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1 of 28
Product Update
Knowledge Graphs
Andreas “ABK” Kollegger
Senior Developer Advocate
1
Agenda
New features in Neo4j & a peek into the future
Knowledge Graphs + LLM = 😁
Demonstration
2
New in Neo4j 5 -
recap
3
Graph Schema: New constraints on nodes,
relationships and properties: Unique relationship
property, Relationship key, Property data types
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
4
Graph Pattern Matching: Improved
expressivity of graph navigation with
quantified path patterns. More powerful and
performant syntax to navigate and traverse
your graph.
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
5
MATCH path=(a:Account)-[rel:TRANSACTION*3..6]->(a)
WHERE size(apoc.coll.toSet(nodes(path))) = size(nodes(path)) - 1
AND ALL(idx in range(0, size(rel)-2)
WHERE (rel[idx]).date < (rel[idx+1]).date
AND (rel[idx+1].amount / rel[idx].amount) * 100 <= 20
)
RETURN path
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
Graph Pattern Matching Example
6
Pre-QPP
MATCH path=(a:Account)-[:PERFORMS]->(first_tx)
((tx_i)-[:BENEFITS_TO]->(a_i)-[:PERFORMS]->(tx_j)
WHERE tx_i.date < tx_j.date
AND 0.80 <= tx_i.amount / tx_j.amount <= 1.00
){3,6}
(last_tx)-[:BENEFITS_TO]->(a)
WHERE size(apoc.coll.toSet([a]+a_i)) = size([a]+a_i)
RETURN path
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
Graph Pattern Matching Example
7
QPP
Parallel Runtime
10
NEO4J 5.0 NEW CAPABILITIES
Parallel Runtime: Speed up analytical query up to 100x
11
Parallel Runtime Speedup
Up to 100x faster analytical queries by adding CPU cores
12
More cores
Faster
Queries
Change Data
Capture (CDC)
13
NEO4J 5.0 NEW CAPABILITIES
Change Data Capture: Automated Real-Time Change Tracking
14
Looking Ahead:
Auto-Sharding
15
NEO4J 5.0 NEW CAPABILITIES
Fabric Auto-Sharding
16
Coming 2024+
Mapping
Sharding
Rules
NEO4J 5.0 NEW CAPABILITIES
Fabric Auto-Sharding
17
Coming 2024+
Mapping
Sharding
Rules
AI Enabler
Graph Data Science & Generative AI
19
Why RAG With Vector Databases Fall Short
1
3
2
4
Similarity is insufficient for rich enterprise reasoning
Only leverage a fraction of
your data: Beyond simple
“metadata”, vector databases
alone fail to capture relationships
from structured data
Miss critical context: Struggle to
capture connections across
nuanced facts, making it
challenging to answer multi-step,
domain-specific, questions
Vector Similarity ≠ Relevance:
Vector search uses an incomplete
measure of similarity. Relying on it
solely can result in irrelevant and
duplicative results
Lack explainability:
The black-box nature of
vectors lacks transparency
and explainability
20 Neo4j Inc. All rights reserved 2024
Neo4j Inc. All rights reserved 2023
21
RAG with Neo4j
Find similar documents
and content
Identify entities
associated to content and
patterns
in connected data
Improve GenAI inferences
and insights. Discover new
relationships and entities
Unify vector search, knowledge graph and data science
capabilities to improve RAG quality and effectiveness
Vector Search Data Science
Knowledge
Graph
Irish Bank
Legislation
Dependency Mapping
23
1 Knowledge Graph Construction
Gen AI use cases
Knowledge Graph
Creation with
Cypher Templates
Knowledge Graph
Creation with
Cypher Templates
26
1
2
3
Knowledge Graph Construction
LLMs for Language Generation
Gen AI use cases
RAG-based Applications
27
Natural Language
Search combining
explicit and implicit
relationships
28
Natural Language
Search combining
explicit and implicit
relationships
● Co-Pilot in Neo4j Browser
for autocomplete & Cypher
generation
● Bloom & NeoDash NL
integration
29
POWERING GENERATIVE AI APPS
Neo4j’s GenAI Roadmap Coming 2024+
● Co-Pilot in Neo4j Browser
for autocomplete & Cypher
generation
● Bloom & NeoDash NL
integration
30
POWERING GENERATIVE AI APPS
Neo4j’s GenAI Roadmap Coming 2024+
Thank
You!
andreas@neo4j.com
31

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