Local LLMs, AI Agents, and Crew AI, Oh My!

from crewai import Agent, Task, Crew
from langchain_community.llms import Ollama


class MyAgents:
  def __init__(self):
    self.model = 'mistral:7b'
    self.llm = Ollama(model=self.model, base_url='http://192.168.1.124:11434')
    self.all_tools = []
    self.allow_delegation = False
    self.verbose = False

  def researcher(self) -> Agent:
    return Agent(
      role='Senior Research Analyst',
      goal='Uncover cutting-edge developments in AI and data science',
      backstory='You work at a leading tech think tank. Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting actionable insights.',
      allow_delegation=self.allow_delegation,
      tools=self.all_tools,
      llm=self.llm,
      verbose=self.verbose
    )

  def writer(self) -> Agent:
    return Agent(
      role='Tech Content Strategist',
      goal='Craft compelling content on tech advancements',
      backstory='You are a renowned Content Strategist, known for your insightful and engaging articles. You transform complex concepts into compelling narratives.',
      allow_delegation=self.allow_delegation,
      tools=self.all_tools,
      llm=self.llm,
      verbose=self.verbose
    )


  def formatter(self) -> Agent:
    return Agent(
      role='formatter',
      goal='Format the text as asked. Leave out actions from discussion members that happen between brackets, eg (smiling).',
      backstory='You are an expert text formatter.',
      allow_delegation=self.allow_delegation,
      tools=self.all_tools,
      llm=self.llm,
      verbose=self.verbose
    )


# Prepare our agents
my_agents = MyAgents()
researcher_agent = my_agents.researcher()
writer_agent = my_agents.writer()
formatter_agent = my_agents.formatter_agent()

# Define our tasks
task1 = Task(
  description='Conduct a comprehensive analysis.',
  expected_output='Full analysis report in bullet points of the health industry in the united states.',
  agent=researcher_agent
)
task2 = Task(
  description='Using the insights provided, develop an engaging blog post that highlights the most significant concepts of the health care industry in the US. Your post should be informative yet accessible, catering to an internet audience. Make it sound cool, avoid complex words so it does not sound like AI.',
  expected_output='Full blog post of at least 4 paragraphs',
  agent=writer_agent
)
task3 = Task(
  description='Using the information provided, format the written blog post in the style of markdown. Take advantage of headers, bold, italics, ordered lists, unordered lists, links, etc. Ensure that the formatting is easier to read for the end user.',
  expected_output='A formatted writeup in markdown.',
  agent=formatter_agent
)

# Configure out crew
crew = Crew(
  agents=[researcher_agent, writer_agent, formatter_agent],
  tasks=[task1, task2, task3],
  function_calling_llm=agents.llm
)

# Run our crew
result = crew.kickoff()
print(result)

# Investigate how the crew did
print(crew.usage_metrics)

# Welcome to the Future of US Healthcare: AI and Data Science Transforming the Industry

Imagine a world where our healthcare system is smarter, more efficient, and personalized to each individual's unique needs. welcome to the future of US healthcare, where Artificial Intelligence (AI) and data science are transforming the industry in incredible ways.

## Generating Insight from Healthcare Data
Our healthcare system generates an astounding amount of data daily - from electronic health records to medical imaging. But how do we make sense of it all? That's where AI comes in.

By analyzing vast amounts of data, AI can help:
- **Diagnose diseases earlier and more accurately**
- **Predict disease progression**
- **Discover new drugs**
- **Assist in surgeries**

## Overcoming Challenges: Data Privacy, Security, and Regulatory Compliance
But the potential benefits of AI in healthcare aren't without challenges. Three critical areas require our attention:

1. **Data privacy and security**: We don't want sensitive medical information falling into the wrong hands.
2. **Regulatory compliance**: The US healthcare industry is complex and heavily regulated, making it a tough nut to crack for innovative technologies.

## Embracing the Future: Personalized Treatment Plans and Early Diagnosis
Despite these challenges, the future of healthcare in the US looks promising. We can expect continued advancements in technology that will revolutionize how we approach healthcare. As consumers, we can look forward to:
- **Personalized treatment plans based on genetic makeup or lifestyle factors**
- **Early diagnosis for potential health issues before they become serious**

## Conclusion
The US healthcare industry is on the brink of a technological revolution. With AI and data science at the forefront, we can look forward to more efficient, personalized, and accurate healthcare services. But as we embrace these advancements, it's essential that we prioritize data privacy, regulatory compliance, and ethical implications to ensure a bright future for all. Stay tuned for more exciting developments in US healthcare!

self.llm = Ollama(model=self.model, base_url='http://192.168.1.124:11434')

  os.environ['OPENAI_API_KEY'] = 'sk-...'
  self.llm=OpenAI(
      temperature=0.8,
      model_name='gpt-3.5-turbo'
  )

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt ./

RUN pip install -r requirements.txt

version: '3.8'

services:
  ai-agents:
    image: ai-agents
    container_name: ai-agents
    build:
      context: .
      dockerfile: Dockerfile
    working_dir: /app
    volumes:
      - .:/app
    # command: tail -f /dev/null # If we want to shell exec and play around, keep the container running.
    command: ["python", "main.py"] # Default command to run
    restart: unless-stopped
    depends_on:
      - ollama # Defined in my previous blog post
      - ollama-webui # Defined in my previos blog post