07fa576de1
CI lint jobs use `uv run --all-groups` for all tools, but ruff doesn't need dependency resolution — only mypy does. By splitting into `UV_RUN_LINT` (ruff) and `UV_RUN_TYPE` (mypy), the CI-facing targets run ruff with `--group lint` only, giving fast-fail feedback before mypy triggers the full environment sync. For packages where source code only conditionally imports heavy deps (text-splitters, huggingface), `lint_package` also overrides `UV_RUN_TYPE` to `--group lint --group typing`, skipping the ~3.5GB `test_integration` download entirely. `lint_tests` keeps `--all-groups` since test code legitimately imports those deps. Additionally, `lint_imports.sh` was inconsistently wired — most packages had the script but weren't calling it. ## Changes **Makefile optimization** - Introduce `UV_RUN_LINT` and `UV_RUN_TYPE` Make variables, both defaulting to `uv run --all-groups`. For `lint_package` and `lint_tests`, `UV_RUN_LINT` is overridden to `uv run --group lint` so ruff runs instantly without syncing heavy deps - For `text-splitters` and `huggingface`, override `UV_RUN_TYPE` on `lint_package` to `uv run --group lint --group typing` — mypy runs without downloading torch, CUDA, spacy, etc. **mypy config for lean groups** - Add `transformers` and `transformers.*` to `ignore_missing_imports` in `text-splitters` pyproject.toml (conditional `try/except` import, same treatment as existing `konlpy`/`nltk` entries) - Add `torch`, `torch.*`, `langchain_community`, `langchain_community.*` to `ignore_missing_imports` in `huggingface` pyproject.toml - Add dual `# type: ignore[unreachable, unused-ignore]` in `text-splitters/base.py` to handle the `PreTrainedTokenizerBase` isinstance check that behaves differently depending on whether transformers is installed **lint_imports.sh consistency** - Add `./scripts/lint_imports.sh` to the lint recipe in every package that wasn't calling it (standard-tests, model-profiles, all 15 partners), and create the script for the two packages missing it entirely (`model-profiles`, `openrouter`) - Update all `lint_imports.sh` scripts to allow `from langchain.agents` and `from langchain.tools` imports (legitimate v1 middleware dependencies used by `langchain-anthropic` and `langchain-openai`)
🦜️🔗 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')