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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/distance-metrics.md
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title: "Distance Metrics"
short_description: "Compare cosine, dot product, and Euclidean distance for vector search, and learn how to pick the right metric for your embedding model."
description: Learn how distance metrics like cosine, Euclidean, Manhattan, and dot product shape vector similarity in Qdrant. Discover which metric fits your data and use case.
weight: 3
isLesson: true