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Fluffy and Fido on the Go: Applying Graph Data and AI to Hack Pet Travel

Fluffy and Fido on the Go: Applying Graph Data and AI to Hack Pet Travel

Ever grappled with the difficulties of traveling with your cherished pet? Discovering the perfect location can require considerable research as you scour the web for pet-friendly hotels, restaurants, green spaces, and more. Furthermore, the urgency of finding an available veterinarian nearby in the event of a pet medical emergency can add to the stress. In this session, the presenters will guide you on how to leverage publicly-available data to locate pet-friendly accommodations, store this information in Neo4j, and combine Neo4j with Artificial Intelligence to find ideal places for you and your pet to stay, dine, and enjoy. The presenters will build an application using Spring Boot and Spring AI, deploy it to Azure Spring Apps, and demonstrate both the app and the Neo4j Bloom visualization tool for additional data insights. By attending this session, you will learn how to streamline your pet travel planning process, allowing more time to enjoy your adventure with your four-legged friend.

Jennifer Reif

October 26, 2023
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  1. Mark Heckler
    Email: [email protected]
    Twitter: @mkheck
    LinkedIn: linkedin.com/in/markheckler
    Github: github.com/mkheck
    Website: thehecklers.com
    Fluffy and Fido on the Go
    Applying Graph Data and AI to Hack Pet Travel
    Jennifer Reif
    Email: [email protected]
    Twitter: @JMHReif
    LinkedIn: linkedin.com/in/jmhreif
    Github: github.com/JMHReif
    Website: jmhreif.com

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  2. Who Are We?
    • Developer Advocate, Neo4j

    • Technical content writer

    • Conference speaker

    • Other: geek
    • Author

    • Architect & Developer

    • Developer Advocate, Java/JVM

    • Java Champion, Rockstar

    • Kotlin Developer Expert

    • Pilot
    bit.ly/springbootbook

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  3. Spring AI
    Bringing AI to Spring applications
    • Implementations for OpenAI, Azure OpenAI

    • Text-based prompts -> Language/code

    • Prompt-stu
    ffi
    ng (vs
    fi
    ne-tuning)

    • Retrieval Augmented Generation (RAG)

    • Provide context and guidance

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  4. Neo4j
    Vector database and data context
    • Grounding answers through connected data

    • Prompt -> query -> graph data -> AI

    • Steps:

    • Calculate vectors (embeddings)

    • Compare prompt vector

    • Return most similar

    • Neo4j Vector Search
    https://docs.spring.io/spring-ai/reference/api/vectordbs.html

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  5. Let’s code!

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  6. Resources
    • Source code: github.com/mkheck/neoai

    • Docs: Spring AI

    • Website: Neo4j GenAI
    Mark Heckler
    Email: [email protected]
    Twitter: @mkheck
    LinkedIn: linkedin.com/in/markheckler
    Github: github.com/mkheck
    Website: thehecklers.com
    Jennifer Reif
    Email: [email protected]
    Twitter: @JMHReif
    LinkedIn: linkedin.com/in/jmhreif
    Github: github.com/JMHReif
    Website: jmhreif.com

    View full-size slide