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1. Jesús Barrasa
Nouveautés Produits
& Graphes de
Connaissances
Head of Solutions Architecture for EMEA
Neo4j

2. Neo4j Inc. All rights reserved 2024
Recent features in Neo4j
Knowledge Graphs
2
Agenda

3. ●Ubiquitous availability of Aurain all major
clouds: GCP, AWS, Azure
●Enterprise-ready Aura
﹣SOC II Type 2 compliance
﹣Better DevOps with AuraDB APIs
﹣Easier RBAC configuration via Aura
console
﹣Better observability with security log
forwarding (EAP) and Performance
metrics forwarding (EAP)
﹣Private Link
Neo4j Inc. All rights reserved 20243
NEO4J AURA
2023 Key Capabilities

4. Customer Managed Keys (Encryption)
4Neo4j Inc. All rights reserved 2024
Whatis it
Aura encrypts all data at transit &
rest by default.
Customer Managed Keys(CMK)
is an alternative way to protect
cloud data for security conscious
Enterprises, enabling customers
to manage their own keys for
encryption / decryption at disk on
Aura using Key Management
Services (KMS) from their Cloud
Service Provider.
Whyit is important
Customers can protect their own
data, control access and have
the ability to revoke access, even
from Neo4j.
Customers can adhere to their
own stringent security policy
around access and key rotation,
on top of Aura’s Enterprise grade
default security and compliance
posture.

5. Customer Managed Keys (Encryption)
5

6. Neo4j Inc. All rights reserved 20246
Graph Schema

7. Graph Schema:New constraints on
nodes, relationships and properties:
●Unique relationship property
●Relationship key
●Property data types
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
Neo4j Inc. All rights reserved 20247

8. Graph Schema / Graph Type
Neo4j Inc. All rights reserved 20248
The definition of the informational content of a schema
(or rather a graph type), comprising:
●A set of node type descriptors
(also known as a node type set).
●A set of edge type descriptors
(also known as an edge type set).
●A node type name dictionary that maps node type
names,
which are identifiers, to node types contained in the node
type set of this graph type descriptor such that each
node type name is mapped to a single node type.
●An edge type name dictionary that maps edge
type names,
which are identifiers, to edge types contained in the
edge type set of this graph type descriptor such that
each edge type name is mapped to a single edge type.
CREATE OR REPLACE GRAPH TYPEFraudDet
(a:AccountHolder { FirstName :: STRING!,
LastName :: STRING!,
UniqueId :: STRING! }
...) REQUIREUniqueId IS KEY,
(c:CreditCard {AccountNumber :: STRING!,
Balance :: FLOAT!,
...} ...) REQUIREAccountNumber IS KEY, ...
(a)-[:HAS_CARD ...]->(c),
(a)-[:HAS_ACCOUNT ...]->(b),...
CREATE OR REPLACE DATABASEfoo
...
[WITH GRAPH TYPEFrautDet]
...

9. Neo4j Inc. All rights reserved 20249
QPP

10. Graph Pattern Matching:Improved
expressivity of graph navigation with
quantified path patterns,
a more powerful and performant syntax
to navigate and traverse your graph.
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
Neo4j Inc. All rights reserved 202410

11. NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
Graph Pattern Matching Example →Fraud Rings
Neo4j Inc. All rights reserved 202411
QPP
MATCHpath=(a:Account)-[:PERFORMS]->(first_tx)
((tx_i)-[:BENEFITS_TO]->(a_i)-[:PERFORMS]->(tx_j)
WHEREtx_i.date < tx_j.date
AND 0.80<= tx_i.amount / tx_j.amount <= 1.00
){3,6}
(last_tx)-[:BENEFITS_TO]->(a)
WHEREsize(apoc.coll.toSet([a]+a_i)) = size([a]+a_i)
RETURNpath
??????read more: https://bit.ly/pierre-qpp

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Parallel Runtime

13. NEO4J 5.0 NEW CAPABILITIES
Parallel Runtime: Speed up analytical query up to 100x
Neo4j Inc. All rights reserved 202413

14. Parallel Runtime Speedup
Up to 100x faster analytical queries by adding CPU
cores
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More cores
Faster
Queries

15. Neo4j Inc. All rights reserved 202420
New in Neo4j 5 -
recap

16. Neo4j
Product capabilities launched in 2023/2024
Neo4j Inc. All rights reserved 202421
●Parallel Runtime-faster analytical Queries
●Change Data Capture-better data integration
●Autonomous clustering & Fabric-limitless
scalability
●Graph Schema & constraints
●Backup with point-in-time recovery
●Incremental import
●Neo4j/AuraDB Ops Managerfor managing
databases
●Aura Enterprise Databaseon all clouds
(AWS, GCP, Azure)
●SOC II Type 2compliance, AuraDB APIs, RBAC
configuration
●Private Link & CMEK
●Log forwarding & performance metrics-better
observability
●Workspace-unified developer experience
●GraphQLSupport & Simplified Drivers API
●Bloomsupport for GDSalgorithms
●GDS Python API
●Knowledge Graph Embeddings
●Longest Path & Topological SortAlgorithm
●VectorSearch & index
●Embedding APIs & LLM Models -Real Time
integration
●OpenAI + MS Azure OpenAI, VertexAI, AWS
Bedrock, Langchain, LlamaIndexetc. -Real Time
GenAI integration

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Graphes de
Connaissances
Le partenaire idéal des LLMs pour une IA fiable
22

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1.The Graph
2.The Knowledge
3.LLMS & GraphRAG
4.mini-Demo and
takeaways
23

19. Neo4j Inc. All rights reserved 2023
The Graph
The Shape of data
24

20. Explore the connections in your data to
unlock deeper insights

21. 26
The Property Graph: Simply Powerful
Employee CityCompany
Nodes represent
objects (nouns)
Relationships are directional
Relationships connect nodes are
represent actions (verbs)
Relationships can have properties
(name/value pairs)
Nodes can have properties
(name/value pairs)
name: Amy Peters
date_of_birth:1984-03-01
employee_ID: 1
:HAS_CEO
start_date: 2008-01-20
:LOCATED_IN

22. 27
Cypher (GQL): Pattern Based
MATCH(p:Employee {employee_ID: 1}) -[r:WORKS_AT*..3]-(c:Company)
RETURNc.name as company, count(*) as strength
ORDER BYstrength DESC
Node
Pattern
Relationship
Pattern

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The Knowledge
The Semantic layer
29

24. Two “types”of semantics
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25. Two “types”of semantics
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26. Two “types”of semantics
Neo4j Inc. All rights reserved 202432

27. 333
3

28. Knowledge graphs enable search with
explicit and implicit (vector) relationships
34Neo4j Inc. All rights reserved 2024

29. A Knowledge Graphcaptures
key enterprise knowledge in
the form of entities and
relationships between them.
Some nodes in the graph have
properties with NL text
353
5

30. These property values get
embedded(transformed into a
numeric representation) and
added to a vector index to
enable vector-based semantic
search.
363
6

31. A semantic search on the
vector index returns the k
approximate nearest
neighboursto the search
concept (word, question, image,
etc)
373
7

32. Each result from the vector
search is “dereferenced“ to get
the corresponding node in the
graph and a subsequent graph
explorationfinds semantically
related elementsthat enrich
and augment the final search
result.
383
8

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LLMs & GraphRAG
Reliable AI
39

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Grounding LLMs with KG: GraphRAG

35. © 2023 Neo4j, Inc. All rights reserved.
Demo time
Code:
https://github.com/jbarrasa/goingmeta/tree/main/session
24

36. Neo4j Inc. All rights reserved 202443
Demo: Q&A on document with rich internal structure
https://www.irishstatutebook.ie/eli/2015/si/516/made/en/p
df

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The document as a graph

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Merci
[email protected]