Pass or Play What does GenAI mean for the Java developer? Jennifer Reif jennifer.reif@neo4j.com @JMHReif github.com/JMHReif jmhreif.com linkedin.com/in/jmhreif

Who is Jennifer Reif? Developer Advocate, Neo4j • Continuous learner • Conference speaker • Tech blogger • Other: geek Jennifer Reif jennifer.reif@neo4j.com @JMHReif github.com/JMHReif jmhreif.com linkedin.com/in/jmhreif

Photo by Matt Walsh on Unsplash AI Vector RAG LLM Algorithm Chaining DS Entity resolution Knowledge graph ML NLP GenAI Hallucination Embedding k-ANN Cosine similarity Euclidean distance Fine-tune Few-shot Grounding Model Prompt Semantic search Similarity Temperature Tokens Natural language ChatGPT Chatbot Context window

Generative Arti fi cial Intelligence Artificial intelligence capable of generating text, images, or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. https://en.wikipedia.org/wiki/Generative_arti fi cial_intelligence

Large Language Model (LLM)

General info… About LLMs • Lots of data • Answers on probabilities • Training takes tons of hardware, money, time • Models • Di ff erent providers / companies train their own

And here enters…

ChatGPT Public interface to LLM • Chat Generative Pre-trained Transformer • OpenAI, Nov 2022 • Natural language response • Predict next word • Feedback / reward to rank responses • Use cases: professional, personal, everything!

Is it all that? Worth the hype? • General information • Public domain knowledge • Historical data • Creative / arts • Human assistant • Task delegation Photo by Igor Omilaev on Unsplash

LLM issues • Lacking most recent data • Not always natural language • Language complexities, sarcasm, emotion • No sources • Hallucinations / Temperature • IP, bias, privacy

Improving LLM accuracy

Approaches • Custom model • Fine-tuning / Few-shot learning • Retrieval Augmented Generation (RAG)

RAG Pull data from external data sources • Retrieval • Data retrieved from database • 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

Explainable AI With RAG + LLM • How did the LLM get this answer? • Grounding LLM answer with veri fi ed data Photo by No Revisions on Unsplash

Vectors and Databases

What is a vector? • Length • Direction • Components have meaning horizontal vertical

How do we use vectors? https://www.mathsisfun.com/algebra/vectors.html

Vector arithmetic C = a + b 1 a b 2 a b 3 a + b

Example Kings and Queens king − man + woman ≈ queen king man wom an 1 king man wom an 2 queen? 3

Vectors to compare things What makes things similar? Length Width

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

Similarity search Vector indexes • Expensive queries (compare to every vector) • Approximate nearest neighbor (k-ANN) • Example: Library • Book classi fi cation - author vs location of plot • Smaller search set = smaller retrieval time! Photo by Martin Adams on Unsplash

Choosing a database

Vector databases Example: Pinecone, 2019 (not fi rst) • Pros: • Fast retrieval of highly-dimensional data • Similarity searches based on vectors (not speci fi c values) • Cons: • Requires a lot of infrastructure and power • Cannot store much outside vector+metadata

Graph databases Example: Neo4j, 2007 • Pros: • Flexible, agile data model • Relationships stored with entities (visual JOINs) • Cons: • Write overhead to store relationships • No bene fi t for low-connected data (relational)

(Knowledge) Graphs in GenAI Bene fi ts • Generate LLM response from knowledge in database • Security rules in database (what’s viewable) • Structured + unstructured data side-by-side • Vector dbs only store unstructured • Retrieve connected entities • Connected = extra, relevant context Combine for more accurate results within a relevant context. Knowledge Graph Similarity Search Find similar documents. Vector Index Find related information. Graph Structure Pattern Matching

Nothing is a silver bullet LLM is (of sorts) mind of its own • Can’t guarantee a consistent answer • Prompt engineering • Context window limits

Demo! Our data model

No content

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 • NODES 2024: dev.neo4j.com/nodes24 Jennifer Reif jennifer.reif@neo4j.com @JMHReif github.com/JMHReif jmhreif.com linkedin.com/in/jmhreif