ruvector/crates/ruvector-crv/README.md

ruvector-crv

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.

Installation

cargo add ruvector-crv

Overview

CRV (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

Quick Start

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);

Architecture

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.

Cross-Session Convergence

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 entries

Related Crates

• ruvector-core — Core vector database with HNSW indexing
• ruvector-attention — Multi-head attention for Stage II sensory vectors
• ruvector-gnn — Graph neural network for Stage III topology
• ruvector-mincut — MinCut partitioning for Stage VI clustering

Architecture

Part of the RuVector ecosystem.

License

MIT OR Apache-2.0