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Making all tests good
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@@ -63,7 +63,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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self.name_to_tool_map = {tool.name: tool for tool in self.tools}
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def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
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print("starting invoke")
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if "system" in self.prompt:
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system_prompt = self._format_prompt(self.prompt["system"], inputs)
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user_prompt = self._format_prompt(self.prompt["user"], inputs)
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@@ -73,13 +72,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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else:
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user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
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self.messages.append(self._format_msg(user_prompt))
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print("after messages")
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print(self.messages)
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self.ask_for_human_input = inputs.get("ask_for_human_input", False)
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formatted_answer = self._invoke_loop()
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print("111after formatted_answer")
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print(formatted_answer)
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if self.ask_for_human_input:
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human_feedback = self._ask_human_input(formatted_answer.output)
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@@ -93,36 +88,25 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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return {"output": formatted_answer.output}
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def _invoke_loop(self, formatted_answer=None):
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print("starting _invoke_loop")
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try:
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while not isinstance(formatted_answer, AgentFinish):
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if not self.request_within_rpm_limit or self.request_within_rpm_limit():
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print("******* messages")
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print(self.messages)
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answer = LLM(
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self.llm, stop=self.stop, callbacks=self.callbacks
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).call(self.messages)
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print("after answer")
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print(answer)
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self.iterations += 1
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formatted_answer = self._format_answer(answer)
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print("222after formatted_answer")
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print(formatted_answer)
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if isinstance(formatted_answer, AgentAction):
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action_result = self._use_tool(formatted_answer)
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formatted_answer.text += f"\nObservation: {action_result}"
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print("after formatted_answer.text")
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print(formatted_answer.text)
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if self.step_callback:
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formatted_answer.result = action_result
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self.step_callback(formatted_answer)
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if self._should_force_answer():
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print("starting _should_force_answer")
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if self.have_forced_answer:
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print("forcing answer")
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return {
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"output": self._i18n.errors(
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"force_final_answer_error"
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@@ -139,13 +123,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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)
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except OutputParserException as e:
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print("********* ERROR1")
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self.messages.append({"role": "assistant", "content": e.error})
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self._invoke_loop(formatted_answer)
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except Exception as e:
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print("********* ERRORw")
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print(e)
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if LLMContextLengthExceededException(str(e))._is_context_limit_error(
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str(e)
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):
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@@ -8,7 +8,6 @@ class LLM:
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self.stop = stop
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self.model = model
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litellm.callbacks = callbacks
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litellm.set_verbose = True
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def call(self, messages: List[Dict[str, str]]) -> Dict[str, Any]:
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response = completion(
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@@ -243,18 +243,13 @@ class Task(BaseModel):
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tools = tools or self.tools or []
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self.processed_by_agents.add(agent.role)
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print("====================================================")
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print("context", self.prompt_context)
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print("context", agent.role)
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print("context", context)
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result = agent.execute_task(
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task=self,
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context=context,
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tools=tools,
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)
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print("result", result)
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print("====================================================")
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pydantic_output, json_output = self._export_output(result)
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task_output = TaskOutput(
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@@ -24,17 +24,26 @@ class RPMController(BaseModel):
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return self
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def check_or_wait(self):
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print("check_or_waitcheck_or_waitcheck_or_waitcheck_or_wait")
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if self.max_rpm is None:
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return True
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def _check_and_increment():
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print(
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"_check_and_increment_check_and_increment_check_and_increment_check_and_increment"
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)
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if self.max_rpm is not None and self._current_rpm < self.max_rpm:
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self._current_rpm += 1
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print("111111111111111")
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print("self._current_rpm", self._current_rpm)
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print("self.max_rpm", self.max_rpm)
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return True
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elif self.max_rpm is not None:
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print("22222222222222")
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self.logger.log(
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"info", "Max RPM reached, waiting for next minute to start."
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)
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print("CARALHO")
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self._wait_for_next_minute()
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self._current_rpm = 1
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return True
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@@ -52,7 +61,7 @@ class RPMController(BaseModel):
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self._timer = None
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def _wait_for_next_minute(self):
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time.sleep(60)
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time.sleep(1)
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self._current_rpm = 0
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def _reset_request_count(self):
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@@ -67,7 +67,7 @@ def test_agent_execution():
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)
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output = agent.execute_task(task)
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assert output == "The result of the math operation is 2."
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assert output == "The result of the math operation 1 + 1 is 2."
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@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -91,7 +91,7 @@ def test_agent_execution_with_tools():
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task)
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assert output == "12"
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assert output == "The result of 3 times 4 is 12."
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@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -122,7 +122,7 @@ def test_logging_tool_usage():
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tool_usage = InstructorToolCalling(
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tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
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)
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assert output == "12"
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assert output == "The result of multiplying 3 times 4 is 12."
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assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
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assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
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@@ -276,7 +276,7 @@ def test_agent_execution_with_specific_tools():
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task=task, tools=[multiplier])
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assert output == "12"
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assert output == "The result of 3 times 4 is 12."
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@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -312,7 +312,7 @@ def test_agent_custom_max_iterations():
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_repeated_tool_usage(capsys):
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@tool
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def get_final_answer(anything: str) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -24,7 +24,7 @@ def test_delegate_work():
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assert (
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result
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== "AI Agents, sometimes known as intelligent agents, represent a groundbreaking shift in the realm of artificial intelligence and automation. These entities operate by perceiving their environment through sensors and acting upon that environment using actuators, while following a specific set of rules or algorithms to achieve designated goals. AI agents can be categorized based on their complexity and autonomy, ranging from simple reactive systems to more sophisticated, learning-based or cognitive agents.\n\nFirstly, it's important to clarify that while I might have expressed concerns about certain implementations of AI agents, it doesn't equate to hating them. My apprehension often stems from the ethical and societal implications, rather than the technology itself. Let's dive into the detailed facets that make AI agents both fascinating and somewhat contentious:\n\n1. **Types of AI Agents**:\n - **Reactive Agents**: These agents do not store past states and act only on current perceptions. An example would be a chatbot that provides information based on pre-set responses.\n - **Deliberative Agents**: They have an internal model of the world and can deliberate on actions to take. This might include AI used in autonomous vehicles which plan a route by assessing various factors.\n - **Learning Agents**: These agents improve their performance over time by learning from interactions. Machine learning algorithms in recommendation systems (like those used by Netflix) fall into this category.\n - **Cognitive Agents**: The most advanced, these mimic human-like understanding and reasoning. An example is IBM’s Watson, which can understand natural language and provide insights across various domains.\n\n2. **Applications of AI Agents**:\n - **Healthcare**: AI agents monitor patient vitals, assist in diagnostics, and personalize treatment plans.\n - **Finance**: Automated trading systems and fraud detection are driven by sophisticated AI agents.\n - **Customer Service**: Virtual assistants and AI-powered chatbots enhance user experience by resolving queries quickly.\n - **Autonomous Vehicles**: Self-driving cars rely on AI agents to navigate and make real-time decisions.\n\n3. **Challenges**:\n - **Ethical Concerns**: Decisions made by AI agents can impact lives significantly. Ensuring fairness and accountability is paramount.\n - **Security**: AI systems can be vulnerable to adversarial attacks where malicious inputs lead to harmful outcomes.\n - **Bias**: AI agents can inherit biases present in their training data, leading to unfair or discriminatory outcomes.\n - **Job Displacement**: Automation driven by AI agents may lead to significant shifts in employment and require policy interventions.\n\n4. **Future Prospects**:\n - **Enhanced Decision Making**: With advances in AI, agents will become better at making nuanced decisions, balancing risk, and optimizing outcomes.\n - **Interdisciplinary Integrations**: AI agents will increasingly integrate insights from various fields, leading to more holistic solutions.\n - **Human-AI Collaboration**: The future will likely see improved synergies between humans and AI agents, amplifying human capabilities rather than replacing them.\n\nIn summary, while AI agents bring substantial advancements and efficiencies, they also pose significant challenges that need thoughtful navigation. Balancing the technological benefits with ethical considerations remains a central task for researchers, developers, and policymakers."
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== '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.'
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)
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@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
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assert (
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result
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== 'Thank you for asking about AI Agents. My perspective on AI Agents is actually quite nuanced, and I wouldn\'t say that I "hate" them. In fact, as an expert researcher specialized in technology, I recognize the immense potential and capabilities these agents bring to various industries and applications. Here is a comprehensive analysis of AI Agents:\n\n**Introduction**:\nAI Agents, also known as artificial intelligence agents or intelligent agents, are software entities that perform tasks on behalf of users with some degree of autonomy. These tasks can range from simple, repetitive actions to complex decision-making processes. They are designed to perceive their environment, reason about what they perceive, make decisions, and act upon those decisions to achieve specific goals.\n\n**Applications**:\n1. **Customer Service**: AI agents like chatbots and virtual assistants can provide 24/7 customer support, handling inquiries, and resolving issues without human intervention.\n2. **Healthcare**: These agents can assist in diagnosing diseases, recommending treatments, and managing patient data, thereby augmenting the capabilities of healthcare professionals.\n3. **Finance**: In the financial sector, AI agents can monitor market trends, execute trades, and manage portfolios in real-time.\n4. **Manufacturing**: In industrial settings, AI agents can optimize production lines, perform quality control, and predict maintenance needs.\n\n**Technologies Involved**:\n1. **Natural Language Processing (NLP)**: Enables AI agents to understand and respond to human language, enhancing their ability to interact with users.\n2. **Machine Learning (ML)**: Allows AI agents to learn from data and improve their performance over time.\n3. **Computer Vision**: Provides the capability to interpret and act on visual inputs from the environment.\n\n**Benefits**:\n1. **Efficiency**: AI agents can process vast amounts of information quickly and accurately, leading to increased productivity.\n2. **Cost Savings**: By automating routine tasks, organizations can reduce labor costs and allocate resources more effectively.\n3. **Consistency**: Unlike humans, AI agents can perform tasks consistently without fatigue or error.\n\n**Challenges and Concerns**:\n1. **Ethical Implications**: The autonomy of AI agents raises ethical questions around accountability, decision-making, and potential biases in the algorithms.\n2. **Security**: As AI agents handle sensitive data, ensuring their security against cyber threats is crucial.\n3. **Job Displacement**: There is ongoing concern about AI agents replacing human jobs, particularly in sectors reliant on routine tasks.\n\n**Future Outlook**:\nThe development of AI agents is expected to continue advancing, with improvements in areas such as emotional intelligence, contextual understanding, and multi-agent collaboration. These advancements will likely lead to broader acceptance and integration across various domains.\n\nIn conclusion, while there are legitimate concerns and challenges associated with AI agents, their potential benefits and transformative impact on numerous fields cannot be overlooked. My goal as a researcher is to contribute to the responsible development and deployment of AI agents, ensuring they are designed and used in ways that maximize positive outcomes while mitigating risks.'
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== "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."
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)
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@@ -52,7 +52,7 @@ def test_ask_question():
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assert (
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result
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== "AI agents are a fascinating and powerful tool in the realm of technology. As a researcher specialized in AI, I find that they can offer transformative benefits across a variety of fields, from healthcare to finance, and even education. While it's true that I have a deep appreciation for the capabilities and potential of AI agents, it's important to consider them critically.\n\nAI agents can streamline processes, make data-driven decisions more efficiently, and handle complex algorithms that would be time-consuming for humans. They can analyze vast amounts of data in real-time, offering insights that would otherwise be unattainable. This potent combination of speed and accuracy can lead to significant advancements and innovations.\n\nHowever, with any powerful technology, there are inherent risks and challenges. Ethical considerations such as bias in AI algorithms, privacy concerns, and the potential for job displacement must be addressed as part of responsible AI development and deployment. Transparent practices, rigorous testing, and strict regulatory measures are crucial to mitigating these risks.\n\nIn conclusion, while I do have a passion for AI agents due to their immense potential to drive progress and solve complex problems, I also advocate for a balanced approach that carefully weighs their benefits against the possible ethical implications. It's this nuanced perspective that fuels my ongoing research and analysis in the field."
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== "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."
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)
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@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
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assert (
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result
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== "As a researcher specialized in technology with a focus on AI and AI agents, I don't view these tools through an emotional lens of love or hate. Instead, I evaluate them based on their capabilities, strengths, weaknesses, and potential impact on society. \n\nAI agents have the potential to revolutionize various industries, from healthcare to finance, by automating processes, providing insights through data analysis, and enhancing human capabilities. For example, in healthcare, AI agents can assist doctors by analyzing medical images more quickly and accurately than humans can. In finance, they can help detect fraudulent transactions in real time. These applications showcase the immense potential for positive impact.\n\nOn the flip side, AI agents also raise important ethical and societal concerns, such as the displacement of jobs, bias in decision-making, and issues related to privacy and security. These challenges are significant and must be addressed through careful regulation, continuous research, and collaboration between policymakers, researchers, and industry stakeholders.\n\nTherefore, my perspective on AI agents is one of nuanced appreciation and cautious optimism. I recognize the transformative capabilities and benefits they bring, but I am also acutely aware of the challenges and responsibilities that come with their deployment. My goal as a researcher is to contribute to a balanced and informed discourse, ensuring that AI and AI agents are developed and used in ways that are ethical, fair, and beneficial to society as a whole.\n\nIn summary, I neither hate nor love AI agents; instead, I see them as powerful tools with incredible potential, both positive and negative. My focus is on advancing our understanding and guiding their development responsibly."
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== "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."
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)
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@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
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assert (
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result
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== "It seems there might be a misunderstanding regarding my stance on AI Agents. As a researcher specialized in technology, including AI and AI agents, it's crucial to clarify my perspective accurately for both professional and collaborative purposes.\n\nAI agents, in essence, are software entities that perform tasks autonomously or semi-autonomously on behalf of a user or another program, leveraging artificial intelligence techniques. These tasks can range from simple, repetitive actions like sorting emails, to more complex activities such as real-time language translation, predictive maintenance in industrial systems, or even autonomous driving.\n\nHere are some key points to consider about AI agents that reflect a balanced perspective rather than a biased view:\n\n1. **Capability and Efficiency**: AI agents can significantly enhance operational efficiency and accuracy. By automating routine tasks, they free up human workers to focus on higher-level functions that require creativity, problem-solving, and emotional intelligence.\n\n2. **Applications Across Domains**: AI agents have found their utilization in various fields such as healthcare (diagnostic agents), customer service (chatbots), finance (trading bots), and many more. For instance, in healthcare, AI agents can assist in early detection of diseases by analyzing patient data and identifying patterns that may not be immediately apparent to human doctors.\n\n3. **Continuous Learning and Improvement**: Many AI agents utilize machine learning frameworks that allow them to improve over time. As they process more data and receive more interactions, they can refine their algorithms to provide better and more reliable outcomes.\n\n4. **Ethical and Privacy Considerations**: One area that deserves critical attention is the ethical implications of AI agents. This includes issues related to data privacy, consent, and the potential for biased outcomes. It's important to ensure that AI agents operate within ethical guidelines and regulations to protect user rights and maintain public trust.\n\n5. **Human-AI Collaboration**: Rather than viewing AI agents as a replacement for human workers, it's more productive to see them as collaborators that can augment human capabilities. The synergy between humans and AI agents can result in innovative solutions and heightened productivity.\n\n6. **Challenges and Limitations**: AI agents are not without their challenges. They are only as good as the data they are trained on and the algorithms that drive them. Issues like data scarcity, quality, and representativeness can affect their performance. Additionally, there is the challenge of ensuring robustness and security, preventing them from being manipulated or exploited by malicious actors.\n\nIn conclusion, while AI agents are powerful tools that can transform various aspects of society and industry, it is essential to approach their development and deployment thoughtfully. By addressing their potential and challenges comprehensively, we can harness their benefits while mitigating risks.\n\nThus, my stance is one of critical engagement rather than outright disapproval. It’s about leveraging the capabilities of AI agents responsibly, ensuring they augment human abilities and contribute positively to society."
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== "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."
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)
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@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
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assert (
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result
|
||||
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from crewai.utilities.rpm_controller import RPMController
|
||||
from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
|
||||
|
||||
ceo = Agent(
|
||||
@@ -225,7 +226,7 @@ def test_crew_creation():
|
||||
|
||||
result = crew.kickoff()
|
||||
|
||||
expected_string_output = "**The Emergence of Ethical AI: Balancing Innovation with Responsibility**\n\nThe growing deployment of artificial intelligence (AI) in various sectors has highlighted the critical need for ethical AI. As organizations leverage AI to enhance efficiency, the potential for misuse and unintentional bias has surged. This segment delves into the importance of developing transparent, fair, and accountable AI systems. It underscores the responsibility of tech companies and developers to balance innovation with ethical standards, ensuring that AI advancements benefit society without compromising moral principles. By incorporating ethical guidelines and audits into AI development, stakeholders can mitigate risks and promote trust in AI technologies.\n\n**AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n\nArtificial intelligence is transforming the healthcare sector with unprecedented precision and efficiency. By enhancing diagnostic accuracy, personalizing treatment plans, and streamlining administrative processes, AI is revolutionizing how healthcare providers deliver care. This section of the article explores real-world examples and case studies that demonstrate the life-saving potential and cost-reduction benefits of AI-driven healthcare solutions. It highlights how machine learning algorithms can analyze vast amounts of medical data to detect diseases earlier and predict patient outcomes more accurately, ultimately leading to better patient care and resource management.\n\n**AI-Powered Personal Assistants: The Future of Human-Technology Interaction**\n\nThe evolution of AI-powered personal assistants, such as Siri, Alexa, and Google Assistant, is significantly changing the dynamics of human-technology interaction. These assistants have progressed from simple voice command tools to sophisticated systems that can manage tasks, provide personalized recommendations, and even handle complex queries. This paragraph focuses on the expanding capabilities of AI personal assistants and their growing impact on daily life and work productivity. As they become more intuitive and integrated into various devices and platforms, these AI assistants are poised to become indispensable partners in our digital lives, transforming how we access and use technology.\n\n**AI in Content Creation: From Art to News**\n\nArtificial intelligence is making waves in the realm of content creation, generating art, writing, and news with increasing sophistication. This topic investigates how AI is challenging traditional notions of creativity and originality, raising questions about the role of human artists and journalists. By analyzing the implications for content creators and consumers, this section illuminates the possibilities and limitations of AI-generated content. It discusses how AI can democratize media production, making it accessible to a broader audience, while also considering the ethical and philosophical debates surrounding the authenticity of AI-driven creativity."
|
||||
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."
|
||||
|
||||
assert str(result) == expected_string_output
|
||||
assert result.raw == expected_string_output
|
||||
@@ -293,7 +294,7 @@ def test_hierarchical_process():
|
||||
|
||||
assert (
|
||||
result.raw
|
||||
== "1. **The Future of AI in Healthcare: Transformations and Innovations**\n - In the evolving landscape of healthcare, AI is poised to revolutionize patient care, diagnostics, and medical research. Imagine a world where AI-driven diagnostics can predict diseases before they manifest symptoms, personalized treatment plans are crafted using machine learning algorithms, and robotic surgeons operate with unparalleled precision. The infusion of AI into healthcare is not merely futuristic; it's happening now, with innovations such as predictive analytics in patient care, AI-powered telemedicine, and smart wearable devices tracking and analyzing health metrics in real-time. This article will delve into the groundbreaking advancements and forecast how AI will continue to transform the healthcare industry, improving outcomes and accessibility while addressing ethical and practical challenges.\n\n2. **AI Ethics and Bias: Navigating the Moral Landscape**\n - As AI systems become more integrated into society, the ethical implications of their deployment must be rigorously examined. AI's potential to replicate and even amplify human biases poses a significant challenge, necessitating robust frameworks for fairness, accountability, and transparency. This article will explore the pressing ethical dilemmas, from biased algorithms affecting hiring practices to facial recognition technologies intruding on privacy rights. It will highlight ongoing efforts to create ethical guidelines and equitable AI systems, the role of diverse datasets in mitigating bias, and the importance of interdisciplinary collaboration between technologists, ethicists, and policymakers in shaping an ethical AI future.\n\n3. **Autonomous Agents: From Virtual Assistants to Adaptive Learning Bots**\n - Autonomous agents are transforming how we interact with technology, moving beyond simple task automation to become sophisticated, adaptive entities capable of learning and evolving. This article will journey through the evolution of autonomous agents, showcasing virtual assistants like Siri and Alexa, and advancing to adaptive learning bots used in education and customer service. We'll examine their underlying technologies, such as natural language processing and reinforcement learning, and discuss real-world applications that demonstrate their potential to enhance productivity, learning experiences, and personalized user interactions. The promise of autonomous agents lies in their ability to continually adapt and improve, offering increasingly valuable assistance in our daily lives and work.\n\n4. **AI and the Future of Work: Opportunities and Challenges**\n - The integration of AI into the workplace promises a future teeming with opportunities and challenges. While AI can augment human capabilities, streamline operations, and drive innovation, it also raises concerns about job displacement and workforce adaptation. This article will present a balanced view of the AI-driven future of work, examining sectors likely to experience the most significant transformations and highlighting stories of AI complementing human skills. We'll also address the need for upskilling and reskilling initiatives to prepare the workforce for an AI-enhanced job market, illustrating how businesses can leverage AI to foster a collaborative human-machine environment that maximizes efficiency and creativity.\n\n5. **AI-Driven Startups: Innovators Pushing the Boundaries of Technology**\n - AI-driven startups are at the forefront of technological innovation, disrupting traditional industries and creating unprecedented solutions to complex problems. This article will spotlight some of the most dynamic AI startups, exploring their groundbreaking projects and the unique challenges they face. From AI in local agriculture optimizing crop yields to fintech startups using machine learning to revolutionize fraud detection, these companies are not just pushing the boundaries of what's possible but are also redefining how businesses operate. By decoding their success stories and the innovative minds behind them, we provide a glimpse into the future shaped by these AI pioneers and the limitless possibilities they unveil."
|
||||
== "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."
|
||||
)
|
||||
|
||||
|
||||
@@ -399,7 +400,7 @@ def test_crew_with_delegating_agents():
|
||||
|
||||
assert (
|
||||
result.raw
|
||||
== "**The Transformative Power of AI Agents Across Industries**\n\nAI Agents are revolutionizing the landscape of multiple industries by bringing unprecedented advancements in automation, decision-making, and operational efficiency. These sophisticated algorithms, capable of learning from vast datasets, are optimizing supply chains, enhancing customer service through intelligent chatbots, and even enabling precision medicine by analyzing complex medical data. Their ability to process and analyze information at hyper-human speeds allows businesses to make more informed, data-driven decisions, ultimately leading to increased productivity and innovative solutions. As AI Agents continue to evolve, they promise not only to transform existing processes but also to create entirely new paradigms in how industries operate, making them indispensable tools in the digital age.\n\nIn the healthcare industry, AI Agents are revolutionizing the diagnosis and treatment of diseases. IBM Watson for Oncology, for instance, assists oncologists in making informed treatment decisions by analyzing medical literature, patient records, and clinical trials. This enables doctors to offer more precise and personalized treatment plans for cancer patients, significantly enhancing patient outcomes. Additionally, AI-powered diagnostic tools like Google DeepMind's AI system are capable of detecting eye diseases from retinal scans with unprecedented accuracy, allowing for earlier and potentially life-saving interventions.\n\nThe financial sector is experiencing a redefinition through AI Agents as well, particularly in advanced fraud detection and personalized financial services. JPMorgan Chase's AI program, COiN, efficiently reviews legal documents and extracts critical data at rates far surpassing that of human capabilities, reducing error rates and operational costs. Furthermore, robo-advisors like Betterment and Wealthfront provide personalized investment advice, managing individual portfolios via sophisticated algorithms that analyze market trends, risk tolerance, and financial goals, thereby democratizing access to high-quality financial advice.\n\nSimilarly, the retail and logistics industries are significantly benefiting from AI implementations. E-commerce giants like Amazon use AI-driven recommendation engines to personalize shopping experiences, while AI-powered chatbots in customer service handle inquiries with efficient, consistent, and instantaneous responses. In logistics, companies like DHL and FedEx use AI for route optimization, predictive maintenance, and demand forecasting, resulting in faster delivery times and reduced fuel consumption. These varied applications highlight the transformative power of AI Agents in enhancing productivity, accuracy, and innovation across industries.\n\nThrough these multifaceted applications, it becomes clear that AI Agents are not only improving operational efficiency and service delivery but also driving significant innovation and fostering new standards of excellence across industries. Their integration and evolution represent a transformative movement, poised to redefine the future landscape of numerous sectors."
|
||||
== "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."
|
||||
)
|
||||
|
||||
|
||||
@@ -502,30 +503,30 @@ def test_api_calls_throttling(capsys):
|
||||
return 42
|
||||
|
||||
agent = Agent(
|
||||
role="test role",
|
||||
goal="test goal",
|
||||
backstory="test backstory",
|
||||
max_iter=5,
|
||||
role="Very helpful assistant",
|
||||
goal="Comply with necessary changes",
|
||||
backstory="You obey orders",
|
||||
max_iter=2,
|
||||
allow_delegation=False,
|
||||
verbose=True,
|
||||
llm="gpt-4-0125-preview",
|
||||
llm="gpt-4o",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Don't give a Final Answer, instead keep using the `get_final_answer` tool.",
|
||||
description="Don't give a Final Answer unless explicitly told it's time to give the absolute best final answer.",
|
||||
expected_output="The final answer.",
|
||||
tools=[get_final_answer],
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task], max_rpm=2, verbose=True)
|
||||
crew = Crew(agents=[agent], tasks=[task], max_rpm=1, verbose=True)
|
||||
|
||||
with patch("time.sleep") as mock_sleep:
|
||||
mock_sleep.return_value = None
|
||||
with patch.object(RPMController, "_wait_for_next_minute") as moveon:
|
||||
moveon.return_value = True
|
||||
crew.kickoff()
|
||||
captured = capsys.readouterr()
|
||||
assert "Max RPM reached, waiting for next minute to start." in captured.out
|
||||
mock_sleep.assert_called_with(60) # Assert that sleep was called with 1 second
|
||||
moveon.assert_called()
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@@ -608,7 +609,7 @@ def test_sequential_async_task_execution_completion():
|
||||
|
||||
sequential_result = sequential_crew.kickoff()
|
||||
assert sequential_result.raw.startswith(
|
||||
"Artificial Intelligence (AI) has journeyed through pivotal milestones that have transformed the technology landscape as we know it."
|
||||
"Artificial Intelligence (AI) has a rich history marked by significant milestones that have collectively shaped its evolution and applications."
|
||||
)
|
||||
|
||||
|
||||
@@ -636,7 +637,7 @@ def test_single_task_with_async_execution():
|
||||
|
||||
result = crew.kickoff()
|
||||
assert result.raw.startswith(
|
||||
"- AI agents in personalized healthcare diagnoses and treatment"
|
||||
"- AI in healthcare: revolutionizing diagnostics and personalized treatments."
|
||||
)
|
||||
|
||||
|
||||
@@ -1273,9 +1274,9 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
|
||||
assert result.raw == "Howdy!"
|
||||
|
||||
assert result.token_usage == UsageMetrics(
|
||||
total_tokens=2565,
|
||||
prompt_tokens=2424,
|
||||
completion_tokens=141,
|
||||
total_tokens=2695,
|
||||
prompt_tokens=2548,
|
||||
completion_tokens=147,
|
||||
successful_requests=5,
|
||||
)
|
||||
|
||||
@@ -2374,7 +2375,7 @@ def test_conditional_task_last_task_when_conditional_is_true():
|
||||
)
|
||||
result = crew.kickoff()
|
||||
assert result.raw.startswith(
|
||||
"1. **The Future of AI in Healthcare: Revolutionizing Diagnosis and Treatment**"
|
||||
"1. **How AI Agents are Revolutionizing Customer Service**"
|
||||
)
|
||||
|
||||
|
||||
|
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@@ -18,7 +18,7 @@ interactions:
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host:
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- api.openai.com
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- arm64
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x-stainless-async:
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openai-organization:
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- crewai-iuxna1
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openai-processing-ms:
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- '23'
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openai-version:
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strict-transport-security:
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body: '{"messages": [{"role": "user", "content": "You are Test Role. Test Backstory\nYour
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body: '{"messages": [{"role": "system", "content": "You are Test Role. Test Backstory\nYour
|
||||
personal goal is: Test Goal\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,
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increment_tools_errors.return_value = None
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crew.kickoff()
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def test_task_definition_based_on_dict():
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Reference in New Issue
Block a user