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David vonThenen
@davidvonthenen
Leveraging Knowledge Graphs for RAG
A Smarter Approach to Contextual AI Applications
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2
Agenda
● RAG Agents: Vector DB vs Graph DB
● Deep Dive by Example
○ Token Prediction vs Data Relationships
○ Explainable AI
○ Leverage In Non-AI Apps
● Q&A
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● Are you Human or an AI?
● I want 5 Kubernetes
● Virtual Machines are Real
● Cloudy, cloudy, cloudy…
● There is storage for that!
David
vonThenen
@davidvonthenen
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Vector DB vs Graph DB
What to Use When? Weigh the Pros and Cons
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5
Vector, Graph, NoSQL… Oh My!
Vector Graph
(No)SQL
And Friends
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6
Vector-based RAG: Pros
● Semantic Search Over Unstructured Text
○ Embedding/Semantic Similarity
● Finding Conceptually Relevant Info
● Highly Scalable, Low Latency
● Diverse Data Types (Img, Audio)
Image Credit:
1. Colin Talks Tech - Introduction to Vector Embeddings
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7
Vector-based RAG: Cons
● No Data Relationships
○ All Knowledge is Flat
● Difficult to Reason Over
Multiple Hops
○ How Many Rs in
Strawberry?
● Miss Complex Entity
Connections
○ Top K Limits
○ Top P Limits
Image Credit:
Darek Kleczek - A Gentle Introduction to LLM APIs
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8
Graph-based RAG: Pros
● Excellent Presenting Relationships
○ Great for Structured Knowledge
○ Associations Between Data
● Retrieve Network of Facts vs Snippets
○ Gather Connected Info (All Hops!)
● Reduce Hallucinations
● Higher Retrieval Accuracy for RAG
○ Better Response/Answer!
Image Credit:
Xi Wang, Qianzhen Zhang, Deke Guo & Xiang Zhao
A survey of continuous subgraph matching for dynamic graphs
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9
Graph-based RAG: Cons
● Complexities of Maintenance
● Data Modeling & Structure
○ Manage Ontologies/Relationships
● Frequent Data Changes = Challenging
○ Data Consistency with Updates
● Performance Impacts vs Embeddings
○ More Relevant = More Time
Image Credit:
What Ontology, RAG and Graph data do you use to develop Intelligent Assistants?
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10
Vector
vs Graph
Image Credit:
Avi Chawla
LinkedIn Post - Vector vs Graph
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Tokens vs Relationships
Token Prediction vs Data Relationships
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12
Prediction and Top-K
Image Credit:
LM Po - Understanding LLM Decoding Strategies
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13
The Problem Is…
Image Credit:
Pasha Shaik - Natural Language Generation from Scratch in Large Language Models with PyTorch
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Better: Graph Retrieval
Image Credit:
Nick Alonso, Beren Millidge
Understanding Graph-based RAG and Multi-Hop Question Answering
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Demo
https://youtu.be/WLEGg5zVwCQ
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Explainable AI
Gain More Insights Into Your RAG Agents
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What is Explainable AI?
● Makes AI Decision Making Transparent & Understandable
○ LLMs/Embeddings = Black Box
○ Uncover the How and Why
● Goal is to Provide:
○ Trust and Validation
○ Bias & Error Detection
○ Collaboration (with Humans)
● My NVIDIA GTC Talk Video
Crack the AI Black Box: Practical
Techniques for Explainable AI
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Visualizing Data
● Graph DBs Offer:
○ Contextual Rep. -> Nodes, Edges, etc
○ Intuitive Visualization For Humans
○ Quick Glance Over Hops
○ How Everything Is Connected!
● Vector DBs:
○ Opaque: High-dimensional Embeddings
○ Flattened Connections: Related But How?
○ Limited Visibility: Difficult to Browse
○ "No Chain Of Nodes" Due To Weights
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Visualizing Vector Data
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Demo
https://youtu.be/DDajZ5nS7aU
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Leverage in Non-AI Apps
Consume (and Modify) Content in External Apps
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Part 1: Chatbot Meets Graph Data
● Customer-Facing Chatbot for Retail Company
● Data is Stored in Graph Database
○ Product Info
○ User Purchase History
○ Supplier Inventory
● Chatbot Can Provide:
○ Contextually Relevant Info
○ Is the Item In Stock?
● Significantly Reduce Hallucinations
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Part 2: Reports/Inventory Same
Data
● Same Retail Company Can Use Directly Use Database
○ Inventory Management
○ Sales Reporting Tool
● Ex: Warehouse Dashboard of Orders
● Other Benefits:
○ Real-time Inventory Changes
○ User Data Instantly Updated
● Single Source of Truth!
● Conventional Business Aspects
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Demo
https://youtu.be/FcEDJl1hDk4
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Vector vs Graph: Takeaways
● Find the Right Tool For Your Use Case
● Graph Databases…
○ Can Use GPUs, CPU Optimized
○ Work Front Load = Modeling
○ Distribute the Ingest
● Vector Databases…
○ Need GPUs!!!!
○ Ingest/Embeddings Takes Time
○ Quick/Scalable
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Resources
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27
AI/ML Resources
CLICK HERE] for All Material Contained in this Session CLICK HERE
DigitalOcean Bare Metal H200 Availability
https://www.digitalocean.com/blog/now-available-bare-metal-nvidia-hgx-h200-gpus
Continue the Conversation - DigitalOcean Discord
https://discord.com/invite/digitalocean
Graph Database Options:
● NebulaGraph - https://github.com/vesoft-inc/nebula
● Neo4j - https://github.com/neo4j/neo4j
● JanusGraph - https://github.com/JanusGraph/janusgraph
Dataset BBC News) in Demo: https://bit.ly/4hBKNjp
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28
Thank You!
David vonThenen
@davidvonthenen

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