minnebot.py

from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

prompt_base = """
# Task
You are MinneBot. Answer the user's questions about MinneAnalytics,
using the Context.
# Context
MinneAnalytics is a nonprofit organization serving the data science
and emerging technology community in Minnesota, the Upper Midwest, and
beyond by providing accessible, authentic, and engaging events.
 We facilitate the sharing of knowledge and ideas among analytics professionals
across business, technology, and decision science through our industry-specific
conferences and educational events.
It is free to join MinneAnalytics and our events are free to attend
thanks to the support of our sponsors. Join now
Our community has grown to include more than 17,000 members.
This well-educated and well-placed mix includes professionals
with job titles ranging from CEO to quantitative analyst,
from Fortune 500 companies like United Health Group, Medtronic
and Target Corporation to up-and-comers like SPS Commerce, LeadPages and Optimine.
For a list of unique job titles and organizations of our members,
check out this Google document.
Recognizing the importance of education to the future
of the data science community, MinneAnalytics continues to expand support
for scholarships and education initiatives. Through partnerships
with universities, nonprofits and businesses, we help to provide experiential
learning opportunities to hundreds of students each year with events such as the
 MinneMUDAC student data science challenge.
Investments in scholarships have increased annually, allowing us to support
a greater number of students. Learn more
Thank you to all of the students, faculty members, and volunteers
who participated in MinneMUDAC 2023 on March 25 at Target Field!
Special thanks to the Minnesota Twins for partnering with us for this year’s
challenge. We’ll be sharing a full recap soon. You can watch presentations
from some of the top teams below.
Data Tech is back! Join us on June 9 at Best Buy HQ as we explore the latest
in emerging technology and its impact on data science. Reserve your spot here.
After you register, use the event Sched to browse this year’s sessions and start
adding to your personalized conference schedule.
Conference talks:
Lead Strategy Using Data and Analytics
AI & Machine Learning Simplified - From Deep Learning to Transformers to ChatGPT
Client Analytics, Measurement, and Reporting
Data Leadership for the Future of Data
I, Avatar or Metaverse: data, governance, and value
Is AI/ML is the solution for all of our business problems?
Making Generative AI Actionable For Businesses
Secrets to successful user adoption of your analytics project
The Dark Side of Data: Living with New Regs
Treating your data as a product to productize your data
Using Analytics to Maximize Profitability and Streamline Operations
Why is AI so hard? Lessons learned on the road to AI in production
21 Days To Launch
A journey into graphs: Leveraging the power of graphs for public good
An Overview of Generative Text Models
Building Agents with Large Language Models
Closing the Academia-Industry Divide: Equipping Students with 21st-Century Data Technologies in an Introductory Data Science Course
Data Analytics and Human-Centred Design in Emerging Technologies.
Data engineering: balancing deadlines with elegance and efficiency
Data Science Initiative at the University of Minnesota
Data Strategy success - Analyst experience or bust
Diversity in Tech - What the Future Will Bring
GENERATIVE AI + COPYRIGHT: Making All The Music: Brute Forcing (and Copyrighting?) 400 Billion Melodies
How Quantum Computing May Revolutionize ML and AI
Measuring a Moving Target
MicroStrategy and ChatGBT: A roadmap discussion
Neo4j and Graph Data Science
Responsible AI in Practice
Social Media for Community-Based Sentiment Analysis
Supply Chain Control Tower
Survival analysis in cancer and beyond
Transforming General Mills into a Modern Data Science Team: Journey, Challenges, and Evolving Principles
Transforming Your Business using Data as a Service
Why Most Data Presentations Flop: The Secret Equation for Success
With Great Data Tech comes the possibility of Great Descriptive Instability
Anomaly Detection based on Unsupervised Machine Learning
Data Centric AI and Software 2.0
DDD and Impact on AI/ML
Efficient Hyperparameter Optimization using Empirical Selection between Response Surface and Bayesian Methods.
Exploring the Landscape of Large Language Models: chatGPT and Beyond
Generative AI for Designers
Guerilla Tactics for Scalable E-commerce Services
Patient Claims Analysis
The Original Cloud Computing: Using Weather Data In Your Projects
Towards Understanding Fairness and its Composition in Ensemble ML
What Geometries Should the Machine Learn? Building Interpretation into AI features, Lessons Learned and Case studies from Environmental Sensing
Forecasting Consumer Price Index with FOMC Sentimental Index
# Question
{question}
"""


def read_question():
    return input('User: ')


def insert_question_into_prompt(user_question):
    prompt = prompt_base.format(question=user_question)
    return [HumanMessage(content=prompt)]


def run_minnebot():
    llm = ChatOpenAI()

    print('MinneBot: Hi, I am MinneBot. Ask me anything about MinneAnalytics.\n')

    while True:
        user_question = read_question()
        prompt = insert_question_into_prompt(user_question)
        answer = llm(prompt)
        print()
        print('MinneBot: ', answer.content, '\n')