GraphSummit Milan - Visione e roadmap del prodotto Neo4j

van Zoratti, VP of Product Management, Neo4j

Scoprite le ultime innovazioni di Neo4j che consentono un’intelligenza guidata dalle relazioni su scala. Scoprite le più recenti integrazioni nel cloud e i miglioramenti del prodotto che rendono Neo4j una scelta essenziale per gli sviluppatori che realizzano applicazioni con dati interconnessi e IA generativa.

1. Visione e roadmap del prodotto Neo4j Ivan Zoratti VP of Product Management

2. SAFE HARBOR ROADMAP DISCLAIMER The information presented here is Neo4j, Inc. confidential and does not constitute, and should not be construed as, a promise or commitment by Neo4j to develop, market or deliver any particular product, feature or function. Neo4j reserves the right to change its product plans or roadmap at any time, without obligation to notify any person of such changes. The timing and content of Neo4j’s future product releases could differ materially from the expectations discussed herein. ‹#›

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

4. New in Neo4 j 5

5. 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

6. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Cloud Scale Procure through Aura Console or via Cloud Marketplace Zero maintenance, automated upgrades and highly available Scalable and elastic, on-demand Enterprise-grade security SOC II Type 2 compliance Easier RBAC configuration with Aura Console Private link CMEK Observability with Ops Manager, performance metrics and logs forwarding

7. 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.

8. Customer Managed Keys (Encryption) ‹#›

9. Unified Product Experience ‹#› Neo4j Inc. All rights reserved 2024 Aura Console Graph Tools Ops Management

10. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit April 12, 2024 Welcome GQL! GQL - Graph Query Language The first new ISO language since 1987 GQL-fueled additions in Cypher: Node and relationship expressions WHERE clause Richer label expressions Sophisticated pattern repetitions SQL-like synonims GQL Error codes GQL is Here: Your Cypher Queries in a GQL World GQL: The ISO Standard for Graphs Has Arrived ISO GQL: A Defining Moment in the History of Database Innovation

11. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit New constraints on nodes, relationships and properties: Unique relationship property Relationship key Property data types NEO4J 5 NEW CAPABILITIES Graph Schema

12. 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] ...

13. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Graph Pattern Matching Improved expressivity of graph navigation with Quantified Path Patterns , a more powerful and performant syntax to navigate and traverse your graph.

14. 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 accountNumber:2 amount: 1000 date: 2023-01-01T10:10:10.000+0000 accountNumber: 1 amount: 900 date: 2023-01-02T10:10:10.000+0000 amount: 729 date: 2023-01-04T10:10:10.000+0000 accountNumber: 4 accountNumber: 3

15. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Parallel Runtime Speed up analytical queries up to 100x

16. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Parallel Runtime Speed up analytical queries up to 100x

17. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Parallel Runtime Speed up analytical queries up to 100x MORE CORES

18. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Parallel Runtime Speed up analytical queries up to 100x FASTER QUERIES MORE CORES

19. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit BLOCK FORMAT Memory Optimized and Future Proof An implementation of graph-native that’s informed by more than a decade of experience supporting real-world production graph workloads. Neo4j is still graph-first; block format is: Native graph storage Optimized for connected data Index-free adjacency Block format supersedes all previous store formats. Migrate, convert, import into Block Format

20. CHANGE DATA CAPTURE Automated Real-Time Change Tracking ‹#› Neo4j Inc. All rights reserved 2024

21. Graph Data at Scale ‹#› Neo4j Inc. All rights reserved 2024 Autonomous Clustering Easy, automated horizontal scale-out Composite Databases Federated queries and sharded graphs

22. Graph Data at Scale ‹#› Neo4j Inc. All rights reserved 2024

23. Properties Sharding ‹#› Neo4j Inc. All rights reserved 2024 Users’ Connections TOPOLOGY DATABASE SHARDED PROPERTY DATABASES Parallel data load Rolling updates on demand

24. AI Enabler Graph Data Science & Generative AI Neo4j Inc. All rights reserved 2024 ‹#›

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

26. A Perfect Match Artificial Intelligence Machine Learning Information Architecture Data Architecture LLM Knowledge Graph Linguistic Pattern Matching Hierarchical Emergent Features Neo4j Inc. All rights reserved 2024 ‹#›

27. Could this be vector search? Artificial Intelligence Machine Learning Information Architecture Data Architecture LLM Knowledge Graph Linguistic Pattern Matching Hierarchical Emergent Features vector Neo4j Inc. All rights reserved 2024 ‹#›

28. Why RAG With Vector Databases Fall Short Similarity is insufficient for rich enterprise reasoning Neo4j Inc. All rights reserved 2024 ‹#› 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

29. ‹#› Neo4j Inc. All rights reserved 2024 DATA INFORMATION KNOWLEDGE INSIGHT MEANING records sets relationships patterns layers What is a Knowledge Graph? An information architecture with layered connections.

30. RAG with Neo4j Neo4j Inc. All rights reserved 2024 ‹#› 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

31. Knowledge Graph Complementary Benefits LLM Human Application Knowledge Graph Extend LLM knowledge through RAG Invite human exploration & curation Advanced application features & analysis Neo4j Inc. All rights reserved 2024 ‹#›

32. 1 Knowledge Graph Construction Gen AI use cases LLM Knowledge Graph Neo4j Inc. All rights reserved 2024 ‹#›

33. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Knowledge Graph Construction with Cypher Templates

34. Graph Builder Neo4j Inc. All rights reserved 2024 ‹#›

35. Human 1 Knowledge Graph Construction Gen AI use cases LLM Knowledge Graph 2 RAG-based Chat Applications Neo4j Inc. All rights reserved 2024 ‹#›

36. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Natural Language Search combining explicit and implicit relationships

37. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit Browser Co-Pilot Uses Text2Cypher model provided by LLM API service layer UI/UX improvements underway for surfacing the copilot feature in Query Soon to be available in Workspace / UPX

38. Application Human 1 Knowledge Graph Construction Gen AI use cases LLM Knowledge Graph 2 RAG-based Chat Applications 3 RAG-enhanced General Applications Neo4j Inc. All rights reserved 2024 ‹#›

39. Neo4j Inc. All rights reserved 2024 ‹#› Vector Index LLM Embeddings API LLM Chat API User Vector Search Prompt + Relevant Information Prompt Response Relevant Results / Documents Embedding Knowledge graph Prompt (Description of Desired Query) Cypher Query LLM API explicit implicit 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

40. 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 Neo4j Inc. All rights reserved 2024 ‹#› GenAI Stack Application Generative AI & Embedding Models Orchestration Grounding Knowledge Graph Neo4j GenAI Integrations Text | Chat | Embedding NL Query | Image Gen Neo4j Drivers Python JavaScript Java Neo4jGraph Neo4jVector GraphCypherQAChain Neo4jGraphStore Neo4jVectorStore KnowledgeGraphIndex

41. 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 Neo4j Inc. All rights reserved 2024 ‹#› Coming 2024+