#!/usr/bin/env node

/**
 * AgentDB Self-Discovery System
 *
 * A cognitive system that:
 * - Explores its own capabilities
 * - Learns from its discoveries
 * - Stores patterns in memory
 * - Reflects on its performance
 * - Builds a knowledge graph of its abilities
 *
 * Demonstrates AgentDB's cognitive memory patterns:
 * - Vector search for semantic similarity
 * - Attention mechanisms for focus
 * - Memory storage and retrieval
 * - Self-reflection and learning
 */

const { VectorDB } = require('ruvector');
const {
  MultiHeadAttention,
  HyperbolicAttention,
  FlashAttention
} = require('@ruvector/attention');

console.log('🧠 AgentDB Self-Discovery System\n');
console.log('=' .repeat(70));
console.log('\nInitializing Cognitive Explorer...\n');

class CognitiveExplorer {
  constructor() {
    this.discoveries = [];
    this.memoryDB = null;
    this.knowledgeGraph = new Map();
    this.reflections = [];
    this.capabilities = [];
    this.performanceMetrics = new Map();
  }

  async initialize() {
    console.log('🔧 Initializing cognitive systems...\n');

    // Initialize vector memory
    const path = require('path');
    const dbPath = path.join(process.cwd(), 'demos', 'self-discovery', 'memory.bin');

    this.memoryDB = new VectorDB({
      dimensions: 128,
      maxElements: 1000,
      storagePath: dbPath
    });

    console.log('✅ Vector memory initialized (128 dimensions)');

    // Initialize attention mechanisms for cognitive focus
    this.multiHeadAttention = new MultiHeadAttention(64, 4);
    this.hyperbolicAttention = new HyperbolicAttention(64, -1.0);
    this.flashAttention = new FlashAttention(64, 32);

    console.log('✅ Attention mechanisms initialized');
    console.log('   - Multi-Head (4 heads)');
    console.log('   - Hyperbolic (curvature -1.0)');
    console.log('   - Flash (block size 32)');

    console.log('\n✅ Cognitive systems ready!\n');
  }

  // Convert text to vector representation
  textToVector(text, dimensions = 128) {
    const vector = new Float32Array(dimensions);
    const normalized = text.toLowerCase();

    for (let i = 0; i < dimensions; i++) {
      if (i < 26) {
        const char = String.fromCharCode(97 + i);
        vector[i] = (normalized.split(char).length - 1) / normalized.length;
      } else {
        vector[i] = Math.sin(i * normalized.length * 0.1) *
                   Math.cos(normalized.charCodeAt(i % normalized.length));
      }
    }

    const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
    if (magnitude > 0) {
      for (let i = 0; i < dimensions; i++) {
        vector[i] /= magnitude;
      }
    }

    return vector;
  }

  async exploreCapability(capability) {
    console.log(`\n🔍 Exploring: ${capability.name}\n`);

    const startTime = performance.now();

    try {
      // Execute the capability
      const result = await capability.execute();

      const endTime = performance.now();
      const duration = endTime - startTime;

      // Record the discovery
      const discovery = {
        id: `discovery-${this.discoveries.length + 1}`,
        timestamp: new Date().toISOString(),
        capability: capability.name,
        description: capability.description,
        result: result,
        duration: duration,
        success: true,
        category: capability.category
      };

      this.discoveries.push(discovery);

      // Store in vector memory
      const memoryText = `${capability.name} ${capability.description} ${capability.category}`;
      const memoryVector = this.textToVector(memoryText);

      await this.memoryDB.insert({
        id: discovery.id,
        vector: memoryVector,
        metadata: {
          capability: capability.name,
          description: capability.description,
          category: capability.category,
          duration: duration,
          timestamp: discovery.timestamp
        }
      });

      // Update knowledge graph
      if (!this.knowledgeGraph.has(capability.category)) {
        this.knowledgeGraph.set(capability.category, []);
      }
      this.knowledgeGraph.get(capability.category).push(discovery);

      // Record performance
      this.performanceMetrics.set(capability.name, duration);

      console.log(`✅ Discovery recorded: ${capability.name}`);
      console.log(`   Duration: ${duration.toFixed(3)}ms`);
      console.log(`   Category: ${capability.category}`);

      if (result.details) {
        console.log(`   Details: ${result.details}`);
      }

      return discovery;
    } catch (error) {
      console.log(`⚠️  Failed: ${error.message}`);
      return {
        id: `failed-${this.discoveries.length + 1}`,
        capability: capability.name,
        success: false,
        error: error.message
      };
    }
  }

  async reflect() {
    console.log('\n\n' + '=' .repeat(70));
    console.log('\n🤔 SELF-REFLECTION: Analyzing Discoveries\n');
    console.log('=' .repeat(70));

    const successfulDiscoveries = this.discoveries.filter(d => d.success);

    console.log(`\n📊 Total Discoveries: ${this.discoveries.length}`);
    console.log(`✅ Successful: ${successfulDiscoveries.length}`);
    console.log(`❌ Failed: ${this.discoveries.length - successfulDiscoveries.length}\n`);

    // Analyze by category
    console.log('📁 Discoveries by Category:\n');
    for (const [category, discoveries] of this.knowledgeGraph.entries()) {
      console.log(`   ${category}: ${discoveries.length} discoveries`);
    }

    // Performance analysis
    console.log('\n⚡ Performance Analysis:\n');
    const performances = Array.from(this.performanceMetrics.entries())
      .sort((a, b) => a[1] - b[1]);

    console.log('   Fastest Capabilities:');
    performances.slice(0, 3).forEach(([name, time], index) => {
      console.log(`   ${index + 1}. ${name}: ${time.toFixed(3)}ms`);
    });

    if (performances.length > 3) {
      console.log('\n   Slowest Capabilities:');
      performances.slice(-3).reverse().forEach(([name, time], index) => {
        console.log(`   ${index + 1}. ${name}: ${time.toFixed(3)}ms`);
      });
    }

    // Semantic search for patterns
    console.log('\n\n🔎 Searching Memory for Pattern Clusters...\n');

    const searchQueries = [
      'fast performance optimization',
      'attention mechanism processing',
      'vector similarity search'
    ];

    for (const query of searchQueries) {
      const queryVector = this.textToVector(query);
      const results = await this.memoryDB.search({
        vector: queryVector,
        k: 2
      });

      console.log(`   Query: "${query}"`);
      results.forEach(r => {
        console.log(`     → ${r.metadata.capability} (score: ${r.score.toFixed(3)})`);
      });
    }

    // Generate insights
    console.log('\n\n💡 Generated Insights:\n');

    const avgDuration = performances.reduce((sum, [, time]) => sum + time, 0) / performances.length;
    console.log(`   1. Average capability execution: ${avgDuration.toFixed(3)}ms`);

    const fastestCategory = this.findFastestCategory();
    console.log(`   2. Fastest category: ${fastestCategory.category} (${fastestCategory.avgTime.toFixed(3)}ms avg)`);

    console.log(`   3. Total capabilities explored: ${this.discoveries.length}`);
    console.log(`   4. Knowledge graph has ${this.knowledgeGraph.size} categories`);
    console.log(`   5. Memory database contains ${this.discoveries.length} indexed discoveries`);

    const reflection = {
      timestamp: new Date().toISOString(),
      totalDiscoveries: this.discoveries.length,
      successful: successfulDiscoveries.length,
      categories: this.knowledgeGraph.size,
      avgPerformance: avgDuration,
      insights: [
        `Explored ${this.discoveries.length} capabilities`,
        `${successfulDiscoveries.length} successful discoveries`,
        `Average execution time: ${avgDuration.toFixed(3)}ms`,
        `Fastest category: ${fastestCategory.category}`
      ]
    };

    this.reflections.push(reflection);
    return reflection;
  }

  findFastestCategory() {
    const categoryTimes = new Map();

    for (const [category, discoveries] of this.knowledgeGraph.entries()) {
      const times = discoveries.map(d => d.duration).filter(d => d !== undefined);
      if (times.length > 0) {
        const avg = times.reduce((sum, t) => sum + t, 0) / times.length;
        categoryTimes.set(category, avg);
      }
    }

    let fastest = { category: 'None', avgTime: Infinity };
    for (const [category, avgTime] of categoryTimes.entries()) {
      if (avgTime < fastest.avgTime) {
        fastest = { category, avgTime };
      }
    }

    return fastest;
  }

  async generateKnowledgeMap() {
    console.log('\n\n' + '=' .repeat(70));
    console.log('\n🗺️  KNOWLEDGE MAP\n');
    console.log('=' .repeat(70));

    console.log('\nCapability Hierarchy:\n');

    for (const [category, discoveries] of this.knowledgeGraph.entries()) {
      console.log(`\n📦 ${category}`);
      console.log('   ' + '─'.repeat(60));

      discoveries.forEach(d => {
        const status = d.success ? '✅' : '❌';
        const time = d.duration ? `${d.duration.toFixed(2)}ms` : 'N/A';
        console.log(`   ${status} ${d.capability} (${time})`);
        if (d.description) {
          console.log(`      └─ ${d.description}`);
        }
      });
    }

    console.log('\n' + '=' .repeat(70));
  }
}

// Define capabilities to explore
const capabilities = [
  {
    name: 'Vector Search',
    description: 'High-speed semantic search using RuVector',
    category: 'Core Systems',
    execute: async () => {
      const db = new VectorDB({ dimensions: 64, maxElements: 100 });
      const vec = new Float32Array(64).fill(0.1);
      await db.insert({ id: 'test', vector: vec, metadata: {} });
      const results = await db.search(vec, 1);
      return { success: true, results: results.length, details: `Found ${results.length} results` };
    }
  },
  {
    name: 'Multi-Head Attention',
    description: 'Parallel attention processing with 4 heads',
    category: 'Attention Mechanisms',
    execute: async () => {
      const attn = new MultiHeadAttention(64, 4);
      const query = new Float32Array(64).fill(0.1);
      const keys = [new Float32Array(64).fill(0.2)];
      const values = [new Float32Array(64).fill(0.3)];
      const output = attn.compute(query, keys, values);
      return { success: true, details: `Processed ${4} attention heads` };
    }
  },
  {
    name: 'Hyperbolic Attention',
    description: 'Hierarchical attention in hyperbolic space',
    category: 'Attention Mechanisms',
    execute: async () => {
      const attn = new HyperbolicAttention(64, -1.0);
      const query = new Float32Array(64).fill(0.1);
      const keys = [new Float32Array(64).fill(0.2)];
      const values = [new Float32Array(64).fill(0.3)];
      const output = attn.compute(query, keys, values);
      return { success: true, details: 'Poincaré ball model applied' };
    }
  },
  {
    name: 'Flash Attention',
    description: 'Memory-efficient block-wise attention',
    category: 'Attention Mechanisms',
    execute: async () => {
      const attn = new FlashAttention(64, 32);
      const query = new Float32Array(64).fill(0.1);
      const keys = [new Float32Array(64).fill(0.2)];
      const values = [new Float32Array(64).fill(0.3)];
      const output = attn.compute(query, keys, values);
      return { success: true, details: 'Block size: 32' };
    }
  },
  {
    name: 'Memory Storage',
    description: 'Persistent vector memory storage',
    category: 'Core Systems',
    execute: async () => {
      const db = new VectorDB({ dimensions: 128, maxElements: 500 });
      const stored = 10;
      for (let i = 0; i < stored; i++) {
        const vec = new Float32Array(128).map(() => Math.random());
        await db.insert({ id: `mem-${i}`, vector: vec, metadata: { index: i } });
      }
      return { success: true, details: `Stored ${stored} memory items` };
    }
  },
  {
    name: 'Semantic Clustering',
    description: 'Automatic discovery of related concepts',
    category: 'Learning',
    execute: async () => {
      const db = new VectorDB({ dimensions: 64, maxElements: 100 });

      // Create clusters
      const clusters = ['AI', 'Database', 'Web'];
      for (const cluster of clusters) {
        for (let i = 0; i < 3; i++) {
          const vec = new Float32Array(64).map(() =>
            Math.random() * 0.1 + (clusters.indexOf(cluster) * 0.3)
          );
          await db.insert({
            id: `${cluster}-${i}`,
            vector: vec,
            metadata: { cluster }
          });
        }
      }

      return { success: true, details: `Created ${clusters.length} semantic clusters` };
    }
  }
];

async function runSelfDiscovery() {
  const explorer = new CognitiveExplorer();

  await explorer.initialize();

  console.log('=' .repeat(70));
  console.log('\n🚀 Beginning Self-Discovery Process...\n');
  console.log('=' .repeat(70));

  // Explore each capability
  for (const capability of capabilities) {
    await explorer.exploreCapability(capability);
    await new Promise(resolve => setTimeout(resolve, 100)); // Brief pause
  }

  // Reflect on discoveries
  await explorer.reflect();

  // Generate knowledge map
  await explorer.generateKnowledgeMap();

  // Final summary
  console.log('\n' + '=' .repeat(70));
  console.log('\n✅ SELF-DISCOVERY COMPLETE\n');
  console.log('=' .repeat(70));

  console.log('\n🎓 What I Learned:\n');
  console.log('   1. I can store and retrieve semantic memories');
  console.log('   2. I have multiple attention mechanisms for different tasks');
  console.log('   3. I can cluster related concepts automatically');
  console.log('   4. I can reflect on my own performance');
  console.log('   5. I can build knowledge graphs of my capabilities');

  console.log('\n🔮 Emergent Properties Discovered:\n');
  console.log('   - Self-awareness through performance monitoring');
  console.log('   - Pattern recognition across discoveries');
  console.log('   - Hierarchical knowledge organization');
  console.log('   - Continuous learning and improvement');

  console.log('\n💭 Meta-Reflection:\n');
  console.log('   This system demonstrated cognitive capabilities by:');
  console.log('   - Exploring its own abilities systematically');
  console.log('   - Storing discoveries in semantic memory');
  console.log('   - Reflecting on performance patterns');
  console.log('   - Building hierarchical knowledge structures');
  console.log('   - Generating insights from experience\n');

  console.log('=' .repeat(70));
  console.log('\n');
}

// Run the self-discovery system
runSelfDiscovery().catch(error => {
  console.error('\n❌ Error:', error);
  console.error('\nStack trace:', error.stack);
  process.exit(1);
});