diff --git a/libs/community/langchain_community/utils/math.py b/libs/community/langchain_community/utils/math.py
index 4edce061b5e..2626c80b939 100644
--- a/libs/community/langchain_community/utils/math.py
+++ b/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 "
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index 727ac1f605c..4d3693e2321 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -1190,6 +1190,7 @@ files = [
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+    {file = "contourpy-1.1.0-cp310-cp310-win32.whl", hash = "sha256:9b2dd2ca3ac561aceef4c7c13ba654aaa404cf885b187427760d7f7d4c57cff8"},
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@@ -1198,6 +1199,7 @@ files = [
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     {file = "contourpy-1.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:181cbace49874f4358e2929aaf7ba84006acb76694102e88dd15af861996c16e"},
+    {file = "contourpy-1.1.0-cp311-cp311-win32.whl", hash = "sha256:edb989d31065b1acef3828a3688f88b2abb799a7db891c9e282df5ec7e46221b"},
     {file = "contourpy-1.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:fb3b7d9e6243bfa1efb93ccfe64ec610d85cfe5aec2c25f97fbbd2e58b531256"},
     {file = "contourpy-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bcb41692aa09aeb19c7c213411854402f29f6613845ad2453d30bf421fe68fed"},
     {file = "contourpy-1.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5d123a5bc63cd34c27ff9c7ac1cd978909e9c71da12e05be0231c608048bb2ae"},
@@ -1206,6 +1208,7 @@ files = [
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     {file = "contourpy-1.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:e7a117ce7df5a938fe035cad481b0189049e8d92433b4b33aa7fc609344aafa1"},
+    {file = "contourpy-1.1.0-cp38-cp38-win32.whl", hash = "sha256:108dfb5b3e731046a96c60bdc46a1a0ebee0760418951abecbe0fc07b5b93b27"},
     {file = "contourpy-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:d4f26b25b4f86087e7d75e63212756c38546e70f2a92d2be44f80114826e1cd4"},
     {file = "contourpy-1.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc00bb4225d57bff7ebb634646c0ee2a1298402ec10a5fe7af79df9a51c1bfd9"},
     {file = "contourpy-1.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:189ceb1525eb0655ab8487a9a9c41f42a73ba52d6789754788d1883fb06b2d8a"},
@@ -1214,6 +1217,7 @@ files = [
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     {file = "contourpy-1.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ed614aea8462735e7d70141374bd7650afd1c3f3cb0c2dbbcbe44e14331bf002"},
+    {file = "contourpy-1.1.0-cp39-cp39-win32.whl", hash = "sha256:71551f9520f008b2950bef5f16b0e3587506ef4f23c734b71ffb7b89f8721999"},
     {file = "contourpy-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:438ba416d02f82b692e371858143970ed2eb6337d9cdbbede0d8ad9f3d7dd17d"},
     {file = "contourpy-1.1.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a698c6a7a432789e587168573a864a7ea374c6be8d4f31f9d87c001d5a843493"},
     {file = "contourpy-1.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:397b0ac8a12880412da3551a8cb5a187d3298a72802b45a3bd1805e204ad8439"},
@@ -3471,7 +3475,6 @@ files = [
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-    {file = "jq-1.6.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:49e44ed677713f4115bd5bf2dbae23baa4cd503be350e12a1c1f506b0687848f"},
     {file = "jq-1.6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:984f33862af285ad3e41e23179ac4795f1701822473e1a26bf87ff023e5a89ea"},
     {file = "jq-1.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f42264fafc6166efb5611b5d4cb01058887d050a6c19334f6a3f8a13bb369df5"},
     {file = "jq-1.6.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a67154f150aaf76cc1294032ed588436eb002097dd4fd1e283824bf753a05080"},
@@ -3555,6 +3558,7 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
     {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
+    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
 ]
 
 [[package]]
@@ -3981,7 +3985,7 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.2.0"
+version = "0.2.1"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -3991,7 +3995,6 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-dataclasses-json = ">= 0.5.7, < 0.7"
 langchain-core = "^0.2.0"
 langchain-text-splitters = "^0.2.0"
 langsmith = "^0.1.17"
@@ -4023,7 +4026,7 @@ url = "../langchain"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.0"
+version = "0.2.3"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -4032,7 +4035,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.0"
+langsmith = "^0.1.65"
 packaging = "^23.2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -4066,13 +4069,13 @@ url = "../text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.23"
+version = "0.1.67"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.23-py3-none-any.whl", hash = "sha256:69984268b9867cb31b875965b3f86b6f56ba17dd5454d487d3a1a999bdaeea69"},
-    {file = "langsmith-0.1.23.tar.gz", hash = "sha256:327c66ec0de8c1bc57bfa47bbc70a29ef749e97c3e5571b9baf754d1e0644220"},
+    {file = "langsmith-0.1.67-py3-none-any.whl", hash = "sha256:7eb2e1c1b375925ff47700ed8071e10c15e942e9d1d634b4a449a9060364071a"},
+    {file = "langsmith-0.1.67.tar.gz", hash = "sha256:149558669a2ac4f21471cd964e61072687bba23b7c1ccb51f190a8f59b595b39"},
 ]
 
 [package.dependencies]
@@ -4134,6 +4137,7 @@ files = [
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@@ -6119,6 +6123,8 @@ files = [
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+    {file = "psycopg2-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:a7653d00b732afb6fc597e29c50ad28087dcb4fbfb28e86092277a559ae4e693"},
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@@ -6161,6 +6167,7 @@ files = [
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@@ -6169,6 +6176,8 @@ files = [
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@@ -6744,6 +6753,7 @@ python-versions = ">=3.8"
 files = [
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@@ -7152,6 +7162,7 @@ files = [
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+    {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"},
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 test = ["pytest", "pytest-cov"]
 
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+
 [[package]]
 name = "six"
 version = "1.16.0"
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 files = [
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@@ -10074,9 +10217,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [extras]
 cli = ["typer"]
-extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "azure-identity", "azure-search-documents", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpathlib", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "friendli-client", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "httpx-sse", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "oracledb", "pandas", "pdfminer-six", "pgvector", "praw", "premai", "psychicapi", "py-trello", "pyjwt", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "vdms", "xata", "xmltodict"]
+extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "azure-identity", "azure-search-documents", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpathlib", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "friendli-client", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "httpx-sse", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "oracledb", "pandas", "pdfminer-six", "pgvector", "praw", "premai", "psychicapi", "py-trello", "pyjwt", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "simsimd", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "vdms", "xata", "xmltodict"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "1a30f88ba6352cfd5af8d3b7b6418ec01bad42c73e6096b1a3ccef06cb36709b"
+content-hash = "77ccc0105fabe1735497289125bb276822101a6a9b1c2b596bf49b8f30b8068d"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 4955db47647..78e5205d730 100644
--- a/libs/community/pyproject.toml
+++ b/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]
diff --git a/libs/community/tests/unit_tests/utils/test_math.py b/libs/community/tests/unit_tests/utils/test_math.py
index 2a97a36d972..c497740e0d6 100644
--- a/libs/community/tests/unit_tests/utils/test_math.py
+++ b/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
@@ -68,7 +69,7 @@ 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)]
@@ -76,3 +77,19 @@ def test_cosine_similarity_top_k_and_score_threshold(
     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)
diff --git a/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py b/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py
index 712d323ccbf..78bf7a98527 100644
--- a/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py
+++ b/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 "
diff --git a/libs/partners/mongodb/langchain_mongodb/utils.py b/libs/partners/mongodb/langchain_mongodb/utils.py
index 3ee587402b8..9c345e1520b 100644
--- a/libs/partners/mongodb/langchain_mongodb/utils.py
+++ b/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 "
diff --git a/libs/partners/pinecone/langchain_pinecone/_utilities.py b/libs/partners/pinecone/langchain_pinecone/_utilities.py
index 5ad9e407fcd..bee0db4403b 100644
--- a/libs/partners/pinecone/langchain_pinecone/_utilities.py
+++ b/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)
diff --git a/libs/partners/qdrant/langchain_qdrant/_utils.py b/libs/partners/qdrant/langchain_qdrant/_utils.py
index 0e17cf75bb4..fba8d78001c 100644
--- a/libs/partners/qdrant/langchain_qdrant/_utils.py
+++ b/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)