Graph Databases: Data Storage with Context

In this connected world, traditional data stores often make it difficult to find valuable relationships. By making them a key component of the model, contextualizing a set of data becomes incredibly simple.
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 with tools to interact with the database. Then, we will look at how graphs are lending their strengths to the AI industry through vectors and GraphRAG.
Code: https://github.com/JMHReif/springai-goodreads

Transcript

1. Graph Databases: Data Storage with Context Jennifer Reif jennifer.reif@neo4j.com @JMHReif github.com/JMHReif jmhreif.com linkedin.com/in/jmhreif Photo by Tomasz Frankowski on Unsplash
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 ATTENDED ATTENDED W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN
7. What is a graph? Adrian Degree Degree Company Jennifer Jacob School ATTENDED ATTENDED W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN ENROLLED_IN Degree C O M PLETED COM PLETED
8. What is a graph? Person Degree Degree Company Person Person School ATTENDED ATTENDED 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
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 ATTENDED ATTENDED 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
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 ATTENDED ATTENDED 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
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. Data model
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. Query language
22. Cypher GQL-compliant • Functional and visual • Based on ASCII art • Declarative • Focus on patterns A B LIKES MATCH (A)-[:LIKES]->(B)
23. Cypher: write All about patterns Jennifer Neo4j WORKS_FOR CREATE (:Person { name: ‘Jennifer’}) -[:WORKS_FOR]-> (:Company { name: ‘Neo4j’}) NODE PROPERTY NODE PROPERTY LABEL LABEL
24. Cypher: read All about patterns Jennifer Neo4j WORKS_FOR MATCH (:Person { name: ‘Jennifer’} ) -[:WORKS_FOR]-> ( whom ) RETURN whom
25. Let’s explore!
26. Visualization, Data Science, and more!
27. Integrations Lots of options! • Cloud • Visualization • Data transportation • Data science • Applications • Administration, ops, monitoring
28. Purchasing system Orders, products, suppliers, etc • Easily fi nd unusual patterns • Optimize routes network • Pass bits on to other systems • Pinpoint… • Improvements for inventory • Weak points in supply chain • In fl uential products/customers
29. Financial Investments, ownership, etc • Easily track fi nancial holdings • Understand money movements • Monitor transactions • Pinpoint… • Suspicious activity • True ownership (transactions) • In fl uential, valuable holdings
30. Other Social, interactions, etc • Easily navigate human interaction • Understand collaboration • Track behavior, journeys • Pinpoint… • User journeys, collabs • Communities • In fl uential people, events
31. What about AI?
32. 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?
33. 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
34. Embeddings / Vectors Convert data to a point in space • Series of numbers • 100s or 1000s of dimensions • Dimension = interesting feature / characteristic
35. Vectors in the physical realm https://www.mathsisfun.com/algebra/vectors.html
36. Vectors in the technical realm Kings and Queens king − man + woman ≈ queen king man wom an 1 king man wom an 2 queen? 3
37. How do we search the vectors? Similarity search • Expensive queries (compare to every vector) • Approximate nearest neighbor (k-ANN) • Example: Library • Book classi fi cation - genre vs location of plot • Smaller search set = smaller retrieval time! Photo by Martin Adams on Unsplash
38. GraphRAG Graph DB as a data source • Higher accuracy (more relevance in result) • Using existing, quality data • Explainability, veri fi ability
39. Explainable AI Why graph with GenAI? • How did the LLM get this answer? • Graphs: • Incorporating siloed data into result • Add extra context/related info to graph • Better veri fi cation through understandable format Photo by No Revisions on Unsplash
40. Let’s code!
41. 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