Knowledge Graphs & GraphRAG: Techniques for Building Effective GenAI Applications: Zach Blumenthal

[@twoplustwo5]

🎯 Key points for quick navigation: 00:00:21 *📖 Introduction to Interactive Session* - Overview of interactive workshop using Python notebook and Neo4j, - Introduction of facilitators aiding the session, - Outline of session structure with emphasis on interaction and hands-on activities. 00:02:09 *🚀 Setup and Preparation* - Instructions to create a Neo4j graph data science sandbox, - Guide to accessing and copying Google Colab notebook for exercises, - Importance of running initial setup tasks for smooth participation. 00:05:22 *🧠 Retrieval Augmented Generation with Graphs Overview* - Explanation of retrieval augmented generation for context enrichment, - Introduction to key components: vector search, graph traversal, and graph data science, - Workshop focus on building a fashion recommendation system using real-world data. 00:09:18 *🛒 Use Case: AI Fashion Assistance* - Building a system to recommend clothing items and generate personalized emails, - Utilizing H&M dataset for practical demonstration, - Potential applications in marketing and customer support scenarios. 00:10:28 *💻 Technology Stack and Course Structure* - Tools involved: OpenAI, Langchain, Colab, Gradio, - Breakdown of session structure from graph building to LLM integration, - Aim to utilize embeddings for enhancing recommendation systems. 00:11:23 *🌐 Graph Model Setup and Data Loading* - Explanation of data model and node types: customers, articles, products, - Instructions to load data using Neo4j and Cypher queries, - Clarification of structural data preparation for graph embeddings. 00:18:25 *🔄 Assistance and Connectivity Issues* - Addressing network and setup issues faced by participants, - Offering personal assistance and guidance on troubleshooting connections, - Encouragement and acknowledgment of participant engagement and progress. 00:25:16 *🤔 Vector Search and Embeddings Introduction* - Introduction to embeddings as data compression for machine learning, - Explanation of embeddings for text, images, and audio, - Discussion on mathematical similarity and importance in data search processes. 27:24 *📊 Embedding and Vector Search Techniques* - Introduction to embedding graph structures alongside audio, text, and video. - Usage of vector search for semantic similarity in graphs and text. - Explanation of Neo4j indices and vector index creation using hnsw. 30:10 *🔍 Creating and Visualizing Vector Indexes* - Steps for creating vector indexes and embedding property using OpenAI. - Use of cosine similarity to perform vector searches in Neo4j. - Demonstration of encoding and searching with example queries. 33:15 *🧩 Embedding Generation vs. Vector Index Creation* - Clarification on generating embeddings and creating vector indexes. - Process of embedding text using OpenAI and indexing in Neo4j. - Practical example of searching via simple prompts using Cipher. 36:24 *⚙️ Integrating LangChain and Advanced Search Techniques* - Integration of LangChain for vector store creation and enhanced search functionality. - Example of searching structured vector data using LangChain. - Discussion on optimization and handling large datasets. 43:20 *🔄 Semantic Search with Graph Patterns* - Explanation of graph patterns and semantic search layers. - Utilizing graph databases for personalized recommendations. - Query examples for visualizing customer purchase data and recommendations. 00:54:48 *📊 Graph-Based Customer Recommendations* - Exploring customer purchase patterns using a graph, - Using machine learning to automate recommendation calculations, - Flexibility in adjusting the time window for filtering purchase data. 00:57:19 *🎯 Vector Search and Score Integration* - Combining vector search with graph traversal to enhance recommendations, - Returning personalized product lists based on purchase similarities, - Using both search and purchase scores for better ranking. 01:03:41 *🔍 Real-Time Data Updates in Recommendations* - Emphasizing real-time updates in recommendation systems, - Flexibility in adjusting query parameters for more accurate results, - Discussing the integration of external batch recommendations. 01:09:03 *🧠 Knowledge Graph Inference Techniques* - Introduction to graph data science and knowledge graph inference, - Explanation of node embeddings and their role in relationship inference, - Use of K-nearest neighbor for relationship prediction and co-purchase analysis. 01:17:56 *🛍️ Techniques for Product Recommendation* - Discusses various strategies for enhancing product recommendations using graph databases, - Emphasizes scaling via customer purchase patterns and multi-hop graph queries, - Mentions the integration of different machine learning models for inference. 01:20:31 *📉 Graph Embedding Challenge & Solutions* - Explains the need to recalculate graph embeddings over time and current efforts for real-time updating, - Highlights the role of Fast RP for efficient computation on large graphs, - Suggests alternative methods like averaging neighborhood embeddings. 01:21:49 *🔍 Dimension and Use Cases of Graph Embeddings* - Talks about the scalability of dimension in graph embeddings relative to use case scale, - Differentiates the application size needed for dimensions compared to text embeddings, - Discusses deployed use cases like entity resolution, fraud detection, and customer segmentation. 01:23:43 *📚 Semantic Retrieval and Functions in LLMs* - Explores semantic data retrieval in graph structures, using text-to-cypher patterns, - Investigates function calling in LLMs for dynamic graph queries, - Describes creating domain-specific query functions for improved relevance and precision. 01:26:19 *🤔 Contextual Understanding of Graph Embeddings* - Describes the motivation and intuition behind graph embeddings, - Connects graph embeddings to traditional ML techniques, focusing on inferring unseen relationships, - Clarifies the representation and scalability that graph embeddings offer for various queries. 01:30:12 *📧 LLMs in Action: Personalized Emails* - Demonstrates using LLMs to generate personalized emails based on graph-based recommendations, - Details the use of search products and recommendation products in a structured prompt, - Illustrates the retrieval and query processes for generating relevant data-driven communications. 01:34:09 *🛠️ Building the Retrieval and Recommendation System* - Walks through the setup of a personalized search and recommendation system using Langchain, - Describes the configuration of retrievers and the process of injecting results into prompts, - Offers a detailed look at the construction of a chain incorporating multiple retrieval steps. 01:37:20 *💻 Application Deployment Demonstration* - Shows the deployment of a demo app using Gradio to run recommendation scenarios, - Discusses the frontend interface and backend processes for generating user-specific outputs, - Highlights the variation in recommendations for different users with similar search queries. Made with HARPA AI

[@heyitsaamirj]

I enjoyed the workshop. I think the key point that should be driven is that why this isn't easily achiievable via traditional relational DBs, and why a graph db is required for this. But the explanations and examples were easy to follow. Thanks!

[@mayankvikram6175]

Really enjoyed the workshop. Thanks for sharing

[@Heisenberg2097]

All these efforts look so creepily wrong.

[@kewlking]

care to elaborate?

[@Heisenberg2097]

@@kewlking too many tools. too much complexity. twiddling here. tweaking there. but no control and no precision.

[@manfredmichael_3ia097]

@@Heisenberg2097 Interesting take

[@Crux69]

@@Heisenberg2097 welcome to modern software engineering

[@Heisenberg2097]

@@Crux69 it's not modern software engineering. it's a shortcut to more crap apps and more fools not knowing what they do.