Simplify store tools: minimal .bas using PRODUCTS, SEARCH PRODUCTS, LLM keywords
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3 changed files with 13 additions and 420 deletions
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' Shipping and Cubage Calculator
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' Calculates shipping dimensions, volume, and estimated costs
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PARAM product_id AS INTEGER
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PARAM quantity AS INTEGER OPTIONAL
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PARAM product_id AS INTEGER DESCRIPTION "Product ID to calculate shipping for"
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PARAM quantity AS INTEGER OPTIONAL DESCRIPTION "Quantity of items (default: 1)"
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PARAM destination_zip AS STRING OPTIONAL DESCRIPTION "Destination ZIP code for shipping estimate"
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product = PRODUCT product_id
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shipping = LLM "Calculate shipping for: " + product + ", qty: " + quantity + ". Return JSON {dimensions: {length_cm, width_cm, height_cm}, weight_kg, volume_cm3, shipping_options: [{carrier, cost, days}]}"
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DESCRIPTION "Calculate shipping dimensions, volumetric weight, and estimated shipping cost"
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' Set defaults
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IF NOT quantity THEN
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quantity = 1
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END IF
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' Find product by ID
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product = FIND "Products.csv", "ProductID = " + product_id
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IF NOT product THEN
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TALK "❌ Product not found with ID: " + product_id
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RETURN { "error": "Product not found" }
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END IF
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' Get product dimensions from extended fields or estimate from quantity per unit
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' Default dimensions in cm (length x width x height)
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dimensions = {}
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' Parse quantity per unit for dimension hints
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qty_unit = product.QuantityPerUnit
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IF qty_unit THEN
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' Extract numeric values and unit types
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IF CONTAINS(qty_unit, "kg") THEN
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dimensions.estimated_weight = EXTRACT_NUMBER(qty_unit)
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dimensions.weight_unit = "kg"
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ELSE IF CONTAINS(qty_unit, "ml") OR CONTAINS(qty_unit, "cc") THEN
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volume_ml = EXTRACT_NUMBER(qty_unit)
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dimensions.estimated_weight = volume_ml / 1000
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dimensions.weight_unit = "kg"
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ELSE IF CONTAINS(qty_unit, "oz") THEN
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oz_value = EXTRACT_NUMBER(qty_unit)
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dimensions.estimated_weight = oz_value * 0.0283495
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dimensions.weight_unit = "kg"
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ELSE IF CONTAINS(qty_unit, "lb") THEN
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lb_value = EXTRACT_NUMBER(qty_unit)
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dimensions.estimated_weight = lb_value * 0.453592
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dimensions.weight_unit = "kg"
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ELSE
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' Default weight estimate based on category
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dimensions.estimated_weight = 0.5
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dimensions.weight_unit = "kg"
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END IF
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END IF
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' Use AI to estimate package dimensions if not available
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BEGIN SYSTEM PROMPT
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You are a logistics expert. Estimate realistic package dimensions for shipping.
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Consider the product type, typical packaging, and quantity per unit.
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Return dimensions in centimeters and weight in kilograms.
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END SYSTEM PROMPT
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estimation_prompt = "Estimate shipping package dimensions for: " + product.ProductName + " (Quantity per unit: " + product.QuantityPerUnit + "). Return JSON: {\"length_cm\": number, \"width_cm\": number, \"height_cm\": number, \"weight_kg\": number, \"package_type\": string}"
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ai_estimate = ASK estimation_prompt
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package = PARSE JSON ai_estimate
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' Calculate for requested quantity
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total_packages = CEIL(quantity / EXTRACT_NUMBER(product.QuantityPerUnit))
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IF total_packages < 1 THEN
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total_packages = 1
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END IF
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' Calculate cubic dimensions
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length_cm = package.length_cm
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width_cm = package.width_cm
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height_cm = package.height_cm
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weight_kg = package.weight_kg * total_packages
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' Volume in cubic centimeters
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volume_cm3 = length_cm * width_cm * height_cm * total_packages
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' Volume in cubic meters
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volume_m3 = volume_cm3 / 1000000
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' Volumetric weight (dimensional weight)
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' Standard divisor: 5000 for international, 6000 for domestic
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volumetric_weight_intl = volume_cm3 / 5000
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volumetric_weight_domestic = volume_cm3 / 6000
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' Billable weight is the greater of actual and volumetric
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billable_weight_intl = MAX(weight_kg, volumetric_weight_intl)
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billable_weight_domestic = MAX(weight_kg, volumetric_weight_domestic)
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' Calculate shipping estimates
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shipping_estimates = []
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' Ground shipping estimate
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ground = {}
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ground.carrier = "Ground"
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ground.billable_weight = billable_weight_domestic
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ground.estimated_days = "5-7 business days"
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ground.base_rate = 5.99
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ground.per_kg_rate = 1.50
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ground.estimated_cost = ground.base_rate + (ground.billable_weight * ground.per_kg_rate)
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PUSH shipping_estimates, ground
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' Express shipping estimate
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express = {}
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express.carrier = "Express"
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express.billable_weight = billable_weight_domestic
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express.estimated_days = "2-3 business days"
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express.base_rate = 12.99
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express.per_kg_rate = 2.50
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express.estimated_cost = express.base_rate + (express.billable_weight * express.per_kg_rate)
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PUSH shipping_estimates, express
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' Overnight shipping estimate
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overnight = {}
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overnight.carrier = "Overnight"
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overnight.billable_weight = billable_weight_intl
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overnight.estimated_days = "Next business day"
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overnight.base_rate = 24.99
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overnight.per_kg_rate = 4.00
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overnight.estimated_cost = overnight.base_rate + (overnight.billable_weight * overnight.per_kg_rate)
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PUSH shipping_estimates, overnight
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' Build response
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result = {}
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result.product_id = product_id
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result.product_name = product.ProductName
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result.quantity = quantity
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result.packages = total_packages
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result.dimensions = {
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"length_cm": length_cm,
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"width_cm": width_cm,
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"height_cm": height_cm,
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"package_type": package.package_type
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}
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result.weight = {
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"actual_kg": weight_kg,
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"volumetric_domestic_kg": volumetric_weight_domestic,
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"volumetric_intl_kg": volumetric_weight_intl,
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"billable_domestic_kg": billable_weight_domestic,
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"billable_intl_kg": billable_weight_intl
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}
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result.volume = {
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"cm3": volume_cm3,
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"m3": volume_m3
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}
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result.shipping_options = shipping_estimates
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' Display results
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TALK "📦 **Shipping Calculator Results**"
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TALK ""
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TALK "**Product:** " + product.ProductName
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TALK "**Quantity:** " + quantity + " (" + total_packages + " package(s))"
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TALK ""
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TALK "**Package Dimensions:**"
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TALK " 📏 " + length_cm + " × " + width_cm + " × " + height_cm + " cm"
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TALK " 📦 Volume: " + FORMAT(volume_cm3, "#,##0") + " cm³ (" + FORMAT(volume_m3, "0.000") + " m³)"
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TALK " ⚖️ Type: " + package.package_type
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TALK ""
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TALK "**Weight Analysis:**"
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TALK " Actual Weight: " + FORMAT(weight_kg, "0.00") + " kg"
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TALK " Volumetric (Domestic): " + FORMAT(volumetric_weight_domestic, "0.00") + " kg"
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TALK " Volumetric (Intl): " + FORMAT(volumetric_weight_intl, "0.00") + " kg"
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TALK " **Billable Weight:** " + FORMAT(billable_weight_domestic, "0.00") + " kg"
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TALK ""
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TALK "**Shipping Options:**"
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FOR EACH option IN shipping_estimates
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TALK " 🚚 **" + option.carrier + "**: $" + FORMAT(option.estimated_cost, "0.00") + " (" + option.estimated_days + ")"
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NEXT
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' Log calculation for analytics
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LOG "shipping_calculation", {
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"product_id": product_id,
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"quantity": quantity,
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"volume_cm3": volume_cm3,
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"weight_kg": weight_kg,
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"timestamp": NOW()
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}
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RETURN result
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RETURN shipping
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@ -1,117 +1,7 @@
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' Product Classification Tool
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' Classifies products into categories using AI and store taxonomy
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PARAM query AS STRING
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PARAM product_name AS STRING DESCRIPTION "Product name to classify (e.g., 'nike air max shoes')"
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PARAM include_suggestions AS BOOLEAN OPTIONAL DESCRIPTION "Include alternative category suggestions"
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DESCRIPTION "Classify a product into the store's category taxonomy using AI"
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' Load store categories for context
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categories = FIND "Categories.csv"
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result = SEARCH PRODUCTS query, 5
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enhanced = LLM "Classify '" + query + "' into categories: " + categories + ". Similar products: " + result + ". Return JSON {category_id, category_name, confidence, attributes: {brand, type}}"
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' Build taxonomy tree for AI context
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taxonomy = ""
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FOR EACH cat IN categories
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taxonomy = taxonomy + cat.CategoryID + ": " + cat.CategoryName + " - " + cat.Description + "\n"
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NEXT
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' Search existing products for similar items
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similar_products = SEARCH "Products.csv", product_name, 5
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similar_context = ""
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IF similar_products THEN
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FOR EACH p IN similar_products
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similar_context = similar_context + "- " + p.ProductName + " (Category: " + p.CategoryID + ")\n"
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NEXT
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END IF
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' Use AI to classify the product
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BEGIN SYSTEM PROMPT
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You are a product classification expert. Your task is to classify products into the correct category based on the store's taxonomy.
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Available Categories:
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${taxonomy}
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Similar existing products in catalog:
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${similar_context}
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Rules:
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1. Return the most specific category that fits
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2. Consider product attributes like brand, type, material
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3. If uncertain, suggest the most likely category with confidence level
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4. Provide reasoning for the classification
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END SYSTEM PROMPT
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classification_prompt = "Classify this product: '" + product_name + "'\n\nReturn JSON format: {\"category_id\": number, \"category_name\": string, \"confidence\": number (0-100), \"reasoning\": string, \"attributes\": {\"brand\": string, \"type\": string, \"material\": string}}"
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result = ASK classification_prompt
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' Parse AI response
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classification = PARSE JSON result
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' Validate category exists
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valid_category = FIND "Categories.csv", "CategoryID = " + classification.category_id
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IF NOT valid_category THEN
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' Fallback to closest match
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TALK "⚠️ AI suggested invalid category, finding closest match..."
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valid_category = FIND "Categories.csv", "CategoryName LIKE '%" + classification.category_name + "%'"
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IF valid_category THEN
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classification.category_id = valid_category.CategoryID
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END IF
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END IF
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' Build response
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response = {}
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response.product_name = product_name
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response.category_id = classification.category_id
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response.category_name = classification.category_name
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response.confidence = classification.confidence
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response.reasoning = classification.reasoning
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response.attributes = classification.attributes
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' Add suggestions if requested
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IF include_suggestions THEN
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suggestions_prompt = "Suggest 2 alternative categories for '" + product_name + "' from: " + taxonomy
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alt_result = ASK suggestions_prompt
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response.alternative_categories = PARSE JSON alt_result
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END IF
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' Log classification for analytics
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LOG "product_classification", {
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"product": product_name,
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"category": classification.category_id,
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"confidence": classification.confidence,
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"timestamp": NOW()
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}
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TALK "📦 **Product Classification Result**"
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TALK ""
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TALK "**Product:** " + product_name
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TALK "**Category:** " + classification.category_name + " (ID: " + classification.category_id + ")"
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TALK "**Confidence:** " + classification.confidence + "%"
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TALK "**Reasoning:** " + classification.reasoning
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TALK ""
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IF classification.attributes THEN
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TALK "**Detected Attributes:**"
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IF classification.attributes.brand THEN
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TALK " • Brand: " + classification.attributes.brand
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END IF
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IF classification.attributes.type THEN
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TALK " • Type: " + classification.attributes.type
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END IF
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IF classification.attributes.material THEN
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TALK " • Material: " + classification.attributes.material
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END IF
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END IF
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IF include_suggestions AND response.alternative_categories THEN
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TALK ""
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TALK "**Alternative Categories:**"
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FOR EACH alt IN response.alternative_categories
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TALK " • " + alt.category_name + " (" + alt.confidence + "% confidence)"
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NEXT
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END IF
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RETURN response
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RETURN enhanced
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@ -1,124 +1,6 @@
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' Product Search with Autocomplete and AI Descriptions
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' Provides fast product search with intelligent suggestions and enriched descriptions
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PARAM query AS STRING
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PARAM query AS STRING DESCRIPTION "Search query (e.g., 'chai tea', 'chocolate', 'sauce')"
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PARAM limit AS INTEGER OPTIONAL DESCRIPTION "Maximum results to return (default: 10)"
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PARAM include_description AS BOOLEAN OPTIONAL DESCRIPTION "Generate AI-enhanced product descriptions"
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result = SEARCH PRODUCTS query, 10
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enhanced = LLM "Enhance these product results with descriptions: " + result + ". Return JSON array with id, name, price, description"
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DESCRIPTION "Search products with autocomplete and AI-generated descriptions"
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' Set defaults
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IF NOT limit THEN
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limit = 10
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END IF
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' Perform fast search across products
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results = SEARCH "Products.csv", query, limit
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IF NOT results OR LEN(results) = 0 THEN
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TALK "🔍 No products found matching '" + query + "'"
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' Use AI to suggest alternatives
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BEGIN SYSTEM PROMPT
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You are a helpful store assistant. The user searched for a product that wasn't found.
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Suggest what they might be looking for or alternative search terms.
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END SYSTEM PROMPT
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suggestion = ASK "The user searched for '" + query + "' but no products were found. Suggest alternatives or clarify what they might want."
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TALK suggestion
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RETURN { "results": [], "suggestions": suggestion }
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END IF
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' Load categories for enrichment
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categories = FIND "Categories.csv"
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' Build category lookup
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category_map = {}
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FOR EACH cat IN categories
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category_map[cat.CategoryID] = cat.CategoryName
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NEXT
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' Enrich results with category names
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enriched_results = []
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FOR EACH product IN results
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item = {}
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item.id = product.ProductID
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item.name = product.ProductName
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item.price = product.UnitPrice
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item.stock = product.UnitsInStock
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item.category_id = product.CategoryID
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item.category_name = category_map[product.CategoryID]
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item.quantity_per_unit = product.QuantityPerUnit
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item.discontinued = product.Discontinued
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item.reorder_level = product.ReorderLevel
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' Generate AI description if requested
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IF include_description THEN
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BEGIN SYSTEM PROMPT
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You are a product copywriter. Write a brief, compelling product description (2-3 sentences).
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Focus on benefits and key features. Be concise and engaging.
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END SYSTEM PROMPT
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desc_prompt = "Write a product description for: " + product.ProductName + " (Category: " + item.category_name + ", Quantity: " + product.QuantityPerUnit + ", Price: $" + product.UnitPrice + ")"
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item.ai_description = ASK desc_prompt
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END IF
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PUSH enriched_results, item
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NEXT
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' Build autocomplete suggestions for future searches
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related_terms = []
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FOR EACH product IN results
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words = SPLIT(product.ProductName, " ")
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FOR EACH word IN words
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IF LEN(word) > 3 AND NOT CONTAINS(related_terms, word) THEN
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PUSH related_terms, word
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END IF
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NEXT
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NEXT
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' Display results
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TALK "🔍 **Search Results for '" + query + "'** (" + LEN(enriched_results) + " found)"
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TALK ""
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FOR EACH item IN enriched_results
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stock_status = "✅ In Stock"
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IF item.stock = 0 THEN
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stock_status = "❌ Out of Stock"
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ELSE IF item.stock < item.reorder_level THEN
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stock_status = "⚠️ Low Stock"
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END IF
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IF item.discontinued = 1 THEN
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stock_status = "🚫 Discontinued"
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END IF
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TALK "**" + item.name + "** - $" + item.price
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TALK " Category: " + item.category_name + " | " + item.quantity_per_unit
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TALK " " + stock_status + " (" + item.stock + " units)"
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IF include_description AND item.ai_description THEN
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TALK " 📝 " + item.ai_description
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END IF
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TALK ""
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NEXT
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' Show related search terms
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IF LEN(related_terms) > 0 THEN
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TALK "**Related searches:** " + JOIN(related_terms, ", ")
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END IF
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' Log search for analytics
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LOG "product_search", {
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"query": query,
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"results_count": LEN(enriched_results),
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"timestamp": NOW()
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}
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RETURN {
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"query": query,
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"results": enriched_results,
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"total": LEN(enriched_results),
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"related_terms": related_terms
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}
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RETURN enhanced
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