expose turbopuffer through CLI and make compatible with latest sdk

pyproject.toml

@@ -75,6 +75,7 @@ all = [
     "pyvespa",
     "lancedb",
     "mysql-connector-python",
+    "turbopuffer[fast]",
 ]
 
 qdrant          = [ "qdrant-client" ]
@@ -102,6 +103,7 @@ lancedb         = [ "lancedb" ]
 oceanbase       = [ "mysql-connector-python" ]
 alisql          = [ "mysql-connector-python" ]
 doris           = [ "doris-vector-search" ]
+turbopuffer     = [ "turbopuffer" ]
 
 [project.urls]
 "repository" = "https://github.com/zilliztech/VectorDBBench"

vectordb_bench/backend/clients/__init__.py

@@ -55,7 +55,7 @@ class DB(Enum):
     TencentElasticsearch = "TencentElasticsearch"
     AliSQL = "AlibabaCloudRDSMySQL"
     Doris = "Doris"
-    TurboPuffer = "TurpoBuffer"
+    TurboPuffer = "TurboPuffer"
 
     @property
     def init_cls(self) -> type[VectorDB]:  # noqa: PLR0911, PLR0912, C901, PLR0915

vectordb_bench/backend/clients/turbopuffer/cli.py

@@ -0,0 +1,62 @@
+from typing import Annotated, TypedDict, Unpack
+
+import click
+from pydantic import SecretStr
+
+from ....cli.cli import (
+    CommonTypedDict,
+    cli,
+    click_parameter_decorators_from_typed_dict,
+    run,
+)
+from .. import DB
+
+
+class TurboPufferTypedDict(TypedDict):
+    api_key: Annotated[
+        str,
+        click.option("--api-key", type=str, help="TurboPuffer API key", required=True),
+    ]
+    api_base_url: Annotated[
+        str,
+        click.option(
+            "--api-base-url",
+            type=str,
+            help="TurboPuffer API base URL",
+            required=False,
+            default="https://api.turbopuffer.com",
+            show_default=True,
+        ),
+    ]
+    namespace: Annotated[
+        str,
+        click.option(
+            "--namespace",
+            type=str,
+            help="TurboPuffer namespace",
+            required=False,
+            default="vdbbench_test",
+            show_default=True,
+        ),
+    ]
+
+
+class TurboPufferIndexTypedDict(CommonTypedDict, TurboPufferTypedDict): ...
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(TurboPufferIndexTypedDict)
+def TurboPuffer(**parameters: Unpack[TurboPufferIndexTypedDict]):
+    from .config import TurboPufferConfig, TurboPufferIndexConfig
+
+    run(
+        db=DB.TurboPuffer,
+        db_config=TurboPufferConfig(
+            db_label=parameters["db_label"],
+            api_key=SecretStr(parameters["api_key"]),
+            api_base_url=parameters["api_base_url"],
+            namespace=parameters["namespace"],
+        ),
+        db_case_config=TurboPufferIndexConfig(),
+        **parameters,
+    )

vectordb_bench/backend/clients/turbopuffer/turbopuffer.py

@@ -42,21 +42,21 @@ def __init__(
         self._scalar_label_field = "label"
 
         self.with_scalar_labels = with_scalar_labels
+
+        # Initialize client with new SDK pattern
+        self.client = tpuf.Turbopuffer(api_key=self.api_key, base_url=self.api_base_url)
+
         if drop_old:
             log.info(f"Drop old. delete the namespace: {self.namespace}")
-            tpuf.api_key = self.api_key
-            tpuf.api_base_url = self.api_base_url
-            ns = tpuf.Namespace(self.namespace)
+            ns = self.client.namespace(self.namespace)
             try:
                 ns.delete_all()
             except Exception as e:
                 log.warning(f"Failed to delete all. Error: {e}")
 
     @contextmanager
     def init(self):
-        tpuf.api_key = self.api_key
-        tpuf.api_base_url = self.api_base_url
-        self.ns = tpuf.Namespace(self.namespace)
+        self.ns = self.client.namespace(self.namespace)
         yield
 
     def optimize(self, data_size: int | None = None):
@@ -78,7 +78,7 @@ def insert_embeddings(
         try:
             if self.with_scalar_labels:
                 self.ns.write(
-                    upsert_columns={
+                    columns={
                         self._scalar_id_field: metadata,
                         self._vector_field: embeddings,
                         self._scalar_label_field: labels_data,
@@ -87,7 +87,7 @@ def insert_embeddings(
                 )
             else:
                 self.ns.write(
-                    upsert_columns={
+                    columns={
                         self._scalar_id_field: metadata,
                         self._vector_field: embeddings,
                     },
@@ -104,19 +104,19 @@ def search_embedding(
         timeout: int | None = None,
     ) -> list[int]:
         res = self.ns.query(
-            rank_by=["vector", "ANN", query],
+            rank_by=("vector", "ANN", query),
             top_k=k,
             filters=self.expr,
         )
-        return [row.id for row in res.rows]
+        return [row.id for row in res.rows] if res.rows is not None else []
 
     def prepare_filter(self, filters: Filter):
         if filters.type == FilterOp.NonFilter:
             self.expr = None
         elif filters.type == FilterOp.NumGE:
-            self.expr = [self._scalar_id_field, "Gte", filters.int_value]
+            self.expr = (self._scalar_id_field, "Gte", filters.int_value)
         elif filters.type == FilterOp.StrEqual:
-            self.expr = [self._scalar_label_field, "Eq", filters.label_value]
+            self.expr = (self._scalar_label_field, "Eq", filters.label_value)
         else:
             msg = f"Not support Filter for TurboPuffer - {filters}"
             raise ValueError(msg)

vectordb_bench/cli/vectordbbench.py

@@ -22,6 +22,7 @@
 from ..backend.clients.tencent_elasticsearch.cli import TencentElasticsearch
 from ..backend.clients.test.cli import Test
 from ..backend.clients.tidb.cli import TiDB
+from ..backend.clients.turbopuffer.cli import TurboPuffer
 from ..backend.clients.vespa.cli import Vespa
 from ..backend.clients.weaviate_cloud.cli import Weaviate
 from ..backend.clients.zilliz_cloud.cli import ZillizAutoIndex
@@ -58,6 +59,7 @@
 cli.add_command(TencentElasticsearch)
 cli.add_command(AliSQLHNSW)
 cli.add_command(Doris)
+cli.add_command(TurboPuffer)
 
 
 if __name__ == "__main__":