Fixed linting errors

Author: luis-gasparschroeder

vectorq/config.py

@@ -15,7 +15,9 @@
     HNSWLibVectorDB,
 )
 from vectorq.vectorq_core.cache.eviction_policy.eviction_policy import EvictionPolicy
-from vectorq.vectorq_core.cache.eviction_policy.strategies.no_eviction import NoEvictionPolicy
+from vectorq.vectorq_core.cache.eviction_policy.strategies.no_eviction import (
+    NoEvictionPolicy,
+)
 
 
 class VectorQConfig:

vectorq/main.py

@@ -20,7 +20,7 @@ def __init__(
         self.vectorq_config = config
         self.vectorq_policy = policy
         self.vectorq_policy.setup(config)
-        
+
     def infer(
         self,
         prompt: str,

vectorq/vectorq_core/cache/eviction_policy/__init__.py

@@ -1,6 +1,8 @@
-from vectorq.vectorq_core.cache.eviction_policy.strategies.lru import LRUEvictionPolicy
-from vectorq.vectorq_core.cache.eviction_policy.strategies.no_eviction import NoEvictionPolicy
 from vectorq.vectorq_core.cache.eviction_policy.eviction_policy import EvictionPolicy
+from vectorq.vectorq_core.cache.eviction_policy.strategies.lru import LRUEvictionPolicy
+from vectorq.vectorq_core.cache.eviction_policy.strategies.no_eviction import (
+    NoEvictionPolicy,
+)
 
 __all__ = [
     "LRUEvictionPolicy",

vectorq/vectorq_core/cache/eviction_policy/strategies/__init__.py

@@ -1,5 +1,7 @@
 from vectorq.vectorq_core.cache.eviction_policy.strategies.lru import LRUEvictionPolicy
-from vectorq.vectorq_core.cache.eviction_policy.strategies.no_eviction import NoEvictionPolicy
+from vectorq.vectorq_core.cache.eviction_policy.strategies.no_eviction import (
+    NoEvictionPolicy,
+)
 
 __all__ = [
     "LRUEvictionPolicy",

vectorq/vectorq_policy/strategies/dynamic_global_threshold.py

@@ -2,7 +2,6 @@
 from enum import Enum
 from typing import Dict, List, Optional, Tuple
 
-import cvxpy as cp
 import numpy as np
 import statsmodels.api as sm
 from scipy.special import expit
@@ -120,10 +119,10 @@ def __init__(self, delta: float):
         self.global_var_t: float = None
 
         self.variance_map: Dict[int, List[float]] = {
-            6:  0.035445,
-            7:  0.028285,
-            8:  0.026436,
-            9:  0.021349,
+            6: 0.035445,
+            7: 0.028285,
+            8: 0.026436,
+            9: 0.021349,
             10: 0.019371,
             11: 0.012615,
             12: 0.011433,
@@ -244,7 +243,7 @@ def _estimate_parameters(
                 print(f"len does not match: {len(similarities)} != {len(labels)}")
             self.logistic_regression.fit(similarities, labels)
             intercept, gamma = self.logistic_regression.coef_[0]
-            
+
             gamma = max(gamma, 1e-6)
             t_hat = -intercept / gamma
             t_hat = float(np.clip(t_hat, 0.0, 1.0))

vectorq/vectorq_policy/strategies/dynamic_local_threshold.py

@@ -111,10 +111,10 @@ def __init__(self, delta: float):
         )
 
         self.variance_map: Dict[int, List[float]] = {
-            6:  0.035445,
-            7:  0.028285,
-            8:  0.026436,
-            9:  0.021349,
+            6: 0.035445,
+            7: 0.028285,
+            8: 0.026436,
+            9: 0.021349,
             10: 0.019371,
             11: 0.012615,
             12: 0.011433,