Neo4j_Anurag Tandon_Product Vision and Roadmap.Benelux.pptx.pdf

Neo4j Inc. All rights reserved 2023
Product Vision
and Roadmap
Anurag Tandon
VP, Product Management
1

New features in Neo4j & a peek into the future
Knowledge Graphs + LLM = 😁
2
Agenda

Cloud Scale with Enterprise Security, Governance & Compliance
Trusted Fundamentals
Cloud First AI Enabler
Ease of Use
Most advanced Graph Algorithms
with in memory projections
Enable intelligent GenAI apps with
Knowledge graphs + LLMs
Integrate with GenAI platforms
Cloud native service integrations to
simplify application development
Fully Managed Database for 0
operations experience so developers
can focus on building applications
Get started with Free Database
Self-service capabilities that enable
developers to improve time to value
Simplify APIs and tools to improve
productivity of developers
Enhanced developer tooling
PRODUCT VISION
Cloud First With Ecosystem Focus
3

4
New in Neo4j 5 -
recap

● 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)
Neo4j product capabilities launched in 2023
5
● 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)

● Ubiquitous availability of Aura in 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
﹣ CMEK-Coming soon
6
NEO4J AURA
2023 Key Capabilities

Graph Schema: New constraints on
nodes, relationships and properties:
● Unique relationship property
● Relationship key
● Property data types
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
7

Graph Pattern Matching: Improved
expressivity of graph navigation with
quantiﬁed path patterns,
a more powerful and performant syntax
to navigate and traverse your graph.
8

Graph Pattern Matching Example → Fraud Rings
9
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

10
Parallel Runtime

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

13
Change Data
Capture (CDC)

Change Data Capture: Automated Real-Time
Change Tracking
14

Workspace Started Unifying Experience of our Graph Tools
15
data importer
browser
bloom
Default experience in Aura for non-Enterprise,
Optional for Enterprise
Neo4j Browser → Query
Possible to connect to self-managed instances

Improvements in Workspace Query
16
New Editor
Saved Cypher (fka Favorites)
Visualization Library from Bloom

Improvements in Bloom / Explore
17
Data Slicer
Scene Actions
GDS Algos
Improved Search UX

18
Looking Ahead

Fabric Auto-Sharding
19
Coming 2024+
Mapping
Sharding
Rules

Uniﬁed Product User Experience
20
Coming 2024+
Aura Console
Graph Tools
Ops
Management

Ephemeral Graph Data Science
21
Coming 2024+
Data transferred via
Arrow from Neo4j
transactional format
into GDS analytics
format
Managed runtimes for running
optimized GDS algorithms or
can package and run full GNN
executions that leverage GDS
features and GDS pipelines
Neo4j
AuraDB
GDS
Session
AuraDS
EVALUATION
Built-in
runtimes for
GDS native
algorithms
Unmanaged GNN execution
Managed Runtimes

AI Enabler
Graph Data Science & Generative AI
22

23
Knowledge Graphs + LLMs
23
Facts
Explicit
Explainable
Words
Implicit
Opaque
KGs LLMs
+
Left Brain + Right Brain

24
A Perfect Match
24
Artiﬁcial Intelligence
Machine Learning
Information Architecture
Data Architecture
LLM
Knowledge
Graph
Linguistic
Pattern
Matching
Hierarchical
Emergent
Features

25
Could this be vector search?
25
Artiﬁcial Intelligence
Machine Learning
Information Architecture
Data Architecture
LLM
Knowledge
Graph
Linguistic
Pattern
Matching
Hierarchical
Emergent
Features
v
e
c
t
o
r

Why RAG With Vector Databases Fall Short
Similarity is insufﬁcient for rich enterprise reasoning
26
1
3
2
4
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

27
What is a Knowledge Graph?
An information architecture with layered connections.
DATA INFORMATION KNOWLEDGE INSIGHT MEANING
records sets relationships patterns layers

RAG with Neo4j
28
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

29
Knowledge Graph Complementary Beneﬁts
29
LLM
Human
Application
Knowledge
Graph
Extend LLM
knowledge
through RAG
Invite human
exploration &
curation
Advanced
application
features & analysis

30
1 Knowledge Graph Construction
Gen AI use cases LLM
Knowledge
Graph

31
Knowledge Graph
Construction with
Cypher Templates

Human
32
Knowledge
Graph
2 RAG-based Chat Applications

33
Natural Language
Search combining
explicit and implicit
relationships

Application Human
34
Knowledge
Graph
2 RAG-based Chat Applications
3 RAG-enhanced General Applications

35
Natural Language
assistants and
co-pilots,
rooted in business
policy
Prompt +
Relevant
Information
Embedding API LLM API
User
Database
Search
Prompt Response
Relevant Results
Knowledge
Graph
Application

● Integrate Neo4j with leading LLM
open-source frameworks such as
LangChain and LlamaIndex
● Call LLM APIs natively via Cypher using
our open-source APOC library
● Agnostic LLM orchestration connecting
graphs to OpenAI, AWS Bedrock, GCP
Vertex AI, Azure, Anthropic, Hugging
Face, and other proprietary and open
source foundation models
Integrate with the GenAI Ecosystem
36
GenAI Stack
Application
Generative AI & Embedding Models
Orchestration
Grounding Knowledge Graph
Neo4jGraph
Neo4jVector
GraphCypherQAChai
n
Neo4jGraphStore
Neo4jVectorStore
KnowledgeGraphIndex
Neo4j GenAI Integrations
Text | Chat | Embedding
NL Query | Image Gen
Neo4j Drivers
Java
Python JavaScript

● Co-Pilot in Neo4j Browser for autocomplete
& Cypher generation
● Bloom & NeoDash NL integration
● More framework integrations:
﹣ Langchain, LlamaIndex,
SemanticKernel, Spring.AI, Haystack
POWERING GENERATIVE AI APPS
Neo4j’s GenAI Roadmap
37
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)
Neo4j product capabilities launched in 2023
38
● 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)

39
Thank
You!
anurag.tandon@neo4j.com

Neo4j_Anurag Tandon_Product Vision and Roadmap.Benelux.pptx.pdf

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