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

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

What is a graph? Jennifer Jacob ??

What is a graph? Company Jennifer Jacob WORKED_FOR WORKED_FOR

What is a graph? Company Jennifer Jacob School W ORKED_FOR WORKED_FOR ATTENDED ATTENDED

What is a graph? Degree Degree Company Jennifer Jacob School ATTENDED ATTENDED W ORKED_FOR WORKED_FOR EN RO LLED_IN ENROLLED_IN

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

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

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

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

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

Example: Logistics Home

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

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

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

Other use cases • Fraud detection • Identity and access management • Recommendations • Supply chain

Graphs add context and meaning Not just point-to-point, but HOW they connect

Data model

One model to rule them all… Conceptual = Logical = Physical data model

One model to rule them all… Conceptual = Logical = Physical data model

Query language

Cypher GQL-compliant • Functional and visual • Based on ASCII art • Declarative • Focus on patterns A B LIKES MATCH (A)-[:LIKES]->(B)

Cypher: write All about patterns Jennifer Neo4j WORKS_FOR CREATE (:Person { name: ‘Jennifer’}) -[:WORKS_FOR]-> (:Company { name: ‘Neo4j’}) NODE PROPERTY NODE PROPERTY LABEL LABEL

Cypher: read All about patterns Jennifer Neo4j WORKS_FOR MATCH (:Person { name: ‘Jennifer’} ) -[:WORKS_FOR]-> ( whom ) RETURN whom

Let’s explore!

Visualization, Data Science, and more!

Integrations Lots of options! • Cloud • Visualization • Data transportation • Data science • Applications • Administration, ops, monitoring

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

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

Other Social, interactions, etc • Easily navigate human interaction • Understand collaboration • Track behavior, journeys • Pinpoint… • User journeys, collabs • Communities • In fl uential people, events

What about AI?

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?

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

Embeddings / Vectors Convert data to a point in space • Series of numbers • 100s or 1000s of dimensions • Dimension = interesting feature / characteristic

Vectors in the physical realm https://www.mathsisfun.com/algebra/vectors.html

Vectors in the technical realm Kings and Queens king − man + woman ≈ queen king man wom an 1 king man wom an 2 queen? 3

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

GraphRAG Graph DB as a data source • Higher accuracy (more relevance in result) • Using existing, quality data • Explainability, veri fi ability

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

Let’s code!

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