GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx

Product Update
Knowledge Graphs
Andreas “ABK” Kollegger
GenAI Lead for Developer Relations
1

© 2024 Neo4j, Inc. All rights reserved.
Agenda
New features in Neo4j & a peek into the future
Knowledge Graphs + LLM = 😁
2

New in Neo4j 5 -
recap
3

Parallel Runtime for faster analytical Queries
Change Data Capture better data integration
Autonomous clustering and Fabric for limitless scalability
Graph Schema, Improved Backup recovery, incremental
import
Neo4j Ops Manager for managing databases
Aura Enterprise Database on all clouds (AWS, GCP, Azure)
SOC II Type 2 compliance, AuraDB APIs, RBAC configuration
Better observability with security log forwarding
Performance metrics forwarding (EAP)
Private Link & CMEK (Coming soon)
Unified Developer Experience with Workspace
Self-service Data Import
GraphQL Support & Simplified Drivers API
Bloom support for GDS algorithms
GDS Python API
Knowledge Graph Embeddings
Longest Path & Topological Sort Algorithm
Vector Search
Real Time integration with Embedding APIs & LLM Models
Cloud Integrations (OpenAI + MS Azure OpenAI, VertexAI, AWS
Bedrock, Langchain, LlamaIndex)
Neo4j product capabilities launched in 2023
4

Graph Schema: New constraints on nodes,
relationships and properties:
● Unique relationship property
● Relationship key
● Property data types
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
5

Graph Pattern Matching: Improved
expressivity of graph navigation with
quantified path patterns,
a more powerful and performant syntax to
navigate and traverse your graph.
6

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
Graph Pattern Matching Example → Fraud Rings
7
QPP

Parallel Runtime
8

Parallel Runtime: Speed up analytical query up to
100x
9

Parallel Runtime Speedup
Up to 100x faster analytical queries by adding CPU
cores
10
More cores
Faster
Queries

Change Data
Capture (CDC)
11

Change Data Capture: Automated Real-Time Change
Tracking
12

Looking Ahead:
Auto-Sharding
13

Fabric Auto-Sharding
14
Coming 2024+
Mapping
Sharding
Rules

Fabric Auto-Sharding
15
Coming 2024+
Mapping
Sharding
Rules

AI Enabler
Graph Data Science & Generative AI
16

17
Facts
Explicit
Explainable
Words
Implicit
Opaque
KGs LLMs
+
Left Brain + Right Brain
Knowledge Graphs + LLMs
Neo4j Inc. All rights reserved 2024

A Perfect Match
Artificial Intelligence
Machine Learning
Information Architecture
Data Architecture
18
LLM
Knowledge
Graph
Linguistic
Pattern
Matching
Hierarchical
Emergent
Features

Could this be vector search?
Artificial Intelligence
Machine Learning
Information Architecture
Data Architecture
19
LLM
Knowledge
Graph
Linguistic
Pattern
Matching
Hierarchical
Emergent
Features

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
20

What is a Knowledge Graph?
An information architecture with layered connections.
21
DATA INFORMATION KNOWLEDGE INSIGHT MEANING
records sets relationships patterns layers

22
RAG with Neo4j
Find similar documents,
content and data
Expanded context for
related information and
ranking results
Improve GenAI inferences
and insights. Discover new
relationships and entities
Unified search, knowledge graph and data science
capabilities to improve RAG quality and effectiveness
Vector Search,
Full-text Search,
Geospatial,
Pattern match
Data Science
Knowledge
Graph

Knowledge Graph
Complementary
Benefits
23
LLM
Human
Application
Knowledge
Graph
Extend LLM
knowledge
through RAG
Invite human
exploration &
curation
Advanced application
features & analysis

24
1 Knowledge Graph Construction
Gen AI use cases LLM
Knowledge
Graph

Knowledge Graph
Construction with
Cypher Templates
25

26
1
2
Knowledge Graph Construction
RAG-based Chat Applications
Gen AI use cases LLM
Human
Knowledge
Graph

27
Natural Language
Search combining
explicit and implicit
relationships
27

28
1
2
3
Knowledge Graph Construction
RAG-based Chat Applications
Gen AI use cases
RAG-enhanced General Applications
LLM
Human
Application
Knowledge
Graph

29
Natural Language
assistants and co-
pilots,
rooted in business
policy
29
Prompt +
Relevant
Information
Embedding API LLM API
User
Database
Search
Prompt Response
Relevant Results
Knowledge
Graph
Application

Integrate with the
GenAI Ecosystem
GenAI Stack
Application
Generative AI & Embedding Models
Orchestration
Grounding Knowledge Graph
Neo4jGraph
Neo4jVector
GraphCypherQAChain
Neo4jGraphStore
Neo4jVectorStore
KnowledgeGraphIndex
Neo4j GenAI Integrations
Text | Chat | Embedding
NL Query | Image Gen
Neo4j Drivers
Java
Python JavaScript
Call LLM APIs natively via
Cypher using our open-
source APOC library
Integrate Neo4j with leading LLM
open-source frameworks such as
LangChain and LlamaIndex
Agnostic LLM orchestration
connecting graphs to OpenAI,
AWS Bedrock, GCP Vertex AI,
Azure, Anthropic, Hugging Face,
and other proprietary and open
source foundation models

● Co-Pilot in Neo4j Browser for autocomplete &
Cypher generation
● Bloom & NeoDash NL integration
● More framework integrations:
○ Langchain, LlamaIndex, SemanticKernel,
Spring.AI, Haystack
31
POWERING GENERATIVE AI APPS
Neo4j’s GenAI Roadmap Coming 2024+

Parallel Runtime for faster analytical Queries
Change Data Capture better data integration
Autonomous clustering and Fabric for limitless scalability
Graph Schema, Improved Backup recovery, incremental
import
Neo4j Ops Manager for managing databases
Aura Enterprise Database on all clouds (AWS, GCP, Azure)
SOC II Type 2 compliance, AuraDB APIs, RBAC configuration
Better observability with security log forwarding
Performance metrics forwarding (EAP)
Private Link & CMEK (Coming soon)
Unified Developer Experience with Workspace
Self-service Data Import
GraphQL Support & Simplified Drivers API
Bloom support for GDS algorithms
GDS Python API
Knowledge Graph Embeddings
Longest Path & Topological Sort Algorithm
Vector Search
Real Time integration with Embedding APIs & LLM Models
Cloud Integrations (OpenAI + MS Azure OpenAI, VertexAI, AWS
Bedrock, Langchain, LlamaIndex)
Neo4j product capabilities launched in 2023
32

Thank
You!
andreas@neo4j.com
33

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