(  # type: ignore
+                self._table_class.id,
+                self._table_class.node_id,
+                self._table_class.text,
+                self._table_class.metadata_,
+                self._table_class.text_search_tsv.label("rank"),
+            )
+            .order_by(text("rank desc"))
+        )
+
+        # type: ignore
+        return self._apply_filters_and_limit(stmt, limit, metadata_filters)
+
     def _build_sparse_query(
         self,
         query_str: Optional[str],
@@ -799,6 +821,24 @@ def _sparse_query_with_rank(
                 for item in res.all()
             ]
 
+    def _query_filters_only(
+        self,
+        limit: int = 10,
+        metadata_filters: Optional[MetadataFilters] = None,
+    ) -> List[DBEmbeddingRow]:
+        stmt = self._build_filters_query(limit, metadata_filters)
+        with self._session() as session, session.begin():
+            res = session.execute(stmt)
+            return [
+                DBEmbeddingRow(
+                    node_id=item.node_id,
+                    text=item.text,
+                    metadata=item.metadata_,
+                    similarity=item.rank,
+                )
+                for item in res.all()
+            ]
+
     async def _async_hybrid_query(
         self, query: VectorStoreQuery, **kwargs: Any
     ) -> List[DBEmbeddingRow]:
@@ -880,6 +920,9 @@ async def aquery(
         self._initialize()
         if query.mode == VectorStoreQueryMode.HYBRID:
             results = await self._async_hybrid_query(query, **kwargs)
+        elif query.mode == VectorStoreQueryMode.FILTERS:
+            filters_top_k = query.filters_top_k or query.similarity_top_k
+            results = self._query_filters_only(filters_top_k, query.filters)
         elif query.mode in [
             VectorStoreQueryMode.SPARSE,
             VectorStoreQueryMode.TEXT_SEARCH,
@@ -904,6 +947,9 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul
         self._initialize()
         if query.mode == VectorStoreQueryMode.HYBRID:
             results = self._hybrid_query(query, **kwargs)
+        elif query.mode == VectorStoreQueryMode.FILTERS:
+            filters_top_k = query.filters_top_k or query.similarity_top_k
+            results = self._query_filters_only(filters_top_k, query.filters)
         elif query.mode in [
             VectorStoreQueryMode.SPARSE,
             VectorStoreQueryMode.TEXT_SEARCH,