From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
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Architecture - Neo4j Tutorial
From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
Architecture
- [Narrator] In this chapter, we'll explore how to use the Knowledge Graph with AI. To start, most graph tooling can slot into existing data architectures and these often include ETL tools, which can be repurposed to load into the graph storage that you select. If you are using a graph database that also has ETL, that ETL will load in from your existing data storage. Making sure your data is as complete and as up to date as possible is critical for high quality and trustworthy answers that you can depend on. This is why doing regular gap analysis and assessing your user queries is important to make sure the model and the data are still supporting your needs. Your semantic data can be used to power things like recommendation systems or chat bots, can be used for grounding your AI via Rag, or it can be used traditionally for data analytics and information retrieval. In those cases, the AI would call your graph with your query it was given. It can perform a fuzzy search of the entities…
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