claude-flow
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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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text/typescript
/**
* RuVector PostgreSQL Bridge - Advanced Attention Mechanisms
*
* Part 3: Graph, Temporal, Multimodal, Retrieval, and Specialized attention.
*
* @module @claude-flow/plugins/integrations/ruvector/attention-advanced
*/
import type {
AttentionMechanism,
AttentionInput,
} from './types.js';
import {
BaseAttentionMechanism,
type AttentionCategory,
} from './attention.js';
// ============================================================================
// Graph Attention Implementations
// ============================================================================
/**
* Graph Attention (GAT-style).
*/
export class GraphAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'graph_attention';
readonly name = 'Graph Attention';
readonly description = 'Graph attention network for structured data';
readonly category: AttentionCategory = 'graph';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
const negativeSlope = 0.2; // LeakyReLU
// Compute attention coefficients with LeakyReLU
const scores = keys.map(k => {
const combined = [...query, ...k];
const attention = combined.reduce((sum, v) => sum + v, 0) / combined.length;
return attention > 0 ? attention / scale : negativeSlope * attention / scale;
});
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.graph_attention(${q}, ${k}, ${v}, ${this.config.numHeads}, 0.2)`;
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Hyperbolic Attention for hierarchical structures.
*/
export class HyperbolicAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'hyperbolic_attention';
readonly name = 'Hyperbolic Attention';
readonly description = 'Attention in hyperbolic space for hierarchical data';
readonly category: AttentionCategory = 'graph';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const curvature = this.config.params?.curvature ?? -1.0;
const scale = this.getScale();
// Compute hyperbolic distances
const scores = keys.map(k => {
const dist = this.poincareDistance(query, k, curvature);
return -dist / scale; // Negative distance as score
});
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
const curvature = this.config.params?.curvature ?? -1.0;
return `SELECT ruvector.hyperbolic_attention(${q}, ${k}, ${v}, ${curvature}, ${this.getScale()})`;
}
private poincareDistance(u: number[], v: number[], c: number): number {
const normU = Math.sqrt(u.reduce((s, x) => s + x * x, 0));
const normV = Math.sqrt(v.reduce((s, x) => s + x * x, 0));
const diff = u.map((x, i) => x - v[i]);
const normDiff = Math.sqrt(diff.reduce((s, x) => s + x * x, 0));
const sqrtC = Math.sqrt(Math.abs(c));
const num = 2 * normDiff * normDiff;
const denom = (1 - normU * normU) * (1 - normV * normV);
return (1 / sqrtC) * Math.acosh(1 + num / Math.max(denom, 1e-6));
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Spherical Attention.
*/
export class SphericalAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'spherical_attention';
readonly name = 'Spherical Attention';
readonly description = 'Attention on the unit sphere using geodesic distances';
readonly category: AttentionCategory = 'graph';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
// Normalize to unit sphere
const normQuery = this.normalize(query);
const normKeys = keys.map(k => this.normalize(k));
// Geodesic distances on sphere
const scores = normKeys.map(k => {
const dot = this.dotProduct(normQuery, k);
const angle = Math.acos(Math.max(-1, Math.min(1, dot)));
return -angle / scale;
});
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.spherical_attention(${q}, ${k}, ${v}, ${this.getScale()})`;
}
private normalize(v: number[]): number[] {
const norm = Math.sqrt(v.reduce((s, x) => s + x * x, 0));
return v.map(x => x / (norm + 1e-6));
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Toroidal Attention for periodic data.
*/
export class ToroidalAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'toroidal_attention';
readonly name = 'Toroidal Attention';
readonly description = 'Attention on torus manifold for periodic structures';
readonly category: AttentionCategory = 'graph';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
// Toroidal distance (periodic in each dimension)
const scores = keys.map(k => {
const dist = this.toroidalDistance(query, k);
return -dist / scale;
});
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.toroidal_attention(${q}, ${k}, ${v}, ${this.getScale()})`;
}
private toroidalDistance(a: number[], b: number[]): number {
let dist = 0;
for (let i = 0; i < a.length; i++) {
const diff = Math.abs(a[i] - b[i]);
const periodic = Math.min(diff, 2 * Math.PI - diff);
dist += periodic * periodic;
}
return Math.sqrt(dist);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
// ============================================================================
// Temporal Attention Implementations
// ============================================================================
/**
* Temporal Attention for time-series data.
*/
export class TemporalAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'temporal_attention';
readonly name = 'Temporal Attention';
readonly description = 'Time-aware attention with temporal decay';
readonly category: AttentionCategory = 'temporal';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
const decayRate = 0.1;
const queryIdx = keys.length - 1;
const scores = keys.map((k, i) => {
const timeDiff = queryIdx - i;
const decay = Math.exp(-decayRate * timeDiff);
return this.dotProduct(query, k) / scale * decay;
});
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map((q, idx) => {
const scale = this.getScale();
const decayRate = 0.1;
const scores = keys.map((k, i) => {
const timeDiff = idx - i;
const decay = Math.exp(-decayRate * Math.abs(timeDiff));
return this.dotProduct(q, k) / scale * decay;
});
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.temporal_attention(${q}, ${k}, ${v}, 0.1, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Recurrent Attention (LSTM-style gating).
*/
export class RecurrentAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'recurrent_attention';
readonly name = 'Recurrent Attention';
readonly description = 'LSTM-style gated attention for sequential processing';
readonly category: AttentionCategory = 'temporal';
private hiddenState: number[] | null = null;
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
const dim = values[0].length;
// Initialize hidden state if needed
if (!this.hiddenState || this.hiddenState.length !== dim) {
this.hiddenState = new Array(dim).fill(0);
}
// Compute attention with gating
const scores = keys.map(k => this.dotProduct(query, k) / scale);
const weights = this.softmax(scores);
const context = this.weightedSum(values, weights);
// LSTM-style gating
const gate = this.sigmoid(context.map((c, i) => c + this.hiddenState![i]));
const output = context.map((c, i) => gate[i] * c + (1 - gate[i]) * this.hiddenState![i]);
// Update hidden state
this.hiddenState = output;
return output;
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
const results: number[][] = [];
for (const q of queries) {
results.push(await this.compute(q, keys, values));
}
return results;
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.recurrent_attention(${q}, ${k}, ${v}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private sigmoid(x: number[]): number[] {
return x.map(v => 1 / (1 + Math.exp(-v)));
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* State Space Model Attention (S4/Mamba-style).
*/
export class StateSpaceAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'state_space';
readonly name = 'State Space Attention';
readonly description = 'State space model attention for efficient sequence modeling';
readonly category: AttentionCategory = 'temporal';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
const dim = values[0].length;
// Simplified SSM: compute via convolution-like operation
const state = new Array(dim).fill(0);
const deltaT = 1.0 / keys.length;
for (let i = 0; i < keys.length; i++) {
const score = this.dotProduct(query, keys[i]) / scale;
const weight = Math.exp(-deltaT * (keys.length - i - 1)) * score;
for (let d = 0; d < dim; d++) {
state[d] += weight * values[i][d];
}
}
// Normalize
const norm = Math.sqrt(state.reduce((s, v) => s + v * v, 0)) + 1e-6;
return state.map(v => v / norm);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.state_space_attention(${q}, ${k}, ${v}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
}
// ============================================================================
// Multimodal Attention Implementations
// ============================================================================
/**
* Cross-Modal Attention.
*/
export class CrossModalAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'cross_modal';
readonly name = 'Cross-Modal Attention';
readonly description = 'Attention across different modalities (text, image, audio)';
readonly category: AttentionCategory = 'multimodal';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
const scores = keys.map(k => this.dotProduct(query, k) / scale);
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.cross_modal_attention(${q}, ${k}, ${v}, ${this.config.numHeads}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Perceiver IO Attention.
*/
export class PerceiverAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'perceiver';
readonly name = 'Perceiver Attention';
readonly description = 'Perceiver IO style attention with latent array';
readonly category: AttentionCategory = 'multimodal';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
const scores = keys.map(k => this.dotProduct(query, k) / scale);
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.perceiver_attention(${q}, ${k}, ${v}, ${this.config.numHeads}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Flamingo-style Attention.
*/
export class FlamingoAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'flamingo';
readonly name = 'Flamingo Attention';
readonly description = 'Flamingo-style gated cross-attention for vision-language';
readonly category: AttentionCategory = 'multimodal';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
// Gated cross-attention
const scores = keys.map(k => this.dotProduct(query, k) / scale);
const weights = this.softmax(scores);
const context = this.weightedSum(values, weights);
// Tanh gating
const gate = Math.tanh(this.dotProduct(query, context) / query.length);
return context.map(c => gate * c);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.flamingo_attention(${q}, ${k}, ${v}, ${this.config.numHeads}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
// ============================================================================
// Retrieval-Augmented Attention Implementations
// ============================================================================
/**
* Retrieval-Augmented Attention.
*/
export class RetrievalAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'retrieval_attention';
readonly name = 'Retrieval Attention';
readonly description = 'Attention augmented with retrieved documents';
readonly category: AttentionCategory = 'retrieval';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
const scores = keys.map(k => this.dotProduct(query, k) / scale);
const weights = this.softmax(scores);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.retrieval_attention(${q}, ${k}, ${v}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* k-NN Augmented Attention.
*/
export class KNNAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'knn_attention';
readonly name = 'k-NN Attention';
readonly description = 'k-nearest neighbor augmented attention';
readonly category: AttentionCategory = 'retrieval';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const k = Math.min(10, keys.length);
const scale = this.getScale();
// Compute all scores
const indexedScores = keys.map((key, i) => ({
index: i,
score: this.dotProduct(query, key) / scale,
}));
// Get top-k
indexedScores.sort((a, b) => b.score - a.score);
const topK = indexedScores.slice(0, k);
// Compute weights only for top-k
const topScores = topK.map(item => item.score);
const weights = this.softmax(topScores);
// Weighted sum of top-k values
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < topK.length; i++) {
const valueIdx = topK[i].index;
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[valueIdx][j];
}
}
return result;
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.knn_attention(${q}, ${k}, ${v}, 10, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
}
/**
* Memory-Augmented Attention.
*/
export class MemoryAugmentedAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'memory_augmented';
readonly name = 'Memory-Augmented Attention';
readonly description = 'Attention with external memory bank';
readonly category: AttentionCategory = 'retrieval';
private memoryBank: number[][] = [];
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const scale = this.getScale();
// Combine keys with memory bank
const allKeys = [...keys, ...this.memoryBank];
const allValues = [...values, ...this.memoryBank];
const scores = allKeys.map(k => this.dotProduct(query, k) / scale);
const weights = this.softmax(scores);
return this.weightedSum(allValues, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.memory_augmented_attention(${q}, ${k}, ${v}, ${this.getScale()})`;
}
/**
* Add vectors to memory bank.
*/
addToMemory(vectors: number[][]): void {
this.memoryBank.push(...vectors);
}
/**
* Clear memory bank.
*/
clearMemory(): void {
this.memoryBank = [];
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
// ============================================================================
// Specialized Attention Implementations
// ============================================================================
/**
* Synthesizer Attention (learned patterns).
*/
export class SynthesizerAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'synthesizer';
readonly name = 'Synthesizer Attention';
readonly description = 'Attention with learned synthetic patterns';
readonly category: AttentionCategory = 'linear';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const seqLen = keys.length;
const dim = query.length;
// Dense synthesizer: learn attention from query alone
const synthetic = new Array(seqLen).fill(0).map((_, i) =>
query.reduce((sum, q, j) => sum + q * Math.sin((i + 1) * (j + 1) * 0.1), 0)
);
const weights = this.softmax(synthetic);
return this.weightedSum(values, weights);
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const v = this.formatMatrix(input.value);
return `SELECT ruvector.synthesizer_attention(${q}, ${v}, ${input.key.length})`;
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Routing Attention (MoE style).
*/
export class RoutingAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'routing';
readonly name = 'Routing Attention';
readonly description = 'Attention with routing to specialized experts';
readonly category: AttentionCategory = 'linear';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const numExperts = this.config.params?.numExperts ?? 4;
const topK = this.config.params?.topK ?? 2;
const scale = this.getScale();
// Compute expert routing scores
const routingScores = Array(numExperts).fill(0).map((_, e) =>
query.reduce((sum, q, i) => sum + q * Math.cos(e * i * 0.1), 0)
);
// Get top-k experts
const indexedScores = routingScores.map((s, i) => ({ index: i, score: s }));
indexedScores.sort((a, b) => b.score - a.score);
const topExperts = indexedScores.slice(0, topK);
const expertWeights = this.softmax(topExperts.map(e => e.score));
// Each expert processes a subset of keys
const keysPerExpert = Math.ceil(keys.length / numExperts);
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let e = 0; e < topExperts.length; e++) {
const expertIdx = topExperts[e].index;
const start = expertIdx * keysPerExpert;
const end = Math.min(start + keysPerExpert, keys.length);
if (start < keys.length) {
const expertKeys = keys.slice(start, end);
const expertValues = values.slice(start, end);
const scores = expertKeys.map(k => this.dotProduct(query, k) / scale);
const weights = this.softmax(scores);
const expertOutput = this.weightedSum(expertValues, weights);
for (let d = 0; d < dim; d++) {
result[d] += expertWeights[e] * expertOutput[d];
}
}
}
return result;
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
const numExperts = this.config.params?.numExperts ?? 4;
const topK = this.config.params?.topK ?? 2;
return `SELECT ruvector.routing_attention(${q}, ${k}, ${v}, ${numExperts}, ${topK}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}
/**
* Mixture of Experts Attention.
*/
export class MixtureOfExpertsAttention extends BaseAttentionMechanism {
readonly type: AttentionMechanism = 'mixture_of_experts';
readonly name = 'Mixture of Experts Attention';
readonly description = 'MoE attention with specialized expert networks';
readonly category: AttentionCategory = 'linear';
async compute(query: number[], keys: number[][], values: number[][]): Promise<number[]> {
const numExperts = this.config.params?.numExperts ?? 8;
const topK = this.config.params?.topK ?? 2;
const scale = this.getScale();
// Router: compute gating scores
const gatingScores = Array(numExperts).fill(0).map((_, e) => {
return query.reduce((sum, q, i) => sum + q * Math.sin(e * i * 0.05), 0);
});
// Top-k gating
const indexed = gatingScores.map((s, i) => ({ idx: i, score: s }));
indexed.sort((a, b) => b.score - a.score);
const selected = indexed.slice(0, topK);
const gateWeights = this.softmax(selected.map(s => s.score));
// Expert computation
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let k = 0; k < selected.length; k++) {
const expertIdx = selected[k].idx;
const expertScale = scale * (1 + expertIdx * 0.1);
const scores = keys.map(key => this.dotProduct(query, key) / expertScale);
const weights = this.softmax(scores);
const output = this.weightedSum(values, weights);
for (let d = 0; d < dim; d++) {
result[d] += gateWeights[k] * output[d];
}
}
return result;
}
async computeBatch(queries: number[][], keys: number[][], values: number[][]): Promise<number[][]> {
return Promise.all(queries.map(q => this.compute(q, keys, values)));
}
toSQL(input: AttentionInput): string {
const q = this.formatMatrix(input.query);
const k = this.formatMatrix(input.key);
const v = this.formatMatrix(input.value);
const numExperts = this.config.params?.numExperts ?? 8;
const topK = this.config.params?.topK ?? 2;
return `SELECT ruvector.moe_attention(${q}, ${k}, ${v}, ${numExperts}, ${topK}, ${this.getScale()})`;
}
private dotProduct(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
private softmax(x: number[]): number[] {
const max = Math.max(...x);
const exp = x.map(v => Math.exp(v - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(v => v / sum);
}
private weightedSum(values: number[][], weights: number[]): number[] {
const dim = values[0].length;
const result = new Array(dim).fill(0);
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < dim; j++) {
result[j] += weights[i] * values[i][j];
}
}
return result;
}
}