Vector Similarity Search
Vector similarity search is a technique for finding semantically similar items in high-dimensional vector spaces. It combines vector representations (e.g., embeddings) with efficient index structures for nearest-neighbor queries. Common applications include semantic search, recommendations, and deduplication. Choice of index and distance metric affects performance and result quality.
This block bundles baseline information, context, and relations as a neutral reference in the model.
Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context in the model
Structural placement
Where this block lives in the structure.
No structure path available.
Relations
Connected blocks
Directly linked content elements.