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kanungle
2026-05-01 09:07:30 -07:00
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qdrant-landing/content/course/essentials/day-1/_index.md
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title: "Day 1: Vector Search Fundamentals"
short_description: "Day 1 of Qdrant Essentials: points, vectors, payloads, distance metrics, chunking strategies, and a hands-on semantic search build."
description: Learn vector search fundamentals in Qdrant. Explore points, payloads, and distance metrics, then apply them in a hands-on semantic movie search project.
isLesson: true
weight: 20
qdrant-landing/content/course/essentials/day-1/chunking-strategies.md
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title: "Text Chunking Strategies"
short_description: "Compare text chunking strategies and learn how to split documents into chunks that align with embedding model context windows for precise retrieval."
description: Learn how to split text into meaningful chunks for vector search. Compare six chunking strategies and discover how metadata improves retrieval precision in Qdrant.
weight: 4
isLesson: true
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
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
qdrant-landing/content/course/essentials/day-1/movie-search-system.md
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title: "Demo: Semantic Movie Search"
short_description: "Walk through a semantic movie search demo using sentence embeddings, payload metadata, and chunking to retrieve films by theme and concept."
description: Build a semantic movie search with Qdrant. Compare chunking strategies, embed descriptions, and combine cosine similarity with metadata filters and grouping for accurate, theme-aware recommendations.
weight: 5
isLesson: true
qdrant-landing/content/course/essentials/day-1/pitstop-project.md
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title: "Project: Building a Semantic Search Engine"
short_description: "Build a domain-specific semantic search engine and compare chunking strategies to see how preprocessing shapes retrieval quality."
description: Build a semantic search engine with Qdrant. Compare chunking strategies, index embeddings, and query by meaning to discover what works best for your domain.
weight: 6
isLesson: true