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initial commit; Claude generated SEO descriptions

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kanungle
2026-05-01 09:07:30 -07:00
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qdrant-landing/content/course/essentials/day-1/embedding-models.md
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title: "Points, Vectors and Payloads"
short_description: "Understand Qdrant's core data model: points, payloads, dense vectors, sparse vectors, and multivectors for flexible vector search."
description: Learn Qdrant’s core data model with points, vectors, payloads, and named vectors. Compare dense, sparse, and multivectors, understand dimensionality trade-offs, and master filtering with payload indexes for precise retrieval.
weight: 2
isLesson: true