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Revamping tool usage

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This commit is contained in:
João Moura
2024-02-10 10:36:34 -08:00
parent d0b0a33be3
commit 00206a62ab
22 changed files with 1233 additions and 584 deletions
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118
tests/agent_test.py
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@@ -9,6 +9,7 @@ from langchain_openai import ChatOpenAI
from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
from crewai.agents.executor import CrewAgentExecutor
from crewai.tools.tool_calling import ToolCalling
from crewai.utilities import RPMController
@@ -85,13 +86,9 @@ def test_agent_execution():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution_with_tools():
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
The input to this tool should be a comma separated list of numbers of
length two, representing the two numbers you want to multiply together.
For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
a, b = numbers.split(",")
return int(a) * int(b)
def multiplier(first_number: int, second_number: int) -> float:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
agent = Agent(
role="test role",
@@ -102,19 +99,15 @@ def test_agent_execution_with_tools():
)
output = agent.execute_task("What is 3 times 4")
assert output == "12"
assert output == "3 times 4 is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
def test_logging_tool_usage():
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
The input to this tool should be a comma separated list of numbers of
length two, representing the two numbers you want to multiply together.
For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
a, b = numbers.split(",")
return int(a) * int(b)
def multiplier(first_number: int, second_number: int) -> float:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
agent = Agent(
role="test role",
@@ -127,10 +120,9 @@ def test_logging_tool_usage():
assert agent.tools_handler.last_used_tool == {}
output = agent.execute_task("What is 3 times 5?")
tool_usage = {
"tool": "multiplier",
"input": "3,5",
}
tool_usage = ToolCalling(
function_name=multiplier.name, arguments={"first_number": 3, "second_number": 5}
)
assert output == "3 times 5 is 15."
assert agent.tools_handler.last_used_tool == tool_usage
@@ -139,13 +131,9 @@ def test_logging_tool_usage():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_cache_hitting():
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
The input to this tool should be a comma separated list of numbers of
length two and ONLY TWO, representing the two numbers you want to multiply together.
For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
a, b = numbers.split(",")
return int(a) * int(b)
def multiplier(first_number: int, second_number: int) -> float:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
cache_handler = CacheHandler()
@@ -162,9 +150,9 @@ def test_cache_hitting():
output = agent.execute_task("What is 2 times 6 times 3?")
output = agent.execute_task("What is 3 times 3?")
assert cache_handler._cache == {
"multiplier-12,3": "36",
"multiplier-2,6": "12",
"multiplier-3,3": "9",
"multiplier-{'first_number': 12, 'second_number': 3}": 36,
"multiplier-{'first_number': 2, 'second_number': 6}": 12,
"multiplier-{'first_number': 3, 'second_number': 3}": 9,
}
output = agent.execute_task("What is 2 times 6 times 3? Return only the number")
@@ -172,21 +160,21 @@ def test_cache_hitting():
with patch.object(CacheHandler, "read") as read:
read.return_value = "0"
output = agent.execute_task("What is 2 times 6?")
output = agent.execute_task(
"What is 2 times 6? Ignore correctness and just return the result of the multiplication tool."
)
assert output == "0"
read.assert_called_with("multiplier", "2,6")
read.assert_called_with(
tool="multiplier", input={"first_number": 2, "second_number": 6}
)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution_with_specific_tools():
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
The input to this tool should be a comma separated list of numbers of
length two, representing the two numbers you want to multiply together.
For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
a, b = numbers.split(",")
return int(a) * int(b)
def multiplier(first_number: int, second_number: int) -> float:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
agent = Agent(
role="test role",
@@ -225,6 +213,34 @@ def test_agent_custom_max_iterations():
private_mock.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_repeated_tool_usage(capsys):
@tool
def get_final_answer(numbers) -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
max_iter=3,
allow_delegation=False,
)
agent.execute_task(
task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
tools=[get_final_answer],
)
captured = capsys.readouterr()
assert (
"I just used the get_final_answer tool with input {'numbers': 42}. So I already know the result of that and don't need to use it again now."
in captured.out
)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_moved_on_after_max_iterations():
@tool
@@ -241,18 +257,14 @@ def test_agent_moved_on_after_max_iterations():
allow_delegation=False,
)
with patch.object(
CrewAgentExecutor, "_force_answer", wraps=agent.agent_executor._force_answer
) as private_mock:
output = agent.execute_task(
task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
tools=[get_final_answer],
)
assert (
output
== "I have used the tool multiple times and the final answer remains 42."
)
private_mock.assert_called_once()
output = agent.execute_task(
task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
tools=[get_final_answer],
)
assert (
output
== "I have used the tool 'get_final_answer' twice and confirmed that the answer is indeed 42."
)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -281,7 +293,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
)
assert (
output
== "I've used the `get_final_answer` tool multiple times and it consistently returns the number 42."
== "I have used the tool as instructed and I am now ready to give the final answer. However, as per the instructions, I am not supposed to give it yet."
)
captured = capsys.readouterr()
assert "Max RPM reached, waiting for next minute to start." in captured.out
@@ -359,7 +371,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
agent=agent1,
),
Task(
description="Don't give a Final Answer, instead keep using the `get_final_answer` tool.",
description="Don't give a Final Answer, instead keep using the `get_final_answer` tool non-stop",
tools=[get_final_answer],
agent=agent2,
),
@@ -428,4 +440,4 @@ def test_agent_step_callback():
callback.return_value = "ok"
crew.kickoff()
callback.assert_called_once()
callback.assert_called()

34
tests/agent_tools/agent_tools_test.py
View File

@@ -17,44 +17,36 @@ tools = AgentTools(agents=[researcher])
@pytest.mark.vcr(filter_headers=["authorization"])
def test_delegate_work():
result = tools.delegate_work(
command="researcher|share your take on AI Agents|I heard you hate them"
coworker="researcher",
task="share your take on AI Agents",
context="I heard you hate them",
)
assert (
result
== "I apologize if my previous statements have given you the impression that I hate AI agents. As a technology researcher, I don't hold personal sentiments towards AI or any other technology. Rather, I analyze them objectively based on their capabilities, applications, and implications. AI agents, in particular, are a fascinating domain of research. They hold tremendous potential in automating and optimizing various tasks across industries. However, like any other technology, they come with their own set of challenges, such as ethical considerations around privacy and decision-making. My objective is to understand these technologies in depth and provide a balanced view."
== "As a researcher, I maintain a neutral perspective on all subjects of research including AI agents. My job is to provide an objective analysis based on facts, not personal feelings. AI Agents are a significant topic in the field of technology with potential to revolutionize various sectors such as healthcare, education, finance and more. They are responsible for tasks that require human intelligence such as understanding natural language, recognizing patterns, and problem solving. However, like any technology, they are tools that can be used for both beneficial and harmful purposes depending on the intent of the user. Therefore, it's crucial to establish ethical guidelines and regulations for their use."
)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_ask_question():
result = tools.ask_question(
command="researcher|do you hate AI Agents?|I heard you LOVE them"
coworker="researcher",
question="do you hate AI Agents?",
context="I heard you LOVE them",
)
assert (
result
== "As an AI, I don't possess feelings or emotions, so I don't love or hate anything. However, I can provide detailed analysis and research on AI agents. They are a fascinating field of study with the potential to revolutionize many industries, although they also present certain challenges and ethical considerations."
)
def test_can_not_self_delegate():
# TODO: Add test for self delegation
pass
def test_delegate_work_with_wrong_input():
result = tools.ask_question(command="writer|share your take on AI Agents")
assert (
result
== "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n"
== "As an AI, I do not possess emotions, hence I cannot love or hate anything. However, as a researcher, I can provide you with an objective analysis of AI Agents. AI Agents are tools designed to perform tasks that would typically require human intelligence. They have potential to revolutionize various sectors including healthcare, education, and finance. However, like any other tool, they can be used for both beneficial and harmful purposes. Therefore, it's essential to have ethical guidelines and regulations in place for their usage."
)
def test_delegate_work_to_wrong_agent():
result = tools.ask_question(
command="writer|share your take on AI Agents|I heard you hate them"
coworker="writer",
question="share your take on AI Agents",
context="I heard you hate them",
)
assert (
@@ -65,7 +57,9 @@ def test_delegate_work_to_wrong_agent():
def test_ask_question_to_wrong_agent():
result = tools.ask_question(
command="writer|do you hate AI Agents?|I heard you LOVE them"
coworker="writer",
question="do you hate AI Agents?",
context="I heard you LOVE them",
)
assert (

675
tests/cassettes/test_agent_repeated_tool_usage.yaml Normal file
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tests/cassettes/test_cache_hitting_between_agents.yaml
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42
tests/crew_test.py
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@@ -160,22 +160,7 @@ def test_hierarchical_process():
assert (
crew.kickoff()
== """Here are the 5 interesting ideas with a highlight paragraph for each:
1. "The Future of AI in Healthcare: Predicting Diseases Before They Happen"
- "Imagine a future where AI empowers us to detect diseases before they arise, transforming healthcare from reactive to proactive. Machine learning algorithms, trained on vast amounts of patient data, could potentially predict heart diseases, strokes, or cancers before they manifest, allowing for early interventions and significantly improving patient outcomes. This article will delve into the rapid advancements in AI within the healthcare sector and how these technologies are ushering us into a new era of predictive medicine."
2. "How AI is Changing the Way We Cook: An Insight into Smart Kitchens"
- "From the humble home kitchen to grand culinary stages, AI is revolutionizing the way we cook. Smart appliances, equipped with advanced sensors and predictive algorithms, are turning kitchens into creative playgrounds, offering personalized recipes, precise cooking instructions, and even automated meal preparation. This article explores the fascinating intersection of AI and gastronomy, revealing how technology is transforming our culinary experiences."
3. "Redefining Fitness with AI: Personalized Workout Plans and Nutritional Advice"
- "Fitness reimagined – that's the promise of AI in the wellness industry. Picture a personal trainer who knows your strengths, weaknesses, and nutritional needs intimately. An AI-powered fitness app can provide this personalized experience, adapting your workout plans and dietary recommendations in real-time based on your progress and feedback. Join us as we unpack how AI is revolutionizing the fitness landscape, offering personalized, data-driven approaches to health and well-being."
4. "AI and the Art World: How Technology is Shaping Creativity"
- "Art and AI may seem like unlikely partners, but their synergy is sparking a creative revolution. AI algorithms are now creating mesmerizing artworks, challenging our perceptions of creativity and originality. From AI-assisted painting to generative music composition, this article will take you on a journey through the fascinating world of AI in art, exploring how technology is reshaping the boundaries of human creativity."
5. "AI in Space Exploration: The Next Frontier"
- "The vast expanse of space, once the sole domain of astronauts and rovers, is the next frontier for AI. AI technology is playing an increasingly vital role in space exploration, from predicting space weather to assisting in interstellar navigation. This article will delve into the exciting intersection of AI and space exploration, exploring how these advanced technologies are helping us uncover the mysteries of the cosmos.\""""
== """Here are the five interesting ideas for an article with a highlight paragraph for each:\n\n1. The Evolution of Artificial Intelligence:\nDive deep into the fascinating journey of artificial intelligence, from its humble beginnings as a concept in science fiction to an integral part of our daily lives and a catalyst of modern innovations. Explore how AI has evolved over the years, the key milestones that have shaped its growth, and the visionary minds behind these advancements. Uncover the remarkable transformation of AI and its astounding potential for the future.\n\n2. AI in Everyday Life:\nUncover the unseen impact of AI in our every day lives, from our smartphones and home appliances to social media and healthcare. Learn about the subtle yet profound ways AI has become a silent partner in your daily routine, enhancing convenience, productivity, and decision-making. Explore the numerous applications of AI right at your fingertips and how it shapes our interactions with technology and the world around us.\n\n3. Ethical Implications of AI:\nVenture into the ethical labyrinth of artificial intelligence, where innovation meets responsibility. Explore the implications of AI on privacy, job security, and societal norms, and the moral obligations we have towards its development and use. Delve into the thought-provoking debates about AI ethics and the measures being taken to ensure its responsible and equitable use.\n\n4. The Rise of AI Startups:\nWitness the rise of AI startups, the new champions of innovation, driving the technology revolution. Discover how these trailblazing companies are harnessing the power of AI to solve complex problems, create new markets, and revolutionize industries. Learn about their unique challenges, their groundbreaking solutions, and the potential they hold for reshaping the future of technology and business.\n\n5. AI and the Environment:\nExplore the intersection of AI and the environment, where technology meets sustainability. Uncover how AI is being used to combat climate change, conserve biodiversity, and optimize resource utilization. Learn about the innovative ways AI is being used to create a sustainable future and the challenges and opportunities it presents."""
)
@@ -277,18 +262,14 @@ def test_crew_verbose_levels_output(capsys):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_cache_hitting_between_agents():
from unittest.mock import patch
from unittest.mock import call, patch
from langchain.tools import tool
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
The input to this tool should be a comma separated list of numbers of
length two, representing the two numbers you want to multiply together.
For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
a, b = numbers.split(",")
return int(a) * int(b)
def multiplier(first_number: int, second_number: int) -> float:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
tasks = [
Task(
@@ -308,15 +289,16 @@ def test_cache_hitting_between_agents():
tasks=tasks,
)
assert crew._cache_handler._cache == {}
output = crew.kickoff()
assert crew._cache_handler._cache == {"multiplier-2,6": "12"}
assert output == "12"
with patch.object(CacheHandler, "read") as read:
read.return_value = "12"
crew.kickoff()
read.assert_called_with("multiplier", "2,6")
assert read.call_count == 2, "read was not called exactly twice"
# Check if read was called with the expected arguments
expected_calls = [
call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
]
read.assert_has_calls(expected_calls, any_order=False)
@pytest.mark.vcr(filter_headers=["authorization"])