What you need to know: Data, apps, and AI for graph databases

Graphs are different from other types of databases because they store relationships between data points. This makes them ideal for applications where the connections between data provides additional context, improving decision-making with existing data.
In this session, we will walk through what a graph database is and how it can transform your applications and data. We will explore creating, querying, and displaying data and learn how to integrate a graph database into applications. Finally, we will discuss how graph databases can be used in AI applications and the strengths they bring to the table. Code: https://github.com/JMHReif/springai-goodreads

Transcript

1. What you need to know: Data, apps, and AI for graph databases Jennifer Reif jennifer.reif@neo4j.com @JMHReif github.com/JMHReif jmhreif.com linkedin.com/in/jmhreif
2. Who is Jennifer Reif? Developer Advocate, Neo4j • Continuous learner • Technical speaker • Tech blogger, podcaster • Other: geek Jennifer Reif jennifer.reif@neo4j.com @JMHReif github.com/JMHReif jmhreif.com linkedin.com/in/jmhreif
3. What is a graph? Jennifer Jacob ??
4. What is a graph? Company Jennifer Jacob WORKED_FOR WORKED_FOR
5. What is a graph? Company Jennifer Jacob School W ORKED_FOR WORKED_FOR ATTENDED ATTENDED
6. What is a graph? Degree Degree Company Jennifer Jacob School W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN ATTENDED ATTENDED
7. What is a graph? Adrian Degree Degree Company Jennifer Jacob School W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN ENROLLED_IN Degree C O M PLETED COM PLETED ATTENDED ATTENDED
8. What is a graph? Person Degree Degree Company Person Person School W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN ENROLLED_IN Degree C O M PLETED COM PLETED Edward Jones Jacob Jennifer SIUE Music CMIS CS Adrian ATTENDED ATTENDED
9. What is a graph? Answers through relationships • How many coworkers shared classes/degrees? • What are common degree journeys? • How many alumni re- enroll for higher degrees? • Who else went to a school and works for company? Person Degree Degree Company Person Person School W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN ENROLLED_IN Degree C O M PLETED COM PLETED Edward Jones Jacob Jennifer SIUE Music CMIS CS Adrian ATTENDED ATTENDED
10. Nodes (vertices) Objects or entities • Can have labels • May have properties Person Degree Degree Company Person Person School Degree Edward Jones Jacob Jennifer SIUE Music CMIS CS Adrian
11. Relationships (edges) Connect entities • Must have type (label) • Must have direction • May have properties Person Degree Degree Company Person Person School W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN ENROLLED_IN Degree C O M PLETED COM PLETED Edward Jones Jacob Jennifer SIUE Music CMIS CS Adrian ATTENDED ATTENDED
12. Example: Logistics Home
13. Logistics • Where fi rst? • Depends on when ready to leave house Home School Opens: 08:00 Donation Opens: 09:00 Shipping Opens: 10:00 Grocery Opens: 07:00
14. Logistics Criteria • Where is time critical? • Personal preferences for refrigerated food? • How long at each store? Music Home School Opens: 08:00 Donation Opens: 09:00 Shipping Opens: 10:00 Grocery Opens: 07:00 WENT_1 W ENT_2 WENT_3 WENT_4 W ENT_5 WENT_6
15. Answer questions Habits/preferences • E ffi ciency improvements? • Adding stop? Where/time? • Share load across more people? • Distance and location calculations? Music Home School Opens: 08:00 Donation Opens: 09:00 Shipping Opens: 10:00 Grocery Opens: 07:00 WENT_1 W ENT_2 WENT_3 WENT_4 W ENT_5 WENT_6
16. Other use cases • Fraud detection • Identity and access management • Recommendations • Supply chain
17. Graphs add context and meaning Not just point-to-point, but HOW they connect
18. Applications
19. One model to rule them all… Conceptual = Logical = Physical data model
20. One model to rule them all… Conceptual = Logical = Physical data model
21. Translates to app model, too! • Java, OOM language • Entity-based mapping • Very little change • Neo4j/Java types nearly same @Node public class Order { @Id String orderId; LocalDate orderDate; LocalTime orderTime; String inStore; @Relationship(value = "BOUGHT", direction = Relationship.Direction.INCOMING) Customer customer; [ { "customer": { "customerId": "1", "customerName": "Kelly Key", "loyaltyId": "908-424-2890" }, "inStore": "N", "orderDate": "2019-04-01", "orderId": "76", "orderTime": "07:52:17" }, { "customer": { "customerId": "1", "customerName": "Kelly Key", "loyaltyId": "908-424-2890" }, "inStore": "N", "orderDate": "2019-04-01", "orderId": "662", "orderTime": "14:53:06" },
22. Let’s code!
23. AI
24. Throw an LLM at it Doesn’t often work • LLM strengths: • human-consumable data • general/public info • LLM weaknesses: • context/big picture • private knowledge • Solo LLM = great -> integrated LLM = more great?
25. RAG architecture • Retrieval • Data retrieved from external source • Augmented • Augments response with facts • Generation • Response in natural language Prompt + Relevant Information LLM API LLM
 Chat API User Database Search Prompt Response Relevant Results / Documents 2 3 1 Database
26. GraphRAG Graph DB as a data source • Graph fi lls in LLM weaknesses! • Store relevant knowledge
27. GraphRAG benefits Graph DB as a data source • Higher accuracy (more relevance in result) • Using existing, quality data • Explainability, veri fi ability
28. Let’s code!
29. Resources • Github repository (today’s code): github.com/JMHReif/springai-goodreads • GraphAcademy LLM courses: graphacademy.neo4j.com/categories/llms/ • Docs for Spring AI: docs.spring.io/spring-ai/reference/api/vectordbs/neo4j.html • Knowledge graph ebook: https://neo4j.com/whitepapers/developers-guide-how- to-build-knowledge-graph/ Jennifer Reif jennifer.reif@neo4j.com @JMHReif github.com/JMHReif jmhreif.com linkedin.com/in/jmhreif