update docstring

semantic_router/index/pinecone.py

@@ -466,6 +466,23 @@ def query(
         route_filter: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Tuple[np.ndarray, List[str]]:
+        """
+        Search the index for the query vector and return the top_k results.
+
+        :param vector: The query vector to search for.
+        :type vector: np.ndarray
+        :param top_k: The number of top results to return, defaults to 5.
+        :type top_k: int, optional
+        :param route_filter: A list of route names to filter the search results, defaults to None.
+        :type route_filter: Optional[List[str]], optional
+        :param kwargs: Additional keyword arguments for the query, including sparse_vector.
+        :type kwargs: Any
+        :keyword sparse_vector: An optional sparse vector to include in the query.
+        :type sparse_vector: Optional[dict]
+        :return: A tuple containing an array of scores and a list of route names.
+        :rtype: Tuple[np.ndarray, List[str]]
+        :raises ValueError: If the index is not populated.
+        """
         if self.index is None:
             raise ValueError("Index is not populated.")
         query_vector_list = vector.tolist()
@@ -492,6 +509,23 @@ async def aquery(
         route_filter: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Tuple[np.ndarray, List[str]]:
+        """
+        Asynchronously search the index for the query vector and return the top_k results.
+
+        :param vector: The query vector to search for.
+        :type vector: np.ndarray
+        :param top_k: The number of top results to return, defaults to 5.
+        :type top_k: int, optional
+        :param route_filter: A list of route names to filter the search results, defaults to None.
+        :type route_filter: Optional[List[str]], optional
+        :param kwargs: Additional keyword arguments for the query, including sparse_vector.
+        :type kwargs: Any
+        :keyword sparse_vector: An optional sparse vector to include in the query.
+        :type sparse_vector: Optional[dict]
+        :return: A tuple containing an array of scores and a list of route names.
+        :rtype: Tuple[np.ndarray, List[str]]
+        :raises ValueError: If the index is not populated.
+        """
         if self.async_client is None or self.host is None:
             raise ValueError("Async client or host are not initialized.")
         query_vector_list = vector.tolist()