AI Deep Dive: A Journey through Heroku/OpenAI Integration with Shelly Sousa

1. AI Deep Dive
A Journey through
Heroku/OpenAI Integration

2. Shelly Sousa
Enterprise Architect, CoBank
Credentialing SME
TDX 24 Speaker
14x Salesforce Certified
10x Industry Certified
Five Star Ranger
https://www.linkedin.com/in/shellybsousa

3. Agenda
● Heroku Employee Database GPT
● Heroku Ask PDF GPT
● Architectural Considerations for Custom GPTs
● Salesforce Certified AI Associate
○ Bonus Demo: GPT Study Buddy

4. Thank you Julián!
Julián Duque
Principal Developer Advocate at Heroku

5. Heroku Employee
Database GPT

6. User Story
Title: Search an Employee Database using Generative AI
As a Human Resources Representative
I want to use Generative AI to search and retrieve information from our employee database
So that I can quickly and efficiently find details about employees without manually sifting through records
Acceptance Criteria
1. Search Capability: I can search using natural language (e.g. “Provide the names of all employees hired in 2022.”)
2. Accurate Results: The AI understands the context of my inquiries and returns accurate and relevant information.
3. Fast response times: The system provides requested information in seconds.
4. User-Friendly Interface: The interface is intuitive, requiring minimal technical knowledge to operate
5. Data Security and Privacy Compliance: The system adheres to applicable data privacy laws and company policies
regarding employee data.

7. Demo

8. Components
● Heroku
○ Cloud Development platform supporting multiple programming languages. Provides web hosting, server management,
monitoring, and features like managed database services
● PostgreSQL Database
○ Open source object-relational database system.
● OpenAI Chat Completions API
○ The Chat completions endpoint accepts text as input and uses an OpenAI model (GPT-4) to generate an output.
● Node.js
○ Backend API server using OpenAI Node API library. Provides access to the OpenAI REST API.

10. Getting started
● Heroku
● OpenAI API
● Github
● https://blog.heroku.com/gpt-backed-heroku-api
● https://github.com/heroku-reference-apps/employee-directory-gpt-action

11. Heroku Ask PDF GPT

12. User Story
Title: Search a PDF using Generative AI
As a company employee
I want to use Generative AI to search complex PDF documents
So that I can easily gain insights and generate summaries without manually analyzing a full PDF
Acceptance Criteria
1. Search Capability: I can search using natural language (e.g. “Provide a summary of this document.”).
2. Accurate Results: The AI understands the context of my inquiries and returns accurate and relevant information.
3. Fast response times: The system provides requested information in seconds.
4. User-Friendly Interface: The interface is intuitive, requiring minimal technical knowledge to operate
5. Data Security and Privacy Compliance: The system adheres to applicable data privacy laws and company policies
regarding sensitive documents.

13. Demo

14. Components
● Heroku
○ Cloud Development platform supporting multiple programming languages.
● PostgreSQL Database
○ Open source object-relational database system
● pgvector
○ Open-source extension for PostgreSQL designed to efficiently store and search through large volumes of vector data.
● OpenAI Embeddings API
○ The Embeddings endpoint accepts text as input and measures the relatedness of text strings
● AWS S3
○ Simple Storage Service to store the PDF so it can be accessed throughout the process

16. Vector What?
Vectors, in this context, refer to lists or arrays of numbers which can represent various types
of data, such as features extracted from images, text, or other multidimensional data.
Embeddings are like an index that group similar pieces of data together. So “dog” might be
similar to “cat” because they are both pets and animals.
pgvector makes it easier and faster to handle and search through complex data directly
where it’s stored (like in a PostgreSQL database).

17. Getting started
● Heroku
● OpenAI API
● AWS
● Github
● https://github.com/heroku-reference-apps/ask-pdf

18. Architectural
Considerations for
Custom GPTs

19. Considerations
● Pros
○ Tailored performance
○ Control over data
○ Privacy and security
○ Unique features
○ Competitive advantage
● Cons
○ High Costs
○ Technical expertise
○ Resource intensive
○ Scalability challenges
○ Ethics and compliance

20. AI Associate Exam
Overview

21. Who Is a Salesforce Certified AI Associate?
Invested in Salesforce Customer 360 Platform
Validate foundational knowledge of and interest in Salesforce
Committed to driving success with AI + Data
Understand importance of data quality and its application to AI
Ready to bring positive change with AI
Develop a foundation of trust with customers

22. Validate Foundational Skills in Four Key Areas
39%
Ethical Considerations
of AI
36%
Data for AI
8%
AI Capabilities in CRM
17%
AI Fundamentals

23. Prepare for Your Exam
Exam Guide
About the Exam
Audience Description
Recommended Training and References
Exam Outline
Candidate Code of Conduct
Maintenance Information
Trailmix
Artificial Intelligence Fundamentals
Generative AI Basics
Natural Language Processing Basics
Meet Salesforce’s Trusted AI Principles
Responsible Creation of Artificial
Intelligence
Data Analytics Fundamentals
PLUS:
Cert Prep
Module

24. Navigate the Exam Experience
Exam Details
40 multiple-choice questions
70 minutes
65% passing score
Cost
$75 USD
$0 USD retake fee
Delivery Options
Proctored exam delivered onsite at testing center
Online proctored environment

25. AI Study Buddy Demo