d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
94 lines
3.5 KiB
Markdown
94 lines
3.5 KiB
Markdown
# ruvector-crv
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[](https://crates.io/crates/ruvector-crv)
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[](https://docs.rs/ruvector-crv)
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[](https://github.com/ruvnet/ruvector)
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**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.
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## Installation
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```bash
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cargo add ruvector-crv
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```
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## Overview
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CRV (Coordinate Remote Viewing) protocol integration for ruvector.
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Maps the 6-stage CRV signal line methodology to ruvector's subsystems:
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| CRV Stage | Data Type | ruvector Component |
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|-----------|-----------|-------------------|
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| Stage I (Ideograms) | Gestalt primitives | Poincaré ball hyperbolic embeddings |
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| Stage II (Sensory) | Textures, colors, temps | Multi-head attention vectors |
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| Stage III (Dimensional) | Spatial sketches | GNN graph topology |
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| Stage IV (Emotional) | AOL, intangibles | SNN temporal encoding |
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| Stage V (Interrogation) | Signal line probing | Differentiable search |
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| Stage VI (3D Model) | Composite model | MinCut partitioning |
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## Quick Start
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```rust
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use ruvector_crv::{CrvConfig, CrvSessionManager, GestaltType, StageIData};
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// Create session manager with default config (384 dimensions)
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let config = CrvConfig::default();
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let mut manager = CrvSessionManager::new(config);
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// Create a session for a target coordinate
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manager.create_session("session-001".to_string(), "1234-5678".to_string()).unwrap();
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// Add Stage I ideogram data
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let stage_i = StageIData {
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stroke: vec![(0.0, 0.0), (1.0, 0.5), (2.0, 1.0), (3.0, 0.5)],
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spontaneous_descriptor: "angular rising".to_string(),
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classification: GestaltType::Manmade,
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confidence: 0.85,
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};
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let embedding = manager.add_stage_i("session-001", &stage_i).unwrap();
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assert_eq!(embedding.len(), 384);
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```
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## Architecture
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The Poincaré ball embedding for Stage I gestalts encodes the hierarchical
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gestalt taxonomy (root → manmade/natural/movement/energy/water/land) with
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exponentially less distortion than Euclidean space.
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For AOL (Analytical Overlay) separation, the spiking neural network temporal
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encoding models signal-vs-noise discrimination: high-frequency spike bursts
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correlate with AOL contamination, while sustained low-frequency patterns
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indicate clean signal line data.
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MinCut partitioning in Stage VI identifies natural cluster boundaries in the
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accumulated session graph, separating distinct target aspects.
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## Cross-Session Convergence
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Multiple sessions targeting the same coordinate can be analyzed for
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convergence — agreement between independent viewers strengthens the
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signal validity:
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```rust
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// After adding data to multiple sessions for "1234-5678"...
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let convergence = manager.find_convergence("1234-5678", 0.75).unwrap();
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// convergence.scores contains similarity values for converging entries
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```
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## Related Crates
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- [`ruvector-core`](https://crates.io/crates/ruvector-core) — Core vector database with HNSW indexing
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- [`ruvector-attention`](https://crates.io/crates/ruvector-attention) — Multi-head attention for Stage II sensory vectors
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- [`ruvector-gnn`](https://crates.io/crates/ruvector-gnn) — Graph neural network for Stage III topology
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- [`ruvector-mincut`](https://crates.io/crates/ruvector-mincut) — MinCut partitioning for Stage VI clustering
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## Architecture
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Part of the [RuVector](https://github.com/ruvnet/ruvector) ecosystem.
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## License
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MIT OR Apache-2.0
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