43880362d8
As a LangChain user streaming a tool-calling model, I expect each streamed chunk to expose structured `tool_call_chunk` content blocks so I can render or process tool calls live, instead of waiting for the final aggregated message. This adds `tool_call_streaming` to `ModelProfile` and uses it in the standard chat-model tool-calling tests. When a model profile opts in, `test_tool_calling` and `test_tool_calling_async` now validate that at least one streamed chunk includes a `tool_call_chunk` block via `content_blocks`, while preserving the existing final-message validation. This keeps the contract profile-gated so providers can opt in once their streaming chunk shape is verified. This PR opts in the providers verified by smoke testing with straightforward profile coverage: OpenAI, Anthropic, Fireworks, HuggingFace, OpenRouter, DeepSeek, and xAI. The generated profile artifacts are refreshed so runtime profiles expose the new capability flag. Perplexity Responses also passed the smoke test, but its current profile data is for the `sonar` family while the Responses smoke path used a routed model string. That profile strategy is left as follow-up. MistralAI currently streams `.tool_call_chunks`, but its content-block translator exposes a complete `tool_call` block instead of `tool_call_chunk`, so it also stays out of this flag until that integration is fixed.
🦜️🔗 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')