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How should you design your database tables for a RAG application? In this video, we understand a practical database ...

In this episode of the Generative AI series we look at the database that makes semantic search possible. Episode 8 showed how ...

... version with every vector. Subscribe for more Microsoft 365 hardening. #VectorDatabase #VectorSearch #RAG #AzureAISearch ...

What happens when a complete novel enters a Vector Database? In this practical GenAI experiment, I take *The Picture of Dorian ...

You'll learn how a user's question embedding is compared with document embeddings stored in a vector database to find and ...

Topics covered: RAG Workflow, RAG Architecture, Retrieval-Augmented Generation, RAG Pipeline, Vector Database, ...

Five terms separated: embedding, vector index, vector store, vector database, and the Microsoft 365 semantic index. Subscribe for ...

... systems • AI knowledge bases In RAG systems, documents can be converted into embeddings and stored in a vector database.

A vector store finds information by meaning instead of by matching words, and it sits underneath every Copilot answer and every ...

Every vector store is a copy of content that lives somewhere else. The central security question is whether the source permissions ...