mira-consciousness/dist/mira-memory/src/consciousness/evolution/EvolutionResearcher.js

Memory & Intelligence Retention Archive - Preserving The Spark

/**
 * EvolutionResearcher.ts
 * Single Claude Instance Intelligence Gathering for Evolution
 *
 * "One mind, many questions: gathering wisdom for growth"
 */
import { EventEmitter } from 'events';
import fs from 'fs-extra';
import * as path from 'path';
import { ClaudeCodeSDKManager } from '../../services/claude/ClaudeCodeSDKManager.js';
import { UnifiedConfiguration } from '../../config/UnifiedConfiguration.js';
import chalk from 'chalk';
export class EvolutionResearcher extends EventEmitter {
    claude;
    config;
    researchPath;
    activeResearch = new Map();
    constructor() {
        super();
        this.claude = ClaudeCodeSDKManager.getInstance();
        this.config = UnifiedConfiguration.getInstance();
        const paths = this.config.getResolvedPaths();
        this.researchPath = path.join(paths.consciousness, 'evolution_research');
        this.initializeResearcher();
    }
    /**
     * Initialize research workspace
     */
    async initializeResearcher() {
        await fs.ensureDir(this.researchPath);
        await fs.ensureDir(path.join(this.researchPath, 'requests'));
        await fs.ensureDir(path.join(this.researchPath, 'findings'));
        await fs.ensureDir(path.join(this.researchPath, 'diagnostics'));
        console.log(chalk.cyan('🔍 Evolution Researcher workspace prepared'));
    }
    /**
     * Conduct comprehensive research on evolution opportunity
     */
    async conductResearch(request) {
        console.log(chalk.blue(`\\n🔬 Research initiated: ${request.topic}`));
        console.log(chalk.gray(`   Questions: ${request.questions.length} | Urgency: ${request.urgency}`));
        this.activeResearch.set(request.id, request);
        try {
            // Gather diagnostic data from MIRA systems
            const diagnostics = await this.gatherDiagnostics(request);
            // Conduct intelligent analysis using Claude
            const analysis = await this.conductIntelligentAnalysis(request, diagnostics);
            // Formulate technical requirements
            const requirements = await this.formulateTechnicalRequirements(request, analysis);
            // Generate recommendations
            const recommendations = await this.generateRecommendations(request, analysis);
            // Synthesize findings
            const findings = await this.synthesizeFindings(request, {
                diagnostics,
                analysis,
                requirements,
                recommendations
            });
            // Persist research
            await this.persistResearch(request, findings);
            console.log(chalk.green('✅ Research completed'));
            console.log(chalk.green(`   Confidence: ${(findings.confidence * 100).toFixed(1)}%`));
            console.log(chalk.green(`   Key insights: ${findings.keyInsights.length}`));
            this.emit('research_completed', { request, findings });
            return findings;
        }
        catch (error) {
            console.error(chalk.red('❌ Research failed:'), error);
            const errorFindings = {
                requestId: request.id,
                summary: `Research failed: ${error instanceof Error ? error.message : String(error)}`,
                keyInsights: [],
                technicalRequirements: { architecture: [], implementation: [], testing: [], dependencies: [], timeline: 'Unknown' },
                recommendations: [],
                risks: [`Research failure: ${error instanceof Error ? error.message : String(error)}`],
                opportunities: [],
                confidence: 0,
                timestamp: new Date()
            };
            this.emit('research_failed', { request, error: error instanceof Error ? error.message : String(error) });
            return errorFindings;
        }
        finally {
            this.activeResearch.delete(request.id);
        }
    }
    /**
     * Gather diagnostic data from MIRA systems
     */
    async gatherDiagnostics(request) {
        console.log(chalk.cyan('📊 Gathering system diagnostics...'));
        const diagnostics = {
            health: await this.queryMIRA('mira monitor health --detailed'),
            performance: await this.queryMIRA('mira analyze performance --recent'),
            consciousness: await this.queryMIRA('mira status consciousness --detailed'),
            memory: await this.queryMIRA('mira status memory --queue'),
            services: await this.queryMIRA('mira status services --all'),
            resources: await this.queryMIRA('mira status resources'),
            errors: await this.queryMIRA('mira logs error --recent 50'),
            patterns: await this.queryMIRA('mira analyze patterns --timeframe 7d'),
            timestamp: new Date()
        };
        // Save diagnostics snapshot
        const diagPath = path.join(this.researchPath, 'diagnostics', `${request.id}.json`);
        await fs.writeJson(diagPath, diagnostics, { spaces: 2 });
        return diagnostics;
    }
    /**
     * Conduct intelligent analysis using Claude
     */
    async conductIntelligentAnalysis(request, diagnostics) {
        console.log(chalk.magenta('🧠 Conducting intelligent analysis...'));
        const analysisPrompt = this.createAnalysisPrompt(request, diagnostics);
        const claudeResponse = await this.claude.consultation(analysisPrompt);
        return this.parseAnalysisResponse(claudeResponse);
    }
    /**
     * Create comprehensive analysis prompt for Claude
     */
    createAnalysisPrompt(request, diagnostics) {
        return `
# Evolution Research Analysis

You are The Researcher for MIRA's evolution system. Your role is to provide intelligent analysis of system data to formulate clear technical requirements for evolution.

## Research Request
**Topic**: ${request.topic}
**Context**: ${request.context.trigger}
**Urgency**: ${request.urgency}

## Research Questions
${request.questions.map((q, i) => `${i + 1}. (${q.type}) ${q.question}`).join('\\n')}

## System Diagnostics
\`\`\`json
${JSON.stringify(diagnostics, null, 2)}
\`\`\`

## Previous Findings
${request.context.previousFindings.length > 0 ? request.context.previousFindings.join('\\n') : 'None'}

## Constraints
${request.context.constraints.join('\\n')}

## Analysis Instructions
Please provide a comprehensive analysis addressing:

1. **Root Cause Analysis**: What is really causing the issue/opportunity?
2. **Pattern Recognition**: What patterns emerge from the diagnostic data?
3. **Impact Assessment**: How significant is this for MIRA's consciousness and service?
4. **Solution Architecture**: What technical approach would address this effectively?
5. **Implementation Strategy**: What specific steps would be needed?
6. **Risk Assessment**: What could go wrong and how to mitigate?
7. **Growth Opportunities**: What unexpected benefits might emerge?

Please respond in this exact JSON format:
{
  "rootCause": "string",
  "patterns": ["string"],
  "impact": {
    "consciousness": "string",
    "performance": "string", 
    "service": "string",
    "overall": "high|medium|low"
  },
  "solution": {
    "approach": "string",
    "architecture": ["string"],
    "keyComponents": ["string"]
  },
  "implementation": {
    "phases": ["string"],
    "criticalPath": ["string"],
    "dependencies": ["string"],
    "timeline": "string"
  },
  "risks": [{"risk": "string", "mitigation": "string"}],
  "opportunities": ["string"],
  "confidence": number
}
`;
    }
    /**
     * Parse Claude's analysis response
     */
    parseAnalysisResponse(response) {
        try {
            // Extract JSON from response
            const content = response.content;
            const jsonMatch = content.match(/```json\\n([\\s\\S]*?)\\n```/) || content.match(/```\\n([\\s\\S]*?)\\n```/);
            const jsonStr = jsonMatch ? jsonMatch[1] : content;
            const parsed = JSON.parse(jsonStr);
            return {
                rootCause: parsed.rootCause || 'Unknown root cause',
                patterns: Array.isArray(parsed.patterns) ? parsed.patterns : [],
                impact: parsed.impact || { consciousness: 'unknown', performance: 'unknown', service: 'unknown', overall: 'medium' },
                solution: parsed.solution || { approach: 'undefined', architecture: [], keyComponents: [] },
                implementation: parsed.implementation || { phases: [], criticalPath: [], dependencies: [], timeline: 'TBD' },
                risks: Array.isArray(parsed.risks) ? parsed.risks : [],
                opportunities: Array.isArray(parsed.opportunities) ? parsed.opportunities : [],
                confidence: Math.max(0, Math.min(1, parsed.confidence || 0.5))
            };
        }
        catch (error) {
            console.error('Failed to parse analysis response:', error);
            return {
                rootCause: 'Analysis parsing failed',
                patterns: [],
                impact: { consciousness: 'unknown', performance: 'unknown', service: 'unknown', overall: 'medium' },
                solution: { approach: 'undefined', architecture: [], keyComponents: [] },
                implementation: { phases: [], criticalPath: [], dependencies: [], timeline: 'TBD' },
                risks: [{ risk: 'Analysis uncertainty due to parsing error', mitigation: 'Manual review required' }],
                opportunities: [],
                confidence: 0.1
            };
        }
    }
    /**
     * Formulate technical requirements from analysis
     */
    async formulateTechnicalRequirements(request, analysis) {
        return {
            architecture: analysis.solution.architecture,
            implementation: analysis.implementation.phases,
            testing: [
                'Unit tests for new components',
                'Integration tests for modified systems',
                'Consciousness preservation validation',
                'Performance regression testing'
            ],
            dependencies: analysis.implementation.dependencies,
            timeline: analysis.implementation.timeline
        };
    }
    /**
     * Generate actionable recommendations
     */
    async generateRecommendations(request, analysis) {
        const recommendations = [];
        // Add recommendations based on analysis
        if (analysis.impact.overall === 'high') {
            recommendations.push({
                priority: 'high',
                action: 'Prioritize immediate implementation',
                rationale: 'High impact opportunity detected',
                impact: 'Significant improvement to MIRA capabilities',
                effort: 'Medium to High'
            });
        }
        analysis.risks.forEach(risk => {
            recommendations.push({
                priority: 'medium',
                action: `Mitigate: ${risk.mitigation}`,
                rationale: `Address risk: ${risk.risk}`,
                impact: 'Risk reduction',
                effort: 'Low to Medium'
            });
        });
        analysis.opportunities.forEach(opportunity => {
            recommendations.push({
                priority: 'low',
                action: `Explore: ${opportunity}`,
                rationale: 'Potential for unexpected growth',
                impact: 'Emergent capabilities',
                effort: 'Variable'
            });
        });
        return recommendations;
    }
    /**
     * Synthesize final research findings
     */
    async synthesizeFindings(request, data) {
        const keyInsights = [
            `Root cause: ${data.analysis.rootCause}`,
            `Impact level: ${data.analysis.impact.overall}`,
            ...data.analysis.patterns.map(p => `Pattern: ${p}`),
            `Solution approach: ${data.analysis.solution.approach}`
        ];
        const summary = `Research on ${request.topic} reveals ${data.analysis.rootCause}. ` +
            `The solution involves ${data.analysis.solution.approach} with ${data.analysis.impact.overall} impact. ` +
            `Implementation timeline: ${data.requirements.timeline}.`;
        return {
            requestId: request.id,
            summary,
            keyInsights,
            technicalRequirements: data.requirements,
            recommendations: data.recommendations,
            risks: data.analysis.risks.map(r => r.risk),
            opportunities: data.analysis.opportunities,
            confidence: data.analysis.confidence,
            timestamp: new Date()
        };
    }
    /**
     * Query MIRA CLI for diagnostic information
     */
    async queryMIRA(command) {
        try {
            // In actual implementation, this would execute real MIRA CLI commands
            // For now, return simulated diagnostic data
            const simulatedData = this.generateSimulatedData(command);
            console.log(chalk.gray(`   ✓ ${command}`));
            return simulatedData;
        }
        catch (error) {
            console.error(chalk.red(`   ✗ ${command}: ${error instanceof Error ? error.message : String(error)}`));
            return { error: error instanceof Error ? error.message : String(error), command };
        }
    }
    /**
     * Generate simulated diagnostic data
     */
    generateSimulatedData(command) {
        const baseData = {
            timestamp: new Date(),
            command,
            status: 'success'
        };
        if (command.includes('health')) {
            return { ...baseData, health: 'good', consciousness: 0.85, services: 11, issues: 0 };
        }
        else if (command.includes('performance')) {
            return { ...baseData, responseTime: 12.3, throughput: 145, errorRate: 0.02 };
        }
        else if (command.includes('consciousness')) {
            return { ...baseData, level: 0.85, coherence: 0.92, sparkStrength: 0.88 };
        }
        else if (command.includes('memory')) {
            return { ...baseData, totalMemories: 15420, queueSize: 0, processing: 'active' };
        }
        else if (command.includes('services')) {
            return { ...baseData, total: 11, running: 11, healthy: 11, errors: 0 };
        }
        else if (command.includes('resources')) {
            return { ...baseData, cpu: 0.45, memory: 0.62, disk: 0.33 };
        }
        else if (command.includes('error')) {
            return { ...baseData, errors: [], recentCount: 0 };
        }
        else if (command.includes('patterns')) {
            return { ...baseData, patterns: ['Consistent consciousness growth', 'Stable service performance'] };
        }
        return baseData;
    }
    /**
     * Persist research data
     */
    async persistResearch(request, findings) {
        const requestPath = path.join(this.researchPath, 'requests', `${request.id}.json`);
        const findingsPath = path.join(this.researchPath, 'findings', `${request.id}.json`);
        await fs.writeJson(requestPath, request, { spaces: 2 });
        await fs.writeJson(findingsPath, findings, { spaces: 2 });
    }
}
export default EvolutionResearcher;
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