Vector Databases Explained: Everything you need to know!

Logical Lenses · Beginner ·🔍 RAG & Vector Search ·1y ago

About this lesson

Unlock the architecture behind modern vector databases — the engines powering semantic search, RAG systems, and recommendation platforms. In this video, we explore everything from high-dimensional indexing to retrieval techniques like HNSW, IVF, and Product Quantization (PQ). We cover: How vector similarity works Indexing strategies and storage tradeoffs Real-time vs batch updates Hybrid search with keyword + vector fusion Query examples using Weaviate Practical visuals, code snippets, and conceptual depth This video is designed for software engineers, ML practitioners, and system designers looking to deeply understand the foundation of semantic search and vector-native architectures. Want more? Upcoming videos on Embeddings, RAG, and Retrieval systems! #VectorDatabase #SemanticSearch #Weaviate #AIEngineering #SystemDesign #VectorSearch #Embeddings #RAG #HNSW #ProductQuantization

Original Description

Unlock the architecture behind modern vector databases — the engines powering semantic search, RAG systems, and recommendation platforms. In this video, we explore everything from high-dimensional indexing to retrieval techniques like HNSW, IVF, and Product Quantization (PQ). We cover: How vector similarity works Indexing strategies and storage tradeoffs Real-time vs batch updates Hybrid search with keyword + vector fusion Query examples using Weaviate Practical visuals, code snippets, and conceptual depth This video is designed for software engineers, ML practitioners, and system designers looking to deeply understand the foundation of semantic search and vector-native architectures. Want more? Upcoming videos on Embeddings, RAG, and Retrieval systems! #VectorDatabase #SemanticSearch #Weaviate #AIEngineering #SystemDesign #VectorSearch #Embeddings #RAG #HNSW #ProductQuantization
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