make all type hints lowercase

semantic_router/encoders/fastembed.py

-from typing import Any, List, Optional
+from typing import Any, Optional
 
 import numpy as np
 from pydantic import PrivateAttr
@@ -44,8 +44,8 @@ class FastEmbedEncoder(BaseEncoder):
 
     def __call__(self, docs: list[str]) -> list[list[float]]:
         try:
-            embeds: List[np.ndarray] = list(self._client.embed(docs))
-            embeddings: List[List[float]] = [e.tolist() for e in embeds]
+            embeds: list[np.ndarray] = list(self._client.embed(docs))
+            embeddings: list[list[float]] = [e.tolist() for e in embeds]
             return embeddings
         except Exception as e:
             raise ValueError(f"FastEmbed embed failed. Error: {e}")

semantic_router/linear.py

-from typing import Tuple
-
 import numpy as np
 from numpy.linalg import norm
 
@@ -21,7 +19,7 @@ def similarity_matrix(xq: np.ndarray, index: np.ndarray) -> np.ndarray:
     return sim
 
 
-def top_scores(sim: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, np.ndarray]:
+def top_scores(sim: np.ndarray, top_k: int = 5) -> tuple[np.ndarray, np.ndarray]:
     # get indices of top_k records
     top_k = min(top_k, sim.shape[0])
     idx = np.argpartition(sim, -top_k)[-top_k:]

semantic_router/utils/splitters.py

@@ -26,7 +26,7 @@ def semantic_splitter(
         split_method (str): The method to use for splitting.
 
     Returns:
-        Dict[str, list[str]]: Splits with corresponding documents.
+        dict[str, list[str]]: Splits with corresponding documents.
     """
     total_docs = len(docs)
     splits = {}