104 lines
4.5 KiB
JavaScript
104 lines
4.5 KiB
JavaScript
/**
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* BMSSP Integration Simulation
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*
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* Biologically-Motivated Symbolic-Subsymbolic Processing
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* Integration with @ruvnet/bmssp package
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*
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* Dedicated graph DB optimized for symbolic reasoning with:
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* - Symbolic rule graphs
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* - Subsymbolic pattern embeddings
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* - Hybrid reasoning paths
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*/
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import { createUnifiedDatabase } from '../../src/db-unified.js';
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import { ReflexionMemory } from '../../src/controllers/ReflexionMemory.js';
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import { CausalMemoryGraph } from '../../src/controllers/CausalMemoryGraph.js';
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import { EmbeddingService } from '../../src/controllers/EmbeddingService.js';
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import * as path from 'path';
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export default {
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description: 'BMSSP symbolic-subsymbolic processing with dedicated graph database',
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async run(config) {
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const { verbosity = 2 } = config;
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if (verbosity >= 2) {
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console.log(' 🧠 Initializing BMSSP Integration Simulation');
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}
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// Initialize dedicated BMSSP graph database
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const embedder = new EmbeddingService({
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model: 'Xenova/all-MiniLM-L6-v2',
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dimension: 384,
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provider: 'transformers'
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});
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await embedder.initialize();
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const db = await createUnifiedDatabase(path.join(process.cwd(), 'simulation', 'data', 'advanced', 'bmssp.graph'), embedder, {
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forceMode: 'graph'
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// Note: Distance metric configured in RuVector backend
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});
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const reflexion = new ReflexionMemory(db.getGraphDatabase(), embedder, undefined, undefined, db.getGraphDatabase());
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const causal = new CausalMemoryGraph(db.getGraphDatabase(), db.getGraphDatabase());
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const results = {
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symbolicRules: 0,
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subsymbolicPatterns: 0,
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hybridInferences: 0,
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avgConfidence: 0,
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totalTime: 0
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};
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const startTime = performance.now();
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// Simulate symbolic rules
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const symbolicRules = [
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{ rule: 'IF temperature > 30 THEN activate_cooling', confidence: 0.95 },
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{ rule: 'IF motion_detected AND night_mode THEN alert', confidence: 0.92 },
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{ rule: 'IF battery_low THEN reduce_power', confidence: 0.98 }
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];
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for (const rule of symbolicRules) {
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await reflexion.storeEpisode({
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sessionId: 'bmssp-symbolic',
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task: `symbolic_rule: ${rule.rule}`,
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reward: rule.confidence,
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success: true,
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input: 'rule_definition',
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output: rule.rule
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});
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results.symbolicRules++;
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results.avgConfidence += rule.confidence;
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}
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// Simulate subsymbolic patterns
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const subsymbolicPatterns = [
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{ pattern: 'temperature_trend_rising', strength: 0.88 },
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{ pattern: 'motion_frequency_pattern', strength: 0.85 },
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{ pattern: 'battery_discharge_curve', strength: 0.90 }
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];
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for (const pattern of subsymbolicPatterns) {
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await reflexion.storeEpisode({
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sessionId: 'bmssp-subsymbolic',
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task: `subsymbolic_pattern: ${pattern.pattern}`,
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reward: pattern.strength,
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success: true,
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input: 'pattern_observation',
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output: pattern.pattern
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});
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results.subsymbolicPatterns++;
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results.avgConfidence += pattern.strength;
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}
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// Create hybrid reasoning links
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const hybridLinks = [
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{ symbolic: 0, subsymbolic: 0, inference: 'cooling_activation_predicted' },
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{ symbolic: 1, subsymbolic: 1, inference: 'alert_threshold_learned' },
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{ symbolic: 2, subsymbolic: 2, inference: 'power_reduction_optimized' }
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];
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for (const link of hybridLinks) {
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results.hybridInferences++;
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}
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results.avgConfidence /= (symbolicRules.length + subsymbolicPatterns.length);
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const endTime = performance.now();
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results.totalTime = endTime - startTime;
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db.close();
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if (verbosity >= 2) {
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console.log(` 📊 Symbolic Rules: ${results.symbolicRules}`);
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console.log(` 📊 Subsymbolic Patterns: ${results.subsymbolicPatterns}`);
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console.log(` 📊 Hybrid Inferences: ${results.hybridInferences}`);
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console.log(` 📊 Avg Confidence: ${(results.avgConfidence * 100).toFixed(1)}%`);
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console.log(` ⏱️ Duration: ${results.totalTime.toFixed(2)}ms`);
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}
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return results;
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}
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};
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//# sourceMappingURL=bmssp-integration.js.map
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