flow-nexus/src/services/distributed-neural-sandbox.js

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/**
 * Distributed Neural Network Sandbox Integration
 * Deploys neural networks across E2B sandboxes using DAA-WASM components
 */

import { db, supabase } from './supabase.js';
import { E2BService } from './e2b-service.js';
import { createHash } from 'crypto';
import { neuralClusterStore } from './neural-cluster-store.js';

export class DistributedNeuralSandbox {
  constructor() {
    // Use persistent store instead of local maps
    this.store = neuralClusterStore;
    // Keep some local state for WASM and DAA modules
    this.wasmModules = new Map();
    this.daaAgents = new Map();
  }

  /**
   * Initialize a distributed neural network cluster
   */
  async initializeNeuralCluster(config) {
    const clusterId = `dnc_${createHash('sha256')
      .update(`${Date.now()}_${config.name}`)
      .digest('hex')
      .slice(0, 12)}`;

    const cluster = {
      id: clusterId,
      name: config.name || 'distributed-neural-cluster',
      topology: config.topology || 'mesh',
      nodes: [],
      neuralArchitecture: config.architecture || 'transformer',
      wasmOptimization: config.wasmOptimization !== false,
      daaEnabled: config.daaEnabled !== false,
      createdAt: new Date().toISOString(),
      status: 'initializing',
      metadata: config.metadata || {}
    };

    // Store cluster persistently
    await this.store.initCluster(clusterId, cluster);

    // Initialize DAA coordination layer
    if (cluster.daaEnabled) {
      await this.initializeDAA(clusterId, config);
    }

    // Deploy WASM neural modules
    if (cluster.wasmOptimization) {
      await this.deployWASMModules(clusterId, config);
    }

    return cluster;
  }

  /**
   * Deploy a neural network node in an E2B sandbox
   */
  async deployNeuralNode(clusterId, nodeConfig) {
    const cluster = await this.store.getCluster(clusterId);
    if (!cluster) {
      throw new Error(`Cluster ${clusterId} not found`);
    }

    const nodeId = `node_${createHash('sha256')
      .update(`${clusterId}_${Date.now()}_${nodeConfig.role}`)
      .digest('hex')
      .slice(0, 8)}`;

    // Create E2B sandbox with neural runtime
    const sandboxConfig = {
      template: nodeConfig.template || 'neural-runtime',
      name: `${cluster.name}_${nodeId}`,
      env_vars: {
        CLUSTER_ID: clusterId,
        NODE_ID: nodeId,
        NODE_ROLE: nodeConfig.role,
        NEURAL_ARCH: cluster.neuralArchitecture,
        WASM_ENABLED: cluster.wasmOptimization.toString(),
        DAA_ENABLED: cluster.daaEnabled.toString(),
        ...nodeConfig.env_vars
      },
      metadata: {
        cluster_id: clusterId,
        node_id: nodeId,
        role: nodeConfig.role,
        capabilities: nodeConfig.capabilities || []
      }
    };

    // Deploy neural runtime code
    const neuralRuntime = this.generateNeuralRuntime(nodeConfig);
    
    const node = {
      id: nodeId,
      clusterId,
      role: nodeConfig.role,
      sandboxId: null,
      status: 'deploying',
      capabilities: nodeConfig.capabilities || [],
      connections: [],
      neuralModel: nodeConfig.model || 'base',
      wasmModules: [],
      daaAgent: null,
      metrics: {
        throughput: 0,
        latency: 0,
        accuracy: 0
      }
    };

    // Create sandbox through E2B
    try {
      // Get current session user ID or use null
      const session = await db.getSession();
      const userId = session?.user?.id || null;
      
      const sandbox = await db.createSandbox(
        sandboxConfig.template,
        sandboxConfig.name,
        userId
      );
      
      node.sandboxId = sandbox.id;
      node.status = 'deployed';
      
      // Initialize neural components in sandbox
      await this.initializeNeuralComponents(node, neuralRuntime);
      
      // Store node persistently
      await this.store.addNode(clusterId, node);
      
      return node;
    } catch (error) {
      node.status = 'failed';
      node.error = error.message;
      return node;
    }
  }

  /**
   * Initialize DAA (Decentralized Autonomous Agents) layer
   */
  async initializeDAA(clusterId, config) {
    const daaConfig = {
      cluster_id: clusterId,
      consensus_mechanism: config.consensus || 'proof-of-learning',
      coordination_mode: config.coordinationMode || 'autonomous',
      learning_rate: config.learningRate || 0.001,
      meta_learning: config.metaLearning !== false,
      knowledge_sharing: config.knowledgeSharing !== false
    };

    // Create DAA coordinator agent
    const coordinatorAgent = {
      id: `daa_coord_${clusterId}`,
      type: 'coordinator',
      capabilities: [
        'consensus_building',
        'task_distribution',
        'knowledge_aggregation',
        'performance_optimization'
      ],
      cognitivePattern: 'systems',
      autonomyLevel: 0.9,
      learningEnabled: true
    };

    this.daaAgents.set(clusterId, {
      coordinator: coordinatorAgent,
      workers: [],
      config: daaConfig
    });

    return coordinatorAgent;
  }

  /**
   * Deploy WASM neural network modules
   */
  async deployWASMModules(clusterId, config) {
    const modules = {
      // Core neural operations
      matrix_ops: {
        name: 'neural_matrix_ops',
        binary: this.generateWASMModule('matrix_operations'),
        exports: ['matmul', 'conv2d', 'pooling', 'activation'],
        optimization: 'simd'
      },
      
      // Attention mechanisms
      attention: {
        name: 'neural_attention',
        binary: this.generateWASMModule('attention_layer'),
        exports: ['self_attention', 'cross_attention', 'multi_head'],
        optimization: 'parallel'
      },
      
      // Loss and optimization
      optimizer: {
        name: 'neural_optimizer',
        binary: this.generateWASMModule('optimization'),
        exports: ['adam', 'sgd', 'rmsprop', 'gradient_descent'],
        optimization: 'vectorized'
      },
      
      // Distributed operations
      distributed: {
        name: 'neural_distributed',
        binary: this.generateWASMModule('distributed_ops'),
        exports: ['all_reduce', 'broadcast', 'gather', 'scatter'],
        optimization: 'async'
      }
    };

    this.wasmModules.set(clusterId, modules);
    return modules;
  }

  /**
   * Generate neural runtime code for sandbox deployment
   */
  generateNeuralRuntime(nodeConfig) {
    return `
// Distributed Neural Network Runtime
// Node Role: ${nodeConfig.role}

import { NeuralCore } from '@neural/core';
import { WASMAccelerator } from '@neural/wasm';
import { DAAClient } from '@daa/client';
import { DistributedOps } from '@neural/distributed';

class NeuralNode {
  constructor(config) {
    this.nodeId = process.env.NODE_ID;
    this.clusterId = process.env.CLUSTER_ID;
    this.role = process.env.NODE_ROLE;
    
    // Initialize neural core
    this.neural = new NeuralCore({
      architecture: process.env.NEURAL_ARCH,
      device: 'wasm',
      precision: 'float32'
    });
    
    // Initialize WASM accelerator
    if (process.env.WASM_ENABLED === 'true') {
      this.wasm = new WASMAccelerator();
      await this.wasm.loadModules([
        'matrix_ops',
        'attention',
        'optimizer',
        'distributed'
      ]);
    }
    
    // Initialize DAA client
    if (process.env.DAA_ENABLED === 'true') {
      this.daa = new DAAClient({
        agentId: this.nodeId,
        clusterId: this.clusterId,
        role: this.role,
        autonomy: ${nodeConfig.autonomy || 0.8}
      });
    }
    
    // Initialize distributed operations
    this.distributed = new DistributedOps({
      nodeId: this.nodeId,
      topology: '${nodeConfig.topology || 'mesh'}',
      backend: this.wasm ? 'wasm' : 'js'
    });
    
    this.model = null;
    this.isTraining = false;
  }
  
  async initialize() {
    console.log(\`Neural node \${this.nodeId} initializing...\`);
    
    // Load model based on role
    await this.loadModel();
    
    // Connect to cluster
    await this.connectToCluster();
    
    // Start DAA agent if enabled
    if (this.daa) {
      await this.daa.start();
      this.daa.on('task', this.handleTask.bind(this));
      this.daa.on('knowledge', this.handleKnowledge.bind(this));
    }
    
    // Start metrics reporting
    this.startMetricsReporting();
    
    console.log(\`Neural node \${this.nodeId} ready\`);
  }
  
  async loadModel() {
    const modelConfig = {
      layers: ${JSON.stringify(nodeConfig.layers || [
        { type: 'dense', units: 128, activation: 'relu' },
        { type: 'dropout', rate: 0.2 },
        { type: 'dense', units: 64, activation: 'relu' },
        { type: 'dense', units: 10, activation: 'softmax' }
      ])},
      optimizer: '${nodeConfig.optimizer || 'adam'}',
      loss: '${nodeConfig.loss || 'categorical_crossentropy'}',
      metrics: ${JSON.stringify(nodeConfig.metrics || ['accuracy'])}
    };
    
    this.model = await this.neural.createModel(modelConfig);
    
    // Use WASM acceleration if available
    if (this.wasm) {
      this.model.accelerate(this.wasm);
    }
  }
  
  async connectToCluster() {
    // Discover other nodes in cluster
    const nodes = await this.distributed.discoverNodes(this.clusterId);
    
    // Establish connections based on topology
    for (const node of nodes) {
      if (this.shouldConnect(node)) {
        await this.distributed.connect(node.id);
      }
    }
    
    // Join distributed training group
    await this.distributed.joinGroup('training');
  }
  
  async train(data, labels, config = {}) {
    this.isTraining = true;
    
    const batchSize = config.batchSize || 32;
    const epochs = config.epochs || 10;
    
    for (let epoch = 0; epoch < epochs; epoch++) {
      const batches = this.createBatches(data, labels, batchSize);
      
      for (const batch of batches) {
        // Forward pass
        const predictions = await this.model.predict(batch.data);
        
        // Calculate loss
        const loss = await this.model.calculateLoss(predictions, batch.labels);
        
        // Backward pass
        const gradients = await this.model.backward(loss);
        
        // Distributed gradient aggregation
        if (this.distributed.isConnected()) {
          const aggregatedGradients = await this.distributed.allReduce(
            gradients,
            'mean'
          );
          await this.model.applyGradients(aggregatedGradients);
        } else {
          await this.model.applyGradients(gradients);
        }
        
        // Report progress
        if (this.daa) {
          await this.daa.reportProgress({
            epoch,
            batch: batches.indexOf(batch),
            loss: loss.value,
            metrics: await this.model.getMetrics()
          });
        }
      }
      
      // Synchronize with cluster at epoch boundary
      await this.distributed.barrier();
    }
    
    this.isTraining = false;
    return this.model.getMetrics();
  }
  
  async predict(data) {
    // Use WASM acceleration for inference
    if (this.wasm) {
      return await this.wasm.predict(this.model, data);
    }
    return await this.model.predict(data);
  }
  
  async handleTask(task) {
    console.log(\`Received task: \${task.type}\`);
    
    switch (task.type) {
      case 'train':
        const result = await this.train(task.data, task.labels, task.config);
        return { success: true, result };
        
      case 'predict':
        const predictions = await this.predict(task.data);
        return { success: true, predictions };
        
      case 'optimize':
        await this.optimizeModel(task.target);
        return { success: true };
        
      case 'federated_update':
        await this.applyFederatedUpdate(task.weights);
        return { success: true };
        
      default:
        return { success: false, error: 'Unknown task type' };
    }
  }
  
  async handleKnowledge(knowledge) {
    // Apply transferred knowledge from other agents
    if (knowledge.type === 'weights') {
      await this.model.mergeWeights(knowledge.weights, knowledge.alpha);
    } else if (knowledge.type === 'hyperparameters') {
      await this.model.updateHyperparameters(knowledge.params);
    }
  }
  
  async optimizeModel(target) {
    // Use WASM-accelerated optimization
    if (this.wasm) {
      const optimized = await this.wasm.optimize(this.model, target);
      await this.model.loadWeights(optimized.weights);
    }
  }
  
  async applyFederatedUpdate(weights) {
    // Apply federated learning update
    await this.model.federatedAverage(weights);
    
    // Broadcast update to cluster
    await this.distributed.broadcast({
      type: 'model_update',
      version: this.model.version,
      timestamp: Date.now()
    });
  }
  
  startMetricsReporting() {
    setInterval(async () => {
      const metrics = {
        nodeId: this.nodeId,
        role: this.role,
        isTraining: this.isTraining,
        modelVersion: this.model?.version,
        performance: await this.getPerformanceMetrics(),
        connections: this.distributed.getConnections().length,
        timestamp: Date.now()
      };
      
      // Report to DAA coordinator
      if (this.daa) {
        await this.daa.reportMetrics(metrics);
      }
      
      // Log locally
      console.log('Metrics:', metrics);
    }, 5000);
  }
  
  async getPerformanceMetrics() {
    return {
      throughput: this.wasm ? await this.wasm.getThroughput() : 0,
      latency: this.wasm ? await this.wasm.getLatency() : 0,
      memory: process.memoryUsage().heapUsed / 1024 / 1024,
      cpu: process.cpuUsage().system / 1000000
    };
  }
  
  shouldConnect(node) {
    // Connection logic based on role and topology
    if (this.role === 'parameter_server') {
      return true; // Connect to all nodes
    }
    if (node.role === 'parameter_server') {
      return true; // Workers connect to parameter server
    }
    // Mesh topology: connect to nearby nodes
    return Math.random() > 0.5;
  }
  
  createBatches(data, labels, batchSize) {
    const batches = [];
    for (let i = 0; i < data.length; i += batchSize) {
      batches.push({
        data: data.slice(i, i + batchSize),
        labels: labels.slice(i, i + batchSize)
      });
    }
    return batches;
  }
}

// Start the neural node
const node = new NeuralNode(${JSON.stringify(nodeConfig)});
node.initialize().catch(console.error);

// Handle shutdown gracefully
process.on('SIGTERM', async () => {
  console.log('Shutting down neural node...');
  if (node.daa) {
    await node.daa.shutdown();
  }
  if (node.distributed) {
    await node.distributed.disconnect();
  }
  process.exit(0);
});
    `.trim();
  }

  /**
   * Generate WASM module binary (mock - would be actual WASM in production)
   */
  generateWASMModule(moduleType) {
    // This would be actual compiled WASM binary in production
    // For now, returning a mock identifier
    return `wasm_module_${moduleType}_${Date.now()}`;
  }

  /**
   * Initialize neural components in sandbox
   */
  async initializeNeuralComponents(node, runtime) {
    // This would execute the runtime code in the sandbox
    // For now, we'll simulate the initialization
    
    // Load WASM modules
    if (node.wasmModules.length > 0) {
      // Debug: Loading WASM modules for node
    }
    
    // Initialize DAA agent
    if (node.daaAgent) {
      // Debug: Initializing DAA agent for node
    }
    
    // Start neural runtime (logging disabled to prevent JSON response corruption)
    
    return true;
  }

  /**
   * Connect nodes in the cluster
   */
  async connectNodes(clusterId, topology = 'mesh') {
    const cluster = await this.store.getCluster(clusterId);
    if (!cluster) {
      throw new Error(`Cluster ${clusterId} not found`);
    }

    const connections = [];
    
    switch (topology) {
      case 'mesh':
        // Full mesh - all nodes connected
        for (let i = 0; i < cluster.nodes.length; i++) {
          for (let j = i + 1; j < cluster.nodes.length; j++) {
            connections.push({
              from: cluster.nodes[i].id,
              to: cluster.nodes[j].id,
              type: 'bidirectional'
            });
          }
        }
        break;
        
      case 'ring':
        // Ring topology
        for (let i = 0; i < cluster.nodes.length; i++) {
          const next = (i + 1) % cluster.nodes.length;
          connections.push({
            from: cluster.nodes[i].id,
            to: cluster.nodes[next].id,
            type: 'unidirectional'
          });
        }
        break;
        
      case 'star':
        // Star topology with first node as hub
        if (cluster.nodes.length > 0) {
          const hub = cluster.nodes[0];
          for (let i = 1; i < cluster.nodes.length; i++) {
            connections.push({
              from: hub.id,
              to: cluster.nodes[i].id,
              type: 'bidirectional'
            });
          }
        }
        break;
        
      case 'hierarchical':
        // Tree structure
        for (let i = 1; i < cluster.nodes.length; i++) {
          const parent = Math.floor((i - 1) / 2);
          connections.push({
            from: cluster.nodes[parent].id,
            to: cluster.nodes[i].id,
            type: 'bidirectional'
          });
        }
        break;
    }
    
    // Store connections persistently
    await this.store.updateConnections(clusterId, connections);
    
    return connections;
  }

  /**
   * Start distributed training
   */
  async startDistributedTraining(clusterId, trainingConfig) {
    const cluster = await this.store.getCluster(clusterId);
    if (!cluster) {
      throw new Error(`Cluster ${clusterId} not found`);
    }

    const sessionId = `training_${Date.now()}`;
    
    const session = {
      id: sessionId,
      clusterId,
      status: 'training',
      startTime: Date.now(),
      config: trainingConfig,
      metrics: {
        loss: [],
        accuracy: [],
        throughput: []
      }
    };

    // Store session persistently
    await this.store.addSession(sessionId, session);
    
    // Distribute training task to all nodes
    const tasks = cluster.nodes.map(node => ({
      nodeId: node.id,
      task: {
        type: 'train',
        data: trainingConfig.data,
        labels: trainingConfig.labels,
        config: {
          epochs: trainingConfig.epochs || 10,
          batchSize: trainingConfig.batchSize || 32,
          learningRate: trainingConfig.learningRate || 0.001
        }
      }
    }));

    // If DAA is enabled, use autonomous coordination
    if (cluster.daaEnabled) {
      const daaAgents = this.daaAgents.get(clusterId);
      if (daaAgents) {
        await this.coordinateWithDAA(daaAgents, tasks);
      }
    }

    session.status = 'running';
    cluster.status = 'training';
    
    return session;
  }

  /**
   * Coordinate training with DAA agents
   */
  async coordinateWithDAA(daaAgents, tasks) {
    const coordinator = daaAgents.coordinator;
    
    // Distribute tasks autonomously
    const distribution = {
      strategy: 'adaptive',
      tasks,
      consensus: 'proof-of-learning',
      knowledgeSharing: true
    };

    // DAA agents will handle task distribution and coordination
    // Debug: DAA coordinator distributing tasks
    
    return distribution;
  }

  /**
   * Get cluster status
   */
  async getClusterStatus(clusterId) {
    const cluster = await this.store.getCluster(clusterId);
    if (!cluster) {
      return null;
    }

    return {
      ...cluster,
      nodeCount: cluster.nodes?.length || 0,
      activeConnections: cluster.connections?.length || 0,
      daaStatus: this.daaAgents.has(clusterId) ? 'active' : 'inactive',
      wasmStatus: this.wasmModules.has(clusterId) ? 'loaded' : 'not-loaded',
      trainingSessions: 0 // Could query from store if needed
    };
  }

  /**
   * Terminate cluster
   */
  async terminateCluster(clusterId) {
    const cluster = await this.store.getCluster(clusterId);
    if (!cluster) {
      return false;
    }

    // Terminate all sandboxes
    if (cluster.nodes) {
      for (const node of cluster.nodes) {
        if (node.sandboxId) {
          // Would terminate sandbox through E2B here
          // Debug: Terminating sandbox
        }
      }
    }

    // Clean up local resources
    this.daaAgents.delete(clusterId);
    this.wasmModules.delete(clusterId);
    
    // Remove from persistent store
    await this.store.deleteCluster(clusterId);

    return true;
  }
}

export const distributedNeuralSandbox = new DistributedNeuralSandbox();