CRV (Coordinate Remote Viewing) protocol integration for RuVector — maps the 6-stage signal line methodology to vector database subsystems with Poincaré ball embeddings, multi-head attention, and MinCut partitioning.
cargo add ruvector-crvCRV (Coordinate Remote Viewing) protocol integration for ruvector.
Maps the 6-stage CRV signal line methodology to ruvector's subsystems:
| CRV Stage | Data Type | ruvector Component |
|---|---|---|
| Stage I (Ideograms) | Gestalt primitives | Poincaré ball hyperbolic embeddings |
| Stage II (Sensory) | Textures, colors, temps | Multi-head attention vectors |
| Stage III (Dimensional) | Spatial sketches | GNN graph topology |
| Stage IV (Emotional) | AOL, intangibles | SNN temporal encoding |
| Stage V (Interrogation) | Signal line probing | Differentiable search |
| Stage VI (3D Model) | Composite model | MinCut partitioning |
use ruvector_crv::{CrvConfig, CrvSessionManager, GestaltType, StageIData};
// Create session manager with default config (384 dimensions)
let config = CrvConfig::default();
let mut manager = CrvSessionManager::new(config);
// Create a session for a target coordinate
manager.create_session("session-001".to_string(), "1234-5678".to_string()).unwrap();
// Add Stage I ideogram data
let stage_i = StageIData {
stroke: vec![(0.0, 0.0), (1.0, 0.5), (2.0, 1.0), (3.0, 0.5)],
spontaneous_descriptor: "angular rising".to_string(),
classification: GestaltType::Manmade,
confidence: 0.85,
};
let embedding = manager.add_stage_i("session-001", &stage_i).unwrap();
assert_eq!(embedding.len(), 384);The Poincaré ball embedding for Stage I gestalts encodes the hierarchical gestalt taxonomy (root → manmade/natural/movement/energy/water/land) with exponentially less distortion than Euclidean space.
For AOL (Analytical Overlay) separation, the spiking neural network temporal encoding models signal-vs-noise discrimination: high-frequency spike bursts correlate with AOL contamination, while sustained low-frequency patterns indicate clean signal line data.
MinCut partitioning in Stage VI identifies natural cluster boundaries in the accumulated session graph, separating distinct target aspects.
Multiple sessions targeting the same coordinate can be analyzed for convergence — agreement between independent viewers strengthens the signal validity:
// After adding data to multiple sessions for "1234-5678"...
let convergence = manager.find_convergence("1234-5678", 0.75).unwrap();
// convergence.scores contains similarity values for converging entriesruvector-core— Core vector database with HNSW indexingruvector-attention— Multi-head attention for Stage II sensory vectorsruvector-gnn— Graph neural network for Stage III topologyruvector-mincut— MinCut partitioning for Stage VI clustering
Part of the RuVector ecosystem.
MIT OR Apache-2.0