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whitespace consistency across docs (#407)
I saw a rendedered whitespace inconsistency in the Tasks docs here:
ed31860071/docs/core-concepts/Tasks.md (L173)
So I set out to patch that up to make it easier to read. I then noticed
there were a few whitespace inconsistencies:
- 2 spaces
- 4 whitespaces
- tabs
It appears that the 4 whitespaces is the prevalent whitesapce usage, so
I overwrote other whitespace usages with that in this commit.
Co-authored-by: Rueben Ramirez <rramirez@ruebens-mbp.tail7c016.ts.net>
Co-authored-by: João Moura <joaomdmoura@gmail.com>
This commit is contained in:
Rueben Ramirez
@@ -96,26 +96,26 @@ This is useful when you have a task that depends on the output of another task t
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# ...
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research_ai_task = Task(
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description='Find and summarize the latest AI news',
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expected_output='A bullet list summary of the top 5 most important AI news',
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async_execution=True,
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agent=research_agent,
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tools=[search_tool]
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description='Find and summarize the latest AI news',
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expected_output='A bullet list summary of the top 5 most important AI news',
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async_execution=True,
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agent=research_agent,
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tools=[search_tool]
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)
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research_ops_task = Task(
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description='Find and summarize the latest AI Ops news',
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expected_output='A bullet list summary of the top 5 most important AI Ops news',
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async_execution=True,
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agent=research_agent,
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tools=[search_tool]
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description='Find and summarize the latest AI Ops news',
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expected_output='A bullet list summary of the top 5 most important AI Ops news',
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async_execution=True,
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agent=research_agent,
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tools=[search_tool]
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)
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write_blog_task = Task(
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description="Write a full blog post about the importance of AI and its latest news",
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expected_output='Full blog post that is 4 paragraphs long',
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agent=writer_agent,
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context=[research_ai_task, research_ops_task]
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description="Write a full blog post about the importance of AI and its latest news",
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expected_output='Full blog post that is 4 paragraphs long',
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agent=writer_agent,
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context=[research_ai_task, research_ops_task]
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)
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#...
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@@ -131,24 +131,24 @@ You can then use the `context` attribute to define in a future task that it shou
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#...
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list_ideas = Task(
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description="List of 5 interesting ideas to explore for an article about AI.",
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expected_output="Bullet point list of 5 ideas for an article.",
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agent=researcher,
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async_execution=True # Will be executed asynchronously
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description="List of 5 interesting ideas to explore for an article about AI.",
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expected_output="Bullet point list of 5 ideas for an article.",
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agent=researcher,
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async_execution=True # Will be executed asynchronously
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)
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list_important_history = Task(
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description="Research the history of AI and give me the 5 most important events.",
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expected_output="Bullet point list of 5 important events.",
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agent=researcher,
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async_execution=True # Will be executed asynchronously
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description="Research the history of AI and give me the 5 most important events.",
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expected_output="Bullet point list of 5 important events.",
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agent=researcher,
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async_execution=True # Will be executed asynchronously
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)
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write_article = Task(
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description="Write an article about AI, its history, and interesting ideas.",
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expected_output="A 4 paragraph article about AI.",
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agent=writer,
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context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
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description="Write an article about AI, its history, and interesting ideas.",
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expected_output="A 4 paragraph article about AI.",
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agent=writer,
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context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
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)
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#...
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@@ -162,20 +162,20 @@ The callback function is executed after the task is completed, allowing for acti
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# ...
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def callback_function(output: TaskOutput):
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# Do something after the task is completed
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# Example: Send an email to the manager
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print(f"""
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Task completed!
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Task: {output.description}
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Output: {output.raw_output}
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""")
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# Do something after the task is completed
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# Example: Send an email to the manager
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print(f"""
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Task completed!
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Task: {output.description}
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Output: {output.raw_output}
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""")
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research_task = Task(
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description='Find and summarize the latest AI news',
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expected_output='A bullet list summary of the top 5 most important AI news',
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agent=research_agent,
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tools=[search_tool],
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callback=callback_function
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description='Find and summarize the latest AI news',
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expected_output='A bullet list summary of the top 5 most important AI news',
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agent=research_agent,
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tools=[search_tool],
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callback=callback_function
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)
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#...
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@@ -188,27 +188,27 @@ Once a crew finishes running, you can access the output of a specific task by us
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```python
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# ...
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task1 = Task(
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description='Find and summarize the latest AI news',
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expected_output='A bullet list summary of the top 5 most important AI news',
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agent=research_agent,
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tools=[search_tool]
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description='Find and summarize the latest AI news',
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expected_output='A bullet list summary of the top 5 most important AI news',
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agent=research_agent,
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tools=[search_tool]
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)
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#...
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crew = Crew(
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agents=[research_agent],
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tasks=[task1, task2, task3],
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verbose=2
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agents=[research_agent],
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tasks=[task1, task2, task3],
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verbose=2
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)
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result = crew.kickoff()
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# Returns a TaskOutput object with the description and results of the task
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print(f"""
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Task completed!
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Task: {task1.output.description}
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Output: {task1.output.raw_output}
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Task completed!
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Task: {task1.output.description}
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Output: {task1.output.raw_output}
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""")
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```
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@@ -22,15 +22,15 @@ from crewai_tools import GithubSearchTool
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# Initialize the tool for semantic searches within a specific GitHub repository
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tool = GithubSearchTool(
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github_repo='https://github.com/example/repo',
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content_types=['code', 'issue'] # Options: code, repo, pr, issue
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github_repo='https://github.com/example/repo',
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content_types=['code', 'issue'] # Options: code, repo, pr, issue
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)
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# OR
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# Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
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tool = GithubSearchTool(
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content_types=['code', 'issue'] # Options: code, repo, pr, issue
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content_types=['code', 'issue'] # Options: code, repo, pr, issue
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)
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```
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