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101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
from collections.abc import Callable
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from crewai.tools import BaseTool, tool
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from crewai.tools.base_tool import to_langchain
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def test_creating_a_tool_using_annotation():
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@tool("Name of my tool")
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def my_tool(question: str) -> str:
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"""Clear description for what this tool is useful for, you agent will need this information to use it."""
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return question
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assert my_tool.name == "Name of my tool"
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assert (
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my_tool.description
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== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
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)
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assert my_tool.args_schema.model_json_schema()["properties"] == {
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"question": {"title": "Question", "type": "string"}
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}
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assert (
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my_tool.func("What is the meaning of life?") == "What is the meaning of life?"
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)
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# Assert the langchain tool conversion worked as expected
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converted_tool = to_langchain([my_tool])[0]
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assert converted_tool.name == "Name of my tool"
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assert (
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converted_tool.description
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== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
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)
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assert converted_tool.args_schema.model_json_schema()["properties"] == {
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"question": {"title": "Question", "type": "string"}
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}
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assert (
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converted_tool.func("What is the meaning of life?")
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== "What is the meaning of life?"
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)
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def test_creating_a_tool_using_baseclass():
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class MyCustomTool(BaseTool):
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name: str = "Name of my tool"
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description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
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def _run(self, question: str) -> str:
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return question
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my_tool = MyCustomTool()
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assert my_tool.name == "Name of my tool"
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assert (
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my_tool.description
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== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
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)
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assert my_tool.args_schema.model_json_schema()["properties"] == {
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"question": {"title": "Question", "type": "string"}
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}
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assert (
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my_tool._run("What is the meaning of life?") == "What is the meaning of life?"
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)
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# Assert the langchain tool conversion worked as expected
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converted_tool = to_langchain([my_tool])[0]
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assert converted_tool.name == "Name of my tool"
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assert (
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converted_tool.description
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== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
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)
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assert converted_tool.args_schema.model_json_schema()["properties"] == {
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"question": {"title": "Question", "type": "string"}
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}
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assert (
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converted_tool.invoke({"question": "What is the meaning of life?"})
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== "What is the meaning of life?"
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)
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def test_setting_cache_function():
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class MyCustomTool(BaseTool):
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name: str = "Name of my tool"
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description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
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cache_function: Callable = lambda: False
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def _run(self, question: str) -> str:
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return question
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my_tool = MyCustomTool()
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assert not my_tool.cache_function()
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def test_default_cache_function_is_true():
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class MyCustomTool(BaseTool):
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name: str = "Name of my tool"
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description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
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def _run(self, question: str) -> str:
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return question
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my_tool = MyCustomTool()
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assert my_tool.cache_function()
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