lint

semantic_router/layer.py

 import json
 import os
+import random
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import yaml
+from tqdm.auto import tqdm
 
 from semantic_router.encoders import BaseEncoder, OpenAIEncoder
 from semantic_router.linear import similarity_matrix, top_scores
@@ -11,8 +13,6 @@ from semantic_router.llms import BaseLLM, OpenAILLM
 from semantic_router.route import Route
 from semantic_router.schema import Encoder, EncoderType, RouteChoice
 from semantic_router.utils.logger import logger
-from tqdm.auto import tqdm
-import random
 
 
 def is_valid(layer_config: str) -> bool:
@@ -311,16 +311,14 @@ class RouteLayer:
         )
 
     def _encode(self, text: str) -> np.ndarray:
-        """Given some text, encode it.
-        """
+        """Given some text, encode it."""
         # create query vector
         xq = np.array(self.encoder([text]))
         xq = np.squeeze(xq)  # Reduce to 1d array.
         return xq
 
     def _retrieve(self, xq: np.ndarray, top_k: int = 5) -> List[dict]:
-        """Given a query vector, retrieve the top_k most similar records.
-        """
+        """Given a query vector, retrieve the top_k most similar records."""
         if self.index is not None:
             # calculate similarity matrix
             sim = similarity_matrix(xq, self.index)
@@ -358,11 +356,8 @@ class RouteLayer:
             return max(scores) > threshold
         else:
             return False
-    
-    def _update_thresholds(
-        self,
-        score_thresholds: Optional[Dict[str, float]] = None
-    ):
+
+    def _update_thresholds(self, score_thresholds: Optional[Dict[str, float]] = None):
         """
         Update the score thresholds for each route.
         """
@@ -420,7 +415,7 @@ class RouteLayer:
                 best_thresholds = thresholds
         # update route layer to best thresholds
         self._update_thresholds(score_thresholds=best_thresholds)
-    
+
     def evaluate(self, X: List[Union[str, float]], y: List[str]) -> float:
         """
         Evaluate the accuracy of the route selection.
@@ -440,8 +435,7 @@ def threshold_random_search(
     route_layer: RouteLayer,
     search_range: Union[int, float],
 ) -> Tuple[float, Dict[str, float]]:
-    """Performs a random search iteration given a route layer and a search range.
-    """
+    """Performs a random search iteration given a route layer and a search range."""
     # extract the route names
     routes = route_layer.get_route_thresholds()
     route_names = list(routes.keys())
@@ -451,13 +445,14 @@ def threshold_random_search(
     for threshold in route_thresholds:
         score_threshold_values.append(
             np.linspace(
-                start=max(threshold-search_range, 0.0),
-                stop=min(threshold+search_range, 1.0),
-                num=100
+                start=max(threshold - search_range, 0.0),
+                stop=min(threshold + search_range, 1.0),
+                num=100,
             )
         )
     # Generate a random threshold for each route
     score_thresholds = {
-        route: random.choice(score_threshold_values[i]) for i, route in enumerate(route_names)
+        route: random.choice(score_threshold_values[i])
+        for i, route in enumerate(route_names)
     }
     return score_thresholds

semantic_router/schema.py

@@ -10,7 +10,7 @@ from semantic_router.encoders import (
     FastEmbedEncoder,
     OpenAIEncoder,
 )
-from semantic_router.utils.splitters import semantic_splitter, DocumentSplit
+from semantic_router.utils.splitters import DocumentSplit, semantic_splitter
 
 
 class EncoderType(Enum):