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77 lines
3.2 KiB
Markdown
77 lines
3.2 KiB
Markdown
# Human Input in Agent Execution
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Human input is crucial in numerous agent execution scenarios, enabling agents to request additional information or clarification when necessary. This feature is particularly useful in complex decision-making processes or when agents require further details to complete a task effectively.
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## Using Human Input with CrewAI
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Incorporating human input with CrewAI is straightforward, enhancing the agent's ability to make informed decisions. While the documentation previously mentioned using a "LangChain Tool" and a specific "DuckDuckGoSearchRun" tool from `langchain_community.tools`, it's important to clarify that the integration of such tools should align with the actual capabilities and configurations defined within your `Agent` class setup.
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### Example:
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```python
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import os
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from crewai import Agent, Task, Crew, Process
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from langchain_community.tools import DuckDuckGoSearchRun
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from langchain.agents import load_tools
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search_tool = DuckDuckGoSearchRun()
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# Loading Human Tools
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human_tools = load_tools(["human"])
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# Define your agents with roles and goals
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researcher = Agent(
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role='Senior Research Analyst',
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goal='Uncover cutting-edge developments in AI and data science',
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backstory="""You are a Senior Research Analyst at a leading tech think tank.
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Your expertise lies in identifying emerging trends and technologies in AI and
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data science. You have a knack for dissecting complex data and presenting
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actionable insights.""",
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verbose=True,
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allow_delegation=False,
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tools=[search_tool]+human_tools # Passing human tools to the agent
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)
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writer = Agent(
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role='Tech Content Strategist',
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goal='Craft compelling content on tech advancements',
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backstory="""You are a renowned Tech Content Strategist, known for your insightful
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and engaging articles on technology and innovation. With a deep understanding of
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the tech industry, you transform complex concepts into compelling narratives.""",
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verbose=True,
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allow_delegation=True
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)
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# Create tasks for your agents
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task1 = Task(
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description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
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Identify key trends, breakthrough technologies, and potential industry impacts.
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Compile your findings in a detailed report.
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Make sure to check with a human if the draft is good before finalizing your answer.""",
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expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
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agent=researcher
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)
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task2 = Task(
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description="""Using the insights from the researcher's report, develop an engaging blog
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post that highlights the most significant AI advancements.
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Your post should be informative yet accessible, catering to a tech-savvy audience.
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Aim for a narrative that captures the essence of these breakthroughs and their
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implications for the future.
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Your final answer MUST be the full blog post of at least 3 paragraphs.""",
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expected_output='A compelling 3 paragraphs blog post formated as markdown about the latest AI advancements in 2024',
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agent=writer
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)
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# Instantiate your crew with a sequential process
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crew = Crew(
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agents=[researcher, writer],
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tasks=[task1, task2],
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verbose=2
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)
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# Get your crew to work!
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result = crew.kickoff()
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print("######################")
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print(result)
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``` |