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Neo4j y GenAI
Neo4j provides graph databases and graph algorithms that can be used to ground language models. Grounding LLMs in a knowledge graph stored with Neo4j allows the language models to leverage both explicit relationships in the graph as well as implicit relationships derived from vector embeddings. This improves the ability of LLMs to provide accurate, non-hallucinated responses by basing their answers on real world facts and connections within the knowledge graph.
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Neo4j y GenAI
- 1. Neo4j Inc. Allrights reserved 2023 Neo4j and GenAI Luis Salvador PreSales Engineer EMEA South
- 2. Graph creates a moreintuitive and connected view of data relationships, unlocking deeper insights and context 2
- 3. 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: Emil Eifrem date_of_birth: 1979-03-01 employee_ID: 1 :HAS_CEO start_date: 2008-01-20 :LOCATED_IN 3
- 4. Hybrid Workload Duality Intelligent Applications Transactions- Security - Performance & Scalability - ACID Consistency - Intelligent Modeling - Extensive & Supported Algo Library - Scalable - Graph Visualization - Graph Transformations Graph Transactions Graph Analytics & Data Science 4
- 5. © 2023 Neo4j,Inc. All rights reserved. Neo4j Graph Data Science 5
- 6. What’s in itfor you: ● Improve model accuracy by 30% ● Simplify processes and remove headaches ● More projects into production without additional hiring Neo4j Graph Data Science Analytics Feature Engineering Data Exploration Graph Data Science Queries & Search Machine Learning Visualization 6
- 7. When do youneed Graph Algorithms? Query (e.g. Cypher) Real-time, local decisioning and pattern matching Graph Algorithms Global analysis and iterations You know what you’re looking for and making a decision You’re learning the overall structure of a network, updating data, and predicting Local Patterns Global Computation 7
- 8. What’s important? Prioritization Who hasthe most connections? Who has the highest page rank? Who is an influencer? What’s unusual? Anomaly & Fraud Detection Where is a community forming? What are the group dynamics? What’s unusual about this data? What’s next? Predictions What’s the most common path? Who is in the same community? What relationship will form? Pl ay s Lives_in In_sport Likes F a n _ o f Plays_for K n o w s Knows Knows K n o w s Answer critical business questions 8
- 9. © 2023 Neo4j,Inc. All rights reserved. Vector Index Search 9
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- 11. What is avector ● Length ● Direction ● Components have meaning horizontal vertical 11
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- 13. What are vectorembeddings ● Same concepts, just “an arrow” ● 100s or 1000s dimensions ● Each dimension corresponds to an interesting feature or characteristic 13
- 14. Kings and Queens king− man + woman ≈ queen k i n g man wom an 1 k i n g man wom an 2 q u e e n ? 3 14
- 15. Neighborhoods ● cosine ● direction/ angle based vector point query nearest 4 ● Euclidean ● distance based 15
- 16. KGs enable searcheswith explicit and implicit (vectors) relationships 16
- 17. Neo4j and VectorSearch Find relevant documents and content for user queries Find entities associated to content and patterns in connected data. Improve search relevance & insights by enhancing a Knowledge Graph. Use graph algorithms and ML to discover new relationships, entities, and groups. Vector Similarity Search Graph Traversals & Pattern Matching Knowledge Graph Inference & ML Vector Search Graph Database 17
- 18. © 2023 Neo4j,Inc. All rights reserved. LLMs and GenAI 18
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- 21. Gen AI iseverywhere We’re at mile one of the marathon 21 The next big thing
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- 25. Learns random sentences from randompeople Talks like a person but doesn’t really understand what it’s saying Occasionally speaks absolute nonsense Is a cute little bird Yes Yes Yes Yes Yes Yes Yes No The limitations of GenAI 25
- 26. LLM Hallucinations Definition: Languagemodels generate text that is incorrect, nonsensical, or unreal. • Appear to answer questions confidently even if they don’t have facts • May provide contradicting or inconsistent responses to similar prompts 26
- 27. The limitations ofGenAI Hallucinates Limited input sizes for fine tuning Lack of enterprise domain knowledge Inability to verify answers Sensitive to prompt (input) phrasing Ethical and data bias concerns 27
- 28. How to HelpLLMs Do Better? Fine-Tuning Grounding Provide completed examples “shots” to the AI as context in prompts. a.k.a In-Context Learning Provide additional training data to better tune GenAI to your use case Provide AI with the information to use for generating responses All of these are useful, but grounding is where Neo4j adds value Neo4j as the data source for Grounding 28 Few-Shot Learning
- 29. Ground LLMs inNeo4j’s Knowledge Graph 29 29
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- 31. 31 1 2 LLMs for LanguageGeneration Gen AI use cases
- 32. Generate personalized Natural Language experiences Neo4j Inc.All rights reserved 2023
- 33. 33 1 2 LLMs for LanguageGeneration RAG-based Applications Gen AI use cases
- 34. Grounding with Retrieval Augmented Generation(RAG) Neo4j Inc. All rights reserved 2023
- 35. Natural Language (NL) Searchon explicit relationships Neo4j Inc. All rights reserved 2023
- 36. Neo4j Inc. Allrights reserved 2023 36 Natural Language Search combining explicit and implicit relationships
- 37. 37 1 2 3 LLMs for LanguageGeneration RAG-based Applications Gen AI use cases Knowledge Graph Construction
- 38. Creating a Knowledge Graph FromUnstructured Text was Difficult Neo4j Inc. All rights reserved 2023
- 39. Knowledge Graph Creation with CypherTemplates Neo4j Inc. All rights reserved 2023 39
- 40. © 2023 Neo4j,Inc. All rights reserved. How will you transform your organization?
- 41. © 2023 Neo4j,Inc. All rights reserved. Thank You! See you 9th April 2024 at our next GraphSummit Madrid! 41