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Ep47: Langchain4j GraphRAG Friction + MCP Authorization Headaches
Manage episode 487226218 series 3579839
jmhreif에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 jmhreif 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
In this episode, I share some hands-on insights from building apps with Langchain4j using Quarkus and Neo4j, and compare it with Spring AI—especially around how each framework handles vector search and GraphRAG workflows. Spoiler: customization in Langchain4j feels a bit clunky.
I also dig into one article's critical take on the MCP authorization spec and why its current approach to security is misaligned with how enterprises actually structure identity and access. The article I discuss breaks down both the architectural intentions and the practical enterprise concerns—token handling, overhead, and developer friction.
If you’re working at the intersection of GenAI infrastructure and enterprise systems, this one’s for you.
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