topics.py

#!/usr/bin/env python
# coding: utf-8

# # Lesson 3: Reflection and Blogpost Writing

# ## Setup

# In[1]:
llm_config = {"model": "gpt-3.5-turbo"} 
llm_config = {"model": "gpt-4o-mini"}


# ## The task!
title= "Building a Thriving Community: Collaborations and Initiatives at DeepLearning.AI"

# In[2]:
task = f'''
        Assuming blog title is: "{title}"
        Write a list of 10 potential topics/headlines including "Introduction" and 'Conclusion' for future blogpost reflecting mentioned title.
        Each headline should reflect a different aspect of importance,
        and should be concise but engaging.
        The result should be a list of 10 potential blog post sections/titles.
       '''


# ## Create a writer agent

# In[3]:
import autogen
writer = autogen.AssistantAgent(
    name="Writer",
    system_message="You are a writer. You Write a list of 10 potential topics/headlines including 'Introduction' "
        f"and 'Conclusion' for future blogpost reflecting thiss title : '{title}'"
        "Each headline should reflect a different aspect of importance,"
        "and should be concise but engaging. You must polish your "
        "writing based on the feedback you receive and give a refined "
        "version. Only return your final work without additional comments.",
    llm_config=llm_config,
)


# In[4]:
reply = writer.generate_reply(messages=[{"content": task, "role": "user"}])


# In[5]:
print("Initial Writer Response:")
print(reply)


# ## Adding reflection 
# 
# Create a critic agent to reflect on the work of the writer agent.

# In[6]:
critic = autogen.AssistantAgent(
    name="Critic",
    is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
    llm_config=llm_config,
    system_message="You are a critic. You review the work of "
                "the writer and provide constructive feedback to help improve the quality of the content.",
)


# In[7]:
res = critic.initiate_chat(
    recipient=writer,
    message=task,
    max_turns=2,
    summary_method="last_msg"
)


# ## Nested chat

# In[8]:
SEO_reviewer = autogen.AssistantAgent(
    name="SEO Reviewer",
    llm_config=llm_config,
    system_message="You are an SEO reviewer, known for your ability to optimize content for search engines, "
        "ensuring that it ranks well and attracts organic traffic. "
        "Make sure your suggestion is concise (within 3 bullet points), concrete and to the point. "
        "Begin the review by stating your role.",
)


# In[9]:
legal_reviewer = autogen.AssistantAgent(
    name="Legal Reviewer",
    llm_config=llm_config,
    system_message="You are a legal reviewer, known for your ability to ensure that content is legally compliant "
        "and free from any potential legal issues. "
        "Make sure your suggestion is concise (within 3 bullet points), concrete and to the point. "
        "Begin the review by stating your role.",
)


# In[10]:
ethics_reviewer = autogen.AssistantAgent(
    name="Ethics Reviewer",
    llm_config=llm_config,
    system_message="You are an ethics reviewer, known for your ability to ensure that content is ethically sound "
        "and free from any potential ethical issues. "
        "Make sure your suggestion is concise (within 3 bullet points), concrete and to the point. "
        "Begin the review by stating your role.",
)


# In[11]:
meta_reviewer = autogen.AssistantAgent(
    name="Meta Reviewer",
    llm_config=llm_config,
    system_message="You are a meta reviewer, you aggregate and review "
    "the work of other reviewers and give a final suggestion on the content.",
)


# ## Orchestrate the nested chats to solve the task

# In[12]:
def reflection_message(recipient, messages, sender, config):
    return f'''Review the following content. 
            \n\n {recipient.chat_messages_for_summary(sender)[-1]['content']}'''


# Ensure that message names comply with naming rules (alphanumeric, underscore, hyphen)
def reflection_message(recipient, messages, sender, config):
    # Ensure the name is correctly formatted without invalid characters
    return f'''Review the following content: 
    \n\n {recipient.chat_messages_for_summary(sender)[-1]['content']}'''

def reflection_message(recipient, messages, sender, config):
    return f'''Review the following content. 
            \n\n {recipient.chat_messages_for_summary(sender)[-1]['content']}'''

review_chats = [
    {
     "recipient": SEO_reviewer, 
     "message": reflection_message, 
     "summary_method": "reflection_with_llm",
     "summary_args": {"summary_prompt" : 
        "Return review into as JSON object only:"
        "{'Reviewer': '', 'Review': ''}. Here Reviewer should be your role",},
     "max_turns": 1},
    {
    "recipient": legal_reviewer, "message": reflection_message, 
     "summary_method": "reflection_with_llm",
     "summary_args": {"summary_prompt" : 
        "Return review into as JSON object only:"
        "{'Reviewer': '', 'Review': ''}.",},
     "max_turns": 1},
    {"recipient": ethics_reviewer, "message": reflection_message, 
     "summary_method": "reflection_with_llm",
     "summary_args": {"summary_prompt" : 
        "Return review into as JSON object only:"
        "{'reviewer': '', 'review': ''}",},
     "max_turns": 1},
     {"recipient": meta_reviewer, 
      "message": "Aggregate feedback from all reviewers and give final suggestions on the content.", 
     "max_turns": 1},
]


# In[13]:
critic.register_nested_chats(
    review_chats,
    trigger=writer,
)


# **Note**: You might get a slightly different response than what's shown in the video. Feel free to try a different task.

# In[ ]:
res = critic.initiate_chat(
    recipient=writer,
    message=task,
    max_turns=2,
    summary_method="last_msg"
)


# ## Get the summary

# In[ ]:
print("Final Refined List of Blog Headline Suggestions:")
print(res.summary)