GraphSummit Madrid - Product Vision and Roadmap - Luis Salvador Neo4j

Luis Salvador
Sales Engineer
1

© 2024 Neo4j, Inc. All rights reserved.
New features in Neo4j and roadmap
Knowledge Graphs + LLM = 😁
2

• 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
Neo4j Inc. All rights reserved 20244
4
• 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)

Graph Schema: New constraints on
nodes, relationships and properties:
• Unique relationship property
• Relationship key
• Property data types
NEO4J 5.x 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

Graph Pattern Matching Example → Fraud Rings
7
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

Parallel Runtime: Speed up analytical query up to 100x
8

Parallel Runtime Speedup
Up to 100x faster analytical queries by adding CPU
cores
9
More cores
Faster
Queries

Change Data Capture: Automated Real-Time
Change Tracking
10

Vector embeddings
12
● Same concepts, just “an arrow”
● 100s or 1000s dimensions
● Each dimension corresponds to an
interesting feature or characteristic
king − man + woman ≈ queen
1 2 3

Fabric Auto-Sharding
15
Coming 2024+
Mapping
Sharding
Rules

17
94%of business
leaders agree that AI is
critical to success over
the next five years1
1. Deloitte’s State of AI in the Enterprise

● Lack of enterprise domain knowledge
● Inability to verify answers
● Hallucination
● Ethical and data bias concerns
19

Ground LLMs in Neo4j’s Knowledge Graph
20 Neo4j Inc. All rights reserved 2024
20

Why RAG With Vector Databases Fall Short
Similarity is insufficient for rich enterprise reasoning
21
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

22
What is a Knowledge Graph?
An information architecture with layered connections.
DATA INFORMATION KNOWLEDGE INSIGHT MEANING
records sets relationships patterns layers

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

RAG with Neo4j
24
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

25
1 Knowledge Graph Construction
Gen AI use cases LLM
Knowledge
Graph

26
Knowledge Graph
Construction with
Cypher Templates

Human
27
Knowledge
Graph
2 RAG-based Chat Applications

28
Natural Language
Search combining
explicit and implicit
relationships

Application Human
29
Knowledge
Graph
2 RAG-based Chat Applications
3 RAG-enhanced General Applications

30
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
31
GenAI Stack
Application
Generative AI & Embedding Models
Orchestration
Grounding Knowledge Graph
Neo4jGraph
Neo4jVector
GraphCypherQAChain
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
32
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
33
• 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)

34

GraphSummit Madrid - Product Vision and Roadmap - Luis Salvador Neo4j

Recommandé

Recommandé

Contenu connexe

Similaire à GraphSummit Madrid - Product Vision and Roadmap - Luis Salvador Neo4j

Similaire à GraphSummit Madrid - Product Vision and Roadmap - Luis Salvador Neo4j (20)

Plus de Neo4j

Plus de Neo4j (20)

Dernier

Dernier (20)

GraphSummit Madrid - Product Vision and Roadmap - Luis Salvador Neo4j