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No experience” should not stop your IT dream Learn with real-time projects, client scenarios, dashboards & interview ...

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

PostgreSQL for AI Applications: Vector Databases, Embeddings, and RAG How can PostgreSQL be used to build modern AI ...

Oracle Exadata + ExaCC/ExaCS + Oracle Database 26ai — how do these technologies come together to build an AI-ready ...

Why This Session is Critical Unified AI & Data Architecture: Avoid running disconnected vector databases by leveraging AlloyDB ...

How vector stores work on any platform: why exact search stops scaling, the three families of approximate search, how accuracy is ...

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

We go from meaning as geometry, through exact search and why it doesn't scale, to the graph behind modern vector databases ...

How embeddings are created How vector databases store information How similarity search retrieves relevant chunks ...

Vector database 13:26 21. RAG 14:15 22. Fine-tuning 14:57 23. Quantization 15:50 24. Tool calling 16:48 25. AI agent 17:47 How ...

Miguel Toscano's PGDay Lowlands Utrecht (2026) session. Building a vector search prototype is easy; keeping it running in ...

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

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

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

Semantic Search - Embeddings in RAG and Vector Databases - Real-world applications, limitations, and interview questions ...

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