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.
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#VectorDatabase #SemanticSearch #Weaviate #AIEngineering #SystemDesign #VectorSearch #Embeddings #RAG #HNSW #ProductQuantization