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UMAP: Mapping Vector Embeddings
Learn how to transform high‑dimensional vector embeddings into 2D maps using UMAP, preserving local and global relationships for clear visual analysis.
Vector embeddings are powerful but nebulous. They are essentially high-dimensional arrays of floating point numbers that encode semantic meaning. But vector embeddings are hard to visualize particularly in context of other embeddings.
But we can transform vector embeddings into 2D space and then graph them. By using UMAP we can preserve the relationship between high-dimensional vectors in 2D space.
UMAP constructs a high-dimensional graph of the data, then optimizes a low-dimensional representation to preserve both:
Local structure: Keeps nearby points together
Global structure: Maintains relative cluster positions
Non-linear: Can capture complex manifold structures
Topological: Based on manifold learning theory
Ragwalla builds RAG-powered enterprise AI agents, replacing OpenAI Assistants.