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5cc230263c
Update to Pydantic v2: Transitioned all references from pydantic.v1 to pydantic (v2), ensuring compatibility with the latest Pydantic features and improvements. Affected components include agent tools, prompts, crew, and task modules. Refactoring & Alignment with Pydantic Standards: Refactored the agent module away from traditional __init__ to align more closely with Pydantic best practices. Updated the crew module to Pydantic v2 and enhanced configurations, allowing JSON and dictionary inputs. Additionally, some (not all) exceptions have been migrated to leverage Pydantic's error-handling capabilities. Enhancements to Validators and Typings: Improved validators and type annotations across multiple modules, enhancing code readability and maintainability. Streamlined the validation process in line with Pydantic v2's methodologies. Import and Configuration Adjustments: Updated to test-related absolute imports due to issues with Pytest finding packages through relative imports.
126 lines
3.6 KiB
Python
126 lines
3.6 KiB
Python
"""Test Agent creation and execution basic functionality."""
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import pytest
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from langchain.chat_models import ChatOpenAI as OpenAI
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from crewai.agent import Agent
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def test_agent_creation():
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agent = Agent(role="test role", goal="test goal", backstory="test backstory")
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assert agent.role == "test role"
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assert agent.goal == "test goal"
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assert agent.backstory == "test backstory"
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assert agent.tools == []
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def test_agent_default_values():
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agent = Agent(role="test role", goal="test goal", backstory="test backstory")
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assert isinstance(agent.llm, OpenAI)
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assert agent.llm.model_name == "gpt-4"
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assert agent.llm.temperature == 0.7
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assert agent.llm.verbose == False
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assert agent.allow_delegation == True
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def test_custom_llm():
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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llm=OpenAI(temperature=0, model="gpt-4"),
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)
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assert isinstance(agent.llm, OpenAI)
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assert agent.llm.model_name == "gpt-4"
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assert agent.llm.temperature == 0
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_without_memory():
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no_memory_agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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memory=False,
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llm=OpenAI(temperature=0, model="gpt-4"),
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)
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memory_agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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memory=True,
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llm=OpenAI(temperature=0, model="gpt-4"),
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)
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result = no_memory_agent.execute_task("How much is 1 + 1?")
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assert result == "1 + 1 equals 2."
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assert no_memory_agent.agent_executor.memory is None
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assert memory_agent.agent_executor.memory is not None
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_execution():
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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allow_delegation=False,
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)
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output = agent.execute_task("How much is 1 + 1?")
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assert output == "2"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_execution_with_tools():
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from langchain.tools import tool
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@tool
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def multiplier(numbers) -> float:
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"""Useful for when you need to multiply two numbers together.
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The input to this tool should be a comma separated list of numbers of
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length two, representing the two numbers you want to multiply together.
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For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
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a, b = numbers.split(",")
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return int(a) * int(b)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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tools=[multiplier],
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allow_delegation=False,
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)
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output = agent.execute_task("What is 3 times 4")
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assert output == "12"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_execution_with_specific_tools():
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from langchain.tools import tool
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@tool
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def multiplier(numbers) -> float:
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"""Useful for when you need to multiply two numbers together.
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The input to this tool should be a comma separated list of numbers of
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length two, representing the two numbers you want to multiply together.
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For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
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a, b = numbers.split(",")
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return int(a) * int(b)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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allow_delegation=False,
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)
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output = agent.execute_task(task="What is 3 times 4", tools=[multiplier])
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assert output == "3 times 4 is 12."
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