Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris

Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j

Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.

1. Nouveautés Produits & Graphes de Connaissances Dr. Jesús Barrasa Head of Solutions Architecture EMEA ‹#›

2. Recent features in Neo4j Knowledge Graphs ‹#› Agenda

3. 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 Neo4j Inc. All rights reserved 2024 ‹#› NEO4J AURA 2023 Key Capabilities

4. Customer Managed Keys (Encryption) ‹#› Neo4j Inc. All rights reserved 2024 What is it Aura encrypts all data at transit & rest by default. Customer Managed Keys (CMK) is an alternative way to protect cloud data for security conscious Enterprises, enabling customers to manage their own keys for encryption / decryption at disk on Aura using Key Management Services (KMS) from their Cloud Service Provider. Why it is important Customers can protect their own data, control access and have the ability to revoke access, even from Neo4j. Customers can adhere to their own stringent security policy around access and key rotation, on top of Aura’s Enterprise grade default security and compliance posture.

5. Customer Managed Keys (Encryption) ‹#›

6. Graph Schema

7. Graph Schema: New constraints on nodes, relationships and properties: Unique relationship property Relationship key Property data types NEO4J 5.0 NEW CAPABILITIES Database Enhancements Neo4j Inc. All rights reserved 2024 ‹#›

8. Graph Schema / Graph Type Neo4j Inc. All rights reserved 2024 ‹#› The definition of the informational content of a schema (or rather a graph type), comprising: A set of node type descriptors (also known as a node type set). A set of edge type descriptors (also known as an edge type set). A node type name dictionary that maps node type names, which are identifiers, to node types contained in the node type set of this graph type descriptor such that each node type name is mapped to a single node type. An edge type name dictionary that maps edge type names, which are identifiers, to edge types contained in the edge type set of this graph type descriptor such that each edge type name is mapped to a single edge type. CREATE OR REPLACE GRAPH TYPE FraudDet (a:AccountHolder { FirstName :: STRING!, LastName :: STRING!, UniqueId :: STRING! } ...) REQUIRE UniqueId IS KEY , (c:CreditCard {AccountNumber :: STRING!, Balance :: FLOAT!, ...} ...) REQUIRE AccountNumber IS KEY , ... (a)-[:HAS_CARD ...]->(c), (a)-[:HAS_ACCOUNT ...]->(b),... CREATE OR REPLACE DATABASE foo ... [ WITH GRAPH TYPE FrautDet] ...

9. QPP

10. 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 Neo4j Inc. All rights reserved 2024 ‹#›

11. NEO4J 5.0 NEW CAPABILITIES Database Enhancements Graph Pattern Matching Example → Fraud Rings Neo4j Inc. All rights reserved 2024 ‹#› 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 👉 read more: https://bit.ly/pierre-qpp

12. Parallel Runtime

13. NEO4J 5.0 NEW CAPABILITIES Parallel Runtime: Speed up analytical query up to 100x Neo4j Inc. All rights reserved 2024 ‹#›

14. Parallel Runtime Speedup Up to 100x faster analytical queries by adding CPU cores Neo4j Inc. All rights reserved 2024 ‹#› More cores Faster Queries

15. Change Data Capture (CDC)

16. NEO4J 5.0 NEW CAPABILITIES Change Data Capture: Automated Real-Time Change Tracking Neo4j Inc. All rights reserved 2024 ‹#›

17. Workspace Started Unifying Experience of our Graph Tools Neo4j Inc. All rights reserved 2024 ‹#› data importer browser bloom Default experience in Aura for non-Enterprise, Optional for Enterprise Neo4j Browser → Query Possible to connect to self-managed instances

18. Improvements in Workspace Query Neo4j Inc. All rights reserved 2024 ‹#› New Editor Saved Cypher (fka Favorites) Visualization Library from Bloom

19. Improvements in Bloom / Explore Neo4j Inc. All rights reserved 2024 ‹#› Data Slicer Scene Actions GDS Algos Improved Search UX

20. New in Neo4j 5 - recap

21. Neo4j Product capabilities launched in 2023/2024 Neo4j Inc. All rights reserved 2024 ‹#› Parallel Runtime - faster analytical Queries Change Data Capture - better data integration Autonomous clustering & Fabric - limitless scalability Graph Schema & constraints Backup with point-in-time recovery Incremental import Neo4j/AuraDB Ops Manager for managing databases Aura Enterprise Database on all clouds (AWS, GCP, Azure) SOC II Type 2 compliance, AuraDB APIs, RBAC configuration Private Link & CMEK Log forwarding & performance metrics - better observability Workspace - unified developer experience GraphQL Support & Simplified Drivers API Bloom support for GDS algorithms GDS Python API Knowledge Graph Embeddings Longest Path & Topological Sort Algorithm Vector Search & index Embedding APIs & LLM Models - Real Time integration OpenAI + MS Azure OpenAI, VertexAI, AWS Bedrock, Langchain, LlamaIndex etc. - Real Time GenAI integration

22. Graphes de Connaissances Le partenaire idéal des LLMs pour une IA fiable ‹#›

23. The Graph The Knowledge LLMS & GraphRAG mini- Demo and takeaways ‹#›

24. Explore the connections in your data to unlock deeper insights

25. ‹#› The Property Graph: Simply Powerful Employee City Company Nodes represent objects (nouns) Relationships are directional Relationships connect nodes are represent actions (verbs) Relationships can have properties (name/value pairs) Nodes can have properties (name/value pairs) name: Amy Peters date_of_birth: 1984-03-01 employee_ID: 1 :HAS_CEO start_date: 2008-01-20 :LOCATED_IN

26. ‹#› Cypher (GQL): Pattern Based MATCH (p: Employee {employee_ID: 1} ) -[r:WORKS_AT*.. 3 ]- (c: Company ) RETURN c.name as company, count(*) as strength ORDER BY strength DESC Node Pattern Relationship Pattern

27. ‹#› https://www.iso.org/standard/76120.html “ Neo4j Welcomes New GQL International Standard in Major Milestone for Database Industry ” https://neo4j.com/press-releases/gql-standard/ SAN MATEO, Calif. – April 17, 2024

28. The Knowledge The Semantic layer ‹#›

29. Two “types” of semantics Neo4j Inc. All rights reserved 2024 ‹#›

30. Two “types” of semantics Neo4j Inc. All rights reserved 2024 ‹#›

31. Two “types” of semantics Neo4j Inc. All rights reserved 2024 ‹#›

32. ‹#› ‹#›

33. Knowledge graphs enable search with explicit and implicit (vector) relationships ‹#› Neo4j Inc. All rights reserved 2024

34. A Knowledge Graph captures key enterprise knowledge in the form of entities and relationships between them . Some nodes in the graph have properties with NL text ‹#› ‹#›

35. These property values get embedded (transformed into a numeric representation) and added to a vector index to enable vector-based semantic search . ‹#› ‹#›

36. A semantic search on the vector index returns the k approximate nearest neighbours to the search concept (word, question, image, etc) ‹#› ‹#›

37. Each result from the vector search is “dereferenced“ to get the corresponding node in the graph and a subsequent graph exploration finds semantically related elements that enrich and augment the final search result. ‹#› ‹#›

38. LLMs & GraphRAG Reliable AI ‹#›

39. ‹#› 40.4165 > 41.9 ??? 🤨 41.3275 < 41.9 👍 🤯

40. Neo4j Inc. All rights reserved 2024 ‹#› Grounding LLMs with KG: GraphRAG