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fixing parser, tools and executor

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This commit is contained in:
João Moura
2024-09-15 18:41:59 -03:00
parent c2795307f6
commit f216b0a78c
98 changed files with 21270 additions and 26437 deletions
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22
poetry.lock generated
View File

@@ -2793,17 +2793,17 @@ langchain-core = ">=0.2.38,<0.3.0"
[[package]]
name = "langchain-openai"
version = "0.1.23"
version = "0.1.24"
description = "An integration package connecting OpenAI and LangChain"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_openai-0.1.23-py3-none-any.whl", hash = "sha256:8e3d215803e157f26480c6108eb4333629832b1a0e746723060c24f93b8b78f4"},
{file = "langchain_openai-0.1.23.tar.gz", hash = "sha256:ed7f16671ea0af177ac5f82d5645a746c5097c56f97b31798e5c07b5c84f0eed"},
{file = "langchain_openai-0.1.24-py3-none-any.whl", hash = "sha256:7dc27f1dd50090a409fb5e970b07aaf3ff32c027cc1e8da1d1272f950e93e02c"},
{file = "langchain_openai-0.1.24.tar.gz", hash = "sha256:3f7ef5c26d232685d3c367b7ec523799563c468725e4ca2e616d7c4a96bd16f3"},
]
[package.dependencies]
langchain-core = ">=0.2.35,<0.3.0"
langchain-core = ">=0.2.39,<0.3.0"
openai = ">=1.40.0,<2.0.0"
tiktoken = ">=0.7,<1"
@@ -2823,13 +2823,13 @@ langchain-core = ">=0.2.38,<0.3.0"
[[package]]
name = "langsmith"
version = "0.1.119"
version = "0.1.120"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.119-py3-none-any.whl", hash = "sha256:152202bd9e856f64d227ab36089965de1818c960fe822858d367241e12cda984"},
{file = "langsmith-0.1.119.tar.gz", hash = "sha256:89ac51a1d76e8f2c12318c5d5e3f3f591cd2b8434ab861ddffbb04dc5f75e9d2"},
{file = "langsmith-0.1.120-py3-none-any.whl", hash = "sha256:54d2785e301646c0988e0a69ebe4d976488c87b41928b358cb153b6ddd8db62b"},
{file = "langsmith-0.1.120.tar.gz", hash = "sha256:25499ca187b41bd89d784b272b97a8d76f60e0e21bdf20336e8a2aa6a9b23ac9"},
]
[package.dependencies]
@@ -2843,13 +2843,13 @@ requests = ">=2,<3"
[[package]]
name = "litellm"
version = "1.44.27"
version = "1.44.28"
description = "Library to easily interface with LLM API providers"
optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
files = [
{file = "litellm-1.44.27-py3-none-any.whl", hash = "sha256:88d514aba2041f7805632db343225f8a71230409a7ce3210911be5e6f6ec13b6"},
{file = "litellm-1.44.27.tar.gz", hash = "sha256:34f11d802a373465648defe619ae6e0dd72d524c0fa9fd11096da2669b134048"},
{file = "litellm-1.44.28-py3-none-any.whl", hash = "sha256:a4476c1f076b7996a97bd5d51e53760be482f1ec888c0c626c7877d5c6ff0849"},
{file = "litellm-1.44.28.tar.gz", hash = "sha256:9a9055ce3f655201e4527786c219eaa98579c0134c031418bc38744fed3cd265"},
]
[package.dependencies]
@@ -2858,7 +2858,7 @@ click = "*"
importlib-metadata = ">=6.8.0"
jinja2 = ">=3.1.2,<4.0.0"
jsonschema = ">=4.22.0,<5.0.0"
openai = ">=1.40.0"
openai = ">=1.45.0"
pydantic = ">=2.0.0,<3.0.0"
python-dotenv = ">=0.2.0"
requests = ">=2.31.0,<3.0.0"

5
src/crewai/agent.py
View File

@@ -73,6 +73,10 @@ class Agent(BaseAgent):
default=None,
description="Callback to be executed after each step of the agent execution.",
)
use_stop_words: bool = Field(
default=True,
description="Use stop words for the agent.",
)
use_system_prompt: Optional[bool] = Field(
default=True,
description="Use system prompt for the agent.",
@@ -238,6 +242,7 @@ class Agent(BaseAgent):
stop_words=stop_words,
max_iter=self.max_iter,
tools_handler=self.tools_handler,
use_stop_words=self.use_stop_words,
tools_names=self.__tools_names(parsed_tools),
tools_description=self._render_text_description_and_args(parsed_tools),
step_callback=self.step_callback,

1
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -20,7 +20,6 @@ class CrewAgentExecutorMixin:
task: Optional["Task"]
iterations: int
have_forced_answer: bool
max_iter: int
_i18n: I18N
def _should_force_answer(self) -> bool:

39
src/crewai/agents/crew_agent_executor.py
View File

@@ -12,7 +12,12 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
from crewai.utilities.logger import Logger
from crewai.utilities.training_handler import CrewTrainingHandler
from crewai.llm import LLM
from crewai.agents.parser import AgentAction, AgentFinish, OutputParserException
from crewai.agents.parser import (
AgentAction,
AgentFinish,
OutputParserException,
FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE,
)
class CrewAgentExecutor(CrewAgentExecutorMixin):
@@ -28,6 +33,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
max_iter: int,
tools: List[Any],
tools_names: str,
use_stop_words: bool,
stop_words: List[str],
tools_description: str,
tools_handler: ToolsHandler,
@@ -52,6 +58,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.tools_handler = tools_handler
self.original_tools = original_tools
self.step_callback = step_callback
self.use_stop_words = use_stop_words
self.tools_description = tools_description
self.function_calling_llm = function_calling_llm
self.respect_context_window = respect_context_window
@@ -73,7 +80,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
self.messages.append(self._format_msg(user_prompt))
self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
formatted_answer = self._invoke_loop()
if self.ask_for_human_input:
@@ -92,36 +98,46 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
while not isinstance(formatted_answer, AgentFinish):
if not self.request_within_rpm_limit or self.request_within_rpm_limit():
answer = LLM(
self.llm, stop=self.stop, callbacks=self.callbacks
self.llm,
stop=self.stop if self.use_stop_words else None,
callbacks=self.callbacks,
).call(self.messages)
if not self.use_stop_words:
try:
self._format_answer(answer)
except OutputParserException as e:
if (
FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
in e.error
):
answer = answer.split("Observation:")[0].strip()
self.iterations += 1
formatted_answer = self._format_answer(answer)
if isinstance(formatted_answer, AgentAction):
action_result = self._use_tool(formatted_answer)
formatted_answer.text += f"\nObservation: {action_result}"
if self.step_callback:
formatted_answer.result = action_result
self.step_callback(formatted_answer)
if self._should_force_answer():
if self.have_forced_answer:
return {
"output": self._i18n.errors(
return AgentFinish(
output=self._i18n.errors(
"force_final_answer_error"
).format(formatted_answer.text)
}
).format(formatted_answer.text),
text=formatted_answer.text,
)
else:
formatted_answer.text += (
f'\n{self._i18n.errors("force_final_answer")}'
)
self.have_forced_answer = True
self.messages.append(
self._format_msg(formatted_answer.text, role="assistant")
)
except OutputParserException as e:
self.messages.append({"role": "assistant", "content": e.error})
self._invoke_loop(formatted_answer)
@@ -132,7 +148,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
):
self._handle_context_length()
self._invoke_loop(formatted_answer)
else:
raise e
return formatted_answer
def _use_tool(self, agent_action: AgentAction) -> Any:

2
src/crewai/agents/parser.py
View File

@@ -74,7 +74,7 @@ class CrewAgentParser:
if action_match:
if includes_answer:
raise OutputParserException(
f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}"
)
action = action_match.group(1)
clean_action = self._clean_action(action)

8
src/crewai/tools/tool_usage.py
View File

@@ -300,7 +300,11 @@ class ToolUsage:
return "\n--\n".join(descriptions)
def _is_gpt(self, llm) -> bool:
return False if not llm else "gpt" in llm.lower()
return (
"gpt" in str(llm).lower()
or "o1-preview" in str(llm).lower()
or "o1-mini" in str(llm).lower()
)
def _tool_calling(
self, tool_string: str
@@ -313,7 +317,7 @@ class ToolUsage:
else ToolCalling
)
converter = Converter(
text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n{tool_string}```",
text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
llm=self.function_calling_llm,
model=model,
instructions=dedent(

8
src/crewai/translations/en.json
View File

@@ -5,15 +5,15 @@
"backstory": "You are a seasoned manager with a knack for getting the best out of your team.\nYou are also known for your ability to delegate work to the right people, and to ask the right questions to get the best out of your team.\nEven though you don't perform tasks by yourself, you have a lot of experience in the field, which allows you to properly evaluate the work of your team members."
},
"slices": {
"observation": "\nObservation",
"observation": "\nObservation:",
"task": "\nCurrent Task: {input}\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:",
"memory": "\n\n# Useful context: \n{memory}",
"role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
"tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n",
"no_tools": "\nTo give my best complete final answer to the task use the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
"format": "I MUST either use a tool (use one at time) OR give my best final answer. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n ",
"format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n ",
"final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
"format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
"format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
"task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
"expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
"human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
@@ -23,7 +23,7 @@
"summary": "This is a summary of our conversation so far:\n{merged_summary}"
},
"errors": {
"force_final_answer_error": "I can't keep going, this was the best I could do.\n {formatted_answer.text}",
"force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",
"force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
"agent_tool_unexsiting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
"task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",

4
src/crewai/utilities/__init__.py
View File

@@ -1,7 +1,7 @@
from .converter import Converter, ConverterError
from .file_handler import FileHandler
from .i18n import I18N
from .instructor import Instructor
from .internal_instructor import InternalInstructor
from .logger import Logger
from .parser import YamlParser
from .printer import Printer
@@ -16,7 +16,7 @@ __all__ = [
"ConverterError",
"FileHandler",
"I18N",
"Instructor",
"InternalInstructor",
"Logger",
"Printer",
"Prompts",

17
src/crewai/utilities/converter.py
View File

@@ -61,11 +61,10 @@ class Converter(OutputConverter):
def _create_instructor(self):
"""Create an instructor."""
from crewai.utilities import Instructor
from crewai.utilities import InternalInstructor
inst = Instructor(
inst = InternalInstructor(
llm=self.llm,
max_attempts=self.max_attempts,
model=self.model,
content=self.text,
instructions=self.instructions,
@@ -90,7 +89,11 @@ class Converter(OutputConverter):
@property
def is_gpt(self) -> bool:
"""Return if llm provided is of gpt from openai."""
return "gpt" in str(self.llm).lower()
return (
"gpt" in str(self.llm).lower()
or "o1-preview" in str(self.llm).lower()
or "o1-mini" in str(self.llm).lower()
)
def convert_to_model(
@@ -214,7 +217,11 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
def is_gpt(llm: Any) -> bool:
"""Return if llm provided is of gpt from openai."""
return "gpt" in str(llm).lower()
return (
"gpt" in str(llm).lower()
or "o1-preview" in str(llm).lower()
or "o1-mini" in str(llm).lower()
)
def create_converter(

6
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -92,7 +92,11 @@ class TaskEvaluator:
return converter.to_pydantic()
def _is_gpt(self, llm) -> bool:
return "gpt" in str(self.llm).lower()
return (
"gpt" in str(self.llm).lower()
or "o1-preview" in str(self.llm).lower()
or "o1-mini" in str(self.llm).lower()
)
def evaluate_training_data(
self, training_data: dict, agent_id: str

36
src/crewai/utilities/instructor.py → src/crewai/utilities/internal_instructor.py
View File

@@ -2,29 +2,27 @@ from typing import Any, Optional, Type
import instructor
from litellm import completion
from pydantic import BaseModel, Field, PrivateAttr, model_validator
class Instructor(BaseModel):
class InternalInstructor:
"""Class that wraps an agent llm with instructor."""
_client: Any = PrivateAttr()
content: str = Field(description="Content to be sent to the instructor.")
agent: Optional[Any] = Field(
description="The agent that needs to use instructor.", default=None
)
llm: Optional[str] = Field(
description="The agent that needs to use instructor.", default=None
)
instructions: Optional[str] = Field(
description="Instructions to be sent to the instructor.",
default=None,
)
model: Type[BaseModel] = Field(
description="Pydantic model to be used to create an output."
)
def __init__(
self,
content: str,
model: Type,
agent: Optional[Any] = None,
llm: Optional[str] = None,
instructions: Optional[str] = None,
):
self.content = content
self.agent = agent
self.llm = llm
self.instructions = instructions
self.model = model
self._client = None
self.set_instructor()
@model_validator(mode="after")
def set_instructor(self):
"""Set instructor."""
if self.agent and not self.llm:
@@ -34,7 +32,6 @@ class Instructor(BaseModel):
completion,
mode=instructor.Mode.TOOLS,
)
return self
def to_json(self):
model = self.to_pydantic()
@@ -44,7 +41,6 @@ class Instructor(BaseModel):
messages = [{"role": "user", "content": self.content}]
if self.instructions:
messages.append({"role": "system", "content": self.instructions})
model = self._client.chat.completions.create(
model=self.llm, response_model=self.model, messages=messages
)

9
src/crewai/utilities/rpm_controller.py
View File

@@ -24,26 +24,17 @@ class RPMController(BaseModel):
return self
def check_or_wait(self):
print("check_or_waitcheck_or_waitcheck_or_waitcheck_or_wait")
if self.max_rpm is None:
return True
def _check_and_increment():
print(
"_check_and_increment_check_and_increment_check_and_increment_check_and_increment"
)
if self.max_rpm is not None and self._current_rpm < self.max_rpm:
self._current_rpm += 1
print("111111111111111")
print("self._current_rpm", self._current_rpm)
print("self.max_rpm", self.max_rpm)
return True
elif self.max_rpm is not None:
print("22222222222222")
self.logger.log(
"info", "Max RPM reached, waiting for next minute to start."
)
print("CARALHO")
self._wait_for_next_minute()
self._current_rpm = 1
return True

101
tests/agent_test.py
View File

@@ -67,7 +67,7 @@ def test_agent_execution():
)
output = agent.execute_task(task)
assert output == "The result of the math operation 1 + 1 is 2."
assert output == "The result of 1 + 1 is 2."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -91,7 +91,7 @@ def test_agent_execution_with_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task)
assert output == "The result of 3 times 4 is 12."
assert output == "The result of the multiplication is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -106,7 +106,6 @@ def test_logging_tool_usage():
goal="test goal",
backstory="test backstory",
tools=[multiplier],
allow_delegation=False,
verbose=True,
)
@@ -122,7 +121,7 @@ def test_logging_tool_usage():
tool_usage = InstructorToolCalling(
tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
)
assert output == "The result of multiplying 3 times 4 is 12."
assert output == "12"
assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
@@ -276,7 +275,61 @@ def test_agent_execution_with_specific_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task=task, tools=[multiplier])
assert output == "The result of 3 times 4 is 12."
assert output == "The result of the multiplication is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
@tool
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",
goal="test goal",
backstory="test backstory",
llm="o1-preview",
max_iter=3,
use_system_prompt=False,
allow_delegation=False,
use_stop_words=False,
)
task = Task(
description="What is 3 times 4?",
agent=agent,
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task=task, tools=[multiplier])
assert output == "12"
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_powered_by_new_o_model_family_that_uses_tool():
@tool
def comapny_customer_data() -> float:
"""Useful for getting customer related data."""
return "The company has 42 customers"
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
llm="o1-preview",
max_iter=3,
use_system_prompt=False,
allow_delegation=False,
use_stop_words=False,
)
task = Task(
description="How many customers does the company have?",
agent=agent,
expected_output="The number of customers",
)
output = agent.execute_task(task=task, tools=[comapny_customer_data])
assert output == "The company has 42 customers"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -296,7 +349,7 @@ def test_agent_custom_max_iterations():
)
with patch.object(
LLM, "call", wraps=LLM("gpt-4o", stop=["\nObservation"]).call
LLM, "call", wraps=LLM("gpt-4o", stop=["\nObservation:"]).call
) as private_mock:
task = Task(
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
@@ -385,7 +438,7 @@ def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_moved_on_after_max_iterations():
@tool
def get_final_answer(numbers) -> float:
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
@@ -497,6 +550,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
goal="test goal",
backstory="test backstory",
max_rpm=10,
max_iter=2,
verbose=True,
allow_delegation=False,
)
@@ -548,6 +602,7 @@ def test_agent_error_on_parsing_tool(capsys):
role="test role",
goal="test goal",
backstory="test backstory",
max_iter=1,
verbose=True,
)
tasks = [
@@ -563,7 +618,7 @@ def test_agent_error_on_parsing_tool(capsys):
agents=[agent1],
tasks=tasks,
verbose=True,
function_calling_llm="gpt-4",
function_calling_llm="gpt-4o",
)
with patch.object(ToolUsage, "_render") as force_exception:
@@ -671,10 +726,10 @@ def test_agent_step_callback():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_function_calling_llm():
llm = "gpt-3.5-turbo-0125"
llm = "gpt-4o"
@tool
def learn_about_AI(topic) -> str:
def learn_about_AI() -> str:
"""Useful for when you need to learn about AI to write an paragraph about it."""
return "AI is a very broad field."
@@ -683,7 +738,8 @@ def test_agent_function_calling_llm():
goal="test goal",
backstory="test backstory",
tools=[learn_about_AI],
llm="gpt-3.5-turbo-0125",
llm="gpt-4o",
max_iter=2,
function_calling_llm=llm,
)
@@ -694,17 +750,26 @@ def test_agent_function_calling_llm():
)
tasks = [essay]
crew = Crew(agents=[agent1], tasks=tasks)
from unittest.mock import patch, Mock
import instructor
from crewai.utilities import Instructor
with patch.object(instructor, "from_litellm") as mock_from_litellm:
mock_client = Mock()
mock_from_litellm.return_value = mock_client
mock_chat = Mock()
mock_client.chat = mock_chat
mock_completions = Mock()
mock_chat.completions = mock_completions
mock_create = Mock()
mock_completions.create = mock_create
with patch.object(Instructor, "__init__", return_value=None) as mock_instructor:
crew.kickoff()
mock_instructor.assert_called()
calls = mock_instructor.call_args_list
print("callscallscallscallscalls")
print(calls)
mock_from_litellm.assert_called_once()
mock_create.assert_called()
calls = mock_create.call_args_list
assert any(
call.kwargs.get("llm") == "gpt-3.5-turbo-0125" for call in calls
call.kwargs.get("model") == "gpt-4o" for call in calls
), "Instructor was not created with the expected model"

12
tests/agent_tools/agent_tools_test.py
View File

@@ -24,7 +24,7 @@ def test_delegate_work():
assert (
result
== 'AI agents have been a transformative force in various industries, but it\'s important to note that personal opinions about them can be nuanced. While I wouldn’t use the term "hate," there are both positive aspects and significant concerns that need to be considered. \n\nOn the positive side, AI agents excel in performing repetitive tasks with high efficiency and accuracy, enabling businesses to optimize their operations and reduce costs. They play a vital role in areas such as customer service, where they can handle inquiries around the clock without fatigue, and in data analysis, where they can process vast amounts of information far quicker than a human could. Additionally, AI agents can be trained to adapt and learn from new data, making them increasingly valuable over time.\n\nHowever, there are also valid concerns. Privacy and security are major issues; AI agents often require access to large datasets that can include sensitive personal information, and breaches can have serious repercussions. There\'s also the ethical dimension, as the deployment of AI agents can lead to job displacement for roles that were traditionally performed by humans. This raises important questions about the future of work and the socio-economic impact of automation. Furthermore, the decision-making of AI agents can sometimes be opaque, making it challenging to understand how they arrive at specific conclusions or actions, which is particularly concerning in high-stakes areas like medical diagnoses or judicial decisions.\n\nSo, it\'s not a matter of "hating" AI agents, but rather a case of needing to balance their incredible potential with careful consideration of the associated risks and ethical implications. Thoughtful regulation and ongoing public discourse will be crucial in ensuring that AI agents are developed and deployed in ways that are beneficial to society as a whole.'
== "AI agents are autonomous programs that perform tasks or services on behalf of users with a considerable degree of autonomy, often based on intelligence derived from data and analytics. They can operate within specific domains or across multiple areas, executing tasks ranging from mundane repetitive actions to complex decision-making processes. Given their applications, it's essential to examine both the benefits and criticisms to provide a balanced perspective.\n\n## The Positive Aspects of AI Agents\n\n1. **Efficiency and Productivity:**\n AI agents can handle tedious and repetitive tasks efficiently, freeing up human workers to focus on more complex and creative endeavors. This results in significant time and cost savings for businesses.\n\n2. **24/7 Availability:**\n AI agents can work continuously without breaks, providing round-the-clock service. This is especially beneficial for customer support, where they can resolve issues and answer queries at any time.\n\n3. **Consistency and Accuracy:**\n Unlike humans, AI agents do not suffer from fatigue and can perform tasks without error, ensuring a high level of consistency in operations.\n\n4. **Data Handling and Analysis:**\n AI agents can process and analyze vast amounts of data quickly, identifying patterns and generating insights that would be otherwise difficult to discern manually.\n\n## Criticisms and Concerns\n\n1. **Job Displacement:**\n One of the primary concerns is that AI agents could replace human jobs, leading to unemployment. While they create new opportunities in AI development and maintenance, the transition can be disruptive.\n\n2. **Ethical Considerations:**\n With increasing autonomy, ensuring that AI agents act ethically and within the bounds of societal norms is a challenge. Misuse of AI for malicious activities or biased decision-making are valid concerns that need rigorous oversight.\n\n3. **Lack of Emotional Intelligence:**\n AI agents, no matter how advanced, lack the ability to empathize and understand human emotions fully. This can be a drawback in areas that require delicate human interaction, such as mental health support.\n\n4. **Dependence and Reliability:**\n Over-reliance on AI agents might lead to significant issues if they malfunction or are compromised. Ensuring reliability and establishing fail-safes is critical.\n\n5. **Security and Privacy:**\n AI agents typically require access to large data sets, often containing sensitive information. Ensuring robust data security and user privacy is paramount to prevent misuse or data breaches.\n\n## Conclusion\n\nWhile AI agents offer remarkable benefits in terms of efficiency, accuracy, and data handling, their integration into society must be managed carefully to address ethical concerns and potential negative impacts on employment and human interaction. The onus is on developers, policymakers, and society at large to ensure that AI agents are designed and deployed responsibly, balancing innovation with the broader implications for humanity. \n\nDespite the apprehensions, dismissing AI agents entirely overlooks their transformative potential. Instead, the focus should be on harnessing these tools' capabilities while mitigating their risks, ensuring they serve to enhance rather than hinder human progress."
)
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
assert (
result
== "My take on AI agents is multifaceted and nuanced. Contrary to what you may have heard, I do not hate AI agents. I have spent significant time and effort studying them, and I view them as transformative tools with immense potential. However, I also recognize the challenges and ethical considerations they bring to the table.\n\nAI agents are software programs that perform tasks autonomously using artificial intelligence techniques. They can range from simple chatbots to complex systems that navigate and make decisions in dynamic environments. Here’s my detailed perspective on AI agents:\n\n### The Advantages:\n\n1. **Efficiency and Productivity:**\n - **Automation of Repetitive Tasks:** AI agents can handle mundane and repetitive tasks such as data entry, appointment scheduling, and customer support, which frees up human workers to focus on more strategic activities.\n - **24/7 Availability:** Unlike humans, AI agents can operate round-the-clock, providing constant support and operations without the need for breaks.\n\n2. **Enhanced Decision Making:**\n - **Data Processing Speed:** AI agents can process vast amounts of data at lightning speeds, enabling faster decision-making processes.\n - **Predictive Analytics:** They can analyze historical data to predict future trends, helping businesses plan more effectively.\n\n3. **Personalization:**\n - **User Experience:** AI agents can tailor interactions based on user data, providing a more personalized experience in applications like e-commerce and content recommendations.\n - **Customer Insight:** They collect and analyze user preferences, allowing businesses to offer customized solutions and enhance customer satisfaction.\n\n### The Challenges:\n\n1. **Ethical Concerns:**\n - **Bias and Fairness:** AI systems can inadvertently perpetuate biases if trained on non-representative or biased datasets.\n - **Privacy Issues:** The collection and use of personal data by AI agents raise significant privacy concerns. Protecting user data from misuse is crucial.\n\n2. **Job Displacement:**\n - **Automation Impact:** As AI agents automate more tasks, there is a potential for job displacement in certain sectors. Workforce reskilling and upskilling become essential.\n\n3. **Dependability and Trust:**\n - **Reliability:** Ensuring that AI agents are reliable and do not malfunction in critical situations is a significant concern.\n - **Transparency:** Users and stakeholders need to understand how AI agents make decisions, which can be challenging given the complexity of some AI models.\n\n### My Perspective:\n\nWhile I acknowledge the immense potential of AI agents to revolutionize industries and improve our daily lives, I believe it is crucial to approach their development and deployment responsibly. Ethical considerations, such as mitigating biases and ensuring transparency, are paramount. Additionally, I advocate for a balanced view—embracing the benefits while proactively addressing the challenges. \n\nIn conclusion, my view on AI agents is one of cautious optimism. I believe they can be incredibly beneficial if developed and used wisely, with proper oversight and ethical guidelines in place. Contrary to any misconceptions, my stance is not rooted in disdain but in a deep understanding of both their capabilities and the associated responsibilities."
== "While it may have come across that I have a strong dislike for AI agents, the reality is more nuanced. My stance isn't one of outright hate, but rather one of concern and critical examination. AI agents offer immense potential in automating tasks, improving efficiency, and driving innovations across various fields such as healthcare, finance, and customer service. They can handle repetitive tasks, provide data-driven insights, and even enhance user experiences through personalized interactions.\n\nHowever, my apprehensions lie in the ethical and societal implications associated with AI agents. Issues such as data privacy, security, and the potential for biased outputs due to flawed algorithms are significant concerns. Moreover, the rapid proliferation of AI agents raises questions about job displacement and the future of work, potentially leading to socioeconomic disparities if not managed thoughtfully.\n\nTransparency and accountability in AI development are paramount. AI agents must be designed and implemented with clear ethical guidelines, rigorous testing, and improved explainability to ensure they act in the best interests of society. Collaboration between technologists, ethicists, policymakers, and the general public is crucial to cultivate trust and navigate the complexities associated with AI advancements.\n\nIn summary, my critical stance on AI agents stems from a desire to see responsible and ethical development that maximizes their benefits while mitigating risks. By addressing these concerns head-on, we can harness the full potential of AI agents to create a more equitable and prosperous future."
)
@@ -52,7 +52,7 @@ def test_ask_question():
assert (
result
== "As a researcher specialized in technology, I neither love nor hate AI agents. My role is to objectively analyze their capabilities, impacts, and potential. AI agents have many beneficial applications, such as improving healthcare, optimizing logistics, and enhancing user experiences. However, there are also concerns regarding privacy, ethical implications, and job displacement. My professional stance is to critically evaluate both the advantages and the challenges, ensuring that the development and deployment of AI agents are conducted responsibly and ethically."
== "As an expert researcher specialized in technology and AI, it's important to maintain an objective and balanced perspective. I do not harbor emotions such as love or hate towards AI agents. Instead, I appreciate the capabilities and potential they offer.\n\nAI agents can significantly enhance productivity, provide valuable insights through data analysis, and automate repetitive tasks, thereby allowing humans to focus on more complex and creative endeavors. However, it's also crucial to address ethical considerations, potential biases, and the implications of their deployment in various sectors.\n\nTherefore, my stance isn't one of emotional attachment but rather a professional appreciation of both the opportunities and challenges presented by AI agents. This balanced view helps in conducting thorough and unbiased research, which is essential for advancing our understanding and responsible use of AI technologies."
)
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
assert (
result
== "I don't hate AI agents; in fact, I find them fascinating and very useful. My work as an expert researcher in technology, especially in AI and AI agents, has given me a deep appreciation of their capabilities and potential. AI agents have the ability to process vast amounts of data more quickly and accurately than any human could, which can lead to groundbreaking discoveries and efficiencies across numerous fields. They can automate tedious tasks, provide personalized recommendations, and even aid in complex decision-making processes.\n\nWhile it's important to recognize and address ethical and practical concerns with AI agents – such as biases in algorithms, potential job displacement, and ensuring data privacy – the benefits they offer cannot be overlooked. Properly developed and regulated, AI agents have the potential to significantly improve our lives and solve problems that were previously insurmountable.\n\nSo, to clarify, I love AI agents for their potential to drive innovation and make our lives better."
== 'As a researcher specializing in technology and artificial intelligence, I do not "hate" AI agents. Instead, I have a deep appreciation for their capabilities and potential to transform numerous industries. My professional interest lies in understanding, analyzing, and improving these technologies to benefit society. AI agents can streamline tasks, process vast amounts of data quickly, and even assist in decision-making processes. While they do come with challenges and ethical considerations, my focus is on leveraging their strengths and addressing their limitations to create a more efficient and advanced technological landscape. So, in short, no, I do not hate AI agents; I respect and am fascinated by their potential.'
)
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
assert (
result
== "AI agents are software entities that perform tasks on behalf of users autonomously. Their capabilities have significantly expanded with advancements in machine learning, natural language processing, and other AI technologies. My stance on AI agents isn't rooted in hatred or disdain, but rather in a critical perspective regarding their deployment and ethical implications.\n\nOne of the key advantages of AI agents is their ability to handle repetitive tasks efficiently, allowing humans to focus on more complex and creative activities. For instance, AI agents can automate customer service through chatbots, manage schedules, and even assist in data analysis.\n\nHowever, there are concerns that need addressing. The proliferation of AI agents raises critical issues surrounding privacy, security, and accountability. For example, AI agents often require access to vast amounts of personal data, which, if not handled properly, can lead to breaches of privacy. Moreover, the decision-making processes of AI agents can sometimes lack transparency, making it difficult to hold them accountable for errors or biased decisions.\n\nAnother concern is the potential impact on employment. As AI agents become more capable, there's a legitimate fear of job displacement in certain sectors. This necessitates a strategic approach to reskill the workforce and create new opportunities centered around AI.\n\nFurthermore, the design and deployment of AI agents must be inclusive and equitable. There have been instances where AI systems have demonstrated bias, reflecting the data they were trained on. This highlights the importance of diverse and representative data sets, as well as continuous monitoring and adjustment to ensure fair outcomes.\n\nIn conclusion, while AI agents offer considerable benefits, their development and integration into society must be approached with caution. By addressing ethical, privacy, and fairness concerns, we can harness the power of AI agents for positive and equitable advancements."
== 'AI Agents are a diverse set of software programs designed to autonomously carry out tasks, interact with users, and make decisions based on data input. While there seems to be a perception that I dislike AI agents, let\'s delve into a detailed and nuanced view.\n\nFirst, AI agents are remarkable for their ability to handle repetitive tasks efficiently. They can automate mundane tasks, freeing up human workers for more complex, creative, and strategic work. For instance, customer service bots can handle basic inquiries, significantly reducing waiting times and improving customer satisfaction.\n\nMoreover, AI agents excel in data analysis. These agents can process and analyze large volumes of data far more quickly and accurately than humans. This capability is particularly valuable in sectors such as finance, healthcare, and logistics, where rapid and precise decision-making is crucial. For example, in the medical field, AI agents can analyze medical images or patient data to assist in diagnosing diseases, potentially leading to earlier detections and better patient outcomes.\n\nThere are also significant advancements in personalized user experiences through AI agents. These agents can learn from user interactions to tailor recommendations and interactions that better meet individual preferences. Think of how Netflix provides personalized movie recommendations or how AI-driven personalization in e-commerce can suggest products that a user is likely to be interested in.\n\nHowever, the criticism that might be perceived as "hate" stems from valid concerns. One significant issue is the potential for job displacement as AI agents become more capable. While they augment human capabilities, the automation of tasks previously performed by humans can lead to job losses, particularly in sectors reliant on repetitive tasks.\n\nFurthermore, ethical concerns are paramount. The deployment of AI agents often involves massive amounts of data, raising issues around privacy and data security. Additionally, if not properly designed and regulated, AI agents can perpetuate and even exacerbate biases present in their training data, leading to unfair outcomes.\n\nTransparency and accountability are also critical challenges. Many AI models operate as "black boxes," with their decision-making processes being opaque. This lack of transparency can be problematic in scenarios where understanding the rationale behind an AI agent\'s decision is necessary, such as in judicial or medical contexts.\n\nIn summary, while AI agents bring a host of benefits, including increased efficiency, enhanced data analysis, and personalized experiences, they also pose significant challenges. These include potential job displacement, ethical concerns, bias, and lack of transparency. These criticisms are not to dismiss AI agents but to highlight the importance of addressing these issues to ensure that AI develops in a manner that is beneficial, fair, and ethical for society as a whole. This balanced view recognizes the transformative potential of AI agents while urging caution and consideration of the broader societal impacts.'
)
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
assert (
result
== "As an expert researcher specialized in technology, especially in the field of AI and AI agents, I can confidently say that I do not hate AI agents. On the contrary, I find them fascinating and full of potential. AI agents can revolutionize various industries by automating tasks, enhancing decision-making processes, and providing insights that were previously unattainable. They can help in areas as diverse as healthcare, finance, security, and even creative arts.\n\nIt's important to recognize that AI agents, like any technology, come with their challenges and ethical considerations. Issues such as bias, transparency, and the impact on employment need to be thoughtfully addressed. However, these challenges can be mitigated through responsible development, clear regulations, and ongoing research.\n\nIn summary, I don't hate AI agents; I see them as powerful tools that, when used responsibly, can significantly improve our lives and society."
== "As a researcher deeply specialized in technology and artificial intelligence, I can confidently say that I do not hate AI agents. In fact, I find them incredibly fascinating and valuable. AI agents have the potential to revolutionize numerous industries by improving efficiency, accuracy, and enabling innovations that were previously unimaginable. They can assist in data analysis, automate repetitive tasks, and even contribute to advancements in healthcare, finance, and environmental monitoring, just to name a few. It's important to recognize that, like any technology, AI agents must be developed and used responsibly, with ethical considerations in mind. So, to address the context shared, yes, it’s true—I do love AI agents for their potential to drive positive change and enhance our capabilities in meaningful ways."
)

47
tests/agent_tools/cassettes/test_ask_question.yaml
View File

@@ -12,7 +12,7 @@ interactions:
shared.\nyou MUST return the actual complete content as the final answer, not
a summary.\n\nThis is the context you''re working with:\nI heard you LOVE them\n\nBegin!
This is VERY important to you, use the tools available and give your best Final
Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation"]}'
Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nResult"]}'
headers:
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46
tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml
View File

@@ -12,7 +12,7 @@ interactions:
shared.\nyou MUST return the actual complete content as the final answer, not
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tests/crew_test.py
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@@ -226,7 +226,7 @@ def test_crew_creation():
result = crew.kickoff()
expected_string_output = "**AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n\nThe integration of AI into healthcare systems heralds a transformative era in the medical field, with the potential to save lives and significantly enhance the quality of patient care. Advanced AI algorithms excel at diagnosing complex diseases such as cancer by analyzing medical images with precision that rivals human expertise. Additionally, the implementation of AI-powered personalized treatment plans can optimize individual patient care, while predictive analytics anticipates complications before they occur, shifting the healthcare paradigm from reactive to proactive. This revolution not only promises to improve diagnostic accuracy but also aims to enhance overall patient outcomes by making healthcare delivery more efficient and anticipatory.\n\n**Ethical Considerations in AI Development: Balancing Innovation and Responsibility**\n\nAs AI technologies become increasingly embedded in daily life, the ethical implications of their development and deployment come to the forefront. This topic delves into the crucial need for ethical frameworks to manage AI's growth responsibly. Issues such as biases within machine learning models, privacy concerns, and the socio-economic impact on employment highlight the importance of creating AI systems that are fair and just. Ensuring these technologies support human values and dignity requires careful navigation of moral dilemmas and the establishment of stringent ethical standards. Therefore, balancing innovation with responsibility is essential to harness the full potential of AI in a way that benefits society as a whole.\n\n**AI Agents in Customer Service: Enhancing User Experience While Reducing Costs**\n\nAI agents, including chatbots and virtual assistants, are revolutionizing customer service by offering round-the-clock support and addressing routine inquiries with an efficiency unmatched by human representatives. The advancements in Natural Language Processing (NLP) enable these AI agents to understand and respond to customer queries in a more natural and conversational manner, significantly boosting user satisfaction. This seamless interaction not only enhances the customer experience but also reduces operational costs for businesses, as AI agents can manage a high volume of interactions simultaneously. As a result, companies benefit from streamlined customer service operations while customers enjoy quicker, more effective resolutions to their problems.\n\n**The Role of AI in Climate Change: Predicting and Mitigating Environmental Impact**\n\nThe role of AI in combating climate change is becoming increasingly prominent, offering innovative solutions to one of the most pressing issues facing humanity today. AI's ability to process and analyze enormous datasets allows for the accurate prediction of climate trends and the identification of effective strategies to reduce carbon footprints. By optimizing energy consumption and other key factors, AI technologies contribute substantially to efforts aimed at mitigating environmental impact. This exploration into AI's contributions to environmental sustainability highlights the potential for these technologies to go beyond conventional approaches, providing new tools in the global fight against climate change and fostering a more sustainable future."
expected_string_output = "**The Rise of AI in Healthcare: Personalization and Precision Medicine**\n\nThe integration of AI in healthcare is revolutionizing the way treatments are personalized for individual patients, ushering in the era of precision medicine. With the development of sophisticated AI algorithms, it's now possible to predict patient responses to various treatments based on their genetics and personal health data. This leads to highly tailored care plans that can significantly improve outcomes and patient satisfaction. By leveraging vast datasets and machine learning, healthcare providers can make more informed decisions, potentially reducing trial-and-error in treatment plans. This customization not only optimizes patient care but also offers considerable economic benefits by minimizing ineffective treatments, showcasing a profound leap in medical science and patient care.\n\n**AI Agents in Creative Arts: Shaping the Future of Music and Visual Arts**\n\nAI's foray into creative arts is reshaping the landscape of music and visual arts in unprecedented ways. AI-generated art is not just a novelty; it’s redefining the boundaries of creativity and sparking a global debate about the essence of artistic expression. Utilizing advanced algorithms and machine learning, AI can compose music, create paintings, and design visual media that rival human work. For instance, AI composers are creating symphonies that blend classical techniques with modern innovation, while AI visual artists are producing pieces that challenge traditional notions of art and authorship. This fusion of programming and artistry captivates a diverse audience, provoking thought and admiration, and suggesting a future where AI plays a collaborative role in creative endeavors.\n\n**Ethical Implications of AI Surveillance: Balancing Safety and Privacy**\n\nThe rise of AI-powered surveillance systems presents a complex conundrum: balancing enhanced security with the preservation of personal privacy. While these systems can significantly bolster public safety by predicting and preventing criminal activities, they also pose serious ethical challenges. Issues such as constant monitoring, data privacy, and potential misuse of surveillance data are at the forefront of public concern. Real-world examples, such as the implementation of facial recognition technology in public spaces, underscore the delicate balance that must be maintained. Policymakers and tech developers are under immense pressure to draft and enforce regulations that uphold privacy rights without compromising security, making this an urgent and highly relevant topic for our times.\n\n**AI in Education: Transforming Teaching and Learning Methods**\n\nAI is making transformative strides in the education sector by customizing learning experiences, offering real-time feedback, and automating administrative tasks, thereby transforming traditional teaching methods. Advanced AI tools assess individual student's learning patterns and tailor educational content to fit their unique needs, helping bridge learning gaps and optimizing academic performance. Additionally, AI-driven platforms provide teachers with deeper insights into student progress, enabling more targeted interventions. By automating routine administrative tasks, educators can focus more on teaching and less on paperwork. The integration of AI in education promises to democratize learning, making quality education accessible to all, and posing as a game-changer in the global education landscape."
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@@ -294,7 +294,7 @@ def test_hierarchical_process():
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== "1. **The Future of AI Agents: From Healthcare to Education** \nArtificial Intelligence agents are not just a futuristic concept; they are rapidly becoming integral to diverse fields including healthcare and education. Imagine an AI agent capable of diagnosing diseases with pinpoint accuracy or predicting student burnout before it occurs. With continual advancements, AI agents can offer personalized treatments, manage chronic conditions, and optimize educational curriculums to cater to individual learning styles. This dynamic shift could lead to more efficient, effective, and personalized approaches in both sectors, making lives better and educational experiences richer. The future promises an exciting horizon where AI agents revolutionize how we live, learn, and heal.\n\n2. **Blockchain Beyond Bitcoin: How Decentralized Technology is Reshaping Industries** \nWhen most people hear \"blockchain,\" they think of Bitcoin and other cryptocurrencies. However, blockchain technology's potential reaches far beyond digital currencies. From supply chain transparency and secure voting systems to immutable medical records and decentralized finance, blockchain is infusing trust and transparency into multiple sectors. By eliminating intermediaries and ensuring data integrity, blockchain is poised to disrupt traditional business models, offering unprecedented efficiencies and new opportunities. This revolution underscores a foundational shift in how industries operate, emphasizing decentralization and direct peer-to-peer interaction.\n\n3. **The Rise of Remote Work: Transforming Corporate Culture and Collaboration** \nThe COVID-19 pandemic has accelerated a trend that was already catching on: remote work. As companies worldwide adapt to this new norm, it's not just about working from home; it's about transforming the very essence of corporate culture and collaboration. The rise of remote work is leading to a more flexible, inclusive, and diverse work environment. Organizations are leveraging advanced collaboration tools and redefining team dynamics to maintain productivity and innovation. This paradigm shift is reshaping leadership styles, employee engagement, and organizational structure, heralding a future where remote work could become a permanent fixture in the professional landscape.\n\n4. **Sustainable Tech Innovations: Green Solutions for a Planet in Peril** \nWith climate change and environmental degradation posing serious threats, sustainable tech innovations are more crucial than ever. From renewable energy solutions like solar and wind power to energy-efficient devices and sustainable manufacturing processes, technology is paving the way for a greener future. Companies are developing smart grids, eco-friendly gadgets, and recycling technologies that minimize waste and reduce the carbon footprint. These advancements are not just good for the planet; they represent significant business opportunities and regulatory advantages. As we face the urgency of environmental challenges, sustainable tech offers a beacon of hope and a clear path forward.\n\n5. **The Ethical Dilemma of AI: Balancing Innovation with Responsibility** \nAs artificial intelligence rapidly advances, it presents an intriguing ethical dilemma: how do we balance groundbreaking innovation with responsible use? The potential for AI to enhance efficiencies, discover new insights, and create unprecedented opportunities is immense. However, concerns around privacy, biased algorithms, and autonomous decision-making necessitate robust ethical frameworks. This conversation demands a nuanced approach that involves policymakers, technologists, and ethicists working together. Addressing these ethical dilemmas is not just about averting risks but also about ensuring that AI systems are designed, deployed, and regulated in ways that foster trust and societal well-being."
== "1. **The Future of Work: How AI and Automation Are Shaping Careers**:\n In a world rapidly driven by technological advancements, AI and automation are not just buzzwords—they are transforming the very fabric of our professional lives. This article delves into the impact of AI on the job market, exploring how careers are evolving with these innovations. From new job opportunities to the obsolescence of traditional roles, the future of work is a complex landscape of challenges and possibilities. Discover how professionals are adapting, what skills are becoming indispensable, and how organizations are strategically leveraging AI to thrive in a competitive environment.\n\n2. **AI Agents: The Creative Minds Behind Your Personal Assistants**:\n Imagine having a personal assistant who anticipates your needs, learns from your preferences, and streamlines your daily activities, all without human intervention. AI agents, the sophisticated algorithms powering digital assistants like Siri, Alexa, and Google Assistant, are the unsung heroes of modern convenience. This article unveils the intricate world of AI agents, detailing their development, capabilities, and the groundbreaking technologies that enable their creativity and efficiency. Learn how these digital assistants are transforming personal and professional productivity, making life smarter and more efficient in ways we never thought possible.\n\n3. **From Sci-Fi to Reality: The Evolution of AI in Popular Culture**:\n Artificial intelligence, once a figment of science fiction, is now an integral part of our reality. This article traces the fascinating journey of AI from its speculative roots in literature, films, and television to becoming a tangible force in modern society. Explore how popular culture has both predicted and shaped public perception of AI, and how these narratives influence technological advancements and ethical considerations. From the dystopian fears to utopian possibilities, see how the interplay between fiction and reality continues to shape the evolution of AI in our world.\n\n4. **Ethics in AI: Navigating the Moral Landscape of Machine Learning**:\n As AI systems become increasingly autonomous and pervasive, the ethical implications of their use are more critical than ever. This article addresses the urgent need to navigate the complex moral landscape of machine learning and artificial intelligence. From bias in algorithms to privacy concerns and the accountability of AI decisions, delve into the ethical dilemmas that tech companies, policymakers, and society face. Understand the strategies being employed to ensure fair, transparent, and ethical AI development, and the ongoing debates that challenge our moral compass in the age of intelligent machines.\n\n5. **AI and the Arts: How Artificial Intelligence is Revolutionizing Creativity**:\n The intersection of AI and the arts is redefining creativity, pushing the boundaries of what is possible in music, visual arts, literature, and beyond. This article explores the innovative ways in which AI is being utilized by artists to create, enhance, and inspire works of art. From generating original compositions to assisting in the creative process, AI's role in the arts is both collaborative and transformative. Discover how machine learning algorithms are enabling new forms of artistic expression and the implications for the future of creativity, heralding a new era where human ingenuity and artificial intelligence coalesce."
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result.raw
== "Artificial Intelligence (AI) Agents are transformative entities designed to automate processes, make decisions, and solve problems by mimicking human intelligence. These sophisticated systems leverage machine learning, natural language processing, and data analytics to perform tasks that traditionally required human intervention. From personal assistants like Siri and Alexa to complex decision-making systems in autonomous vehicles, AI Agents are increasingly becoming integral to our daily lives, promising enhanced efficiency and productivity across numerous domains.\n\nOne of the most prominent examples of AI Agents is their application in the healthcare industry. These intelligent systems can analyze vast amounts of medical data to assist in diagnostic processes, personalize treatment plans, and even predict outbreaks of diseases. For instance, IBM's Watson for Oncology utilizes AI to provide evidence-based treatment options by analyzing large datasets of medical literature and patient records. These capabilities not only improve the accuracy of diagnoses but also significantly reduce the time it takes to develop effective treatment plans, ultimately enhancing patient outcomes.\n\nBeyond healthcare, AI Agents are making strides in industries such as finance, retail, and manufacturing. In the financial sector, AI-driven chatbots and virtual assistants help manage customer inquiries, detect fraudulent activities, and offer personalized financial advice. Retailers use AI Agents to optimize supply chain operations, manage inventory, and enhance customer experiences through personalized recommendations. Meanwhile, in manufacturing, AI-powered agents are pivotal in predictive maintenance, quality control, and optimizing production lines. These applications underscore the versatility of AI Agents in transforming business operations and driving economic growth.\n\nDespite these notable benefits, the adoption of AI Agents also presents several challenges. Concerns about data privacy and security, ethical considerations regarding decision-making processes, and the potential for job displacement are significant hurdles that need to be addressed. As AI Agents become more autonomous, ensuring transparency and accountability in their operations is critical. Looking forward, the future of AI Agents holds immense potential. Continued advancements in AI technologies, coupled with robust regulatory frameworks, can further unlock the capabilities of AI Agents, making them indispensable assets in our quest for innovation and quality of life improvements. As we navigate this evolving landscape, fostering collaboration between AI developers, policymakers, and society at large will be crucial in harnessing the transformative potential of AI Agents."
== "AI agents are intelligent software entities designed to autonomously perform tasks and make decisions based on data, embodying the next significant leap in the AI evolution. These agents leverage complex algorithms and machine learning techniques to analyze vast datasets, adapt to new patterns, and execute actions without human intervention. From personal assistants like Siri and Alexa to sophisticated financial trading systems and healthcare diagnostics, AI agents are revolutionizing numerous industries by enhancing efficiency, accuracy, and decision-making capabilities. Their ability to operate independently while continuously learning and improving means they hold the transformative potential to redefine how businesses operate, drive innovation, and create unprecedented value across various sectors.\n\nArtificial Intelligence (AI) transcends the notion of simple automation by introducing systems capable of mimicking human intelligence. This evolution in technology doesn't just follow predefined rules but learns from patterns, makes decisions, and even anticipates future outcomes. Machine learning, a subset of AI, has brought about breakthroughs where software evolves by processing data and identifying patterns too complex for traditional algorithms. As a result, AI applications now span from image recognition to natural language processing, offering human-like interactions and unparalleled analytic capabilities.\n\nIn healthcare, AI's influence is particularly profound. It enhances diagnostic accuracy by analyzing medical images, predicting disease outbreaks, and personalizing treatment plans based on patient data. AI-driven innovations such as robotic surgeries and AI-powered diagnostic tools are spearheading a new era of medical breakthroughs, reducing human error rates and facilitating early detection of life-threatening conditions. This is just the tip of the iceberg, as ongoing research continues to uncover new possibilities for integrating AI in patient care, potentially offering solutions that were once the realm of science fiction.\n\nAI's integration into daily life also brings forth significant ethical and societal considerations. Issues surrounding privacy, data security, and the potential displacement of jobs by automation need to be carefully managed. Additionally, ensuring that AI systems are transparent and unbiased is critical to their widespread acceptance and trust. As humanity stands on the brink of this technological revolution, balancing innovation with responsibility will be key to harnessing AI's full potential to drive sustainable growth and improve quality of life globally.\n\nThus, Artificial Intelligence, with all its promises and challenges, is undeniably charting a course toward a future where intelligent systems will increasingly become integral to our lives, driving transformation across all domains of human endeavor."
)
@@ -609,7 +609,7 @@ def test_sequential_async_task_execution_completion():
sequential_result = sequential_crew.kickoff()
assert sequential_result.raw.startswith(
"Artificial Intelligence (AI) has a rich history marked by significant milestones that have collectively shaped its evolution and applications."
"Article: The Pivotal Moments in the History of Artificial Intelligence\n\nArtificial Intelligence (AI)"
)
@@ -636,9 +636,7 @@ def test_single_task_with_async_execution():
)
result = crew.kickoff()
assert result.raw.startswith(
"- AI in healthcare: revolutionizing diagnostics and personalized treatments."
)
assert result.raw.startswith("- Ethical considerations in AI deployment.")
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1101,11 +1099,11 @@ def test_dont_set_agents_step_callback_if_already_set():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_crew_function_calling_llm():
from unittest.mock import patch
from unittest.mock import patch, Mock
from crewai_tools import tool
from crewai.utilities import Instructor
import instructor
llm = "gpt-3.5-turbo-0125"
llm = "gpt-4o"
@tool
def learn_about_AI() -> str:
@@ -1129,12 +1127,23 @@ def test_crew_function_calling_llm():
tasks = [essay]
crew = Crew(agents=[agent1], tasks=tasks)
with patch.object(Instructor, "__init__", return_value=None) as mock_instructor:
with patch.object(instructor, "from_litellm") as mock_from_litellm:
mock_client = Mock()
mock_from_litellm.return_value = mock_client
mock_chat = Mock()
mock_client.chat = mock_chat
mock_completions = Mock()
mock_chat.completions = mock_completions
mock_create = Mock()
mock_completions.create = mock_create
crew.kickoff()
mock_instructor.assert_called()
calls = mock_instructor.call_args_list
mock_from_litellm.assert_called()
mock_create.assert_called()
calls = mock_create.call_args_list
assert any(
call.kwargs.get("llm") == "gpt-3.5-turbo-0125" for call in calls
call.kwargs.get("model") == "gpt-4o" for call in calls
), "Instructor was not created with the expected model"
@@ -1164,7 +1173,7 @@ def test_task_with_no_arguments():
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
assert result.raw == "The total number of sales is 75."
assert result.raw == "January: 5, February: 10, March: 15, April: 20, May: 25"
def test_code_execution_flag_adds_code_tool_upon_kickoff():
@@ -1274,9 +1283,9 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
assert result.raw == "Howdy!"
assert result.token_usage == UsageMetrics(
total_tokens=2695,
prompt_tokens=2548,
completion_tokens=147,
total_tokens=2677,
prompt_tokens=2528,
completion_tokens=149,
successful_requests=5,
)
@@ -2375,7 +2384,7 @@ def test_conditional_task_last_task_when_conditional_is_true():
)
result = crew.kickoff()
assert result.raw.startswith(
"1. **How AI Agents are Revolutionizing Customer Service**"
"1. **The Rise of Autonomous AI Agents in Business Operations**"
)

26
tests/memory/cassettes/test_save_and_search.yaml
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20
tests/pipeline/cassettes/test_router_with_empty_input.yaml
View File

@@ -9,7 +9,7 @@ interactions:
is the expect criteria for your final answer: Test output\nyou MUST return the
actual complete content as the final answer, not a summary.\n\nBegin! This is
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4
tests/task_test.py
View File

@@ -266,7 +266,7 @@ def test_output_pydantic_hierarchical():
)
result = crew.kickoff()
assert isinstance(result.pydantic, ScoreOutput)
assert result.to_dict() == {"score": 5}
assert result.to_dict() == {"score": 4}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -556,7 +556,7 @@ def test_save_task_pydantic_output():
output_file_exists = os.path.exists("score.json")
assert output_file_exists
assert {"score": 5} == json.loads(open("score.json").read())
assert {"score": 4} == json.loads(open("score.json").read())
if output_file_exists:
os.remove("score.json")