Vector Search Made Simple: Getting Started with OpenSearch for AI Applications
summary
OpenSearch positions itself as a full vector database that handles trillions of vectors, offers both exact K-NN and ANN engines, and ships connectors for OpenAI, Cohere and DeepSeek plus integrations with LangChain and LlamaIndex. Embedding math: OpenAI text-embedding-3-small produces 1536‑dim ≈6KB per vector, so a 100 MB document can inflate to ~600 MB of embeddings. Built‑in quantization yields 2–32× compression tradeoffs. DB‑Engines ranks it #2 among vector DBs.
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chapters (18)
- 0:00Introduction
- 1:47Lexical search limits
- 3:56Lexical to semantic evolution
- 5:21Embeddings meaning context
- 7:51Distance metrics nearest neighbor
- 9:40Vector databases in AI
- 10:44OpenSearch top vector DB
- 11:48OpenSearch use cases
- 12:54Vector DB capabilities
- 14:41Semantic search end-to-end
- 16:51Multi-modal data handling
- 18:38Memory cost vector workloads
- 20:45Hybrid search methods
- 22:11Hybrid scoring ranking
- 24:19AI features beyond search
- 26:50OpenSearch V3 MCP
- 27:30Summary OpenSearch AI
- 28:12OpenSearch community
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