Vector Composition in Scry

Scry stores a large public corpus with pre-computed embedding_voyage4 vectors (2048-dim, Voyage-4-lite). You can embed arbitrary concepts as named @handles, then search, mix, and debias them in SQL.

Skill generation: 2026032401

Mental Model

Three layers, each building on the last:

1. Embed -- turn a text description into a named vector stored server-side. Reference it as @handle in SQL.
2. Search -- rank corpus documents by cosine distance (<=>) to your @handle. Smaller distance = more similar.
3. Algebra -- compose vectors before searching. Mix two concepts, subtract unwanted directions, build contrastive axes. The result is still a vector you can search against.

The key insight: embedding_voyage4 <=> @concept is a single SQL expression that does an approximate nearest-neighbor search over the indexed corpus. Vector algebra gives you control over what direction that search points.

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