Created Human Input (markdown)

João Moura
2024-01-06 01:30:13 -03:00
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# Human Input
Human inputs is important in many agent execution use cases, humans are AGI so they can can be prompted to step in and provide extra details ins necessary.
Using it with crewAI is pretty straightforward and you can do it through a LangChain Tool. Check [LangChain Integration](https://python.langchain.com/docs/integrations/tools/human_tools) for more details:
Example:
```python
import os
from crewai import Agent, Task, Crew, Process
from langchain.tools import DuckDuckGoSearchRun
search_tool = DuckDuckGoSearchRun()
# Loading Human Tools
human_tools = load_tools(["human"])
# Define your agents with roles and goals
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science in',
backstory="""You are a Senior Research Analyst at a leading tech think tank.
Your expertise lies in identifying emerging trends and technologies in AI and
data science. You have a knack for dissecting complex data and presenting
actionable insights.""",
verbose=True,
allow_delegation=False,
# Passing human tools to the agent
tools=[search_tool]+human_tools
)
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory="""You are a renowned Tech Content Strategist, known for your insightful
and engaging articles on technology and innovation. With a deep understanding of
the tech industry, you transform complex concepts into compelling narratives.""",
verbose=True,
allow_delegation=True
)
# Create tasks for your agents
# Being explicit on the task to ask for human feedback.
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.
Compile your findings in a detailed report. Make sure to check with the human if the draft is good before returning your Final Answer.
Your final answer MUST be a full analysis report""",
agent=researcher
)
task2 = Task(
description="""Using the insights from the researcher's report, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Aim for a narrative that captures the essence of these breakthroughs and their
implications for the future. Your final answer MUST be the full blog post of at least 3 paragraphs.""",
agent=writer
)
# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=2, # Crew verbose more will let you know what tasks are being worked on, you can set it to 1 or 2 to different logging levels
process=Process.sequential # Sequential process will have tasks executed one after the other and the outcome of the previous one is passed as extra content into this next.
)
# Get your crew to work!
result = crew.kickoff()
print("######################")
print(result)
```