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updating docs

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João Moura
2024-02-26 13:38:14 -03:00
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docs/how-to/Human-Input-on-Execution.md
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@@ -1,10 +1,12 @@
# Human Input on Execution
# Human Input in Agent Execution
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:
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.
Example:
## Using Human Input with CrewAI
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.
### Example:
```python
import os
@@ -20,15 +22,14 @@ 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',
goal='Uncover cutting-edge developments in AI and data science',
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
tools=[search_tool]+human_tools # Passing human tools to the agent
)
writer = Agent(
role='Tech Content Strategist',
@@ -41,13 +42,12 @@ writer = Agent(
)
# 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""",
Make sure to check with a human if the draft is good before finalizing your answer.""",
expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
agent=researcher
)
@@ -58,6 +58,7 @@ task2 = Task(
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.""",
expected_output='A compelling 3 paragraphs blog post formated as markdown about the latest AI advancements in 2024',
agent=writer
)