core,langchain,community[patch]: allow langsmith 0.2 (#28598)

libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py

@@ -3,7 +3,7 @@
 import math
 import os
 import tempfile
-from typing import List
+from typing import List, cast
 
 import numpy as np
 import pytest
@@ -60,13 +60,13 @@ class RandomEmbeddings(Embeddings):
     """Fake embeddings with random vectors. For testing purposes."""
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        return [np.random.rand(100).tolist() for _ in texts]
+        return [cast(list[float], np.random.rand(100).tolist()) for _ in texts]
 
     def embed_query(self, text: str) -> List[float]:
-        return np.random.rand(100).tolist()
+        return cast(list[float], np.random.rand(100).tolist())
 
     def embed_image(self, uris: List[str]) -> List[List[float]]:
-        return [np.random.rand(100).tolist() for _ in uris]
+        return [cast(list[float], np.random.rand(100).tolist()) for _ in uris]
 
 
 class IncrementalEmbeddings(Embeddings):

libs/community/tests/unit_tests/vectorstores/test_utils.py

@@ -1,5 +1,7 @@
 """Test vector store utility functions."""
 
+from typing import cast
+
 import numpy as np
 from langchain_core.documents import Document
 
@@ -53,7 +55,7 @@ def test_maximal_marginal_relevance() -> None:
 def test_maximal_marginal_relevance_query_dim() -> None:
     query_embedding = np.random.random(size=5)
     query_embedding_2d = query_embedding.reshape((1, 5))
-    embedding_list = np.random.random(size=(4, 5)).tolist()
+    embedding_list = cast(list[list[float]], np.random.random(size=(4, 5)).tolist())
     first = maximal_marginal_relevance(query_embedding, embedding_list)
     second = maximal_marginal_relevance(query_embedding_2d, embedding_list)
     assert first == second