diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py
index 18d71cedeb235e024bca52416d851e99fbb5d102..0bfc4eea94b9940b5dc476ed167c92062d31b2ff 100644
--- a/semantic_router/routers/base.py
+++ b/semantic_router/routers/base.py
@@ -4,6 +4,7 @@ import os
import random
import hashlib
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing_extensions import deprecated
from pydantic import BaseModel, Field
import numpy as np
@@ -280,6 +281,20 @@ class RouterConfig:
)
+def xq_reshape(xq: List[float] | np.ndarray) -> np.ndarray:
+ # convert to numpy array if not already
+ if not isinstance(xq, np.ndarray):
+ xq = np.array(xq)
+ # check if vector is 1D and expand to 2D if necessary
+ if len(xq.shape) == 1:
+ xq = np.expand_dims(xq, axis=0)
+ if xq.shape[0] != 1:
+ raise ValueError(
+ f"Expected (1, x) dimensional input for query, got {xq.shape}."
+ )
+ return xq
+
+
class BaseRouter(BaseModel):
encoder: DenseEncoder = Field(default_factory=OpenAIEncoder)
index: BaseIndex = Field(default_factory=BaseIndex)
@@ -402,7 +417,7 @@ class BaseRouter(BaseModel):
def __call__(
self,
text: Optional[str] = None,
- vector: Optional[List[float]] = None,
+ vector: Optional[List[float] | np.ndarray] = None,
simulate_static: bool = False,
route_filter: Optional[List[str]] = None,
) -> RouteChoice:
@@ -411,6 +426,9 @@ class BaseRouter(BaseModel):
if text is None:
raise ValueError("Either text or vector must be provided")
vector = self._encode(text=[text])
+ # convert to numpy array if not already
+ vector = xq_reshape(vector)
+ # calculate semantics
route, top_class_scores = self._retrieve_top_route(vector, route_filter)
passed = self._check_threshold(top_class_scores, route)
if passed and route is not None and not simulate_static:
@@ -444,7 +462,7 @@ class BaseRouter(BaseModel):
async def acall(
self,
text: Optional[str] = None,
- vector: Optional[List[float]] = None,
+ vector: Optional[List[float] | np.ndarray] = None,
simulate_static: bool = False,
route_filter: Optional[List[str]] = None,
) -> RouteChoice:
@@ -453,7 +471,9 @@ class BaseRouter(BaseModel):
if text is None:
raise ValueError("Either text or vector must be provided")
vector = await self._async_encode(text=[text])
-
+ # convert to numpy array if not already
+ vector = xq_reshape(vector)
+ # calculate semantics
route, top_class_scores = await self._async_retrieve_top_route(
vector, route_filter
)
@@ -483,19 +503,21 @@ class BaseRouter(BaseModel):
# if no route passes threshold, return empty route choice
return RouteChoice()
+ # TODO: add multiple routes return to __call__ and acall
+ @deprecated("This method is deprecated. Use `__call__` instead.")
def retrieve_multiple_routes(
self,
text: Optional[str] = None,
- vector: Optional[List[float]] = None,
+ vector: Optional[List[float] | np.ndarray] = None,
) -> List[RouteChoice]:
if vector is None:
if text is None:
raise ValueError("Either text or vector must be provided")
- vector_arr = self._encode(text=[text])
- else:
- vector_arr = np.array(vector)
+ vector = self._encode(text=[text])
+ # convert to numpy array if not already
+ vector = xq_reshape(vector)
# get relevant utterances
- results = self._retrieve(xq=vector_arr)
+ results = self._retrieve(xq=vector)
# decide most relevant routes
categories_with_scores = self._semantic_classify_multiple_routes(results)
return [
@@ -514,16 +536,14 @@ class BaseRouter(BaseModel):
# return route_choices
def _retrieve_top_route(
- self, vector: List[float], route_filter: Optional[List[str]] = None
+ self, vector: np.ndarray, route_filter: Optional[List[str]] = None
) -> Tuple[Optional[Route], List[float]]:
"""
Retrieve the top matching route based on the given vector.
Returns a tuple of the route (if any) and the scores of the top class.
"""
# get relevant results (scores and routes)
- results = self._retrieve(
- xq=vector[0], top_k=self.top_k, route_filter=route_filter
- )
+ results = self._retrieve(xq=vector, top_k=self.top_k, route_filter=route_filter)
# decide most relevant routes
top_class, top_class_scores = self._semantic_classify(results)
# TODO do we need this check?
@@ -531,11 +551,11 @@ class BaseRouter(BaseModel):
return route, top_class_scores
async def _async_retrieve_top_route(
- self, vector: List[float], route_filter: Optional[List[str]] = None
+ self, vector: np.ndarray, route_filter: Optional[List[str]] = None
) -> Tuple[Optional[Route], List[float]]:
# get relevant results (scores and routes)
results = await self._async_retrieve(
- xq=vector[0], top_k=self.top_k, route_filter=route_filter
+ xq=vector, top_k=self.top_k, route_filter=route_filter
)
# decide most relevant routes
top_class, top_class_scores = await self._async_semantic_classify(results)
@@ -939,7 +959,7 @@ class BaseRouter(BaseModel):
"""Given a query vector, retrieve the top_k most similar records."""
# get scores and routes
scores, routes = self.index.query(
- vector=xq, top_k=top_k, route_filter=route_filter
+ vector=xq[0], top_k=top_k, route_filter=route_filter
)
return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)]
@@ -949,7 +969,7 @@ class BaseRouter(BaseModel):
"""Given a query vector, retrieve the top_k most similar records."""
# get scores and routes
scores, routes = await self.index.aquery(
- vector=xq, top_k=top_k, route_filter=route_filter
+ vector=xq[0], top_k=top_k, route_filter=route_filter
)
return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)]
diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py
index 1741865b7f7ffdd9dcd58d94a2ec04f3225bd560..1f743f1c8581fedc6232e57742b90c06d6671fe3 100644
--- a/tests/unit/test_router.py
+++ b/tests/unit/test_router.py
@@ -826,6 +826,8 @@ class TestSemanticRouter:
auto_sync="local",
)
vector = [0.1, 0.2, 0.3]
+ if index_cls is PineconeIndex:
+ time.sleep(PINECONE_SLEEP) # allow for index to be populated
results = route_layer.retrieve_multiple_routes(vector=vector)
assert len(results) >= 1, "Expected at least one result"
assert any(