ruvector-graph-transformer-wasm

WebAssembly bindings for RuVector Graph Transformer — proof-gated graph attention, verified training, and 8 specialized graph layers running client-side in the browser.

Run the full graph transformer in any browser tab — no server, no API calls, no data leaving the device. Every graph mutation is formally verified client-side, so your users get the same mathematical safety guarantees as the Rust version. The WASM binary is size-optimized and loads in milliseconds.

Install

# With wasm-pack (recommended)
wasm-pack build crates/ruvector-graph-transformer-wasm --target web

# Or from npm (when published)
npm install ruvector-graph-transformer-wasm

Quick Start

import init, { JsGraphTransformer } from "ruvector-graph-transformer-wasm";

await init();
const gt = new JsGraphTransformer();
console.log(gt.version()); // "2.0.4"

// Proof-gated mutation
const gate = gt.create_proof_gate(128);
const proof = gt.prove_dimension(128, 128);
console.log(proof.verified); // true

// 82-byte attestation for RVF witness chains
const attestation = gt.create_attestation(proof.proof_id);
console.log(attestation.length); // 82

// Sublinear attention — O(n log n)
const result = gt.sublinear_attention(
  new Float32Array([0.1, 0.2, 0.3, 0.4]),
  [{ src: 0, tgt: 1 }, { src: 0, tgt: 2 }],
  4, 2
);

// Verified training step with certificate
const step = gt.verified_training_step(
  [1.0, 2.0], [0.1, 0.2], 0.01
);
console.log(step.weights, step.certificate);

// Physics: symplectic integration
const state = gt.hamiltonian_step([1.0, 0.0], [0.0, 1.0], 0.01);
console.log(state.energy);

// Biological: spiking attention
const spikes = gt.spiking_attention(
  [0.5, 1.5, 0.3], [[1], [0, 2], [1]], 1.0
);

// Manifold: mixed-curvature distance
const d = gt.product_manifold_distance(
  [1, 0, 0, 1], [0, 1, 1, 0], [0.0, -1.0]
);

// Temporal: causal masking
const scores = gt.causal_attention(
  [1.0, 0.0],
  [[1.0, 0.0], [0.0, 1.0]],
  [1.0, 2.0]
);

// Economic: Nash equilibrium
const nash = gt.game_theoretic_attention(
  [1.0, 0.5, 0.8],
  [{ src: 0, tgt: 1 }, { src: 1, tgt: 2 }]
);
console.log(nash.converged);

// Stats
console.log(gt.stats());

API

Proof-Gated Operations

| Method | Returns | Description | |--------|---------|-------------| | new JsGraphTransformer(config?) | JsGraphTransformer | Create transformer instance | | version() | string | Crate version | | create_proof_gate(dim) | object | Create proof gate for dimension | | prove_dimension(expected, actual) | object | Prove dimension equality | | create_attestation(proof_id) | Uint8Array | 82-byte proof attestation | | verify_attestation(bytes) | boolean | Verify attestation from bytes | | compose_proofs(stages) | object | Type-checked pipeline composition |

Sublinear Attention

| Method | Returns | Description | |--------|---------|-------------| | sublinear_attention(q, edges, dim, k) | object | Graph-sparse top-k attention | | ppr_scores(source, adj, alpha) | Float64Array | Personalized PageRank scores |

Physics-Informed

| Method | Returns | Description | |--------|---------|-------------| | hamiltonian_step(positions, momenta, dt) | object | Symplectic leapfrog step | | verify_energy_conservation(before, after, tol) | object | Energy conservation proof |

Biological

| Method | Returns | Description | |--------|---------|-------------| | spiking_attention(spikes, edges, threshold) | Float64Array | Event-driven spiking attention | | hebbian_update(pre, post, weights, lr) | Float64Array | Hebbian weight update | | spiking_step(features, adjacency) | object | Full spiking step over feature matrix |

Verified Training

| Method | Returns | Description | |--------|---------|-------------| | verified_step(weights, gradients, lr) | object | SGD step + proof receipt | | verified_training_step(features, targets, weights) | object | Training step + certificate |

Manifold

| Method | Returns | Description | |--------|---------|-------------| | product_manifold_distance(a, b, curvatures) | number | Mixed-curvature distance | | product_manifold_attention(features, edges) | object | Product manifold attention |

Temporal-Causal

| Method | Returns | Description | |--------|---------|-------------| | causal_attention(query, keys, timestamps) | Float64Array | Temporally masked attention | | causal_attention_graph(features, timestamps, edges) | Float64Array | Causal graph attention | | granger_extract(history, num_nodes, num_steps) | object | Granger causality DAG |

Economic

| Method | Returns | Description | |--------|---------|-------------| | game_theoretic_attention(features, edges) | object | Nash equilibrium attention |

Meta

| Method | Returns | Description | |--------|---------|-------------| | stats() | object | Aggregate proof/attestation statistics | | reset() | void | Reset all internal state |

Building

# Web target (recommended for browsers)
wasm-pack build crates/ruvector-graph-transformer-wasm --target web

# Node.js target
wasm-pack build crates/ruvector-graph-transformer-wasm --target nodejs

# Cargo check
cargo check -p ruvector-graph-transformer-wasm

Bundle Size

The WASM binary is optimized for size with opt-level = "s", LTO, and single codegen unit.

Related Packages

| Package | Description | |---------|-------------| | ruvector-graph-transformer | Core Rust crate (186 tests) | | @ruvector/graph-transformer | Node.js NAPI-RS bindings | | ruvector-verified-wasm | Formal verification WASM bindings |

License

MIT