diff --git a/pyproject.toml b/pyproject.toml
index a7c5c892b..3a72d15c6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -65,7 +65,7 @@ qdrant = [ "qdrant-client" ]
 pinecone = [ "pinecone-client" ]
 weaviate = [ "weaviate-client" ]
 elastic = [ "elasticsearch" ]
-pgvector = [ "pgvector", "sqlalchemy" ]
+pgvector = [ "pgvector", "psycopg2" ]
 pgvecto_rs = [ "psycopg2" ]
 redis = [ "redis" ]
 chromadb = [ "chromadb" ]
diff --git a/vectordb_bench/backend/clients/pgvecto_rs/config.py b/vectordb_bench/backend/clients/pgvecto_rs/config.py
index 7ad5983f1..73c41c239 100644
--- a/vectordb_bench/backend/clients/pgvecto_rs/config.py
+++ b/vectordb_bench/backend/clients/pgvecto_rs/config.py
@@ -8,42 +8,30 @@
 class PgVectoRSConfig(DBConfig):
     user_name: SecretStr = "postgres"
     password: SecretStr
-    url: SecretStr
+    host: str = "localhost"
+    port: int = 5432
     db_name: str
 
     def to_dict(self) -> dict:
         user_str = self.user_name.get_secret_value()
         pwd_str = self.password.get_secret_value()
-        url_str = self.url.get_secret_value()
-        host, port = url_str.split(":")
         return {
-            "host": host,
-            "port": port,
+            "host": self.host,
+            "port": self.port,
             "dbname": self.db_name,
             "user": user_str,
-            "password": pwd_str,
+            "password": pwd_str
         }
 
-
 class PgVectoRSIndexConfig(BaseModel, DBCaseConfig):
     metric_type: MetricType | None = None
-    quantizationType: Literal["trivial", "scalar", "product"]
-    quantizationRatio: None | Literal["x4", "x8", "x16", "x32", "x64"]
-
-    def parse_quantization(self) -> str:
-        if self.quantizationType == "trivial":
-            return "quantization = { trivial = { } }"
-        elif self.quantizationType == "scalar":
-            return "quantization = { scalar = { } }"
-        else:
-            return f'quantization = {{ product = {{ ratio = "{self.quantizationRatio}" }} }}'
 
     def parse_metric(self) -> str:
         if self.metric_type == MetricType.L2:
-            return "l2_ops"
+            return "vector_l2_ops"
         elif self.metric_type == MetricType.IP:
-            return "dot_ops"
-        return "cosine_ops"
+            return "vector_dot_ops"
+        return "vector_cos_ops"
 
     def parse_metric_fun_op(self) -> str:
         if self.metric_type == MetricType.L2:
@@ -52,16 +40,27 @@ def parse_metric_fun_op(self) -> str:
             return "<#>"
         return "<=>"
 
+class PgVectoRSQuantConfig(PgVectoRSIndexConfig):
+    quantizationType: Literal["trivial", "scalar", "product"]
+    quantizationRatio: None | Literal["x4", "x8", "x16", "x32", "x64"]
 
-class HNSWConfig(PgVectoRSIndexConfig):
+    def parse_quantization(self) -> str:
+        if self.quantizationType == "trivial":
+            return "quantization = { trivial = { } }"
+        elif self.quantizationType == "scalar":
+            return "quantization = { scalar = { } }"
+        else:
+            return f'quantization = {{ product = {{ ratio = "{self.quantizationRatio}" }} }}'
+
+
+class HNSWConfig(PgVectoRSQuantConfig):
     M: int
     efConstruction: int
     index: IndexType = IndexType.HNSW
 
     def index_param(self) -> dict:
         options = f"""
-capacity = 1048576
-[algorithm.hnsw]
+[indexing.hnsw]
 m = {self.M}
 ef_construction = {self.efConstruction}
 {self.parse_quantization()}
@@ -72,17 +71,16 @@ def search_param(self) -> dict:
         return {"metrics_op": self.parse_metric_fun_op()}
 
 
-class IVFFlatConfig(PgVectoRSIndexConfig):
+class IVFFlatConfig(PgVectoRSQuantConfig):
     nlist: int
     nprobe: int | None = None
     index: IndexType = IndexType.IVFFlat
 
     def index_param(self) -> dict:
         options = f"""
-capacity = 1048576
-[algorithm.ivf]
+[indexing.ivf]
 nlist = {self.nlist}
-nprob = {self.nprobe if self.nprobe else 10}
+nsample = {self.nprobe if self.nprobe else 10}
 {self.parse_quantization()}
 """
         return {"options": options, "metric": self.parse_metric()}
@@ -90,14 +88,29 @@ def index_param(self) -> dict:
     def search_param(self) -> dict:
         return {"metrics_op": self.parse_metric_fun_op()}
 
+class IVFFlatSQ8Config(PgVectoRSIndexConfig):
+    nlist: int
+    nprobe: int | None = None
+    index: IndexType = IndexType.IVFSQ8
+
+    def index_param(self) -> dict:
+        options = f"""
+[indexing.ivf]
+nlist = {self.nlist}
+nsample = {self.nprobe if self.nprobe else 10}
+quantization = {{ scalar = {{ }} }}
+"""
+        return {"options": options, "metric": self.parse_metric()}
+
+    def search_param(self) -> dict:
+        return {"metrics_op": self.parse_metric_fun_op()}
 
-class FLATConfig(PgVectoRSIndexConfig):
+class FLATConfig(PgVectoRSQuantConfig):
     index: IndexType = IndexType.Flat
 
     def index_param(self) -> dict:
         options = f"""
-capacity = 1048576
-[algorithm.flat]
+[indexing.flat]
 {self.parse_quantization()}
 """
         return {"options": options, "metric": self.parse_metric()}
@@ -107,9 +120,8 @@ def search_param(self) -> dict:
 
 
 _pgvecto_rs_case_config = {
-    IndexType.AUTOINDEX: HNSWConfig,
     IndexType.HNSW: HNSWConfig,
-    IndexType.DISKANN: HNSWConfig,
     IndexType.IVFFlat: IVFFlatConfig,
+    IndexType.IVFSQ8: IVFFlatSQ8Config,
     IndexType.Flat: FLATConfig,
 }
diff --git a/vectordb_bench/backend/clients/pgvecto_rs/pgvecto_rs.py b/vectordb_bench/backend/clients/pgvecto_rs/pgvecto_rs.py
index 22476522c..22caa43e6 100644
--- a/vectordb_bench/backend/clients/pgvecto_rs/pgvecto_rs.py
+++ b/vectordb_bench/backend/clients/pgvecto_rs/pgvecto_rs.py
@@ -1,18 +1,17 @@
-"""Wrapper around the Pgvector vector database over VectorDB"""
+"""Wrapper around the Pgvecto.rs vector database over VectorDB"""
 
 import io
 import logging
 from contextlib import contextmanager
 from typing import Any
 import pandas as pd
-
 import psycopg2
+import psycopg2.extras
 
 from ..api import VectorDB, DBCaseConfig
 
 log = logging.getLogger(__name__)
 
-
 class PgVectoRS(VectorDB):
     """Use SQLAlchemy instructions"""
 
@@ -66,6 +65,8 @@ def init(self) -> None:
         self.conn = psycopg2.connect(**self.db_config)
         self.conn.autocommit = False
         self.cursor = self.conn.cursor()
+        self.cursor.execute('SET search_path = "$user", public, vectors')
+        self.conn.commit()
 
         try:
             yield
@@ -113,7 +114,7 @@ def _create_index(self):
             self.conn.commit()
         except Exception as e:
             log.warning(
-                f"Failed to create pgvector table: {self.table_name} error: {e}"
+                f"Failed to create pgvecto.rs table: {self.table_name} error: {e}"
             )
             raise e from None
 
@@ -127,13 +128,10 @@ def _create_table(self, dim: int):
                 f'CREATE TABLE IF NOT EXISTS public."{self.table_name}" \
                     (id Integer PRIMARY KEY, embedding vector({dim}));'
             )
-            self.cursor.execute(
-                f'ALTER TABLE public."{self.table_name}" ALTER COLUMN embedding SET STORAGE PLAIN;'
-            )
             self.conn.commit()
         except Exception as e:
             log.warning(
-                f"Failed to create pgvector table: {self.table_name} error: {e}"
+                f"Failed to create pgvecto.rs table: {self.table_name} error: {e}"
             )
             raise e from None
 
@@ -146,22 +144,24 @@ def insert_embeddings(
         assert self.conn is not None, "Connection is not initialized"
         assert self.cursor is not None, "Cursor is not initialized"
 
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
         try:
-            items = {"id": metadata, "embedding": embeddings}
+            items = {
+                "id": metadata,
+                "embedding": embeddings
+            }
             df = pd.DataFrame(items)
             csv_buffer = io.StringIO()
             df.to_csv(csv_buffer, index=False, header=False)
             csv_buffer.seek(0)
-            self.cursor.copy_expert(
-                f'COPY public."{self.table_name}" FROM STDIN WITH (FORMAT CSV)',
-                csv_buffer,
-            )
+            self.cursor.copy_expert(f"COPY public.\"{self.table_name}\" FROM STDIN WITH (FORMAT CSV)", csv_buffer)
             self.conn.commit()
             return len(metadata), None
         except Exception as e:
-            log.warning(
-                f"Failed to insert data into pgvector table ({self.table_name}), error: {e}"
-            )
+            log.warning(f"Failed to insert data into pgvecto.rs table ({self.table_name}), error: {e}")
+            return 0, e
 
     def search_embedding(
         self,
diff --git a/vectordb_bench/frontend/const/dbCaseConfigs.py b/vectordb_bench/frontend/const/dbCaseConfigs.py
index fad5f362d..24de64725 100644
--- a/vectordb_bench/frontend/const/dbCaseConfigs.py
+++ b/vectordb_bench/frontend/const/dbCaseConfigs.py
@@ -397,6 +397,11 @@ class CaseConfigInput(BaseModel):
     inputConfig={
         "options": ["trivial", "scalar", "product"],
     },
+    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None)
+    in [
+        IndexType.HNSW.value,
+        IndexType.IVFFlat.value,
+    ],
 )
 
 CaseConfigParamInput_QuantizationRatio_PgVectoRS = CaseConfigInput(
@@ -406,7 +411,11 @@ class CaseConfigInput(BaseModel):
         "options": ["x4", "x8", "x16", "x32", "x64"],
     },
     isDisplayed=lambda config: config.get(CaseConfigParamType.quantizationType, None)
-    == "product",
+    == "product" and config.get(CaseConfigParamType.IndexType, None)
+    in [
+        IndexType.HNSW.value,
+        IndexType.IVFFlat.value,
+    ],
 )
 
 CaseConfigParamInput_ZillizLevel = CaseConfigInput(