1de100f278
Originally a narrow bump of mypy to `1.20` in four packages. Expanded to get the whole monorepo onto a single, current mypy and a consistent type-check configuration, so contributors no longer hit different mypy versions and divergent behavior depending on which package they touch. ### What changed - **Unified the mypy pin to `>=2.1.0,<2.2.0`** in every mypy-using package (6 libs + 14 partners), replacing the previously scattered pins (`1.10`/`1.17`/`1.18`/`1.19`/`1.20`, with assorted upper bounds). - **Unified the `[tool.mypy]` base per tier:** - libs: `plugins = ["pydantic.mypy"]`, `strict = true`, `enable_error_code = "deprecated"`, `warn_unreachable = true` - partners: `disallow_untyped_defs = true` - Normalized style (`disallow_untyped_defs = "True"` string → bool, quote/key consistency). - **Fixed the 20 real errors** mypy 2.1 surfaces: `redundant-cast` from improved narrowing (`core`, `langchain-classic`), a `var-annotated` for `_LOGGED`, a return-type widening in `langchain-groq`'s `_convert_from_v1_to_groq` (it can legitimately return a bare `str`), and stale `type-arg`/`unused-ignore` in `langchain-model-profiles` tests. ### Deliberate non-uniformity (documented inline in the relevant `pyproject.toml`s) Going fully byte-identical would surface ~196 additional errors that are *not* real bugs, so two settings are kept package-appropriate: - **`warn_unreachable`** is enabled on every strict lib **except `core`**, where it false-flags intentional defensive code — including the SSRF / IP-policy guards in `_security/` — as unreachable. - **`pydantic.mypy` plugin** is used only on `anthropic` and `perplexity` (their code is authored against it and reports ~99/~132 errors without it). It is *not* added to the other partners, where it only flags the public alias constructor API (e.g. `ChatGroq(model=...)`) in tests rather than finding bugs. - **`ollama`** is left on its `ty` type checker; it does not use mypy. --------- Co-authored-by: Mason Daugherty <github@mdrxy.com>
🦜️🔗 langchain-tests
Looking for the JS/TS version? Check out LangChain.js.
Quick Install
pip install langchain-tests
🤔 What is this?
This is a testing library for LangChain integrations. It contains the base classes for a standard set of tests.
📖 Documentation
For full documentation, see the API reference.
📕 Releases & Versioning
See our Releases and Versioning policies.
We encourage pinning your version to a specific version in order to avoid breaking your CI when we publish new tests. We recommend upgrading to the latest version periodically to make sure you have the latest tests.
Not pinning your version will ensure you always have the latest tests, but it may also break your CI if we introduce tests that your integration doesn't pass.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.
Usage
To add standard tests to an integration package (e.g., for a chat model), you need to create
- A unit test class that inherits from
ChatModelUnitTests - An integration test class that inherits from
ChatModelIntegrationTests
tests/unit_tests/test_standard.py:
"""Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_parrot_chain import ChatParrotChain
class TestParrotChainStandard(ChatModelUnitTests):
@pytest.fixture
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatParrotChain
tests/integration_tests/test_standard.py:
"""Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_parrot_chain import ChatParrotChain
class TestParrotChainStandard(ChatModelIntegrationTests):
@pytest.fixture
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatParrotChain
Reference
The following fixtures are configurable in the test classes. Anything not marked as required is optional.
chat_model_class(required): The class of the chat model to be testedchat_model_params: The keyword arguments to pass to the chat model constructorchat_model_has_tool_calling: Whether the chat model can call tools. By default, this is set tohasattr(chat_model_class, 'bind_tools)chat_model_has_structured_output: Whether the chat model can structured output. By default, this is set tohasattr(chat_model_class, 'with_structured_output')