Vector Search Made Simple: Getting Started with OpenSearch for AI Applications
summary
OpenSearch claims end-to-end vector search at production scale: 140 GitHub repos, 1.6 billion downloads, and 3,000 contributors, and it runs billions to trillions of vectors using Lucene, FAISS and a new Lucene-on-FAISS option. It provides GPU indexing for faster embedding ingestion, supports ANN algorithms (HNSW, IVF), and offers vector quantization 2x–32x memory reduction plus a hybrid search optimizer (version 3.2) to tune lexical+semantic weighting automatically.
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chapters (22)
- 0:00Introduction to OpenSearch
- 1:09OpenSearch overview
- 3:47Lexical vs semantic search
- 5:22Full-text and exact match
- 8:26Query DSL and types
- 10:16Ranking and relevance
- 13:13OpenSearch dashboards
- 13:47Distributed architecture
- 16:54Semantic search with vectors
- 20:03Vector embeddings and models
- 21:52Vector proximity and algorithms
- 25:53Implementing vector search
- 29:11Search pipelines and clients
- 32:02ML Commons and integration
- 34:22Optimization and quantization
- 36:55Hybrid search execution
- 39:44Hybrid search optimizer
- 41:58Neural sparse search
- 46:18Sparse vs dense comparison
- 47:38Agentic AI and MCP
- 50:28OpenSearch 3.0 updates
- 51:07Summary and community
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