Broadcast operation failure while using simsimd beyond v3.7.7

libs/community/langchain_community/utils/math.py

@@ -26,10 +26,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
 
         X = np.array(X, dtype=np.float32)
         Y = np.array(Y, dtype=np.float32)
-        Z = 1 - simd.cdist(X, Y, metric="cosine")
-        if isinstance(Z, float):
-            return np.array([Z])
-        return np.array(Z)
+        Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
+        return Z
     except ImportError:
         logger.debug(
             "Unable to import simsimd, defaulting to NumPy implementation. If you want "

libs/community/pyproject.toml

@@ -105,6 +105,7 @@ httpx-sse = {version = "^0.4.0", optional = true}
 pyjwt = {version = "^2.8.0", optional = true}
 oracledb = {version = "^2.2.0", optional = true}
 cloudpathlib = { version = "^0.18", optional = true }
+simsimd = {version = "^4.3.1", optional = true}
 
 
 [tool.poetry.group.test]
@@ -289,6 +290,7 @@ extended_testing = [
  "httpx-sse",
  "pyjwt",
  "oracledb",
+ "simsimd",
 ]
 
 [tool.ruff]

libs/community/tests/unit_tests/utils/test_math.py

@@ -1,4 +1,5 @@
 """Test math utility functions."""
+import importlib
 from typing import List
 
 import numpy as np
@@ -68,11 +69,27 @@ def test_cosine_similarity_score_threshold(
     assert np.allclose(expected_scores, actual_scores)
 
 
-def test_cosine_similarity_top_k_and_score_threshold(
+def invoke_cosine_similarity_top_k_score_threshold(
     X: List[List[float]], Y: List[List[float]]
 ) -> None:
     expected_idxs = [(0, 0), (2, 2), (1, 2), (0, 2)]
     expected_scores = [1.0, 0.93419873, 0.87038828, 0.83743579]
     actual_idxs, actual_scores = cosine_similarity_top_k(X, Y, score_threshold=0.8)
     assert actual_idxs == expected_idxs
     assert np.allclose(expected_scores, actual_scores)
+
+
+def test_cosine_similarity_top_k_and_score_threshold(
+    X: List[List[float]], Y: List[List[float]]
+) -> None:
+    if importlib.util.find_spec("simsimd"):
+        raise ValueError("test should be run without simsimd installed.")
+    invoke_cosine_similarity_top_k_score_threshold(X, Y)
+
+
+@pytest.mark.requires("simsimd")
+def test_cosine_similarity_top_k_and_score_threshold_with_simsimd(
+    X: List[List[float]], Y: List[List[float]]
+) -> None:
+    # Same test, but ensuring simsimd is available in the project through the import.
+    invoke_cosine_similarity_top_k_score_threshold(X, Y)

libs/partners/milvus/langchain_milvus/vectorstores/milvus.py

@@ -35,10 +35,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
 
         X = np.array(X, dtype=np.float32)
         Y = np.array(Y, dtype=np.float32)
-        Z = 1 - simd.cdist(X, Y, metric="cosine")
-        if isinstance(Z, float):
-            return np.array([Z])
-        return np.array(Z)
+        Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
+        return Z
     except ImportError:
         logger.debug(
             "Unable to import simsimd, defaulting to NumPy implementation. If you want "

libs/partners/mongodb/langchain_mongodb/utils.py

@@ -35,10 +35,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
 
         X = np.array(X, dtype=np.float32)
         Y = np.array(Y, dtype=np.float32)
-        Z = 1 - simd.cdist(X, Y, metric="cosine")
-        if isinstance(Z, float):
-            return np.array([Z])
-        return np.array(Z)
+        Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
+        return Z
     except ImportError:
         logger.debug(
             "Unable to import simsimd, defaulting to NumPy implementation. If you want "

libs/partners/pinecone/langchain_pinecone/_utilities.py

@@ -66,10 +66,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
 
         X = np.array(X, dtype=np.float32)
         Y = np.array(Y, dtype=np.float32)
-        Z = 1 - simd.cdist(X, Y, metric="cosine")
-        if isinstance(Z, float):
-            return np.array([Z])
-        return np.array(Z)
+        Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
+        return Z
     except ImportError:
         X_norm = np.linalg.norm(X, axis=1)
         Y_norm = np.linalg.norm(Y, axis=1)

libs/partners/qdrant/langchain_qdrant/_utils.py

@@ -56,10 +56,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
 
         X = np.array(X, dtype=np.float32)
         Y = np.array(Y, dtype=np.float32)
-        Z = 1 - simd.cdist(X, Y, metric="cosine")
-        if isinstance(Z, float):
-            return np.array([Z])
-        return np.array(Z)
+        Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
+        return Z
     except ImportError:
         X_norm = np.linalg.norm(X, axis=1)
         Y_norm = np.linalg.norm(Y, axis=1)