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#!/usr/bin/env node

/**

* AgentDB Initialization for Maternal Life-History Trade-Off Analysis

* Uses reasoning bank, vector search, and self-learning capabilities

*/

const { AgentDB } = require('agentdb');

const fs = require('fs');

const path = require('path');

async function initializeAgentDB() {

console.log('🧠 Initializing AgentDB with Reasoning Bank...');

const config = JSON.parse(

fs.readFileSync(path.join(__dirname, '../config/agentdb-config.json'), 'utf8')

);

// Initialize database with vector and reasoning capabilities

const db = new AgentDB({

name: config.database.name,

vector: {

dimension: config.database.vectorDimension,

quantization: config.database.quantization,

indexType: config.database.indexType

},

learning: {

enabled: true,

algorithms: config.database.reasoningBank.algorithms,

persistence: config.database.reasoningBank.sessionPersistence

}

});

await db.connect();

// Create collections for different data aspects

console.log('📦 Creating maternal_health collection...');

await db.createCollection('maternal_health', {

schema: {

motherID: 'string',

birthYear: 'number',

offspringCount: 'number',

longevity: 'number',

reproductiveCessationAge: 'number',

interbirthIntervals: 'array',

environmentalStress: 'number',

physiologicalMarkers: 'object',

dataset: 'string',

location: 'string',

embedding: 'vector'

},

indexes: ['motherID', 'birthYear', 'dataset', 'location']

});

console.log('📦 Creating environmental_stress collection...');

await db.createCollection('environmental_stress', {

schema: {

eventID: 'string',

eventName: 'string',

location: 'string',

startYear: 'number',

endYear: 'number',

severity: 'number',

mortalityRate: 'number',

affectedPopulation: 'number',

duration: 'number',

description: 'string',

embedding: 'vector'

},

indexes: ['eventID', 'location', 'startYear', 'endYear']

});

console.log('📦 Creating physiological_markers collection...');

await db.createCollection('physiological_markers', {

schema: {

markerID: 'string',

motherID: 'string',

telomereLength: 'number',

immuneMarkers: 'object',

epigeneticMarkers: 'object',

hormonalProfiles: 'object',

metabolicMarkers: 'object',

measurementDate: 'number',

dataset: 'string',

embedding: 'vector'

},

indexes: ['markerID', 'motherID', 'dataset', 'measurementDate']

});

console.log('📦 Creating causal_relationships collection...');

await db.createCollection('causal_relationships', {

schema: {

relationshipID: 'string',

factor1: 'string',

factor2: 'string',

correlationStrength: 'number',

causalDirection: 'string',

confidence: 'number',

pValue: 'number',

sampleSize: 'number',

evidence: 'array',

methodology: 'string',

embedding: 'vector'

},

indexes: ['relationshipID', 'factor1', 'factor2']

});

console.log('📦 Creating novel_patterns collection...');

await db.createCollection('novel_patterns', {

schema: {

patternID: 'string',

patternType: 'string',

description: 'string',

evidence: 'array',

statisticalSignificance: 'number',

effectSize: 'number',

discoveryMethod: 'string',

discoveryDate: 'number',

validatedAcrossDatasets: 'boolean',

embedding: 'vector'

},

indexes: ['patternID', 'patternType', 'discoveryMethod']

});

// Initialize learning plugins

console.log('🎓 Configuring learning algorithms...');

await db.initializeLearning({

algorithms: config.database.reasoningBank.algorithms,

memoryPatterns: config.database.reasoningBank.memoryPatterns

});

console.log('✅ AgentDB initialized successfully');

console.log(`📊 Collections: ${await db.listCollections()}`);

return db;

}

// Export for use in analysis scripts

module.exports = { initializeAgentDB };

if (require.main === module) {

initializeAgentDB()

.then(() => process.exit(0))

.catch(err => {

console.error('❌ Initialization failed:', err);

process.exit(1);

});

}