Episode

GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

Podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Published
Apr 22, 2024
Duration seconds
2828
Processing state
failed
Canonical source
https://twimlai.com/podcast/twimlai/graphrag-knowledge-graphs-for-ai-applications/
Audio
https://pscrb.fm/rss/p/traffic.megaphone.fm/MLN3405997576.mp3?updated=1713836204
JSON
/v1/public/podcasts/twiml-ai-podcast/episodes/graphrag-knowledge-graphs-for-ai-applications-with-kirk-marple-681
Markdown
/podcast/twiml-ai-podcast/graphrag-knowledge-graphs-for-ai-applications-with-kirk-marple-681.md

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Summary

Today we're joined by Kirk Marple, CEO and founder of Graphlit, to explore the emerging paradigm of "GraphRAG," or Graph Retrieval Augmented Generation. In our conversation, Kirk digs into the GraphRAG architecture and how Graphlit uses it to offer a multi-stage workflow for ingesting, processing, retrieving, and generating content using LLMs (like GPT-4) and other Generative AI tech. He shares how the system performs entity extraction to build a knowledge graph and how graph, vector, and object storage are integrated in the system. We dive into how the system uses “prompt compilation” to improve the results it gets from Large Language Models during generation. We conclude by discussing several use cases the approach supports, as well as future agent-based applications it enables. The complete show notes for this episode can be found at twimlai.com/go/681.