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Generative AI Workshop
GraphSummit London, 14 Nov 2023
Zach Blumenfeld, Kristof Neys

What We Will Cover
1. Neo4j Introduction: Basics of product (What is our product, Cypher, how it works, etc.)
2. Knowledge Graph Building
a. Load GDB
b. Explore the graph in Browser
3. Search
a. Text Embedding (With OpenAI)
b. Vector Index
c. Search with Vector Index
d. Search with hops (+ graph traversal to search for users, departments, etc.)
4. Q&A Chatbot
a. Using Chat completion API in RAG workﬂow
5. Enhancing AI Inference (Example with Recommendations)
a. Introduce GDS
b. Add Additional relationships to graph to scale recommendations (embeddings, KNN, other algos?)
c. Demonstrate recommendations in semantic search
Neo4j Inc. All rights reserved 2023
2

What We Will Cover
1. Part 0: Introductory comments & Short Demo
2. Part 1: Creating the Knowledge Graph
a. Some Theory
b. Set-up
c. Dataset intro & Data ingestion
3. Part 2: Vector search, Semantic Search with Contex
a. Some Theory
b. LLM integrations & Vector indexes
Coffee Break
c. Vector Search & Graph patterns
4. Part 3: Graph Data Science
a. Some Theory
b. Creating Node embeddings
c. Retrieval Augmented Generation
5. Part 4: Putting it all together: Marketing Campaign App
3

4
Part 0:
The opportunity of GenAI

5
$6.6 Trillion
In Economic Value
Generative AI is Predicted to Unlock
Up to 3.3% productivity improvement annually

6
Automate data
retrieval tasks
1
Improve customer
service experiences
Expedite reading,
understanding, & summarizing
2
3
Generate
content & code
4
Generative AI Provides Amazing Opportunities

7
LLMs Core function…To manipulate strings…
• GPT training focuses primarily on predicting the probability of text sequences and
specifically for predicting which words come next in a sequence
“When you play the
game of thrones….
Input
LLM
…you win or you die.
There is no middle
ground.”
Output

8
Nothing is perfect…
but don’t push it!

There is no need for this…!
10

Whereas we all know what The Truth is…
11

Lack of enterprise domain knowledge
Inability to verify or explain answers
Hallucination
Fine-tuning limitations
Sensitive to prompt (input) phrasing
Ethical & data bias concerns
12
But There Are Challenges…

13
Knowledge
Facts
Context
Language
Statistics
Creativity
KGs LLMs
+
BETTER TOGETHER
Solution: Generative AI, LLMs, and Knowledge Graphs

© 2023 Neo4j, Inc. All rights reserved.
14
Setting the context - From a user point of view

15
We are not making this up…

16
Three main ways to let LLM’s work with your Graph

17
Part 1:
Knowledge Graph

18
A Knowledge Graph is a structured
representation of facts, consisting of
entities, relationships and semantic
descriptions
● Entities can be real-world objects and abstract
concepts
● Relationships represent the connections between
entities
● Semantic description of the entities and relationships

Nodes represent entities in the
graph
19
Person
Person
Car
Knowledge Graph Components

graph
Relationships represent
associations or interactions between
nodes
20
Person
Person
Car
LOVES
KNOWS
KNOWS
LIVES WITH
O
W
N
S
D
R
I
V
E
S

Person
Person
Name: “Andre”
Born: May 29, 1970
Twitter: “@dan”
Name: “Mica”
Born: Dec 5, 1975
Car
Brand “Volvo”
Model: “V70”
Description:
Since:
Jan 10, 2011
LOVES
KNOWS
KNOWS
LIVES WITH
O
W
N
S
D
R
I
V
E
S
graph
Relationships represent
associations or interactions between
nodes
Properties represent attributes of
nodes or relationships
21

User
:VISITED
Website
User
IPLocation
Website
IPLocation
Website
Website
Website
:VISITED
:VISITED
:VISITED
:USED
:USED
:
U
S
E
D
:
V
I
S
I
T
E
D
:
V
I
S
I
T
E
D
:VISITED
:SAME_AS
Graphs allows you to make implicit
relationships….
….explicit
And they Grow too…?!
22

:SAME_AS
User
:VISITED
Website
User
IPLocation
Website
IPLocation
Website
Website
Website
:VISITED
:VISITED
:VISITED
:USED
:USED
:
U
S
E
D
:
V
I
S
I
T
E
D
:
V
I
S
I
T
E
D
:VISITED
User
:SAM
E_AS
:USED
:VISITED
PersonId: 1
PersonId: 1 PersonId: 1
User
PersonId: 2
:VISITED
…and can then group similar nodes…and
create a new graph from the explicit
relationships…
A graph grows organically - gaining
insights and enriching your data
Graphs….Grow!
23

Come and see us for the book on Knowledge Graphs
24

25
What We Will Build
Today

Improve GenAI Using
Knowledge Graphs with
Retrieval Augmented
Generation
(RAG)
26

RAG enables
natural language
search on factual
information
retrieved from a
database
Prompt +
Relevant
Information
LLM API
Embedding and/or
Natural Language
Query
LLM
Completion API
User
Database
Search
Prompt Response
Relevant Results
/ Documents
2
3
1
Knowledge
Graph
Retrieval Augmented Generation
27

Today we Build a Targeted, Personalized, Content
Generator
28

29
Some Cypher Basics

Cypher: powerful and expressive query language
30
MATCH (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} )
Person
NODE NODE
LABEL PROPERTY
LABEL PROPERTY
CREATE
RELATIONSHIP
name: ‘Ann’
LOVES
Person
name: ‘Dan’

Cypher: powerful and expressive query language
31
MARRIED_TO
Person
name: ‘Dan’
MATCH (p:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
spouse
NODE
RETURN p.name as husband, spouse
VARIABLE

MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Easily express complex queries with Cypher
Find all direct reports and how many
people they manage,
up to three levels down
32
Cypher Query
SQL Query

Enough Theory - some
practice… part 1
33

34
Back to Theory…
Semantic Search

Neo4j and Vector Search
35
Find relevant documents and
content for user queries
Find entities associated to
content and patterns in
connected data.
Improve search relevance &
insights by enhancing a
Knowledge Graph. Use graph
algorithms and ML to
discover new relationships,
entities, and groups.
Vector Similarity
Search
Graph Traversals &
Pattern Matching
Knowledge Graph
Inference & ML
Vector Search
Graph Database

What are vector embeddings
36
● Same concepts, just “an arrow”
● 100s or 1000s dimensions

Finding Similar vectors
37
● cosine
● direction / angle based
vector point
query
nearest 4
● Euclidean
● distance based

Why & What is a Vector Index?
● Data applied on: encoding vectors of mainly unstructured data such
as text, audio, video that is converted using embedding models
(“Raw” vectors).
● Main purpose: deploy approximate methods to perform similarity
search at lower computational cost.
● Once an embedding vector has been created as a node property a vector
index can be created across those properties.
● This indexing is an algorithm that maps the original vector to a data
structure that enables faster search.
● By creating a vector index a data structure optimized for queries is created
at “store time” (as opposed to GDS similarity search at query time).
38

Type of Algorithms
Some of the algorithms that can be applied are:
● Random projection
● Product Quantization
● Locality-sensitive hashing
● Hierarchical Navigable Small World (HNSW)
39

How is search performed?
40
● The Query vector is any piece of unstructured data that is being converted
to an encoding vector (the “Raw” vector) and is mapped to an index using
the same Algorithm (i.e. Hierarchical Navigable Small World).
● The “Key” vectors are the stored vectors that have been indexed.
● When search is performed between the query vector and the stored
vectors a similarity function is applied.
● Several similarity measures can be used, including:
○ Cosine similarity
○ Euclidean similarity
○ Dot product

• GenAI orchestration Framework
• Many out-of-the-box components
and integrations
• Prebuilt chains and agents
LangChain why & how

practice… part 2
42

43
Back to Theory…
Graph Data Science to
enhance your
Knowledge Graph

Neo4j Graph Data Science
Algorithms, Procedures & ML
Graph Projections
Neo4j Database
Graph DS/ML Workspace
highly optimized, massively parallel, scalable
● Run graph algorithms to generate insights:
65+ algorithms across centrality, path ﬁnding,
community detection, similarity, and more
● Engineer graph features for ML: Leverage
relationship information with algorithms & node
embeddings
● Build graph native ML pipelines: Link
prediction, node classiﬁcation & property
regression
● Integrate with external ML frameworks:
Python client, blazing fast import & export,
formatting for dataframes and tensors
44

45
Graph Data Science
Use unsupervised machine
learning techniques to identify
associations, anomalies, and
trends.
Use embeddings to learn the
features in your graph that
you don’t even know are
important yet.
Train in-graph supervised ML
models to predict links, labels,
and missing data.
Find the patterns you’re looking
for in connected data
Knowledge Graphs
Graph Algorithms
Graph Native
Machine Learning

Similarity
• K-Nearest Neighbors (KNN)
• Node Similarity
• Filtered KNN & Node Similarity
• Cosine & Pearson Similarity Functions
• Euclidean Distance Similarity Function
• Euclidean Similarity Function
• Jaccard & Overlap Similarity Functions
Graph Embeddings
• Fast Random Projection (FastRP)
• FastRP with Property Weights
• GraphSAGE
• Node2Vec
• HashGNN
Machine Learning
• Knowledge Graph Embeddings
• Link Prediction Pipelines
• Node Classification Pipelines
• Node Regression Pipelines
The Largest Library Of Graph Algorithms & ML
Community Detection
• Weakly Connected Components
• Conductance Metric
• K-1 Coloring
• K-Means Clustering
• Label Propagation
• Leiden Algorithm
• Local Clustering Coefficient
• Louvain Algorithm
• Max K-Cut
• Modularity Optimization
• Speaker Listener Label Propagation
• Strongly Connected Components
• Triangle Count
Centrality
• ArticleRank
• Betweenness Centrality & Approx.
• Closeness Centrality
• Degree Centrality
• Eigenvector Centrality
• Harmonic Centrality
• Hyperlink Induced Topic Search (HITS)
• Influence Maximization (CELF)
• PageRank
• Personalized PageRank
Pathfinding & Search
• A* Shortest Path
• All Pairs Shortest Path
• Breadth & Depth First Search
• Delta-Stepping Single-Source
• Dijkstra Single-Source
• Dijkstra Source-Target
• Minimum Spanning Tree & K-Spanning Tree
• Random Walk
• Yen’s K Shortest Path
• Minimum Directed Steiner Tree
• Topological Sort
• Longest Path
Link Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
… and more!
• Collapse Paths
• Graph Sampling
• Graph Stratified Sampling
• One Hot Encoding
• Pregel API (write your own algos)
• Property Scaling
• Split Relationships
• Synthetic Graph Generation
46

The Power of Graph Embeddings
The ‘bridge’ to traditional Machine Learning
47

The representation of nodes as
low-dimensional vectors that
summarize their graph position, the
structure of their local graph
neighborhood as well as any possible
node features
NODE EMBEDDING
48

NODE EMBEDDING
49

Node Embeddings
4 algorithms…and counting
● FastRP (Fast Random Projection) - Calculates embeddings extremely fast using probabilistic
sampling and linear algebra.
● GraphSAGE (Graph SAmple and aggreGatE) - Trains a Graph Neural Network (GNN) to
generate embeddings on old and new graph data. Uses batch sampling procedures for
scalability.
● Node2Vec - Creates embeddings that represent nodes in similar neighborhoods and/or
structural “roles” in the graph using adjustable random walks.
● HashGNN - Quickly generates embeddings on heterogeneous graphs. Like a GNN but much
faster and simpler with comparable benchmarked performance. Leverages a clever
application of hashing functions rather than training a model.
New
50

practice… part 3
51

52
Generating Grounded
Content with Knowledge
Graph and LLM

Improve GenAI Using
Knowledge Graphs with
Retrieval Augmented
Generation
(RAG)
53

RAG enables
natural language
search on factual
information
retrieved from a
database
Prompt +
Relevant
Information
LLM API
Embedding and/or
Natural Language
Query
LLM
Completion API
User
Database
Search
Prompt Response
Relevant Results
/ Documents
2
3
1
Knowledge
Graph
Retrieval Augmented Generation
54

Run the App!
55

56
Github Repo:
https://github.com/neo4j-product-examples/genai-workshop/tree/main
Resources / Education:
Graph academy: https://graphacademy.neo4j.com/
Resources: https://neo4j.com/resources/
Books: https://neo4j.com/books/
Video's: https://neo4j.com/videos/
Blog: https://neo4j.com/blog/
Community: https://community.neo4j.com/
Book on Knowledge Graphs:
https://neo4j.com/books/knowledge-graphs-practitioners-guide/
Neo4j Sandbox: https://sandbox.neo4j.com/
Resources & Material

57
The End…

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