diff --git a/pyproject.toml b/pyproject.toml
index 9e2cfd676aafbc480a475553b2054f33fdc4672e..f78ed13fc5a435d8c391228770e2fdefa3635240 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "semantic-router"
-version = "0.0.52"
+version = "0.0.53"
description = "Super fast semantic router for AI decision making"
authors = [
"James Briggs <james@aurelio.ai>",
diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py
index 50989c62048dd43ba9354484c75f08abad4185bc..19d5381ff51a0c688cbd0bf49c3c4c5f9242ac9b 100644
--- a/semantic_router/__init__.py
+++ b/semantic_router/__init__.py
@@ -4,4 +4,4 @@ from semantic_router.route import Route
__all__ = ["RouteLayer", "HybridRouteLayer", "Route", "LayerConfig"]
-__version__ = "0.0.50"
+__version__ = "0.0.53"
diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py
index 7e32f3a8d676af31a51969b4ec07d6f13060d108..5426ec76d8efc856a2c036a6dcd8afc33d69d091 100644
--- a/semantic_router/index/local.py
+++ b/semantic_router/index/local.py
@@ -99,6 +99,35 @@ class LocalIndex(BaseIndex):
route_names = [self.routes[i] for i in idx]
return scores, route_names
+ async def aquery(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ ) -> Tuple[np.ndarray, List[str]]:
+ """
+ Search the index for the query and return top_k results.
+ """
+ if self.index is None or self.routes is None:
+ raise ValueError("Index or routes are not populated.")
+ if route_filter is not None:
+ filtered_index = []
+ filtered_routes = []
+ for route, vec in zip(self.routes, self.index):
+ if route in route_filter:
+ filtered_index.append(vec)
+ filtered_routes.append(route)
+ if not filtered_routes:
+ raise ValueError("No routes found matching the filter criteria.")
+ sim = similarity_matrix(vector, np.array(filtered_index))
+ scores, idx = top_scores(sim, top_k)
+ route_names = [filtered_routes[i] for i in idx]
+ else:
+ sim = similarity_matrix(vector, self.index)
+ scores, idx = top_scores(sim, top_k)
+ route_names = [self.routes[i] for i in idx]
+ return scores, route_names
+
def delete(self, route_name: str):
"""
Delete all records of a specific route from the index.
diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py
index fdd873206085821be4b3c4b45f85447cb0162c23..a578eb01b2edbf1177dd9a9ddb79c1dbaa425173 100644
--- a/semantic_router/index/pinecone.py
+++ b/semantic_router/index/pinecone.py
@@ -464,7 +464,25 @@ class PineconeIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
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()
@@ -474,6 +492,7 @@ class PineconeIndex(BaseIndex):
filter_query = None
results = self.index.query(
vector=[query_vector_list],
+ sparse_vector=kwargs.get("sparse_vector", None),
top_k=top_k,
filter=filter_query,
include_metadata=True,
@@ -488,7 +507,25 @@ class PineconeIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
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()
@@ -498,6 +535,7 @@ class PineconeIndex(BaseIndex):
filter_query = None
results = await self._async_query(
vector=query_vector_list,
+ sparse_vector=kwargs.get("sparse_vector", None),
namespace=self.namespace or "",
filter=filter_query,
top_k=top_k,
@@ -514,6 +552,7 @@ class PineconeIndex(BaseIndex):
async def _async_query(
self,
vector: list[float],
+ sparse_vector: Optional[dict] = None,
namespace: str = "",
filter: Optional[dict] = None,
top_k: int = 5,
@@ -521,6 +560,7 @@ class PineconeIndex(BaseIndex):
):
params = {
"vector": vector,
+ "sparse_vector": sparse_vector,
"namespace": namespace,
"filter": filter,
"top_k": top_k,