memory-integration-analysis.js•6.35 kB
/**
* Chicken Business Memory Integration Analysis
* How to enhance your AI system with persistent knowledge using MCP Memory Server
*/
// ============================================================================
// MEMORY SERVER CAPABILITIES ANALYSIS
// ============================================================================
/*
The Memory MCP Server provides:
1. ENTITIES - People, places, things in your business
- Suppliers (Magnolia, San Miguel, etc.)
- Customers (regular buyers, restaurants)
- Workers (branch staff, processing team)
- Products (whole chickens, parts, necks)
- Branches (locations, stores)
2. RELATIONS - How entities connect
- "Magnolia" supplies_to "Main_Branch"
- "Customer_Restaurant_A" prefers "chicken_parts"
- "Worker_Maria" works_at "Branch_2"
- "Branch_1" processes "whole_chickens"
3. OBSERVATIONS - Facts about entities
- "Magnolia delivers on Tuesdays and Fridays"
- "Customer_Restaurant_A orders 50kg weekly"
- "Worker_Juan is expert at chicken processing"
- "Branch_2 sells more necks than parts"
*/
// ============================================================================
// INTEGRATION OPPORTUNITIES FOR CHICKEN BUSINESS
// ============================================================================
const CHICKEN_BUSINESS_MEMORY_SCHEMA = {
// 1. SUPPLIER INTELLIGENCE
suppliers: {
entities: [
{
name: "Magnolia_Supplier",
entityType: "supplier",
observations: [
"Delivers whole chickens in 10-piece bags",
"Delivery schedule: Tuesday and Friday",
"Average quality rating: 4.5/5",
"Price per bag: 1200 pesos",
"Reliable delivery time: 8AM-10AM"
]
}
],
relations: [
{ from: "Magnolia_Supplier", to: "Main_Branch", relationType: "supplies_to" },
{ from: "Magnolia_Supplier", to: "Whole_Chicken", relationType: "provides" }
]
},
// 2. CUSTOMER PATTERNS
customers: {
entities: [
{
name: "Restaurant_Lucky_Dragon",
entityType: "customer",
observations: [
"Orders 30 bags of chicken parts weekly",
"Prefers Wednesday deliveries",
"Pays on time consistently",
"Requests specific cut sizes",
"Peak season: December-January"
]
}
],
relations: [
{ from: "Restaurant_Lucky_Dragon", to: "Chicken_Parts", relationType: "purchases" },
{ from: "Restaurant_Lucky_Dragon", to: "Branch_1", relationType: "served_by" }
]
},
// 3. WORKER EXPERTISE
workers: {
entities: [
{
name: "Worker_Maria",
entityType: "employee",
observations: [
"Expert at chicken processing",
"Can process 20 chickens per hour",
"Works morning shift 6AM-2PM",
"Prefers working on Tuesdays",
"Knows customer preferences well"
]
}
],
relations: [
{ from: "Worker_Maria", to: "Branch_1", relationType: "works_at" },
{ from: "Worker_Maria", to: "Chicken_Processing", relationType: "specializes_in" }
]
},
// 4. SEASONAL PATTERNS
patterns: {
entities: [
{
name: "Christmas_Season",
entityType: "business_period",
observations: [
"High demand for whole chickens",
"Price increases by 20%",
"Extended working hours needed",
"Stock 3x normal inventory",
"Customer orders increase 150%"
]
}
],
relations: [
{ from: "Christmas_Season", to: "Whole_Chicken", relationType: "increases_demand_for" },
{ from: "Christmas_Season", to: "All_Branches", relationType: "affects" }
]
}
};
// ============================================================================
// ENHANCED AI CAPABILITIES WITH MEMORY
// ============================================================================
const ENHANCED_CAPABILITIES = {
// 1. INTELLIGENT NOTE PARSING
noteParsingWithMemory: `
When parsing: "Buy magnolia chicken 20 bags"
WITHOUT MEMORY: Basic parsing
- Supplier: magnolia
- Product: chicken
- Quantity: 20 bags
WITH MEMORY: Intelligent context
- Supplier: Magnolia_Supplier (known entity)
- Expected cost: 20 * 1200 = 24,000 pesos
- Delivery day: Tuesday or Friday
- Processing capacity: Worker_Maria can handle this
- Customer impact: Enough for Restaurant_Lucky_Dragon + 10 bags extra
`,
// 2. PROACTIVE RECOMMENDATIONS
recommendations: `
AI can now suggest:
- "Magnolia usually delivers on Fridays, schedule Worker_Maria"
- "Restaurant_Lucky_Dragon will need parts soon, start processing"
- "Christmas season approaching, increase orders by 200%"
- "New supplier has better prices, but check delivery reliability"
`,
// 3. PATTERN RECOGNITION
patterns: `
Memory enables:
- "This customer always orders before weekends"
- "Summer months have 30% less neck sales"
- "Worker_Juan is most efficient with morning deliveries"
- "Branch_2 needs restocking every 3 days"
`
};
// ============================================================================
// INTEGRATION ARCHITECTURE
// ============================================================================
const INTEGRATION_PLAN = {
// PHASE 1: Basic Memory Integration
phase1: {
goal: "Add memory to existing chicken business AI",
tasks: [
"Connect Memory server to chicken AI system",
"Create entities for known suppliers and customers",
"Store basic business patterns in memory",
"Enhance note parsing with memory lookup"
]
},
// PHASE 2: Intelligent Context
phase2: {
goal: "Context-aware AI responses",
tasks: [
"Query memory during note processing",
"Add observations from successful transactions",
"Build supplier and customer intelligence",
"Implement pattern recognition"
]
},
// PHASE 3: Proactive Intelligence
phase3: {
goal: "AI that learns and predicts",
tasks: [
"Seasonal pattern detection",
"Automatic recommendations",
"Worker scheduling optimization",
"Inventory prediction"
]
}
};
export { CHICKEN_BUSINESS_MEMORY_SCHEMA, ENHANCED_CAPABILITIES, INTEGRATION_PLAN };