upgrade ruff / black, reformat all

Signed-off-by: min.tian <min.tian.cn@gmail.com>

Author: alwayslove2013

vectordb_bench/backend/clients/__init__.py

@@ -47,7 +47,6 @@ class DB(Enum):
     Clickhouse = "Clickhouse"
     Vespa = "Vespa"
     LanceDB = "LanceDB"
-    
 
     @property
     def init_cls(self) -> type[VectorDB]:  # noqa: PLR0911, PLR0912, C901, PLR0915
@@ -76,10 +75,10 @@ def init_cls(self) -> type[VectorDB]:  # noqa: PLR0911, PLR0912, C901, PLR0915
             from .qdrant_cloud.qdrant_cloud import QdrantCloud
 
             return QdrantCloud
-        
+
         if self == DB.QdrantLocal:
             from .qdrant_local.qdrant_local import QdrantLocal
-            
+
             return QdrantLocal
 
         if self == DB.WeaviateCloud:
@@ -207,10 +206,12 @@ def config_cls(self) -> type[DBConfig]:  # noqa: PLR0911, PLR0912, C901, PLR0915
             from .qdrant_cloud.config import QdrantConfig
 
             return QdrantConfig
-        
+
         if self == DB.QdrantLocal:
             from .qdrant_local.config import QdrantLocalConfig
 
+            return QdrantLocalConfig
+
         if self == DB.WeaviateCloud:
             from .weaviate_cloud.config import WeaviateConfig
 
@@ -332,10 +333,10 @@ def case_config_cls(  # noqa: C901, PLR0911, PLR0912
             from .qdrant_cloud.config import QdrantIndexConfig
 
             return QdrantIndexConfig
-        
+
         if self == DB.QdrantLocal:
             from .qdrant_local.config import QdrantLocalIndexConfig
-            
+
             return QdrantLocalIndexConfig
 
         if self == DB.WeaviateCloud:

vectordb_bench/backend/clients/qdrant_local/cli.py

@@ -1,4 +1,4 @@
-from typing import Annotated, TypedDict, Unpack
+from typing import Annotated, Unpack
 
 import click
 from pydantic import SecretStr
@@ -11,7 +11,6 @@
     run,
 )
 
-
 DBTYPE = DB.QdrantLocal
 
 
@@ -22,39 +21,35 @@ class QdrantLocalTypedDict(CommonTypedDict):
     ]
     on_disk: Annotated[
         bool,
-        click.option(
-            "--on-disk", type=bool, default=False, help="Store the vectors and the HNSW index on disk"
-        ),
+        click.option("--on-disk", type=bool, default=False, help="Store the vectors and the HNSW index on disk"),
     ]
     m: Annotated[
         int,
-        click.option(
-            "--m", type=int, default=16, help="HNSW index parameter m, set 0 to disable the index"
-        ),
+        click.option("--m", type=int, default=16, help="HNSW index parameter m, set 0 to disable the index"),
     ]
     ef_construct: Annotated[
         int,
-        click.option(
-            "--ef-construct", type=int, default=200, help="HNSW index parameter ef_construct"
-        ),
+        click.option("--ef-construct", type=int, default=200, help="HNSW index parameter ef_construct"),
     ]
     hnsw_ef: Annotated[
         int,
         click.option(
-            "--hnsw-ef", type=int, default=0, help="HNSW index parameter hnsw_ef, set 0 to use ef_construct for search",
+            "--hnsw-ef",
+            type=int,
+            default=0,
+            help="HNSW index parameter hnsw_ef, set 0 to use ef_construct for search",
         ),
     ]
 
+
 @cli.command()
 @click_parameter_decorators_from_typed_dict(QdrantLocalTypedDict)
 def QdrantLocal(**parameters: Unpack[QdrantLocalTypedDict]):
     from .config import QdrantLocalConfig, QdrantLocalIndexConfig
 
     run(
         db=DBTYPE,
-        db_config=QdrantLocalConfig(
-            url=SecretStr(parameters["url"])
-        ),
+        db_config=QdrantLocalConfig(url=SecretStr(parameters["url"])),
         db_case_config=QdrantLocalIndexConfig(
             on_disk=parameters["on_disk"],
             m=parameters["m"],

vectordb_bench/backend/clients/qdrant_local/config.py

@@ -1,10 +1,11 @@
 from pydantic import BaseModel, SecretStr
 
-from ..api import DBCaseConfig, DBConfig, IndexType, MetricType
+from ..api import DBCaseConfig, DBConfig, MetricType
+
 
 class QdrantLocalConfig(DBConfig):
     url: SecretStr
-    
+
     def to_dict(self) -> dict:
         return {
             "url": self.url.get_secret_value(),
@@ -17,7 +18,7 @@ class QdrantLocalIndexConfig(BaseModel, DBCaseConfig):
     ef_construct: int
     hnsw_ef: int | None = 0
     on_disk: bool | None = False
-    
+
     def parse_metric(self) -> str:
         if self.metric_type == MetricType.L2:
             return "Euclid"
@@ -26,21 +27,21 @@ def parse_metric(self) -> str:
             return "Dot"
 
         return "Cosine"
-    
+
     def index_param(self) -> dict:
         return {
             "distance": self.parse_metric(),
             "m": self.m,
             "ef_construct": self.ef_construct,
             "on_disk": self.on_disk,
         }
-    
+
     def search_param(self) -> dict:
         search_params = {
-            "exact": False, # Force to use ANNs
+            "exact": False,  # Force to use ANNs
         }
-        
+
         if self.hnsw_ef != 0:
             search_params["hnsw_ef"] = self.hnsw_ef
-        
-        return search_params
+
+        return search_params

vectordb_bench/backend/clients/qdrant_local/qdrant_local.py

@@ -28,22 +28,23 @@
 QDRANT_BATCH_SIZE = 100
 
 
-def qdrant_collection_exists(client, collection_name: str) -> bool:
+def qdrant_collection_exists(client: QdrantClient, collection_name: str) -> bool:
     collection_exists = True
-    
+
     try:
         client.get_collection(collection_name)
-    except Exception as e:
+    except Exception:
         collection_exists = False
-    
+
     return collection_exists
-    
+
+
 class QdrantLocal(VectorDB):
     def __init__(
         self,
         dim: int,
         db_config: dict,
-        db_case_config: dict,
+        db_case_config: QdrantLocalIndexConfig,
         collection_name: str = "QdrantLocalCollection",
         drop_old: bool = False,
         name: str = "QdrantLocal",
@@ -56,26 +57,26 @@ def __init__(
         self.search_parameter = self.case_config.search_param()
         self.collection_name = collection_name
         self.client = None
-        
+
         self._primary_field = "pk"
         self._vector_field = "vector"
-        
+
         client = QdrantClient(**self.db_config)
-        
+
         # Lets just print the parameters here for double check
         log.info(f"Case config: {self.case_config.index_param()}")
         log.info(f"Search parameter: {self.search_parameter}")
-        
+
         if drop_old and qdrant_collection_exists(client, self.collection_name):
             log.info(f"{self.name} client drop_old collection: {self.collection_name}")
             client.delete_collection(self.collection_name)
-        
+
         if not qdrant_collection_exists(client, self.collection_name):
             log.info(f"{self.name} create collection: {self.collection_name}")
             self._create_collection(dim, client)
 
         client = None
-    
+
     @contextmanager
     def init(self):
         """
@@ -89,11 +90,15 @@ def init(self):
         yield
         self.client = None
         del self.client
-    
+
     def _create_collection(self, dim: int, qdrant_client: QdrantClient):
         log.info(f"Create collection: {self.collection_name}")
-        log.info(f"Index parameters: m={self.case_config.index_param()['m']}, ef_construct={self.case_config.index_param()['ef_construct']}, on_disk={self.case_config.index_param()['on_disk']}")
-        
+        log.info(
+            f"Index parameters: m={self.case_config.index_param()['m']}, "
+            f"ef_construct={self.case_config.index_param()['ef_construct']}, "
+            f"on_disk={self.case_config.index_param()['on_disk']}"
+        )
+
         # If the on_disk is true, we enable both on disk index and vectors.
         try:
             qdrant_client.create_collection(
@@ -104,10 +109,10 @@ def _create_collection(self, dim: int, qdrant_client: QdrantClient):
                     on_disk=self.case_config.index_param()["on_disk"],
                 ),
                 hnsw_config=HnswConfigDiff(
-                    m = self.case_config.index_param()["m"],
+                    m=self.case_config.index_param()["m"],
                     ef_construct=self.case_config.index_param()["ef_construct"],
                     on_disk=self.case_config.index_param()["on_disk"],
-                )
+                ),
             )
 
             qdrant_client.create_payload_index(
@@ -121,7 +126,7 @@ def _create_collection(self, dim: int, qdrant_client: QdrantClient):
                 return
             log.warning(f"Failed to create collection: {self.collection_name} error: {e}")
             raise e from None
-        
+
     def optimize(self, data_size: int | None = None):
         assert self.client, "Please call self.init() before"
         # wait for vectors to be fully indexed
@@ -139,11 +144,11 @@ def optimize(self, data_size: int | None = None):
                     )
                     log.info(msg)
                     return
-                
+
         except Exception as e:
             log.warning(f"QdrantCloud ready to search error: {e}")
             raise e from None
-    
+
     def insert_embeddings(
         self,
         embeddings: Iterable[list[float]],
@@ -163,7 +168,7 @@ def insert_embeddings(
         assert self.client is not None
         assert len(embeddings) == len(metadata)
         insert_count = 0
-        
+
         # disable indexing for quick insertion
         self.client.update_collection(
             collection_name=self.collection_name,
@@ -185,13 +190,13 @@ def insert_embeddings(
                 collection_name=self.collection_name,
                 optimizer_config=OptimizersConfigDiff(indexing_threshold=100),
             )
-                
+
         except Exception as e:
             log.info(f"Failed to insert data, {e}")
             return insert_count, e
         else:
             return insert_count, None
-    
+
     def search_embedding(
         self,
         query: list[float],
@@ -203,7 +208,7 @@ def search_embedding(
         Should call self.init() first.
         """
         assert self.client is not None
-        
+
         f = None
         if filters:
             f = Filter(
@@ -215,17 +220,13 @@ def search_embedding(
                         ),
                     ),
                 ],
-            ) 
-        res = (
-            self.client.query_points(
-                collection_name=self.collection_name,
-                query=query,
-                limit=k,
-                query_filter=f,
-                search_params=SearchParams(**self.search_parameter),
-                
-            ).points
-        )
-        
-        return [result.id for result in res]
+            )
+        res = self.client.query_points(
+            collection_name=self.collection_name,
+            query=query,
+            limit=k,
+            query_filter=f,
+            search_params=SearchParams(**self.search_parameter),
+        ).points
 
+        return [result.id for result in res]

vectordb_bench/backend/clients/weaviate_cloud/cli.py

@@ -15,33 +15,32 @@
 class WeaviateTypedDict(CommonTypedDict):
     api_key: Annotated[
         str,
-        click.option("--api-key", type=str, help="Weaviate api key", required=False, default=''),
+        click.option("--api-key", type=str, help="Weaviate api key", required=False, default=""),
     ]
     url: Annotated[
         str,
         click.option("--url", type=str, help="Weaviate url", required=True),
     ]
     no_auth: Annotated[
         bool,
-        click.option("--no-auth", is_flag=True, help="Do not use api-key, set it to true if you are using a local setup. Default is False.", default=False),
+        click.option(
+            "--no-auth",
+            is_flag=True,
+            help="Do not use api-key, set it to true if you are using a local setup. Default is False.",
+            default=False,
+        ),
     ]
     m: Annotated[
         int,
-        click.option(
-            "--m", type=int, default=16, help="HNSW index parameter m."
-        ),
+        click.option("--m", type=int, default=16, help="HNSW index parameter m."),
     ]
     ef_construct: Annotated[
         int,
-        click.option(
-            "--ef-construction", type=int, default=256, help="HNSW index parameter ef_construction"
-        ),
+        click.option("--ef-construction", type=int, default=256, help="HNSW index parameter ef_construction"),
     ]
     ef: Annotated[
         int,
-        click.option(
-            "--ef", type=int, default=256, help="HNSW index parameter ef for search"
-        ),
+        click.option("--ef", type=int, default=256, help="HNSW index parameter ef for search"),
     ]
 
 
@@ -54,7 +53,7 @@ def Weaviate(**parameters: Unpack[WeaviateTypedDict]):
         db=DB.WeaviateCloud,
         db_config=WeaviateConfig(
             db_label=parameters["db_label"],
-            api_key=SecretStr(parameters["api_key"]) if parameters["api_key"] != '' else SecretStr("-"),
+            api_key=SecretStr(parameters["api_key"]) if parameters["api_key"] != "" else SecretStr("-"),
             url=SecretStr(parameters["url"]),
             no_auth=parameters["no_auth"],
         ),