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docs: add conceptual testing docs (#28205)
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# Testing | ||
<span data-heading-keywords="tests,testing,unit,integration"></span> | ||
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Testing is a critical part of the development process that ensures your code works as expected and meets the desired quality standards. | ||
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In the LangChain ecosystem, we have 2 main types of tests: **unit tests** and **integration tests**. | ||
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For integrations that implement standard LangChain abstractions, we have a set of **standard tests** (both unit and integration) that help maintain compatibility between different components and ensure reliability of high-usage ones. | ||
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## Unit Tests | ||
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**Definition**: Unit tests are designed to validate the smallest parts of your code—individual functions or methods—ensuring they work as expected in isolation. They do not rely on external systems or integrations. | ||
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**Example**: Testing the `convert_langchain_aimessage_to_dict` function to confirm it correctly converts an AI message to a dictionary format: | ||
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```python | ||
from langchain_core.messages import AIMessage, ToolCall, convert_to_openai_messages | ||
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def test_convert_to_openai_messages(): | ||
ai_message = AIMessage( | ||
content="Let me call that tool for you!", | ||
tool_calls=[ | ||
ToolCall(name='parrot_multiply_tool', id='1', args={'a': 2, 'b': 3}), | ||
] | ||
) | ||
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result = convert_to_openai_messages(ai_message) | ||
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expected = { | ||
"role": "assistant", | ||
"tool_calls": [ | ||
{ | ||
"type": "function", | ||
"id": "1", | ||
"function": { | ||
"name": "parrot_multiply_tool", | ||
"arguments": '{"a": 2, "b": 3}', | ||
}, | ||
} | ||
], | ||
"content": "Let me call that tool for you!", | ||
} | ||
assert result == expected # Ensure conversion matches expected output | ||
``` | ||
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--- | ||
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## Integration Tests | ||
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**Definition**: Integration tests validate that multiple components or systems work together as expected. For tools or integrations relying on external services, these tests often ensure end-to-end functionality. | ||
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**Example**: Testing `ParrotMultiplyTool` with access to an API service that multiplies two numbers and adds 80: | ||
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```python | ||
def test_integration_with_service(): | ||
tool = ParrotMultiplyTool() | ||
result = tool.invoke({"a": 2, "b": 3}) | ||
assert result == 86 | ||
``` | ||
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--- | ||
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## Standard Tests | ||
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**Definition**: Standard tests are pre-defined tests provided by LangChain to ensure consistency and reliability across all tools and integrations. They include both unit and integration test templates tailored for LangChain components. | ||
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**Example**: Subclassing LangChain's `ToolsUnitTests` or `ToolsIntegrationTests` to automatically run standard tests: | ||
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```python | ||
from langchain_tests.unit_tests import ToolsUnitTests | ||
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class TestParrotMultiplyToolUnit(ToolsUnitTests): | ||
@property | ||
def tool_constructor(self): | ||
return ParrotMultiplyTool | ||
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def tool_invoke_params_example(self): | ||
return {"a": 2, "b": 3} | ||
``` | ||
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To learn more, check out our guide on [how to add standard tests to an integration](../../contributing/how_to/integrations/standard_tests). |
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