claude-flow/.claude/worktrees/adr-120-impl/v3/plugins/ruvector-upstream/src/bridges/gnn.ts

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/**
 * Graph Neural Network Bridge
 *
 * Bridge to ruvector-gnn-wasm for graph-based learning and inference.
 */

import type { WasmBridge, WasmModuleStatus, GnnConfig } from '../types.js';
import { GnnConfigSchema } from '../types.js';

/**
 * Graph structure
 */
export interface Graph {
  nodes: Float32Array[];
  edges: Array<[number, number]>;
  edgeWeights?: Float32Array;
}

/**
 * GNN inference result
 */
export interface GnnResult {
  nodeEmbeddings: Float32Array[];
  graphEmbedding: Float32Array;
  predictions?: Float32Array;
}

/**
 * GNN WASM module interface
 */
interface GnnModule {
  forward(graph: Graph, config: GnnConfig): GnnResult;
  nodeClassification(graph: Graph, config: GnnConfig): Float32Array;
  linkPrediction(graph: Graph, source: number, targets: number[], config: GnnConfig): Float32Array;
  graphClassification(graphs: Graph[], config: GnnConfig): Float32Array;
}

/**
 * GNN Bridge implementation
 */
export class GnnBridge implements WasmBridge<GnnModule> {
  readonly name = 'ruvector-gnn-wasm';
  readonly version = '0.1.0';

  private _status: WasmModuleStatus = 'unloaded';
  private _module: GnnModule | null = null;
  private config: GnnConfig;

  constructor(config?: Partial<GnnConfig>) {
    this.config = GnnConfigSchema.parse(config ?? {});
  }

  get status(): WasmModuleStatus {
    return this._status;
  }

  async init(): Promise<void> {
    if (this._status === 'ready') return;
    if (this._status === 'loading') return;

    this._status = 'loading';

    try {
      const wasmModule = await import('@ruvector/gnn-wasm').catch(() => null);

      if (wasmModule) {
        this._module = wasmModule as unknown as GnnModule;
      } else {
        this._module = this.createMockModule();
      }

      this._status = 'ready';
    } catch (error) {
      this._status = 'error';
      throw error;
    }
  }

  async destroy(): Promise<void> {
    this._module = null;
    this._status = 'unloaded';
  }

  isReady(): boolean {
    return this._status === 'ready';
  }

  getModule(): GnnModule | null {
    return this._module;
  }

  /**
   * Forward pass through GNN
   */
  forward(graph: Graph, config?: Partial<GnnConfig>): GnnResult {
    if (!this._module) throw new Error('GNN module not initialized');
    const mergedConfig = { ...this.config, ...config };
    return this._module.forward(graph, mergedConfig);
  }

  /**
   * Node classification
   */
  nodeClassification(graph: Graph, config?: Partial<GnnConfig>): Float32Array {
    if (!this._module) throw new Error('GNN module not initialized');
    const mergedConfig = { ...this.config, ...config };
    return this._module.nodeClassification(graph, mergedConfig);
  }

  /**
   * Link prediction
   */
  linkPrediction(
    graph: Graph,
    source: number,
    targets: number[],
    config?: Partial<GnnConfig>
  ): Float32Array {
    if (!this._module) throw new Error('GNN module not initialized');
    const mergedConfig = { ...this.config, ...config };
    return this._module.linkPrediction(graph, source, targets, mergedConfig);
  }

  /**
   * Graph classification
   */
  graphClassification(graphs: Graph[], config?: Partial<GnnConfig>): Float32Array {
    if (!this._module) throw new Error('GNN module not initialized');
    const mergedConfig = { ...this.config, ...config };
    return this._module.graphClassification(graphs, mergedConfig);
  }

  /**
   * Create mock module for development
   */
  private createMockModule(): GnnModule {
    return {
      forward(graph: Graph, config: GnnConfig): GnnResult {
        const nodeEmbeddings = graph.nodes.map(node => {
          const emb = new Float32Array(config.outputDim);
          for (let i = 0; i < config.outputDim; i++) {
            emb[i] = node[i % node.length] * 0.5;
          }
          return emb;
        });

        const graphEmbedding = new Float32Array(config.outputDim);
        for (const nodeEmb of nodeEmbeddings) {
          for (let i = 0; i < config.outputDim; i++) {
            graphEmbedding[i] += nodeEmb[i] / nodeEmbeddings.length;
          }
        }

        return { nodeEmbeddings, graphEmbedding };
      },

      nodeClassification(graph: Graph, config: GnnConfig): Float32Array {
        return new Float32Array(graph.nodes.length).fill(0.5);
      },

      linkPrediction(graph: Graph, source: number, targets: number[], config: GnnConfig): Float32Array {
        return new Float32Array(targets.length).map(() => Math.random());
      },

      graphClassification(graphs: Graph[], config: GnnConfig): Float32Array {
        return new Float32Array(graphs.length).map(() => Math.random());
      },
    };
  }
}

/**
 * Create a new GNN bridge
 */
export function createGnnBridge(config?: Partial<GnnConfig>): GnnBridge {
  return new GnnBridge(config);
}