upgrade black/ruff and fix lint issues

Signed-off-by: min.tian <[email protected]>

Author: alwayslove2013

vectordb_bench/backend/clients/oss_opensearch/cli.py

@@ -12,7 +12,7 @@
     run,
 )
 from .. import DB
-from .config import OSSOS_Engine, OSSOpenSearchQuantization
+from .config import OSSOpenSearchQuantization, OSSOS_Engine
 
 log = logging.getLogger(__name__)
 

vectordb_bench/backend/clients/oss_opensearch/config.py

@@ -17,8 +17,8 @@ class OSSOpenSearchConfig(DBConfig, BaseModel):
     def to_dict(self) -> dict:
         use_ssl = self.port == 443
         http_auth = (
-            (self.user, self.password.get_secret_value()) 
-            if self.user is not None and self.password is not None and len(self.user) != 0 and len(self.password) != 0 
+            (self.user, self.password.get_secret_value())
+            if self.user is not None and self.password is not None and len(self.user) != 0 and len(self.password) != 0
             else ()
         )
         return {
@@ -78,7 +78,7 @@ class OSSOpenSearchIndexConfig(BaseModel, DBCaseConfig):
     quantization_type: OSSOpenSearchQuantization = OSSOpenSearchQuantization.fp32
 
     @root_validator
-    def validate_engine_name(cls, values):
+    def validate_engine_name(cls, values: dict):
         """Map engine_name string from UI to engine enum"""
         if values.get("engine_name"):
             engine_name = values["engine_name"].lower()
@@ -138,8 +138,6 @@ def index_param(self) -> dict:
         log.info(f"Using metric_type: {self.metric_type_name} for index creation")
         log.info(f"Resulting space_type: {self.parse_metric()} for index creation")
 
-        parameters = {"ef_construction": self.efConstruction, "m": self.M}
-
         return {
             "name": "hnsw",
             "engine": self.engine.value,

vectordb_bench/backend/clients/oss_opensearch/oss_opensearch.py

@@ -93,18 +93,18 @@ def _create_index(self, client: OpenSearch) -> None:
         settings["index"]["knn.algo_param.ef_search"] = ef_search_value
         # Build properties mapping, excluding _id which is automatically handled by OpenSearch
         properties = {}
-        
+
         # Only add id field to properties if it's not the special _id field
         if self.id_col_name != "_id":
             properties[self.id_col_name] = {"type": "integer", "store": True}
-            
+
         properties[self.label_col_name] = {"type": "keyword"}
         properties[self.vector_col_name] = {
             "type": "knn_vector",
             "dimension": self.dim,
             "method": self.case_config.index_param(),
         }
-        
+
         mappings = {
             "properties": properties,
         }
@@ -342,17 +342,16 @@ def search_embedding(
                     preference="_only_local" if self.case_config.number_of_shards == 1 else None,
                     routing=self.routing_key,
                 )
-                
+
             log.debug(f"Search took: {resp['took']}")
             log.debug(f"Search shards: {resp['_shards']}")
             log.debug(f"Search hits total: {resp['hits']['total']}")
             try:
                 if self.id_col_name == "_id":
                     # Get _id directly from hit metadata
                     return [int(h["_id"]) for h in resp["hits"]["hits"]]
-                else:
-                    # Get custom id field from docvalue fields
-                    return [int(h["fields"][self.id_col_name][0]) for h in resp["hits"]["hits"]]
+                # Get custom id field from docvalue fields
+                return [int(h["fields"][self.id_col_name][0]) for h in resp["hits"]["hits"]]
             except Exception:
                 # empty results
                 return []

vectordb_bench/backend/clients/s3_vectors/config.py

@@ -29,10 +29,10 @@ class S3VectorsIndexConfig(DBCaseConfig, BaseModel):
     def parse_metric(self) -> str:
         if self.metric_type == MetricType.COSINE:
             return "cosine"
-        elif self.metric_type == MetricType.L2:
+        if self.metric_type == MetricType.L2:
             return "euclidean"
-        else:
-            raise ValueError(f"Unsupported metric type: {self.metric_type}")
+        msg = f"Unsupported metric type: {self.metric_type}"
+        raise ValueError(msg)
 
     def index_param(self) -> dict:
         return {}

vectordb_bench/backend/clients/s3_vectors/s3_vectors.py

@@ -1,11 +1,11 @@
 """Wrapper around the Milvus vector database over VectorDB"""
 
 import logging
-import time
 from collections.abc import Iterable
 from contextlib import contextmanager
 
 import boto3
+
 from vectordb_bench.backend.filter import Filter, FilterOp
 
 from ..api import VectorDB

vectordb_bench/backend/clients/tidb/config.py

@@ -4,6 +4,7 @@
 
 from ..api import DBCaseConfig, DBConfig, MetricType
 
+
 class TiDBConfigDict(TypedDict):
     host: str
     port: int
@@ -13,6 +14,7 @@ class TiDBConfigDict(TypedDict):
     ssl_verify_cert: bool
     ssl_verify_identity: bool
 
+
 class TiDBConfig(DBConfig):
     user_name: str = "root"
     password: SecretStr

vectordb_bench/cli/vectordbbench.py

@@ -1,12 +1,12 @@
 from ..backend.clients.alloydb.cli import AlloyDBScaNN
 from ..backend.clients.aws_opensearch.cli import AWSOpenSearch
-from ..backend.clients.oss_opensearch.cli import OSSOpenSearch
 from ..backend.clients.clickhouse.cli import Clickhouse
 from ..backend.clients.lancedb.cli import LanceDB
 from ..backend.clients.mariadb.cli import MariaDBHNSW
 from ..backend.clients.memorydb.cli import MemoryDB
 from ..backend.clients.milvus.cli import MilvusAutoIndex
 from ..backend.clients.oceanbase.cli import OceanBaseHNSW, OceanBaseIVF
+from ..backend.clients.oss_opensearch.cli import OSSOpenSearch
 from ..backend.clients.pgdiskann.cli import PgDiskAnn
 from ..backend.clients.pgvecto_rs.cli import PgVectoRSHNSW, PgVectoRSIVFFlat
 from ..backend.clients.pgvector.cli import PgVectorHNSW