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added descriptions to each page
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title: "Day 1: Vector Search Fundamentals"
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description: Learn vector search fundamentals in Qdrant. Explore points, payloads, and distance metrics, then apply them in a hands-on semantic movie search project.
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isLesson: true
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weight: 20
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title: Text Chunking Strategies
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title: "Text Chunking Strategies"
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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.
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weight: 4
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title: Distance Metrics
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title: "Distance Metrics"
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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.
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weight: 3
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title: Points, Vectors and Payloads
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title: "Points, Vectors and Payloads"
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description: Learn Qdrant’s core data model: points, vectors, payloads, and named vectors. Compare dense, sparse, and multivectors, understand dimensionality trade-offs, and master filtering with payload indexes for precise retrieval.
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weight: 2
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title: "Demo: Semantic Movie Search"
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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.
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weight: 5
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title: "Project: Building a Semantic Search Engine"
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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.
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weight: 6
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