chore(standard-tests): add mypy strict checking (#32384)

Co-authored-by: Mason Daugherty <mason@langchain.dev>

libs/standard-tests/langchain_tests/conftest.py

@@ -3,7 +3,7 @@
 import gzip
 from os import PathLike
 from pathlib import Path
-from typing import Union
+from typing import Any, Union
 
 import pytest
 import yaml
@@ -36,7 +36,7 @@ class CustomSerializer:
     def deserialize(data: bytes) -> dict:
         """Decompress data and convert it from YAML."""
         text = gzip.decompress(data).decode("utf-8")
-        cassette = yaml.safe_load(text)
+        cassette: dict[str, Any] = yaml.safe_load(text)
         cassette["requests"] = [
             Request._from_dict(request) for request in cassette["requests"]
         ]

libs/standard-tests/langchain_tests/integration_tests/chat_models.py

@@ -15,7 +15,6 @@ from langchain_core.messages import (
     AIMessage,
     AIMessageChunk,
     BaseMessage,
-    BaseMessageChunk,
     HumanMessage,
     SystemMessage,
     ToolMessage,
@@ -67,8 +66,6 @@ def _get_joke_class(
 
     if schema_type == "json_schema":
         return Joke.model_json_schema(), validate_joke_dict
-    msg = "Invalid schema type"
-    raise ValueError(msg)
 
 
 class _TestCallbackHandler(BaseCallbackHandler):
@@ -1381,7 +1378,7 @@ class ChatModelIntegrationTests(ChatModelTests):
         _validate_tool_call_message(result)
 
         # Test stream
-        full: Optional[BaseMessageChunk] = None
+        full: Optional[BaseMessage] = None
         for chunk in model_with_tools.stream(query):
             full = chunk if full is None else full + chunk  # type: ignore[assignment]
         assert isinstance(full, AIMessage)
@@ -1443,7 +1440,7 @@ class ChatModelIntegrationTests(ChatModelTests):
         _validate_tool_call_message(result)
 
         # Test astream
-        full: Optional[BaseMessageChunk] = None
+        full: Optional[BaseMessage] = None
         async for chunk in model_with_tools.astream(query):
             full = chunk if full is None else full + chunk  # type: ignore[assignment]
         assert isinstance(full, AIMessage)
@@ -1791,7 +1788,7 @@ class ChatModelIntegrationTests(ChatModelTests):
         result = model_with_tools.invoke(query)
         _validate_tool_call_message_no_args(result)
 
-        full: Optional[BaseMessageChunk] = None
+        full: Optional[BaseMessage] = None
         for chunk in model_with_tools.stream(query):
             full = chunk if full is None else full + chunk  # type: ignore[assignment]
         assert isinstance(full, AIMessage)
@@ -1932,7 +1929,11 @@ class ChatModelIntegrationTests(ChatModelTests):
         assert isinstance(result, AIMessage)
 
     @pytest.mark.parametrize("schema_type", ["pydantic", "typeddict", "json_schema"])
-    def test_structured_output(self, model: BaseChatModel, schema_type: str) -> None:
+    def test_structured_output(
+        self,
+        model: BaseChatModel,
+        schema_type: Literal["pydantic", "typeddict", "json_schema"],
+    ) -> None:
         """Test to verify structured output is generated both on invoke and stream.
 
         This test is optional and should be skipped if the model does not support
@@ -1968,7 +1969,7 @@ class ChatModelIntegrationTests(ChatModelTests):
         if not self.has_structured_output:
             pytest.skip("Test requires structured output.")
 
-        schema, validation_function = _get_joke_class(schema_type)  # type: ignore[arg-type]
+        schema, validation_function = _get_joke_class(schema_type)
         chat = model.with_structured_output(schema, **self.structured_output_kwargs)
         mock_callback = MagicMock()
         mock_callback.on_chat_model_start = MagicMock()
@@ -2012,7 +2013,9 @@ class ChatModelIntegrationTests(ChatModelTests):
 
     @pytest.mark.parametrize("schema_type", ["pydantic", "typeddict", "json_schema"])
     async def test_structured_output_async(
-        self, model: BaseChatModel, schema_type: str
+        self,
+        model: BaseChatModel,
+        schema_type: Literal["pydantic", "typeddict", "json_schema"],
     ) -> None:
         """Test to verify structured output is generated both on invoke and stream.
 
@@ -2049,7 +2052,7 @@ class ChatModelIntegrationTests(ChatModelTests):
         if not self.has_structured_output:
             pytest.skip("Test requires structured output.")
 
-        schema, validation_function = _get_joke_class(schema_type)  # type: ignore[arg-type]
+        schema, validation_function = _get_joke_class(schema_type)
 
         chat = model.with_structured_output(schema, **self.structured_output_kwargs)
         ainvoke_callback = _TestCallbackHandler()
@@ -2269,9 +2272,6 @@ class ChatModelIntegrationTests(ChatModelTests):
             punchline: str = FieldProper(description="answer to resolve the joke")
 
         # Pydantic class
-        # Type ignoring since the interface only officially supports pydantic 1
-        # or pydantic.v1.BaseModel but not pydantic.BaseModel from pydantic 2.
-        # We'll need to do a pass updating the type signatures.
         chat = model.with_structured_output(Joke, method="json_mode")
         msg = (
             "Tell me a joke about cats. Return the result as a JSON with 'setup' and "

libs/standard-tests/langchain_tests/integration_tests/vectorstores.py

@@ -4,7 +4,7 @@ from abc import abstractmethod
 
 import pytest
 from langchain_core.documents import Document
-from langchain_core.embeddings.fake import DeterministicFakeEmbedding, Embeddings
+from langchain_core.embeddings import DeterministicFakeEmbedding, Embeddings
 from langchain_core.vectorstores import VectorStore
 
 from langchain_tests.base import BaseStandardTests

libs/standard-tests/langchain_tests/unit_tests/chat_models.py

@@ -1022,8 +1022,8 @@ class ChatModelUnitTests(ChatModelTests):
 
         # Test optional params
         model = self.chat_model_class(
-            max_tokens=10,  # type: ignore[call-arg]
-            stop=["test"],  # type: ignore[call-arg]
+            max_tokens=10,
+            stop=["test"],
             **self.chat_model_params,
         )
         ls_params = model._get_ls_params()

libs/standard-tests/pyproject.toml

@@ -32,25 +32,29 @@ repository = "https://github.com/langchain-ai/langchain"
 [dependency-groups]
 test = ["langchain-core"]
 test_integration = []
-
 lint = ["ruff<0.13,>=0.12.10"]
-typing = ["mypy<2,>=1.17.1", "langchain-core"]
+typing = [
+    "mypy<1.18,>=1.17.1",
+    "types-pyyaml<7.0.0.0,>=6.0.12.2",
+    "langchain-core",
+]
 
 [tool.uv.sources]
 langchain-core = { path = "../core", editable = true }
 
 [tool.mypy]
-disallow_untyped_defs = "True"
+plugins = ["pydantic.mypy"]
+strict = true
+enable_error_code = "deprecated"
+warn_unreachable = true
+
+# TODO: activate for 'strict' checking
+disallow_any_generics = false
 
 [[tool.mypy.overrides]]
-module = "yaml"
+module = ["vcr.*",]
 ignore_missing_imports = true
 
-[[tool.mypy.overrides]]
-module = "vcr.*"
-ignore_missing_imports = true
-
-
 [tool.ruff]
 target-version = "py39"