sphereql-embed

Vector embedding projection engine for the sphereQL project.

Projects high-dimensional embeddings onto S² via one of four families — PCA, Kernel PCA (Gaussian/RBF), Laplacian eigenmap (connectivity-preserving), or random projection — unified behind a ConfiguredProjection enum so the pipeline can switch families without touching generics.

Provides a query pipeline (SphereQLPipeline) with k-NN search, similarity thresholds, concept paths, glob detection, local manifold fitting, and a Category Enrichment Layer: inter-category graph, bridge detection with Genuine / OverlapArtifact / Weak classification, automatic inner spheres, drill-down, and hierarchical domain-group routing for low-EVR regimes (hierarchical_nearest).

Ships a metalearning framework on top: a PipelineConfig hierarchy for every tunable constant (with #[serde(default)] so partial overrides work), a QualityMetric trait plus four concrete metrics (territorial health, bridge coherence, cluster silhouette, graph modularity) with composite presets, an auto_tune sweep over a discrete SearchSpace (Grid / Random / Bayesian TPE-lite), a MetaModel layer (NearestNeighbor, DistanceWeighted) with an on-disk store at ~/.sphereql/meta_records.json, and FeedbackEvent / FeedbackAggregator primitives for blending user satisfaction into the training record.

Includes a TextEmbedder trait (plus NoEmbedder default and FnEmbedder closure wrapper) so downstream crates — GraphQL, REPLs, custom harnesses — can accept natural-language queries without sphereql-embed depending on any specific embedder backend.

See the main repository for full documentation, examples (auto_tune, meta_learn, meta_warm_start, meta_feedback, spatial_analysis, category_enrichment), and architecture overview.