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Graph Retrieval - Let Me Count The Ways // Egor Kraev // AI in Production

Posted Mar 21, 2025 | Views 95
# Graph
# Wise
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Egor Kraev
Head of AI @ Wise

With a foundation in the Russian mathematical tradition, refined at at ETH Zurich and the University of Maryland, Egor's journey in machine learning spans decades, crossing from economic and human development analysis to advanced AI applications for business. As the Head of AI at Wise, Egor has had the privilege of molding its data science landscape, in particular in fraud detection, trading algorithms, causal inference for marketing, and transformative LLM applications throughout the company. Egor's leadership has seen the Wise Data Science team grow from an idea to a powerhouse of over 30 skilled professionals.

Egor's passion for AI is not confined to work context; it extends for example into experiments in reinforcement learning for molecular optimization, and generative AI for educational purposes. Currently, my focus is on multi-agent LLM approaches, pushing the envelope for practical applications of GenAI.

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SUMMARY

Whenever you read about taking Retrieval Augmented Generation beyond simple vector search on embeddings, graphs are almost sure to come up. But what graphs? Old-school knowledge graphs, with entities and their relationships, or document-centric graphs, with text snippets as nodes? And how do you use them to improve your retrieval? Nearest neighborhood? PageRank? Something else? I will provide an overview of what's happening in that space, including what I'm doing, and give you a tour of the different options, with their pros and cons.

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