The document details a hands-on workshop focused on building GenAI applications using Neo4j and knowledge graphs. It covers topics such as knowledge graph basics, vector and semantic search, data enrichment, and the integration of large language models for personalized content generation, specifically for e-commerce fashion assistants. Participants are guided through connecting to a Neo4j workspace, accessing resources like Colab notebooks, and practical exercises to enhance GenAI insights and functionalities.

1. Hands-On GraphRAG Workshop
Building Smarter GenAI
Apps with Knowledge
Graphs
Emil Pastor | Head of Solution Engineering
Samko Yun | Sr. Solution Engineer
Darren Wood | Sr. Solution Engineer
Muddassir Zaidi | Sr. Consulting Engineer
Angelo Bueti | Professional Services Manager

2. ● Ask questions straight away, this is an
interactive session
● Raise your hand if you are stuck
● Have fun
Workshop Rules

3. Before We Start
1. Connect to your Aura instance through
workspace workspace.neo4j.io
2. Open the notebook in Colab (needs a
google account!)
github.com/neo4j-product-examples/
genai-workshop
3. open genai-workshop.ipynb

4. Agenda
Introduction
1. Introduction to Neo4j & GenAI
2. GenAI App Demo! (what we will build today)
Hands On
1. Knowledge Graph Basics
2. Vector Search
3. Semantic Search with Graph Patterns
4. Enrichment with Graph DS & ML
5. LLM Powered Content Generator

5. Retrieval-Augmented Generation Is
Becoming an Industry Standard
RAG augments LLMs by
retrieving up-to-date external
data to inform responses:
● Provide domain-specific,
relevant responses
● Reduce hallucinations with
verified data
● Enable traceability back
to sources

6. Find similar documents
and content
Identify entities associated to
content and patterns in
connected data
Improve & enrich GenAI
insights. Discover new
relationships and entities
Unify vector search, knowledge graph, and data science to
improve RAG quality and effectiveness
Vector
Search
Graph Data
Science
Knowledge
Graph
GraphRAG with Neo4j

7. Today
We Build an LLM
Chain with
GraphRAG

8. This is Eva
For her birthday, Eva wants a
Halter Neck Top
How do you choose
what to buy?

9. For her birthday, Eva wants a
Halter Neck Top
How do you choose
what to buy?
1. Get a list of Halter Neck Top
2. Match the Halter Neck Top
for Eva
3. Accessorize or add
another item
This is Eva

10. This Requires:
1. Data: Kaggle H&M
2. Search & Retrieval
3. Context for Personalization
4. Recommendation Engine
5. LLM Powered Content
Generator
At Scale: Let’s Build an AI
Fashion Assistant

11. Solution: LLMs and Knowledge Graphs
Knowledge
Context
Enrichment
Language
Reasoning
Creativity
KGs LLMs
+
BETTER TOGETHER

12. We'll Build a Targeted, Personalized
Content Generator

13. Structured
Purchase Data
Neo4j Inc. All rights reserved 2024
13
Demo Architecture
Graph Data Science
Unstructured
Product
Descriptions
INGEST
INGEST
Python Notebook

14. Journey
1
Knowledge Graph
(Kaggle H&M Dataset)
2
Vector Search
(Initial Retrieval)
3
Semantic Search with
Graph Patterns
(Personalization)
4
Graph DS & ML
(Recommendations)
LLM Powered Content
Generator & Fashion
Assistant
5

15. Now We Start
1. Connect to your Aura instance through
workspace workspace.neo4j.io
2. Open the notebook in Colab (needs a
google account!)
github.com/neo4j-product-examples/
genai-workshop
3. open genai-workshop.ipynb

16. 1. Knowledge Graph Basics
1
Knowledge Graph
(Kaggle H&M Dataset)
2
Vector Search
(Initial Retrieval)
3
Semantic Search with
Graph Patterns
(Personalization)
4
Graph DS & ML
(Recommendations)
LLM Powered Content
Generator & Fashion
Assistant
5

17. Neo4j Inc. All rights reserved 2024
Name: “Andre”
Born: May 29, 1970
Twitter: “@dan”
Name: “Mica”
Born: Dec 5, 1975
Brand “Volvo”
Model: “V70”
Description:
Since:
Jan 10, 2011
LOVES
KNOWS
Person
Person
LIVES WITH
O
W
N
S
KNOWS
D
R
I
V
E
S
Nodes represent entities in the
graph
17
Knowledge Graph Components
Relationships represent
associations or interactions
between nodes
Properties represent attributes of
nodes or relationships
Car

18. Neo4j Inc. All rights reserved 2024
18
Cypher: A Powerful & Expressive Query Language
MARRIED_TO
Person
name: ‘Dan’
spouse
MATCH (p:Person {name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
NODE RELATIONSHIP TYPE
LABEL PROPERTY
NODE
RETURN p.name as husband, spouse
VARIABLE VARIABLE

19. © 2024 Neo4j, Inc. All rights
reserved.
19
We are turning this:

20. Into this :

21. Data Model
customerId
tDat
Color
name
type
group
description

22. Go to Notebook
Knowledge Graph Basics

23. 2. Vector Search
1
Knowledge Graph
(Kaggle H&M Dataset)
2
Vector Search
(Initial Retrieval)
3
Semantic Search with
Graph Patterns
(Personalization)
4
Graph DS & ML
(Recommendations)
LLM Powered Content
Generator & Fashion
Assistant
5

24. What are Embeddings?
● A type of data compression
● Transform messy data (text, images, audio,
etc.) into a compact format for ML algorithms
● Most often numeric vectors (a.k.a arrays) with
100s or 1000s of elements
● Preserve information such that “similar” items
have proportionally “similar” embedding
vectors
● Similarity is measured with vector algorithms
(cosine, euclidean, etc.)
Graph Graph Model
Graph Vector
Embeddings

25. What Does“Similarity” Mean?
It Depends:
● Text Embeddings => Semantic similarity, the meaning behind a text
sequence
● Graph Embeddings => Similarity in position or structure in a graph-
can have semantic meaning too

26. What will we use embeddings for?
● (Now) Vector search using text embeddings
● (Later) Recommendations using graph node embeddings

27. Search & Vectors in Neo4j
Neo4j makes search efficient through a variety of indexes including
● Range: General index for predicates based on equality and range.
● Point: Predicts on geospatial points like distance bounding boxes, etc.
● Text: Predicates on strings like contains, ends with, etc.
● FullText: Text search based on tokenization and analyzers
● Vector: ANN (Approximate Nearest Neighbor) search on vectors

28. How is similarity measured
vector point
query
nearest 4
The vector index allows us to query vectors on larger datasets.
It uses HNSW to perform k-ANN queries
Euclidean
Distance based
Cosine
Direction / angle based

29. LangChain Why & How
• GenAI orchestration Framework
• Many out-of-the-box components
and integrations
• Prebuilt chains and agents

30. Go to Notebook
Vector Search

31. 3. Semantic Search with Graph Patterns
1
Knowledge Graph
(Kaggle H&M Dataset)
2
Vector Search
(Initial Retrieval)
3
Semantic Search with
Graph Patterns
(Personalization)
4
Graph DS & ML
(Recommendations)
LLM Powered Content
Generator & Fashion
Assistant
5

32. Neo4j & Semantic Search
Vector Similarity
Search
Graph Pattern
Matching
Knowledge Graph
DS/ML
Find relevant documents and
content for user queries.
Find entities associated to
content. Identify 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 Search Graph Database
HNSW
Graph Data Science

33. Semantic Search + Traversal
Purchases in
common
Customers
Target
Customer
Semantically
Similar
Products

34. Go to Notebook
Semantic Search with Graph Patterns

35. 4. Graph Data Science & ML
1
Knowledge Graph
(Kaggle H&M Dataset)
2
Vector Search
(Initial Retrieval)
3
Semantic Search with
Graph Patterns
(Personalization)
4
Graph DS & ML
(Recommendations)
LLM Powered Content
Generator & Fashion
Assistant
5

36. Bring the context of your
connected data into a format that
other pipelines can ingest.
The Largest Catalog of
Graph Algorithms
Native Graph Catalog and
Analytics Workspace
Graph Embeddings for ML
&AI
Pathfinding
& Search Centrality
Community
Detection
Machine
Learning
Link
Prediction Similarity
and
more …
Iterate fast with different data sets,
models, and version trained models.
Over 65 pretuned,
parallelized algorithms.
Neo4j Graph Data Science (GDS)

38. Node
Embedding
Vector representation of a node
that summarizes it’s position in the
graph, local neighborhood
structure, and/or node features

39. K-Nearest
Neighbor (KNN)
w/ Node
Embeddings
Draw connections between highly
interconnected nodes and/or those
that have similar roles in the graph 0.2
0.3
0.6
-0.6
0.1
0.4
0.5
-0.4
-0.1
0.5
0.4
-0.4

40. Go to Notebook
Graph Data Science & ML

41. 5. LLM Powered Content Generator
1
Knowledge Graph
(Kaggle H&M Dataset)
2
Vector Search
(Initial Retrieval)
3
Semantic Search with
Graph Patterns
(Personalization)
4
Graph DS & ML
(Recommendations)
LLM Powered Content
Generator & Fashion
Assistant
5

42. Prompting the LLM
You are a personal assistant named Sally for a fashion, home, and beauty company called HRM.Write an
engaging email to {customerName} , one of your customers, to promote and summarize products relevant for
them given:
- The current season / time of year: {timeOfYear}
- Recent searches/interests: {customerInterests}
Please only mention the products listed below.Do not come up with or add any new products to the
list.Each product comes with an https `url` field. Make sure to provide that https url with descriptive
name text in markdown for each product.
# RelevantProducts:
These are products from the HRM store the customer may be interested in based
on their recent searches/interests: {customerInterests}
{searchProds}
# Customer May Also Be Interested In the Following
The below candidates are recommended based on the shared purchase patterns of other customers in the HRM
database.
Select the best 4 to 5 product subset from the context that best match the time of year: {timeOfYear}
and to pair with the RelevantProducts above. For example, even if scarfs are listed here, they may not
be appropriate for a summer time of year so best not to include those.
{recProds}

43. LangChain Chain
customerId
searchPrompt Personalized search
Recommendations
timeOfYear
customerName
prompt llm personalized
email
searchProds
recProds
{
searchProds: (searchPrompt , customer_id)| personalizedSearch
recProds:customer_id | recommendations
customerName:
timeOfYear:
}
prompt | llm | outputParser

44. Go to Notebook
LLM Powered Content Generator

45. Integrating AI with
knowledge graphs for
smarter supply chain
management
Merging structured and
unstructured data for
efficient operations
Converting vast amounts of
unstructured data into
actionable knowledge
Leveraging AI
for customized content
at scale
Real-World GenAI Breakthroughs
Powered by Neo4j

46. Banking & Financial
Services
Technology Telecommunications Energy
E-Commerce
Health & Life
Sciences
1,700+ Organizations Use Neo4j

47. Thank you!