feat: support turbopuffer client

vectordb_bench/backend/clients/__init__.py

@@ -55,6 +55,7 @@ class DB(Enum):
     TencentElasticsearch = "TencentElasticsearch"
     AliSQL = "AlibabaCloudRDSMySQL"
     Doris = "Doris"
+    TurboPuffer = "TurpoBuffer"
 
     @property
     def init_cls(self) -> type[VectorDB]:  # noqa: PLR0911, PLR0912, C901, PLR0915
@@ -187,6 +188,10 @@ def init_cls(self) -> type[VectorDB]:  # noqa: PLR0911, PLR0912, C901, PLR0915
             from .doris.doris import Doris
 
             return Doris
+        if self == DB.TurboPuffer:
+            from .turbopuffer.turbopuffer import TurboPuffer
+
+            return TurboPuffer
 
         if self == DB.Test:
             from .test.test import Test
@@ -357,6 +362,10 @@ def config_cls(self) -> type[DBConfig]:  # noqa: PLR0911, PLR0912, C901, PLR0915
             from .doris.config import DorisConfig
 
             return DorisConfig
+        if self == DB.TurboPuffer:
+            from .turbopuffer.config import TurboPufferConfig
+
+            return TurboPufferConfig
 
         if self == DB.Test:
             from .test.config import TestConfig
@@ -537,6 +546,10 @@ def case_config_cls(  # noqa: C901, PLR0911, PLR0912, PLR0915
             from .doris.config import DorisCaseConfig
 
             return DorisCaseConfig
+        if self == DB.TurboPuffer:
+            from .turbopuffer.config import TurboPufferIndexConfig
+
+            return TurboPufferIndexConfig
 
         # DB.Pinecone, DB.Chroma, DB.Redis
         return EmptyDBCaseConfig

vectordb_bench/backend/clients/cockroachdb/cockroachdb.py

@@ -35,7 +35,7 @@ class CockroachDB(VectorDB):
         FilterOp.StrEqual,
     ]
 
-    def __init__(
+    def __init__(  # noqa: PLR0915
         self,
         dim: int,
         db_config: dict,

vectordb_bench/backend/clients/turbopuffer/config.py

@@ -0,0 +1,37 @@
+from pydantic import BaseModel, SecretStr
+
+from ..api import DBCaseConfig, DBConfig, MetricType
+
+
+class TurboPufferConfig(DBConfig):
+    api_key: SecretStr
+    api_base_url: str
+    namespace: str = "vdbbench_test"
+
+    def to_dict(self) -> dict:
+        return {
+            "api_key": self.api_key.get_secret_value(),
+            "api_base_url": self.api_base_url,
+            "namespace": self.namespace,
+        }
+
+
+class TurboPufferIndexConfig(BaseModel, DBCaseConfig):
+    metric_type: MetricType | None = None
+    use_multi_ns_for_filter: bool = False
+    time_wait_warmup: int = 60 * 1  # 1min
+
+    def parse_metric(self) -> str:
+        if self.metric_type == MetricType.COSINE:
+            return "cosine_distance"
+        if self.metric_type == MetricType.L2:
+            return "euclidean_squared"
+
+        msg = f"Not Support {self.metric_type}"
+        raise ValueError(msg)
+
+    def index_param(self) -> dict:
+        return {}
+
+    def search_param(self) -> dict:
+        return {}

vectordb_bench/backend/clients/turbopuffer/turbopuffer.py

@@ -0,0 +1,122 @@
+"""Wrapper around the Pinecone vector database over VectorDB"""
+
+import logging
+import time
+from contextlib import contextmanager
+
+import turbopuffer as tpuf
+
+from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferIndexConfig
+from vectordb_bench.backend.filter import Filter, FilterOp
+
+from ..api import VectorDB
+
+log = logging.getLogger(__name__)
+
+
+class TurboPuffer(VectorDB):
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+        FilterOp.StrEqual,
+    ]
+
+    def __init__(
+        self,
+        dim: int,
+        db_config: dict,
+        db_case_config: TurboPufferIndexConfig,
+        drop_old: bool = False,
+        with_scalar_labels: bool = False,
+        **kwargs,
+    ):
+        """Initialize wrapper around the milvus vector database."""
+        self.api_key = db_config.get("api_key", "")
+        self.api_base_url = db_config.get("api_base_url", "")
+        self.namespace = db_config.get("namespace", "")
+        self.db_case_config = db_case_config
+        self.metric = db_case_config.parse_metric()
+
+        self._vector_field = "vector"
+        self._scalar_id_field = "id"
+        self._scalar_label_field = "label"
+
+        self.with_scalar_labels = with_scalar_labels
+        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)
+            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)
+        yield
+
+    def optimize(self, data_size: int | None = None):
+        # turbopuffer responds to the request
+        #   once the cache warming operation has been started.
+        # It does not wait for the operation to complete,
+        #   which can take multiple minutes for large namespaces.
+        self.ns.hint_cache_warm()
+        log.info(f"warming up but no api waiting for complete. just sleep {self.db_case_config.time_wait_warmup}s")
+        time.sleep(self.db_case_config.time_wait_warmup)
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        labels_data: list[str] | None = None,
+        **kwargs,
+    ) -> tuple[int, Exception]:
+        try:
+            if self.with_scalar_labels:
+                self.ns.write(
+                    upsert_columns={
+                        self._scalar_id_field: metadata,
+                        self._vector_field: embeddings,
+                        self._scalar_label_field: labels_data,
+                    },
+                    distance_metric=self.metric,
+                )
+            else:
+                self.ns.write(
+                    upsert_columns={
+                        self._scalar_id_field: metadata,
+                        self._vector_field: embeddings,
+                    },
+                    distance_metric=self.metric,
+                )
+        except Exception as e:
+            log.warning(f"Failed to insert. Error: {e}")
+        return len(embeddings), None
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+        timeout: int | None = None,
+    ) -> list[int]:
+        res = self.ns.query(
+            rank_by=["vector", "ANN", query],
+            top_k=k,
+            filters=self.expr,
+        )
+        return [row.id for row in res.rows]
+
+    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]
+        elif filters.type == FilterOp.StrEqual:
+            self.expr = [self._scalar_label_field, "Eq", filters.label_value]
+        else:
+            msg = f"Not support Filter for TurboPuffer - {filters}"
+            raise ValueError(msg)

vectordb_bench/backend/runner/rate_runner.py

@@ -7,7 +7,6 @@
 
 from vectordb_bench import config
 from vectordb_bench.backend.clients import api
-from vectordb_bench.backend.clients.doris.doris import Doris
 from vectordb_bench.backend.dataset import DataSetIterator
 from vectordb_bench.backend.utils import time_it
 
@@ -54,7 +53,7 @@ def _insert_embeddings(db: api.VectorDB, emb: list[list[float]], metadata: list[
             db_copy = deepcopy(db)
             with db_copy.init():
                 _insert_embeddings(db_copy, emb, metadata, retry_idx=0)
-        elif isinstance(db, Doris):
+        elif db.name == "Doris":
             # DorisVectorClient is not thread-safe. Similar to pgvector, create a per-thread client
             # by deep-copying the wrapper and forcing lazy re-init inside the thread.
             db_copy = deepcopy(db)

vectordb_bench/frontend/config/styles.py

@@ -69,6 +69,7 @@ def getPatternShape(i):
     DB.S3Vectors: "https://assets.zilliz.com/s3_vectors_daf370b4e5.png",
     DB.Hologres: "https://img.alicdn.com/imgextra/i3/O1CN01d9qrry1i6lTNa2BRa_!!6000000004364-2-tps-218-200.png",
     DB.Doris: "https://doris.apache.org/images/logo.svg",
+    DB.TurboPuffer: "https://turbopuffer.com/logo2.png",
 }
 
 # RedisCloud color: #0D6EFD