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18
Cypher: A Powerful & Expressive Query Language
MARRIED_TO
Person
name: ‘Dan’
spouse
MATCH (p:Person {name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
NODE RELATIONSHIP TYPE
LABEL PROPERTY
NODE
RETURN p.name as husband, spouse
VARIABLE VARIABLE](https://image.slidesharecdn.com/neo4jgraphragworkshop14112024-241119064858-8cf02d86/75/Hands-On-GraphRAG-Workshop-GraphSummit-Melbourne-2024-18-2048.jpg)
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Hands-On GraphRAG Workshop: GraphSummit Melbourne 2024
The document details a hands-on workshop focused on building GenAI applications using Neo4j and knowledge graphs. It covers topics such as knowledge graph basics, vector and semantic search, data enrichment, and the integration of large language models for personalized content generation, specifically for e-commerce fashion assistants. Participants are guided through connecting to a Neo4j workspace, accessing resources like Colab notebooks, and practical exercises to enhance GenAI insights and functionalities.
In this document
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Overview of the GraphRAG workshop, introduction of speakers, and session agenda focusing on Neo4j & GenAI applications.
Introduction to Retrieval-Augmented Generation (RAG) and its significance in building LLM applications.
Example scenario of creating an AI fashion assistant using a knowledge graph and recommendation systems.
Discussion on the synergy of Knowledge Graphs (KGs) and Large Language Models (LLMs) for better content generation.
Architecture of the demo application showcasing the integration of multiple data sources and models.
Instructions for connecting to Neo4j Aura instance and accessing the GitHub repository for practical sessions.
Introduction to Knowledge Graph components, such as nodes and relationships, enhanced with a Cypher query example.
Insights into Graph Data Science techniques, Vector Search, and data embeddings for effective search operations.
Concept of semantic search and graph pattern matching to enhance user query relevance and insights.
Overview of Graph Data Science capabilities, including node embeddings and KNN for advanced analytics.
Techniques for generating personalized content through LLM with customer-centric parameters and recommendations.
Examples of industry applications of Neo4j in enhancing data operations and AI integration.
Closing remarks and gratitude expressed to participants.
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Hands-On GraphRAG Workshop: GraphSummit Melbourne 2024
- 1. Hands-On GraphRAG Workshop BuildingSmarter GenAI Apps with Knowledge Graphs Emil Pastor | Head of Solution Engineering Samko Yun | Sr. Solution Engineer Darren Wood | Sr. Solution Engineer Muddassir Zaidi | Sr. Consulting Engineer Angelo Bueti | Professional Services Manager
- 2. ● Ask questionsstraight away, this is an interactive session ● Raise your hand if you are stuck ● Have fun Workshop Rules
- 3. Before We Start 1.Connect to your Aura instance through workspace workspace.neo4j.io 2. Open the notebook in Colab (needs a google account!) github.com/neo4j-product-examples/ genai-workshop 3. open genai-workshop.ipynb
- 4. Agenda Introduction 1. Introduction toNeo4j & GenAI 2. GenAI App Demo! (what we will build today) Hands On 1. Knowledge Graph Basics 2. Vector Search 3. Semantic Search with Graph Patterns 4. Enrichment with Graph DS & ML 5. LLM Powered Content Generator
- 5. Retrieval-Augmented Generation Is Becomingan Industry Standard RAG augments LLMs by retrieving up-to-date external data to inform responses: ● Provide domain-specific, relevant responses ● Reduce hallucinations with verified data ● Enable traceability back to sources
- 6. Find similar documents andcontent Identify entities associated to content and patterns in connected data Improve & enrich GenAI insights. Discover new relationships and entities Unify vector search, knowledge graph, and data science to improve RAG quality and effectiveness Vector Search Graph Data Science Knowledge Graph GraphRAG with Neo4j
- 7. Today We Build anLLM Chain with GraphRAG
- 8. This is Eva Forher birthday, Eva wants a Halter Neck Top How do you choose what to buy?
- 9. For her birthday,Eva wants a Halter Neck Top How do you choose what to buy? 1. Get a list of Halter Neck Top 2. Match the Halter Neck Top for Eva 3. Accessorize or add another item This is Eva
- 10. This Requires: 1. Data:Kaggle H&M 2. Search & Retrieval 3. Context for Personalization 4. Recommendation Engine 5. LLM Powered Content Generator At Scale: Let’s Build an AI Fashion Assistant
- 11. Solution: LLMs andKnowledge Graphs Knowledge Context Enrichment Language Reasoning Creativity KGs LLMs + BETTER TOGETHER
- 12. We'll Build aTargeted, Personalized Content Generator
- 13. Structured Purchase Data Neo4j Inc.All rights reserved 2024 13 Demo Architecture Graph Data Science Unstructured Product Descriptions INGEST INGEST Python Notebook
- 14. Journey 1 Knowledge Graph (Kaggle H&MDataset) 2 Vector Search (Initial Retrieval) 3 Semantic Search with Graph Patterns (Personalization) 4 Graph DS & ML (Recommendations) LLM Powered Content Generator & Fashion Assistant 5
- 15. Now We Start 1.Connect to your Aura instance through workspace workspace.neo4j.io 2. Open the notebook in Colab (needs a google account!) github.com/neo4j-product-examples/ genai-workshop 3. open genai-workshop.ipynb
- 16. 1. Knowledge GraphBasics 1 Knowledge Graph (Kaggle H&M Dataset) 2 Vector Search (Initial Retrieval) 3 Semantic Search with Graph Patterns (Personalization) 4 Graph DS & ML (Recommendations) LLM Powered Content Generator & Fashion Assistant 5
- 17. Neo4j Inc. Allrights reserved 2024 Name: “Andre” Born: May 29, 1970 Twitter: “@dan” Name: “Mica” Born: Dec 5, 1975 Brand “Volvo” Model: “V70” Description: Since: Jan 10, 2011 LOVES KNOWS Person Person LIVES WITH O W N S KNOWS D R I V E S Nodes represent entities in the graph 17 Knowledge Graph Components Relationships represent associations or interactions between nodes Properties represent attributes of nodes or relationships Car
- 18. Neo4j Inc. Allrights reserved 2024 18 Cypher: A Powerful & Expressive Query Language MARRIED_TO Person name: ‘Dan’ spouse MATCH (p:Person {name:“Dan”} ) -[:MARRIED_TO]-> (spouse) NODE RELATIONSHIP TYPE LABEL PROPERTY NODE RETURN p.name as husband, spouse VARIABLE VARIABLE
- 19. © 2024 Neo4j,Inc. All rights reserved. 19 We are turning this:
- 20.
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- 22. Go to Notebook KnowledgeGraph Basics
- 23. 2. Vector Search 1 KnowledgeGraph (Kaggle H&M Dataset) 2 Vector Search (Initial Retrieval) 3 Semantic Search with Graph Patterns (Personalization) 4 Graph DS & ML (Recommendations) LLM Powered Content Generator & Fashion Assistant 5
- 24. What are Embeddings? ●A type of data compression ● Transform messy data (text, images, audio, etc.) into a compact format for ML algorithms ● Most often numeric vectors (a.k.a arrays) with 100s or 1000s of elements ● Preserve information such that “similar” items have proportionally “similar” embedding vectors ● Similarity is measured with vector algorithms (cosine, euclidean, etc.) Graph Graph Model Graph Vector Embeddings
- 25. What Does“Similarity” Mean? ItDepends: ● Text Embeddings => Semantic similarity, the meaning behind a text sequence ● Graph Embeddings => Similarity in position or structure in a graph- can have semantic meaning too
- 26. What will weuse embeddings for? ● (Now) Vector search using text embeddings ● (Later) Recommendations using graph node embeddings
- 27. Search & Vectorsin Neo4j Neo4j makes search efficient through a variety of indexes including ● Range: General index for predicates based on equality and range. ● Point: Predicts on geospatial points like distance bounding boxes, etc. ● Text: Predicates on strings like contains, ends with, etc. ● FullText: Text search based on tokenization and analyzers ● Vector: ANN (Approximate Nearest Neighbor) search on vectors
- 28. How is similaritymeasured vector point query nearest 4 The vector index allows us to query vectors on larger datasets. It uses HNSW to perform k-ANN queries Euclidean Distance based Cosine Direction / angle based
- 29. LangChain Why &How • GenAI orchestration Framework • Many out-of-the-box components and integrations • Prebuilt chains and agents
- 30. Go to Notebook VectorSearch
- 31. 3. Semantic Searchwith Graph Patterns 1 Knowledge Graph (Kaggle H&M Dataset) 2 Vector Search (Initial Retrieval) 3 Semantic Search with Graph Patterns (Personalization) 4 Graph DS & ML (Recommendations) LLM Powered Content Generator & Fashion Assistant 5
- 32. Neo4j & SemanticSearch Vector Similarity Search Graph Pattern Matching Knowledge Graph DS/ML Find relevant documents and content for user queries. Find entities associated to content. Identify 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 Search Graph Database HNSW Graph Data Science
- 33. Semantic Search +Traversal Purchases in common Customers Target Customer Semantically Similar Products
- 34. Go to Notebook SemanticSearch with Graph Patterns
- 35. 4. Graph DataScience & ML 1 Knowledge Graph (Kaggle H&M Dataset) 2 Vector Search (Initial Retrieval) 3 Semantic Search with Graph Patterns (Personalization) 4 Graph DS & ML (Recommendations) LLM Powered Content Generator & Fashion Assistant 5
- 36. Bring the contextof your connected data into a format that other pipelines can ingest. The Largest Catalog of Graph Algorithms Native Graph Catalog and Analytics Workspace Graph Embeddings for ML &AI Pathfinding & Search Centrality Community Detection Machine Learning Link Prediction Similarity and more … Iterate fast with different data sets, models, and version trained models. Over 65 pretuned, parallelized algorithms. Neo4j Graph Data Science (GDS)
- 38. Node Embedding Vector representation ofa node that summarizes it’s position in the graph, local neighborhood structure, and/or node features
- 39. K-Nearest Neighbor (KNN) w/ Node Embeddings Drawconnections between highly interconnected nodes and/or those that have similar roles in the graph 0.2 0.3 0.6 -0.6 0.1 0.4 0.5 -0.4 -0.1 0.5 0.4 -0.4
- 40. Go to Notebook GraphData Science & ML
- 41. 5. LLM PoweredContent Generator 1 Knowledge Graph (Kaggle H&M Dataset) 2 Vector Search (Initial Retrieval) 3 Semantic Search with Graph Patterns (Personalization) 4 Graph DS & ML (Recommendations) LLM Powered Content Generator & Fashion Assistant 5
- 42. Prompting the LLM Youare a personal assistant named Sally for a fashion, home, and beauty company called HRM.Write an engaging email to {customerName} , one of your customers, to promote and summarize products relevant for them given: - The current season / time of year: {timeOfYear} - Recent searches/interests: {customerInterests} Please only mention the products listed below.Do not come up with or add any new products to the list.Each product comes with an https `url` field. Make sure to provide that https url with descriptive name text in markdown for each product. # RelevantProducts: These are products from the HRM store the customer may be interested in based on their recent searches/interests: {customerInterests} {searchProds} # Customer May Also Be Interested In the Following The below candidates are recommended based on the shared purchase patterns of other customers in the HRM database. Select the best 4 to 5 product subset from the context that best match the time of year: {timeOfYear} and to pair with the RelevantProducts above. For example, even if scarfs are listed here, they may not be appropriate for a summer time of year so best not to include those. {recProds}
- 43. LangChain Chain customerId searchPrompt Personalizedsearch Recommendations timeOfYear customerName prompt llm personalized email searchProds recProds { searchProds: (searchPrompt , customer_id)| personalizedSearch recProds:customer_id | recommendations customerName: timeOfYear: } prompt | llm | outputParser
- 44. Go to Notebook LLMPowered Content Generator
- 45. Integrating AI with knowledgegraphs for smarter supply chain management Merging structured and unstructured data for efficient operations Converting vast amounts of unstructured data into actionable knowledge Leveraging AI for customized content at scale Real-World GenAI Breakthroughs Powered by Neo4j
- 46. Banking & Financial Services TechnologyTelecommunications Energy E-Commerce Health & Life Sciences 1,700+ Organizations Use Neo4j
- 47.