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 0896c46cc850f..8e25d841dcef2 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
@@ -130,6 +130,7 @@ class PGVectorStore(BasePydanticVectorStore):
             user="postgres",
             table_name="paul_graham_essay",
             embed_dim=1536  # openai embedding dimension
+            vector_search_method="cosine_distance" # Optional specify vector search method. Default is cosine_distance.
         )
         ```
     """
@@ -272,6 +273,7 @@ def from_params(
         use_jsonb: bool = False,
         hnsw_kwargs: Optional[Dict[str, Any]] = None,
         create_engine_kwargs: Optional[Dict[str, Any]] = None,
+        vector_search_method: Optional[str] = "cosine_distance",
     ) -> "PGVectorStore":
         """Construct from params.
 
@@ -296,6 +298,7 @@ def from_params(
                 contains "hnsw_ef_construction", "hnsw_ef_search", "hnsw_m", and optionally "hnsw_dist_method". Defaults to None,
                 which turns off HNSW search.
             create_engine_kwargs (Optional[Dict[str, Any]], optional): Engine parameters to pass to create_engine. Defaults to None.
+            vector_search_method (Optional[str], optional): Vector search method. Defaults to cosine_distance.
 
         Returns:
             PGVectorStore: Instance of PGVectorStore constructed from params.
@@ -307,6 +310,7 @@ def from_params(
         async_conn_str = async_connection_string or (
             f"postgresql+asyncpg://{user}:{password}@{host}:{port}/{database}"
         )
+        cls.vector_search_method = vector_search_method
         return cls(
             connection_string=conn_str,
             async_connection_string=async_conn_str,
@@ -597,13 +601,72 @@ def _build_query(
     ) -> Any:
         from sqlalchemy import select, 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.embedding.cosine_distance(embedding).label("distance"),
-        ).order_by(text("distance asc"))
+        match self.vector_search_method:
+            case "cosine_distance":
+                stmt = select(  # type: ignore
+                    self._table_class.id,
+                    self._table_class.node_id,
+                    self._table_class.text,
+                    self._table_class.metadata_,
+                    self._table_class.embedding.cosine_distance(embedding).label(
+                        "distance"
+                    ),
+                ).order_by(text("distance asc"))
+
+            case "max_inner_product":
+                stmt = select(  # type: ignore
+                    self._table_class.id,
+                    self._table_class.node_id,
+                    self._table_class.text,
+                    self._table_class.metadata_,
+                    self._table_class.embedding.max_inner_product(embedding).label(
+                        "distance"
+                    ),
+                ).order_by(text("distance asc"))
+
+            case "l2_distance":
+                stmt = select(  # type: ignore
+                    self._table_class.id,
+                    self._table_class.node_id,
+                    self._table_class.text,
+                    self._table_class.metadata_,
+                    self._table_class.embedding.l2_distance(embedding).label(
+                        "distance"
+                    ),
+                ).order_by(text("distance asc"))
+
+            case "l1_distance":
+                stmt = select(  # type: ignore
+                    self._table_class.id,
+                    self._table_class.node_id,
+                    self._table_class.text,
+                    self._table_class.metadata_,
+                    self._table_class.embedding.l1_distance(embedding).label(
+                        "distance"
+                    ),
+                ).order_by(text("distance asc"))
+
+            case "hamming_distance":
+                stmt = select(  # type: ignore
+                    self._table_class.id,
+                    self._table_class.node_id,
+                    self._table_class.text,
+                    self._table_class.metadata_,
+                    self._table_class.embedding.hamming_distance(embedding).label(
+                        "distance"
+                    ),
+                ).order_by(text("distance asc"))
+
+            case "jaccard_distance":
+                stmt = select(  # type: ignore
+                    self._table_class.id,
+                    self._table_class.node_id,
+                    self._table_class.text,
+                    self._table_class.metadata_,
+                    self._table_class.embedding.jaccard_distance(embedding).label(
+                        "distance"
+                    ),
+                ).order_by(text("distance asc"))
 
         return self._apply_filters_and_limit(stmt, limit, metadata_filters)