Beginner's Guide to Graph RAG
There are different types of RAG architectures to retrieve information from a repository, depending on the goal.
Graph RAGs retrieve interconnected knowledge, focusing on long-range context. This is the case when the corpus is well structured and has its own embedded taxonomy. Or if you build a knowledge graph on top of the corpus. Also, you can blend different RAG architectures together in a single RAG, for instance, leveraging tokens, sequencing (token prediction as in the earliest models), indexes and graphs.
In this session, you will learn:
?? The basics of Graph Retrieval-Augmented Generation (RAG) and its role in AI.
?? How to integrate graph data to optimize machine learning models.
?? Practical use cases for Graph RAG in various industries.
?? A live demonstration of building a Graph RAG pipeline.
Speaker: Tom Yeh, CS Professor University of Colorado Boulder, Expert in Graph Data Systems
This hands-on workshop is for developers and AI professionals, featuring state-of-the-art technology, case studies, code-share, and live demos. Recording and GitHub material will be available to registrants who cannot attend the free 60-min session.
Holistic Management Analysis and Knowledge Representation (Ontology, Taxonomy, Knowledge Graph, Thesaurus/Translator) for Enterprise Architecture, Business Architecture, Zero Trust, Supply Chain, and ML/AI foundation.
2 周What are the prerequisites for this notional Beginner? English, maths, management analysis, quantitative analysis, file systems, web, scripting, coding, formulas, graph databases, graph query, ....?
Pricing Actuary and Actuarial Scientist | Gen AI explorer
3 周Juan Antonio Monroy Kuhn
Business intelligence and automation
3 周I was not able to attend the session. Is there a way I can access a recording (if there is one)
Head of Data Science & Artificial Intelligence @AstraZeneca| Founder & Lead @Women in Big Data Berlin| TEDx and Keynote speaker| Berliner Weltverbesserer 2018| Top10 IT Manager of Tomorrow| Top100 Women in Data Science
3 周Eileen Oberringer
Data Science Aficionado | Expertise in Data Science, Data Analytics, and Business Analytics | Proficient in Python, EDA, ML, MySQL, Power BI, and Data Visualisation | Investment Enthusiast in Share Market & Mutual Funds
3 周"Thank you, Vincent Granville and Tom, for putting together this insightful session on Graph RAG. I appreciate the emphasis on how Graph RAGs enable effective information retrieval, especially when dealing with well-structured corpora and knowledge graphs. The session's focus on practical use cases and the live demonstration of pipeline building sounds invaluable for AI professionals looking to integrate graph-based models in real-world applications. ?One suggestion: it might be helpful to include a segment on the scalability challenges of Graph RAGs and strategies for optimizing their performance in large-scale deployments. Looking forward to the session!"