/**
 * Research-Swarm Integration
 *
 * Distributed research graph DB
 * Integration with research-swarm package
 *
 * Features:
 * - Collaborative research agents
 * - Literature review aggregation
 * - Hypothesis generation and testing
 * - Knowledge synthesis
 */

import { createUnifiedDatabase } from '../../src/db-unified.js';
import { ReflexionMemory } from '../../src/controllers/ReflexionMemory.js';
import { CausalMemoryGraph } from '../../src/controllers/CausalMemoryGraph.js';
import { SkillLibrary } from '../../src/controllers/SkillLibrary.js';
import { EmbeddingService } from '../../src/controllers/EmbeddingService.js';
import * as path from 'path';

export default {
  description: 'Research-swarm distributed research with collaborative agents',

  async run(config: any) {
    const { verbosity = 2, researchers = 5 } = config;

    if (verbosity >= 2) {
      console.log(`   🔬 Initializing Research-Swarm (${researchers} researchers)`);
    }

    // Initialize distributed research graph database
    const embedder = new EmbeddingService({
      model: 'Xenova/all-MiniLM-L6-v2',
      dimension: 384,
      provider: 'transformers'
    });
    await embedder.initialize();

    const db = await createUnifiedDatabase(
      path.join(process.cwd(), 'simulation', 'data', 'advanced', 'research-swarm.graph'),
      embedder,
      { forceMode: 'graph' }
    );

    const reflexion = new ReflexionMemory(
      db.getGraphDatabase() as any,
      embedder,
      undefined,
      undefined,
      db.getGraphDatabase() as any
    );

    const causal = new CausalMemoryGraph(
      db.getGraphDatabase() as any,
      db.getGraphDatabase() as any
    );

    const skills = new SkillLibrary(
      db.getGraphDatabase() as any,
      embedder,
      undefined,
      db.getGraphDatabase() as any
    );

    const results = {
      papers: 0,
      hypotheses: 0,
      experiments: 0,
      synthesizedKnowledge: 0,
      totalTime: 0
    };

    const startTime = performance.now();

    // Literature Review (each researcher finds papers)
    const papers = [
      'neural_architecture_search_techniques',
      'few_shot_learning_methods',
      'transfer_learning_strategies',
      'meta_learning_algorithms',
      'continual_learning_approaches'
    ];

    const paperIds: number[] = [];
    for (let i = 0; i < papers.length; i++) {
      const id = await reflexion.storeEpisode({
        sessionId: `researcher-${i % researchers}`,
        task: `literature_review: ${papers[i]}`,
        reward: 0.80 + Math.random() * 0.15,  // Quality varies
        success: true,
        input: 'academic_search',
        output: `paper_summary_${papers[i]}`
      });
      paperIds.push(id);
      results.papers++;
    }

    // Hypothesis Generation (synthesizing from papers)
    const hypotheses = [
      'combining_meta_learning_with_architecture_search_improves_few_shot',
      'transfer_learning_enables_faster_continual_learning',
      'meta_architecture_search_reduces_hyperparameter_tuning'
    ];

    const hypothesisIds: number[] = [];
    for (const hypothesis of hypotheses) {
      const id = await reflexion.storeEpisode({
        sessionId: 'research-synthesis',
        task: `hypothesis: ${hypothesis}`,
        reward: 0.70,  // Untested hypothesis
        success: false,  // Not yet validated
        input: 'literature_synthesis',
        output: `hypothesis_${hypothesis}`,
        critique: 'Requires experimental validation'
      });
      hypothesisIds.push(id);
      results.hypotheses++;

      // Link hypothesis to supporting papers
      for (let i = 0; i < Math.min(2, paperIds.length); i++) {
        await causal.addCausalEdge({
          fromMemoryId: paperIds[i],
          fromMemoryType: 'episode',
          toMemoryId: id,
          toMemoryType: 'episode',
          similarity: 0.85,
          uplift: 0.20,
          confidence: 0.80,
          sampleSize: 100,
          mechanism: 'paper_supports_hypothesis'
        });
      }
    }

    // Experimental Validation
    const experiments = [
      { hypothesis: 0, result: 'confirmed', confidence: 0.92 },
      { hypothesis: 1, result: 'confirmed', confidence: 0.88 },
      { hypothesis: 2, result: 'partially_confirmed', confidence: 0.75 }
    ];

    for (const exp of experiments) {
      await reflexion.storeEpisode({
        sessionId: 'experimental-validation',
        task: `experiment_validate_hypothesis_${exp.hypothesis}`,
        reward: exp.confidence,
        success: exp.result === 'confirmed',
        input: `hypothesis_${exp.hypothesis}`,
        output: exp.result,
        critique: `Confidence: ${exp.confidence}`
      });
      results.experiments++;
    }

    // Knowledge Synthesis (create reusable research methods)
    const researchMethods = [
      'meta_architecture_search_protocol',
      'few_shot_evaluation_framework',
      'transfer_learning_pipeline'
    ];

    for (const method of researchMethods) {
      await skills.createSkill({
        name: method,
        description: 'Research methodology',
        code: `// Reusable research method: ${method}`,
        successRate: 0.85
      });
      results.synthesizedKnowledge++;
    }

    const endTime = performance.now();
    results.totalTime = endTime - startTime;

    db.close();

    if (verbosity >= 2) {
      console.log(`      📊 Papers Reviewed: ${results.papers}`);
      console.log(`      📊 Hypotheses Generated: ${results.hypotheses}`);
      console.log(`      📊 Experiments Conducted: ${results.experiments}`);
      console.log(`      📊 Synthesized Knowledge: ${results.synthesizedKnowledge} methods`);
      console.log(`      ⏱️  Duration: ${results.totalTime.toFixed(2)}ms`);
    }

    return results;
  }
};