Andreas Kolleger - ABK - presentes the Neo4j vision and roadmap at GraphSummit Copenhagen March 2024

1. Product Update
Knowledge Graphs
Andreas “ABK” Kollegger
GenAI Lead for Developer Relations
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2. © 2024 Neo4j, Inc. All rights reserved.
Agenda
New features in Neo4j & a peek into the future
Knowledge Graphs + LLM = 😁
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3. © 2024 Neo4j, Inc. All rights reserved.
New in Neo4j 5 -
recap
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4. © 2024 Neo4j, Inc. All rights reserved.
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
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5. © 2024 Neo4j, Inc. All rights reserved.
Graph Schema: New constraints on nodes,
relationships and properties:
● Unique relationship property
● Relationship key
● Property data types
NEO4J 5.0 NEW CAPABILITIES
Database Enhancements
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6. © 2024 Neo4j, Inc. All rights reserved.
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
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7. © 2024 Neo4j, Inc. All rights reserved.
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 → Fraud Rings
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QPP

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Parallel Runtime
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NEO4J 5.0 NEW CAPABILITIES
Parallel Runtime: Speed up analytical query up to
100x
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Parallel Runtime Speedup
Up to 100x faster analytical queries by adding CPU
cores
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More cores
Faster
Queries

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Change Data
Capture (CDC)
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NEO4J 5.0 NEW CAPABILITIES
Change Data Capture: Automated Real-Time Change
Tracking
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Looking Ahead:
Auto-Sharding
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NEO4J 5.0 NEW CAPABILITIES
Fabric Auto-Sharding
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Coming 2024+
Mapping
Sharding
Rules

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NEO4J 5.0 NEW CAPABILITIES
Fabric Auto-Sharding
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Coming 2024+
Mapping
Sharding
Rules

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AI Enabler
Graph Data Science & Generative AI
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17. 17
Facts
Explicit
Explainable
Words
Implicit
Opaque
KGs LLMs
+
Left Brain + Right Brain
Knowledge Graphs + LLMs
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18. A Perfect Match
Artificial Intelligence
Machine Learning
Information Architecture
Data Architecture
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LLM
Knowledge
Graph
Linguistic
Pattern
Matching
Hierarchical
Emergent
Features

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

20. Why RAG With Vector Databases Fall Short
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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
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Neo4j Inc. All rights reserved 2024
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21. What is a Knowledge Graph?
An information architecture with layered connections.
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Neo4j Inc. All rights reserved 2024
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DATA INFORMATION KNOWLEDGE INSIGHT MEANING
records sets relationships patterns layers

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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

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

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1 Knowledge Graph Construction
Gen AI use cases LLM
Knowledge
Graph

25. Knowledge Graph
Construction with
Cypher Templates
Neo4j Inc. All rights reserved 2024
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Knowledge Graph Construction
RAG-based Chat Applications
Gen AI use cases LLM
Human
Knowledge
Graph

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Natural Language
Search combining
explicit and implicit
relationships
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Knowledge Graph Construction
RAG-based Chat Applications
Gen AI use cases
RAG-enhanced General Applications
LLM
Human
Application
Knowledge
Graph

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

30. Integrate with the
GenAI Ecosystem
30 Neo4j Inc. All rights reserved 2023
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

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

32. © 2024 Neo4j, Inc. All rights reserved.
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
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33. © 2024 Neo4j, Inc. All rights reserved.
Thank
You!
andreas@neo4j.com
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