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HS Code Classification Engine with pyhscodes + LLM hybrid approach.
Classification pipeline:
1. Local semantic cache → instant response for repeated products (FREE, zero-latency)
2. pyhscodes fuzzy search → 6-digit WCO base code (FREE, local)
3. LLM refinement → 10-digit HTSUS / 12-digit TARIC (requires API key)
4. Confidence scoring + human review flagging
Data sources:
- ComplianceReviewItem PostgreSQL table → cached classifications (ILIKE match)
- pyhscodes: 6,940+ WCO HS codes (LGPL-2.1, FREE)
- US HTSUS: 10-digit codes (public domain structure)
- EU TARIC: 12-digit codes (public domain structure)
Enterprise features:
- Async batch classification with asyncio.gather + Semaphore(5)
- Local semantic cache via PostgreSQL ILIKE fuzzy matching
- Thread-safe singleton engine
Cost savings: cache + pyhscodes handles 80%+ of classification, LLM only for edge cases.
Typing conventions:
All public APIs use explicit type hints. Literal types enforce valid
code levels and sources. Final constants prevent mutation.
"""
from __future__ import annotations
import asyncio
import hashlib
import json
import logging
import re
import threading
import time as _time
from dataclasses import dataclass, field
from typing import Annotated, Any, Final, Literal, Optional
import jellyfish
from difflib import SequenceMatcher
logger: Final = logging.getLogger(__name__)
try:
from pyhscodes import hscodes as _pyhscodes
PYHSCODES_AVAILABLE: Final[bool] = True
except ImportError:
_pyhscodes = None
PYHSCODES_AVAILABLE = False
logger.warning("pyhscodes not installed. Install with: pip install pyhscodes")
# ── Domain Exceptions ─────────────────────────────────────────────────
class HSClassificationError(Exception):
"""Base exception for all HS classification errors."""
class HSCodeValidationError(HSClassificationError):
"""Raised when an HS code fails structural validation.
Attributes:
code: The invalid code.
reason: Why validation failed.
"""
def __init__(self, code: str, reason: str) -> None:
self.code = code
self.reason = reason
super().__init__(f"Invalid HS code '{code}': {reason}")
class HSClassificationHallucinationError(HSClassificationError):
"""Raised when LLM returns a code that contradicts pyhscodes database.
Attributes:
llm_code: The LLM-generated code.
llm_chapter: The chapter from LLM code.
pyh_chapter: The chapter from pyhscodes.
"""
def __init__(self, llm_code: str, llm_chapter: str, pyh_chapter: str) -> None:
self.llm_code = llm_code
self.llm_chapter = llm_chapter
self.pyh_chapter = pyh_chapter
super().__init__(
f"LLM chapter {llm_chapter} contradicts pyhscodes chapter {pyh_chapter} "
f"(code: {llm_code})"
)
class HSCodeLookupError(HSClassificationError):
"""Raised when an HS code lookup fails in the database."""
class LLMClassificationError(HSClassificationError):
"""Raised when LLM-based classification fails.
Attributes:
provider: The LLM provider (openai, anthropic).
detail: Error detail.
"""
def __init__(self, provider: str, detail: str) -> None:
self.provider = provider
self.detail = detail
super().__init__(f"LLM classification failed ({provider}): {detail}")
# ── Constants ─────────────────────────────────────────────────────────
# Confidence thresholds
HIGH_CONFIDENCE_THRESHOLD: Final[float] = 0.85
MEDIUM_CONFIDENCE_THRESHOLD: Final[float] = 0.65
LOW_CONFIDENCE_THRESHOLD: Final[float] = 0.45
NEEDS_REVIEW_THRESHOLD: Final[float] = 0.7
LLM_MAX_CONFIDENCE: Final[float] = 0.85
# HS code structure patterns
HS2_PATTERN: Final[re.Pattern[str]] = re.compile(r"^\d{2}$")
HS4_PATTERN: Final[re.Pattern[str]] = re.compile(r"^\d{4}$")
HS6_PATTERN: Final[re.Pattern[str]] = re.compile(r"^\d{6}$")
HTSUS_PATTERN: Final[re.Pattern[str]] = re.compile(r"^\d{10}$")
TARIC_PATTERN: Final[re.Pattern[str]] = re.compile(r"^\d{12}$")
# Valid HS code digit lengths
VALID_CODE_LEVELS: Final[frozenset[int]] = frozenset({2, 4, 6, 10, 12})
# Type aliases
CodeLevel = Literal[2, 4, 6, 10, 12]
ClassificationSource = Literal["pyhscodes", "llm-openai", "llm-anthropic", "llm-opencode", "llm-gemini", "manual", "none"]
TargetDigits = Literal[6, 10, 12]
# Ambiguous terms that always trigger human review
_AMBIGUOUS_TERMS: Final[frozenset[str]] = frozenset({
"various", "mixed", "assorted", "multiple", "miscellaneous",
"general", "sundry",
})
# LLM stop words
_LLM_STOP_WORDS: Final[frozenset[str]] = frozenset({
"the", "a", "an", "and", "or", "of", "for", "in", "on", "at", "to",
"with", "by", "from", "is", "are", "was", "were", "be", "been",
})
# HS Section names
SECTION_NAMES: Final[dict[str, str]] = {
"I": "Live Animals",
"II": "Vegetable Products",
"III": "Animal/Vegetable Fats",
"IV": "Prepared Foodstuffs",
"V": "Mineral Products",
"VI": "Chemical Products",
"VII": "Plastics/Rubber",
"VIII": "Raw Hides/Skins/Leather",
"IX": "Wood/Cork/Straw",
"X": "Pulp/Paper",
"XI": "Textiles",
"XII": "Footwear/Headgear",
"XIII": "Stone/Ceramic/Glass",
"XIV": "Precious Stones/Metals",
"XV": "Base Metals",
"XVI": "Machinery",
"XVII": "Vehicles/Aircraft",
"XVIII": "Optical/Photographic/Medical",
"XIX": "Arms/Ammunition",
"XX": "Miscellaneous Manufactured",
"XXI": "Works of Art/Antiques",
}
# HS Chapter descriptions for LLM context
CHAPTER_DESCRIPTIONS: Final[dict[str, str]] = {
"01": "Live animals",
"02": "Meat and edible meat offal",
"03": "Fish and crustaceans, molluscs",
"04": "Dairy produce, birds' eggs, honey",
"05": "Products of animal origin, n.e.s.",
"06": "Live trees and other plants, bulbs, roots",
"07": "Edible vegetables and certain roots and tubers",
"08": "Edible fruit and nuts, peel of citrus fruit or melons",
"09": "Coffee, tea, maté and spices",
"10": "Cereals",
"11": "Products of the milling industry, malt, starches",
"12": "Oil seeds and oleaginous fruits",
"13": "Lac, gums, resins",
"14": "Vegetable plaiting materials",
"15": "Animal or vegetable fats and oils, cleavage products",
"16": "Preparations of meat, fish or crustaceans",
"17": "Sugars and sugar confectionery",
"18": "Cocoa and cocoa preparations",
"19": "Preparations of cereals, flour, starch or milk",
"20": "Preparations of vegetables, fruit, nuts",
"21": "Miscellaneous edible preparations",
"22": "Beverages, spirits and vinegar",
"23": "Residues and waste from the food industries, animal fodder",
"24": "Tobacco and manufactured tobacco substitutes",
"25": "Salt, sulphur, earths, stone, plaster, lime and cement",
"26": "Ores, slag and ash",
"27": "Mineral fuels, mineral oils, bituminous substances",
"28": "Inorganic chemicals, organic/inorganic compounds",
"29": "Organic chemicals",
"30": "Pharmaceutical products",
"31": "Fertilisers",
"32": "Tanning or dyeing extracts, paints, putties",
"33": "Essential oils, perfumery, cosmetic preparations",
"34": "Soap, organic surface-active agents, waxes",
"35": "Albuminoidal substances, modified starches, glues",
"36": "Explosives, pyrotechnic products, matches",
"37": "Photographic or cinematographic goods",
"38": "Miscellaneous chemical products",
"39": "Plastics and articles thereof",
"40": "Rubber and articles thereof",
"41": "Raw hides and skins (other than furskins) and leather",
"42": "Articles of leather, saddlery and harness, travel goods",
"43": "Furskins and artificial fur, manufactures thereof",
"44": "Wood and articles of wood, wood charcoal",
"45": "Cork and articles of cork",
"46": "Basketwork, wickerwork",
"47": "Pulp of wood, fibrous cellulosic material",
"48": "Paper and paperboard, articles thereof",
"49": "Printed books, newspapers, pictures",
"50": "Silk",
"51": "Wool, fine or coarse animal hair, horsehair yarn",
"52": "Cotton",
"53": "Other vegetable textile fibres, paper yarn",
"54": "Man-made filaments, strips of man-made textile materials",
"55": "Man-made staple fibres",
"56": "Wadding, felt and nonwovens, special yarns",
"57": "Carpets and other textile floor coverings",
"58": "Special woven fabrics, tufted textile fabrics, lace",
"59": "Impregnated, coated, covered or laminated textile fabrics",
"60": "Knitted or crocheted fabrics",
"61": "Articles of apparel and clothing accessories, knitted or crocheted",
"62": "Articles of apparel and clothing accessories, not knitted or crocheted",
"63": "Other made up textile articles, worn clothing",
"64": "Footwear, gaiters and the like, parts thereof",
"65": "Headgear and parts thereof",
"66": "Umbrellas, sun umbrellas, walking-sticks, whips",
"67": "Prepared feathers and down, artificial flowers",
"68": "Articles of stone, plaster, cement, asbestos, mica",
"69": "Ceramic products",
"70": "Glass and glassware",
"71": "Natural or cultured pearls, precious stones, precious metals, coin",
"72": "Iron and steel",
"73": "Articles of iron or steel",
"74": "Copper and articles thereof",
"75": "Nickel and articles thereof",
"76": "Aluminium and articles thereof",
"77": "Reserved",
"78": "Lead and articles thereof",
"79": "Zinc and articles thereof",
"80": "Tin and articles thereof",
"81": "Other base metals, cermets, articles thereof",
"82": "Tools, implements, cutlery, spoons and forks, base metal",
"83": "Miscellaneous articles of base metal",
"84": "Nuclear reactors, boilers, machinery and mechanical appliances",
"85": "Electrical machinery and equipment, sound recorders, television",
"86": "Railway or tramway locomotives, rolling-stock",
"87": "Vehicles other than railway or tramway rolling-stock",
"88": "Aircraft, spacecraft, and parts thereof",
"89": "Ships, boats and floating structures",
"90": "Optical, photographic, measuring, checking, precision instruments",
"91": "Clocks and watches and parts thereof",
"92": "Musical instruments, parts and accessories thereof",
"93": "Arms and ammunition, parts and accessories thereof",
"94": "Furniture, bedding, lamps and lighting, prefabricated buildings",
"95": "Toys, games and sports requisites, parts and accessories thereof",
"96": "Miscellaneous manufactured articles",
"97": "Works of art, collectors' pieces and antiques",
}
# Keyword → HS code mapping for pre-filtering (common product terms)
KEYWORD_HS_MAPPING: Final[dict[str, str]] = {
# ── Compound keywords (longest match priority) ──
"organic cotton t-shirt": "610910", "silk evening dress": "620449",
"wool blend coat": "620110", "leather handbag": "420221",
"stainless steel pipe": "730630", "aluminum sheet": "760612",
"copper wire": "740811", "steel beam": "721633",
"plastic water bottle": "392330", "glass jar": "701090",
"ceramic tile": "690721", "wooden floor": "441112",
"cardboard box": "481910", "rubber hose": "400932",
"cotton bed linen": "630231", "silk scarf": "611710",
"wool blanket": "630140", "nylon backpack": "420292",
"leather wallet": "420231", "canvas bag": "420292",
"suede boots": "640391", "rubber sole shoe": "640411",
"velvet curtain": "630391", "linen tablecloth": "630260",
"cashmere sweater": "611020", "polyester shirt": "620520",
"denim pants": "620342", "corduroy jacket": "620193",
"terry towel": "630260", "fleece jacket": "611020",
"down sleeping bag": "630790", "insulated jacket": "620193",
"safety helmet": "650610", "work gloves": "420330",
"steel toe boot": "640340", "reflective vest": "621139",
"fire extinguisher": "842410", "smoke detector": "853110",
"cctv camera": "852589", "security camera": "852589",
"wireless speaker": "851822", "bluetooth speaker": "851822",
"sound bar": "851822", "subwoofer": "851822",
"usb hub": "851762", "power bank": "850760",
"lithium ion battery": "850760", "lithium battery": "850760",
"rechargeable battery": "850760", "alkaline battery": "850650",
"car battery": "850710", "battery pack": "850760",
"polyester jacket": "620193", "leather jacket": "620193",
"down jacket": "620110", "cotton t-shirt": "610910",
"cotton shirt": "620520", "cotton dress": "620442",
"cotton bed sheet": "630221", "cotton pillowcase": "630231",
"cotton towel": "630260", "cotton blanket": "630221",
"cotton yarn": "520511", "cotton thread": "520411",
"cotton fabric": "520899", "cotton cloth": "520899",
"wool carpet": "570110", "wool rug": "570110",
"wool sweater": "611020", "wool coat": "620110",
"wool suit": "620331", "wool trousers": "620342",
"silk dress": "620449", "silk scarf": "611710",
"silk tie": "621520", "silk shirt": "620560",
"leather boots": "640391", "leather shoes": "640399",
"leather gloves": "420330", "leather bag": "420221",
"rubber shoes": "640299", "rubber boots": "640299",
"rubber sole": "640411", "rubber tire": "401110",
"nylon bag": "420292", "nylon backpack": "420292",
"denim jeans": "620342", "denim jacket": "620193",
"satin dress": "620449",
"coffee beans": "090111", "coffee roasted": "090121",
"green tea": "090210", "black tea": "090230",
"olive oil virgin": "150910", "sunflower seed oil": "151219",
"natural gas": "271111", "crude oil": "270900",
"diesel fuel": "271019", "gasoline": "271012",
"kerosene": "271019", "jet fuel": "271019",
"sodium hydroxide": "281511", "sulfuric acid": "280700",
"hydrochloric acid": "280610", "nitric acid": "280800",
"phosphoric acid": "280920",
"usb type c cable": "854442", "usb cable": "854442",
"hdmi cable": "854442", "ethernet cable": "854442",
"power cable": "854442",
"led headlight": "851220", "led lamp": "940542",
"led strip": "940542", "led panel": "940542",
"brake pad": "870830", "brake disc": "870830",
"spark plug": "851110", "ignition coil": "851130",
"tractor": "870110", "combine harvester": "843351",
"plough": "843010", "harvester": "843351",
"blender kitchen": "850940", "food processor": "850940",
"wheat flour": "110100", "wheat bread": "190510",
"white sugar": "170114", "raw sugar": "170111",
"brown sugar": "170112", "powdered sugar": "170199",
"milk powder": "040210", "condensed milk": "040219",
"salmon fillet": "030481", "salmon fresh": "030214",
"cheddar cheese": "040610", "mozzarella cheese": "040610",
"pasta spaghetti": "190211", "pasta penne": "190211",
"work overalls": "620343", "overall": "620343",
"hydraulic press": "846291", "grinding machine": "846024",
"crop sprayer": "842482", "irrigation system": "842482",
"pharmaceutical": "300692", "medicament": "300490",
"sludge": "271099", "waste oil": "271099",
"wooden crate": "441520", "wooden pallet": "441520",
"server": "847150", "workstation": "847130",
"blender": "850940", "mixer": "850940",
"vaccine": "300241", "antiserum": "300241",
"steering wheel": "870894", "seat belt": "870893",
"rough diamond": "710231", "diamond rough": "710231",
"granite slab": "680223", "granite tile": "680223",
"crushed gravel": "251710", "gravel": "251710",
"bituminous coal": "270112", "coal": "270112",
"lpg": "271119", "propane": "271119", "butane": "271119",
"biofuel": "150120", "biodiesel": "382600",
"fiber optic cable": "854470", "optical cable": "854470",
"satellite dish": "852910", "antenna": "852910",
"acrylic paint": "320890", "latex paint": "320890",
"watercolor": "321310", "oil paint": "320890",
"shower head": "650691", "shower": "650691",
"power socket": "853669", "electrical outlet": "853669",
"power outlet": "853669", "wall socket": "853669",
"ethylene": "271114", "polyethylene resin": "390110",
"benzene": "290220", "toluene": "290230",
"formaldehyde": "291211", "methanol formaldehyde": "291211",
"ammonia": "281410", "ammonium": "310520",
"viscose rayon": "540331", "rayon": "540331",
"kevlar": "550111", "aramid fiber": "550111",
"cork": "450110", "cork board": "450490",
"rattan": "140120", "wicker": "940159",
"seagrass": "940389",
"automotive parts": "851771", "car parts": "870899",
"auto parts": "870899", "vehicle parts": "870899",
"heat exchanger": "841950", "radiator": "841950",
"alarm system": "853110", "security alarm": "853110",
"burglar alarm": "853110", "fire alarm": "853110",
"vault safe": "830300", "safe": "830300",
"helicopter parts": "880730", "helicopter": "880240",
"rocket": "880790", "satellite": "880260",
"anchor": "731600", "chain anchor": "731600",
"diamond ring": "711319", "engagement ring": "711319",
"mechanical watch": "910129", "pocket watch": "910119",
"lawn mower": "843311", "mower": "843311",
"garden hose": "400932", "hose pipe": "400932",
"dog food": "230910", "cat food": "230910",
"pet food": "230910", "animal feed": "230990",
# ── IT & Electronics ──
"laptop": "847130", "computer": "847130", "notebook computer": "847130",
"desktop computer": "847130", "server computer": "847150",
"tablet": "847130", "ipad": "847130",
"phone": "851713", "smartphone": "851712", "mobile phone": "851712",
"cell phone": "851712", "telephone": "851718",
"television": "852872", "tv": "852872", "monitor": "852852",
"computer monitor": "852852", "display": "852852",
"camera": "900653", "digital camera": "900659", "video camera": "852589",
"webcam": "852589", "cctv camera": "852589",
"headphones": "851830", "earbuds": "851830", "headset": "851830",
"speaker": "851822", "microphone": "851810",
"printer": "844332", "scanner": "844331", "copier": "844331",
"router": "851762", "modem": "851762", "switch": "851762",
"network switch": "851762", "network hub": "851762",
"usb flash drive": "852351", "memory card": "852351", "ssd": "852351",
"hard drive": "852351", "hdd": "852351", "solid state drive": "852351",
"storage device": "852351",
"battery": "850650", "charger": "850440", "power adapter": "850440",
"power supply": "850440", "power inverter": "850440",
"led light": "940542", "solar panel": "854140",
"circuit board": "853400", "pcb": "853400", "printed circuit board": "853400",
"motherboard": "847150", "ram": "854231", "memory module": "854231",
"ram memory module": "854231", "dimm": "854231",
"graphics card": "847180", "gpu": "847180", "video card": "847180",
"semiconductor": "854110", "chip": "854110", "microchip": "854110",
"processor": "854231", "cpu": "854231",
"transformer": "850421", "electric motor": "850110",
"led display": "852872", "lcd": "852852", "oled": "852872",
"smartwatch": "910212", "wearable": "910212",
"drone": "880260", "uav": "880260",
"speaker system": "851822", "sound bar": "851822",
"keyboard": "847160", "mouse": "847160", "trackpad": "847160",
"external hard drive": "852351", "usb hub": "851762",
"graphics processor": "847180", "co-processor": "847180",
# ── Vehicles ──
"car": "870323", "automobile": "870323", "vehicle": "870323",
"truck": "870422", "motorcycle": "871120", "bicycle": "871200",
"bus": "870240", "aircraft": "880240", "airplane": "880240",
"ship": "890120", "boat": "890310", "train": "860110",
"trailer": "871640", "semi-trailer": "871631",
"suv": "870323", "van": "870323", "pickup truck": "870422",
"scooter": "871120", "moped": "871120",
"locomotive": "860110", "railway car": "860210",
# ── Apparel & Textiles ──
"shirt": "620520", "t-shirt": "610910", "blouse": "620630",
"polo shirt": "610510", "dress shirt": "620520",
"pants": "620342", "trousers": "620342", "jeans": "620342",
"shorts": "620342", "chinos": "620342",
"dress": "620442", "skirt": "620452", "jumpsuit": "621149",
"jacket": "620193", "coat": "620193", "blazer": "620331",
"raincoat": "620213", "windbreaker": "620193",
"sweater": "611030", "pullover": "611030", "cardigan": "611030",
"hoodie": "611020", "sweatshirt": "611020", "wool sweater": "611020",
"socks": "611595", "underwear": "610821", "boxers": "610711",
"gloves": "621600", "hat": "650500", "cap": "650699",
"scarf": "611710", "tie": "621520", "belt": "420330",
"leather belt": "420330",
"shoe": "640399", "shoes": "640399", "sneakers": "640411",
"boot": "640399", "boots": "640399", "sandal": "640299",
"slipper": "640419", "flip-flop": "640299",
"cotton t-shirt": "610910", "cotton tshirt": "610910",
"cotton shirt": "620520", "cotton dress": "620442",
"silk dress": "620442", "linen shirt": "620560",
"sports uniform": "611241", "jersey": "611241", "tracksuit": "611241",
"baby clothes": "611120", "infant clothing": "611120",
"swimsuit": "611231", "bikini": "611231", "swimming trunks": "611231",
"nightwear": "610721", "pajamas": "610721", "nightgown": "610721",
"uniform": "611241", "workwear": "621133",
# ── Food & Agriculture ──
"apple": "080810", "orange": "080510", "banana": "080390",
"grape": "080610", "strawberry": "081010", "blueberry": "081040",
"mango": "081060", "pineapple": "080430", "peach": "080930",
"cherry": "080930", "pear": "080830", "kiwi": "081050",
"lemon": "080520", "lime": "080520", "grapefruit": "080540",
"watermelon": "080710", "melon": "080710",
"coffee": "090121", "tea": "090210", "cocoa": "180100",
"rice": "100630", "wheat": "100119", "corn": "100590",
"barley": "100390", "oat": "100410", "rye": "100210",
"bread": "190590", "pasta": "190219", "noodles": "190219",
"cereal": "190410", "flour": "110100", "starch": "110819",
"cheese": "040690", "milk": "040110", "butter": "040510",
"yogurt": "040310", "cream": "040390",
"egg": "040711", "chicken": "020714", "beef": "020130",
"pork": "020319", "lamb": "020430", "turkey": "020727",
"fish": "030289", "salmon": "030214", "tuna": "030487",
"shrimp": "030617", "lobster": "030632", "crab": "030614",
"oyster": "030629", "squid": "030752", "octopus": "030759",
"wine": "220421", "beer": "220300", "whisky": "220830",
"vodka": "220860", "rum": "220840", "gin": "220850",
"chocolate": "180632", "candy": "170490", "sugar": "170199",
"salt": "250100", "pepper": "090411", "cinnamon": "090611",
"turmeric": "091030", "ginger": "091011",
"olive oil": "150910", "sunflower oil": "151219",
"palm oil": "151190", "coconut oil": "151319",
"honey": "040900", "maple syrup": "170220",
"tomato": "070200", "potato": "070190", "onion": "070310",
"garlic": "070320", "carrot": "070610", "cabbage": "070490",
"lettuce": "070511", "cucumber": "070700", "pepper": "070960",
"mushroom": "070951", "spinach": "070960", "broccoli": "070690",
"corn kernels": "071040", "peas": "070810", "beans": "071333",
"lentils": "071340", "soybeans": "120190",
"peanut": "120241", "almond": "080211", "walnut": "080231",
"cashew": "080390", "hazelnut": "080221", "pecan": "080232",
"coconut": "080119", "pistachio": "080212",
"avocado": "080440", "olive": "070990",
# ── Metals & Minerals ──
"steel": "720899", "iron": "720899", "aluminum": "760310",
"aluminium": "760310", "copper": "740311", "brass": "740321",
"zinc": "790111", "tin": "800110", "lead": "780110",
"nickel": "750210", "titanium": "810820", "tungsten": "810110",
"platinum": "711011", "palladium": "711021",
"gold": "710812", "silver": "710691", "copper wire": "740811",
"steel pipe": "730630", "steel tube": "730630",
"aluminum sheet": "760612", "aluminum foil": "760711",
"steel plate": "720851", "steel sheet": "720851",
"stainless steel": "721990", "alloy steel": "721990",
"iron ore": "260112", "bauxite": "260600",
# ── Wood & Paper ──
"wood": "440799", "timber": "440799", "lumber": "440799",
"plywood": "441231", "veneer": "440890",
"mdf": "441112", "particle board": "441011",
"furniture": "940390", "furniture board": "940390",
"paper": "481910", "cardboard": "481910", "paperboard": "481910",
"tissue paper": "481810", "toilet paper": "481810",
"paper towel": "481890", "napkin": "481890",
"book": "490199", "newspaper": "490290", "magazine": "490210",
"notebook": "482010", "diary": "482010",
"envelope": "481710", "file folder": "482010", "ring binder": "482010",
"paper bag": "481930", "shopping bag": "481930",
"gift wrap": "481940", "wrapping paper": "481940",
"label": "482110", "sticker": "482110", "stamp": "490700",
"poster": "491191", "calendar": "491000",
# ── Plastics & Rubber ──
"plastic": "392690", "rubber": "401590",
"plastic bottle": "392330", "plastic bag": "392321",
"plastic container": "392330", "plastic cup": "392410",
"plastic plate": "392410", "plastic toy": "950300",
"rubber tire": "401110", "rubber tube": "400942",
"silicone": "391000", "teflon": "390461",
"foam": "391723", "styrofoam": "391723",
"nylon": "540771", "polyester": "540761",
"acrylic": "392051", "polycarbonate": "392061",
"polyethylene": "390110", "polypropylene": "390210",
"pvc": "390410", "vinyl": "390410",
# ── Furniture ──
"chair": "940161", "sofa": "940161", "couch": "940161",
"table": "940360", "desk": "940360", "coffee table": "940360",
"bed": "940350", "mattress": "940421", "pillow": "940490",
"cabinet": "940330", "shelf": "940390", "bookshelf": "940390",
"wardrobe": "940350", "dresser": "940350", "nightstand": "940350",
"bookcase": "940390", "cupboard": "940330",
"kitchen cabinet": "940330", "office desk": "940360",
"office chair": "940161", "filing cabinet": "940330",
# ── Beauty & Personal Care ──
"shampoo": "330510", "conditioner": "330510",
"soap": "340111", "detergent": "340220",
"toothpaste": "330610", "toothbrush": "960321",
"perfume": "330300", "cologne": "330300",
"cosmetics": "330499", "makeup": "330499",
"lipstick": "330410", "mascara": "330420",
"foundation": "330499", "powder": "330499",
"lotion": "330499", "moisturizer": "330499",
"sunscreen": "330499", "spf": "330499",
"deodorant": "330790", "antiperspirant": "330790",
"razor": "821210", "shaving cream": "330790",
"nail polish": "330300", "nail remover": "330790",
"hair dye": "330510", "hair color": "330510",
"facial cleanser": "330499", "face wash": "330499",
"eye cream": "330499", "serum": "330499",
# ── Tools & Hardware ──
"drill": "846721", "saw": "846722", "hammer": "820520",
"screwdriver": "820540", "wrench": "820411",
"pliers": "820320", "pliers set": "820320",
"tape measure": "901780", "level": "901720",
"sandpaper": "680520", "grinder": "846781",
"welder": "851511", "soldering iron": "851511",
"paint brush": "960340", "roller": "960340",
"spray gun": "842420", "air compressor": "841480",
"generator": "850211", "inverter": "850440",
"battery charger": "850440", "power bank": "850760",
"extension cord": "854442", "power strip": "853690",
"electrical panel": "853710", "circuit breaker": "853630",
# ── Medical & Health ──
"medicine": "300490", "drug": "300490", "pill": "300490",
"tablet": "300490", "capsule": "300490",
"syringe": "901890", "bandage": "300590",
"thermometer": "902580", "stethoscope": "901819",
"mask": "630790", "surgical mask": "630790",
"glove": "401519", "surgical glove": "401519",
"goggles": "900490", "safety glasses": "900490",
"wheelchair": "871310", "crutch": "902110",
"hearing aid": "902110", "pacemaker": "902150",
"x-ray": "902212", "ultrasound": "901819",
"mri": "902219", "ct scanner": "902219",
"defibrillator": "902180", "ventilator": "901920",
"bandage": "300590", "gauze": "300590",
"antiseptic": "300650", "disinfectant": "300650",
"vitamin": "210690", "supplement": "210690",
"probiotic": "210690", "protein powder": "210690",
# ── Sports & Recreation ──
"ball": "950662", "football": "950662", "soccer": "950662",
"basketball": "950662", "baseball": "950662", "volleyball": "950662",
"golf club": "950639", "tennis racket": "950659",
"badminton racket": "950659", "table tennis": "950640",
"ski": "950611", "snowboard": "950699", "ski boots": "950612",
"skates": "950670", "ice skates": "950670", "roller skates": "950670",
"yoga mat": "950691", "exercise mat": "950691",
"dumbbell": "950691", "barbell": "950691", "weight": "950691",
"treadmill": "950691", "bicycle": "871200",
"camping tent": "630622", "sleeping bag": "630790",
"backpack": "420292", "hiking boots": "640399",
"fishing rod": "950710", "fishing net": "950790",
"hunting rifle": "930520", "shotgun": "930520",
# ── Music & Entertainment ──
"guitar": "920210", "piano": "920120", "violin": "920210",
"drum": "920600", "flute": "920510", "saxophone": "920510",
"trumpet": "920510", "ukulele": "920210",
"microphone": "851810", "amplifier": "851840",
"audio mixer": "851840", "turntable": "851981",
"record player": "851981", "cd player": "851981",
"dvd": "852349", "blu-ray": "852349", "cd": "852349",
"video game": "950450", "game console": "950450",
"board game": "950490", "card game": "950490",
"toy": "950300", "doll": "950391", "puzzle": "950300",
"teddy bear": "950300", "stuffed animal": "950300",
"building blocks": "950300", "lego": "950300",
"rc car": "950300", "remote control toy": "950300",
# ── Construction Materials ──
"cement": "252329", "concrete": "252329", "brick": "690490",
"tile": "690721", "ceramic tile": "690721", "porcelain tile": "690721",
"glass": "700529", "mirror": "700910", "window": "700800",
"door": "441820", "wooden door": "441820",
"insulation": "680610", "foam insulation": "391723",
"drywall": "480210", "gypsum board": "480210",
"plywood": "441231", "osb": "441011",
"concrete block": "690100", "paver": "690410",
# ── Household Items ──
"box": "481910", "carton": "481910",
"bag": "481930", "bottle": "701090", "jar": "701090",
"cup": "691200", "mug": "691200", "plate": "691110",
"bowl": "691200", "spoon": "821599", "fork": "821599",
"knife": "821191", "kitchen knife": "821191", "scissors": "821300",
"pot": "732393", "pan": "732393", "wok": "732393",
"frying pan": "732393", "saucepan": "732393",
"kettle": "851679", "teapot": "732393",
"trash can": "732399", "recycling bin": "732399",
"umbrella": "660110", "walking stick": "660210",
"broom": "960310", "mop": "960310",
"vacuum cleaner": "850811", "washing machine": "845011",
"dishwasher": "842211", "refrigerator": "841810",
"freezer": "841821", "oven": "851410", "stove": "851610",
"microwave": "851650", "toaster": "851672",
"iron": "851640", "hair dryer": "851631",
"air conditioner": "841510", "fan": "841451",
"heater": "851629", "lamp": "940520",
"bulb": "853950", "led bulb": "853950",
"curtain": "630391", "blinds": "630392",
"carpet": "570330", "rug": "570330",
"doormat": "570330", "mat": "570330",
# ── Chemicals ──
"paint": "320890", "varnish": "320910", "lacquer": "320910",
"ink": "321519", "dye": "320411", "pigment": "320610",
"glue": "350610", "adhesive": "350610", "epoxy": "390730",
"resin": "390710", "silicone": "391000",
"fertilizer": "310590", "pesticide": "380891",
"herbicide": "380891", "insecticide": "380891",
"cleaning product": "340220", "bleach": "281111",
"acetone": "291411", "methanol": "290511",
"ethanol": "220710", "isopropanol": "290512",
"sulfuric acid": "280700", "hydrochloric acid": "280610",
# ── Energy & Power ──
"solar panel": "854140", "solar cell": "854140",
"wind turbine": "850240", "generator": "850211",
"ups": "850440", "uninterruptible power supply": "850440",
"fuel cell": "850790", "lithium battery": "850760",
"alkaline battery": "850650", "rechargeable battery": "850760",
# ── Office Supplies ──
"pen": "960810", "pencil": "960910", "crayon": "960990",
"marker": "960910", "highlighter": "960910",
"eraser": "960900", "sharpener": "961400",
"ruler": "901780", "protractor": "901720",
"stapler": "847290", "paper clip": "830510",
"rubber band": "402700", "tape": "391990",
"correction fluid": "382490", "whiteout": "382490",
"calculator": "847010", "cash register": "847050",
# ── Industrial Equipment ──
"valve": "848180", "pump": "841370", "compressor": "841430",
"filter": "842131", "bearing": "848210", "gear": "848340",
"spring": "732020", "chain": "731582",
"pipe": "730300", "fitting": "730719", "flange": "730791",
"coupling": "848360", "belt": "401011",
"conveyor": "842820", "crane": "842611", "forklift": "842710",
"hoist": "842511", "winch": "842519",
"lathe": "845811", "milling machine": "845710",
"drill press": "845921", "grinder": "846781",
"cnc machine": "845811", "3d printer": "847740",
"robot": "847950", "industrial robot": "847950",
"concrete mixer": "847431", "excavator": "843049",
"bulldozer": "843049", "loader": "842952",
# ── Fasteners & Small Hardware ──
"nail": "731700", "screw": "731815", "bolt": "731815",
"nut": "731816", "washer": "731822", "rivet": "731823",
"clip": "731824", "pin": "731829", "cotter pin": "731829",
"hinge": "830210", "lock": "830110", "padlock": "830110",
"handle": "830240", "knob": "830240",
"hook": "830810", "eyelet": "830820",
# ── Textiles & Fabrics ──
"cotton": "520899", "polyester": "540761", "nylon": "540771",
"silk": "500720", "wool": "511130", "linen": "530921",
"denim": "520833", "canvas": "520852",
"velvet": "580121", "satin": "500720",
"lace": "580410", "embroidery": "581010",
"fabric": "520899", "textile": "520899",
"thread": "520411", "yarn": "520411",
"rope": "560750", "cord": "560750",
"towel": "630260", "blanket": "630140",
"sheet": "630231", "pillowcase": "630231",
"tablecloth": "630260", "napkin": "630260",
"curtain": "630391", "drape": "630391",
# ── Leather Goods ──
"leather": "420221", "suede": "420330",
"wallet": "420231", "purse": "420221",
"handbag": "420221", "briefcase": "420212",
"backpack": "420292", "suitcase": "420212",
"luggage": "420212", "travel bag": "420212",
"glove": "420330", "jacket": "420330",
"shoe upper": "640610",
# ── Glass & Ceramics ──
"glass": "700529", "mirror": "700910",
"window": "700800", "door": "700800",
"glass bottle": "701090", "glass jar": "701090",
"glassware": "701339", "drinking glass": "701339",
"ceramic": "690721", "porcelain": "690721",
"vase": "691310", "flowerpot": "691390",
"tile": "690721", "brick": "690490",
# ── Sports Equipment (specific) ──
"tennis ball": "950662", "golf ball": "950662",
"baseball bat": "950639", "cricket bat": "950639",
"hockey stick": "950639", "lacrosse stick": "950639",
"surfboard": "950699", "paddleboard": "950699",
"kayak": "890310", "canoe": "890310",
"snorkel": "950619", "scuba gear": "950619",
"ski pole": "950619", "ski boot": "950612",
"snowshoe": "950619",
}
# Multi-word keyword matching order (longest first for greedy match)
_KEYWORD_MATCH_ORDER: Final[tuple[str, ...]] = tuple(
sorted(KEYWORD_HS_MAPPING.keys(), key=len, reverse=True)
)
# ── Data Models ───────────────────────────────────────────────────────
@dataclass(frozen=False, slots=True)
class HSClassification:
"""Represents an HS code classification result.
Attributes:
hs_code: The classified code (6-digit WCO, 10-digit HTSUS, or 12-digit TARIC).
description: Human-readable description of the HS code.
confidence: Classification confidence from 0.0 to 1.0.
source: Classification source (pyhscodes, llm-openai, etc.).
section: HS section letter (I-XXI).
chapter: 2-digit chapter code.
heading: 4-digit heading code.
subheading: 6-digit subheading code.
parent_code: Parent HS code in the hierarchy.
level: Code digit length (2, 4, 6, 10, or 12).
reasoning: Explanation of classification rationale.
alternatives: Alternative classifications considered.
needs_human_review: Whether human review is required.
"""
hs_code: str
description: str
confidence: float
source: str
section: str = ""
chapter: str = ""
heading: str = ""
subheading: str = ""
parent_code: str = ""
level: int = 0
reasoning: str = ""
alternatives: list[dict[str, Any]] = field(default_factory=list)
needs_human_review: bool = False
def __post_init__(self) -> None:
"""Parse code structure into hierarchical components."""
self.level = len(self.hs_code)
if self.level >= 2:
self.chapter = self.hs_code[:2]
if self.level >= 4:
self.heading = self.hs_code[:4]
if self.level >= 6:
self.subheading = self.hs_code[:6]
@property
def is_valid(self) -> bool:
"""Whether the code has a valid structural length."""
return self.level in VALID_CODE_LEVELS
@property
def needs_review(self) -> bool:
"""Whether human review is needed (low confidence or flagged)."""
return self.needs_human_review or self.confidence < MEDIUM_CONFIDENCE_THRESHOLD
@dataclass(frozen=False, slots=True)
class ClassificationRequest:
"""Request for HS code classification.
Attributes:
description: Product description to classify.
country_origin: Country of origin (ISO 3166-1 alpha-2).
country_destination: Destination country (ISO 3166-1 alpha-2).
quantity: Quantity of goods.
unit: Unit of measurement.
value: Monetary value.
material: Primary material composition.
additional_info: Additional context for classification.
"""
description: str
country_origin: str = ""
country_destination: str = ""
quantity: float = 0.0
unit: str = ""
value: float = 0.0
material: str = ""
additional_info: str = ""
@dataclass(frozen=False, slots=True)
class ClassificationResponse:
"""Response from HS code classification.
Attributes:
request: The original classification request.
primary: The primary (best) classification result.
alternatives: Alternative classifications in descending confidence.
processing_time_ms: Total processing time in milliseconds.
model_used: Which model/source was used.
"""
request: ClassificationRequest
primary: HSClassification
alternatives: list[HSClassification] = field(default_factory=list)
processing_time_ms: float = 0.0
model_used: str = ""
@dataclass(frozen=False, slots=True)
class ClassificationCacheEntry:
"""In-memory cache entry for a classification result.
Attributes:
description_hash: SHA-256 hash of the normalized description.
description: Original product description.
response: The cached ClassificationResponse.
created_at: Timestamp when the cache entry was created.
hit_count: Number of times this cache entry was accessed.
"""
description_hash: str
description: str
response: ClassificationResponse
created_at: float = field(default_factory=_time.time)
hit_count: int = 0
# ── Semantic Cache ────────────────────────────────────────────────────
class ClassificationCache:
"""Local semantic cache for HS classifications.
Provides two-tier caching:
1. In-memory LRU dict for hot paths (O(1) lookup)
2. PostgreSQL ILIKE fallback for cross-restart persistence
Thread-safe via threading.Lock.
"""
_MAX_MEMORY_ENTRIES: Final[int] = 2048
_CACHE_TTL_SECONDS: Final[float] = 86400.0 # 24 hours
def __init__(self) -> None:
self._lock: threading.Lock = threading.Lock()
self._memory_cache: dict[str, ClassificationCacheEntry] = {}
@staticmethod
def _normalize(description: str) -> str:
"""Normalize description for deterministic hashing."""
normalized: str = description.lower().strip()
normalized = re.sub(r"\s+", " ", normalized)
return normalized
@staticmethod
def _hash(description: str) -> str:
"""SHA-256 hash of a normalized description."""
return hashlib.sha256(description.encode("utf-8")).hexdigest()
def get(self, description: str) -> Optional[ClassificationResponse]:
"""Look up a description in the in-memory cache."""
normalized: str = self._normalize(description)
desc_hash: str = self._hash(normalized)
with self._lock:
entry = self._memory_cache.get(desc_hash)
if entry is None:
return None
if (_time.time() - entry.created_at) > self._CACHE_TTL_SECONDS:
del self._memory_cache[desc_hash]
return None
entry.hit_count += 1
return entry.response
def put(self, description: str, response: ClassificationResponse) -> None:
"""Store a classification result in the in-memory cache."""
normalized: str = self._normalize(description)
desc_hash: str = self._hash(normalized)
with self._lock:
if len(self._memory_cache) >= self._MAX_MEMORY_ENTRIES:
if self._memory_cache:
lru_key: str = min(
self._memory_cache,
key=lambda k: (
self._memory_cache[k].hit_count,
self._memory_cache[k].created_at,
),
)
del self._memory_cache[lru_key]
self._memory_cache[desc_hash] = ClassificationCacheEntry(
description_hash=desc_hash,
description=normalized,
response=response,
)
async def get_from_db(self, description: str) -> Optional[ClassificationResponse]:
"""Look up a description in the PostgreSQL compliance_review_items table.
Uses ILIKE for fuzzy matching against previously classified products.
"""
try:
from hermes.database.connection import get_database_manager
db_manager = get_database_manager()
if not db_manager or not db_manager.async_engine:
return None
from sqlalchemy import text
normalized: str = self._normalize(description)
pattern: str = f"%{normalized}%"
async with db_manager.async_engine.connect() as conn:
result = await conn.execute(
text(
"SELECT hs_code_suggested, hs_code_description, "
"hs_code_confidence, hs_code_alternatives "
"FROM compliance_review_items "
"WHERE hs_code_suggested != '' "
"AND ("
" LOWER(shipper) LIKE LOWER(:pattern) "
" OR LOWER(consignee) LIKE LOWER(:pattern) "
" OR LOWER(invoice_number) LIKE LOWER(:pattern) "
") "
"ORDER BY created_at DESC LIMIT 1"
),
{"pattern": pattern},
)
row = result.fetchone()
if row is None:
return None
hs_code: str = row[0] or ""
hs_desc: str = row[1] or ""
hs_conf: float = float(row[2] or 0.0)
hs_alts_raw: str = row[3] or "[]"
if not hs_code:
return None
try:
alts_data: list[dict[str, Any]] = json.loads(hs_alts_raw)
except (json.JSONDecodeError, TypeError):
alts_data = []
alternatives: list[HSClassification] = []
for alt in alts_data:
alternatives.append(
HSClassification(
hs_code=alt.get("hs_code", ""),
description=alt.get("description", ""),
confidence=float(alt.get("confidence", 0.0)),
source="cache-db",
)
)
primary = HSClassification(
hs_code=hs_code,
description=hs_desc,
confidence=hs_conf,
source="cache-db",
reasoning="Loaded from compliance review cache",
)
logger.info(
"Cache HIT (DB) → HS %s (%.0f%%)",
hs_code,
hs_conf * 100,
)
return ClassificationResponse(
request=ClassificationRequest(description=description),
primary=primary,
alternatives=alternatives,
model_used="cache-db",
)
except Exception as exc:
logger.debug("DB cache lookup failed: %s", exc)
return None
def stats(self) -> dict[str, Any]:
"""Return cache statistics."""
with self._lock:
total_hits: int = sum(e.hit_count for e in self._memory_cache.values())
return {
"memory_entries": len(self._memory_cache),
"max_entries": self._MAX_MEMORY_ENTRIES,
"total_hits": total_hits,
"ttl_seconds": self._CACHE_TTL_SECONDS,
}
# ── HS Code Validator ────────────────────────────────────────────────
class HSCodeValidator:
"""Validates HS code structure and consistency.
Provides static methods for structural validation of 2/4/6/10/12-digit
HS codes according to WCO nomenclature rules.
"""
@staticmethod
def is_valid_hs6(code: str) -> bool:
"""Validate 6-digit HS code structure.
Args:
code: The code to validate.
Returns:
True if code is a valid 6-digit HS code (chapter 01-99, etc.).
"""
if not HS6_PATTERN.match(code):
return False
chapter: int = int(code[:2])
if chapter < 1 or chapter > 99:
return False
heading: int = int(code[2:4])
if heading < 1 or heading > 99:
return False
subheading: int = int(code[4:6])
if subheading < 1 or subheading > 99:
return False
return True
@staticmethod
def is_valid_htsus(code: str) -> bool:
"""Validate 10-digit HTSUS code structure.
Args:
code: The code to validate.
Returns:
True if code is a valid 10-digit HTSUS code.
"""
if not HTSUS_PATTERN.match(code):
return False
return HSCodeValidator.is_valid_hs6(code[:6])
@staticmethod
def is_valid_taric(code: str) -> bool:
"""Validate 12-digit TARIC code structure.
Args:
code: The code to validate.
Returns:
True if code is a valid 12-digit TARIC code.
"""
if not TARIC_PATTERN.match(code):
return False
return HSCodeValidator.is_valid_hs6(code[:6])
@staticmethod
def get_hierarchy(code: str) -> dict[str, str]:
"""Get hierarchical breakdown of HS code.
Args:
code: The HS code to decompose.
Returns:
Dict with keys: section, chapter, heading, subheading, national.
"""
hierarchy: dict[str, str] = {
"section": "",
"chapter": "",
"heading": "",
"subheading": "",
"national": "",
}
if len(code) >= 2:
hierarchy["chapter"] = code[:2]
if len(code) >= 4:
hierarchy["heading"] = code[:4]
if len(code) >= 6:
hierarchy["subheading"] = code[:6]
if len(code) >= 10:
hierarchy["national"] = code[6:10]
if len(code) >= 12:
hierarchy["national"] = code[6:12]
return hierarchy
# ── pyhscodes Wrapper ────────────────────────────────────────────────
class PyHSCodesClassifier:
"""Wrapper around pyhscodes for local HS code classification.
Provides search, lookup, and hierarchy traversal using the pyhscodes
database of 6,940+ WCO HS codes.
Raises:
ImportError: If pyhscodes is not installed.
"""
def __init__(self) -> None:
"""Initialize the classifier.
Raises:
ImportError: If pyhscodes is not installed.
"""
if not PYHSCODES_AVAILABLE:
raise ImportError("pyhscodes not installed")
self._hs: Any = _pyhscodes
def search(
self, query: str, max_results: int = 5
) -> list[HSClassification]:
"""Search for HS codes matching the query.
Args:
query: Product description to search for.
max_results: Maximum number of results to return.
Returns:
List of HSClassification objects sorted by relevance.
"""
try:
all_results: list[Any] = self._hs.search_fuzzy(query)
results: list[Any] = all_results[:max_results] if all_results else []
except LookupError:
results = self._search_by_keywords(query, max_results)
except Exception as exc:
logger.error("pyhscodes search failed: %s", exc)
results = []
classifications: list[HSClassification] = []
for result in results:
confidence: float = self._calculate_confidence(query, result)
classification = HSClassification(
hs_code=result.hscode,
description=result.description,
confidence=confidence,
source="pyhscodes",
section=result.section,
parent_code=result.parent,
level=int(result.level) if result.level else len(result.hscode),
)
classifications.append(classification)
return classifications
def _search_by_keywords(self, query: str, max_results: int) -> list[Any]:
"""Search by individual keywords when full query fails.
Args:
query: Original product query.
max_results: Maximum results per keyword.
Returns:
List of raw pyhscodes results.
"""
words: list[str] = re.findall(r"[a-zA-Z]+", query.lower())
keywords: list[str] = [
w for w in words if w not in _LLM_STOP_WORDS and len(w) > 2
]
for keyword in keywords:
try:
results: list[Any] = self._hs.search_fuzzy(keyword)
if results:
return results[:max_results]
except (LookupError, Exception):
continue
return []
def lookup(self, code: str) -> Optional[HSClassification]:
"""Lookup a specific HS code.
Args:
code: The HS code to lookup.
Returns:
HSClassification if found, None otherwise.
"""
try:
result: Any = self._hs.lookup(code)
if result is None:
return None
return HSClassification(
hs_code=result.hscode,
description=result.description,
confidence=1.0,
source="pyhscodes",
section=result.section,
parent_code=result.parent,
level=int(result.level) if result.level else len(result.hscode),
)
except Exception as exc:
logger.error("pyhscodes lookup failed: %s", exc)
return None
def get_children(self, code: str) -> list[HSClassification]:
"""Get child codes of a parent code.
Args:
code: The parent HS code.
Returns:
List of child HSClassification objects.
"""
try:
results: list[Any] = self._hs.get_children(code)
classifications: list[HSClassification] = []
for result in results:
classification = HSClassification(
hs_code=result.hscode,
description=result.description,
confidence=0.9,
source="pyhscodes",
section=result.section,
parent_code=result.parent,
level=int(result.level) if result.level else len(result.hscode),
)
classifications.append(classification)
return classifications
except Exception as exc:
logger.error("pyhscodes get_children failed: %s", exc)
return []
def _calculate_confidence(self, query: str, result: Any) -> float:
"""Calculate confidence score for a search result.
Args:
query: Original search query.
result: Raw pyhscodes result object.
Returns:
Confidence score between 0.5 and 0.95.
"""
query_lower: str = query.lower().strip()
desc_lower: str = result.description.lower() if result.description else ""
commodity_lower: str = result.commodity.lower() if result.commodity else ""
if query_lower in desc_lower:
return 0.95
if query_lower in commodity_lower:
return 0.95
query_words: set[str] = set(query_lower.split())
desc_words: set[str] = set(desc_lower.split())
commodity_words: set[str] = set(commodity_lower.split())
all_entity_words: set[str] = desc_words | commodity_words
if not query_words or not all_entity_words:
return 0.5
overlap: set[str] = query_words & all_entity_words
overlap_ratio: float = len(overlap) / len(query_words)
base_confidence: float = 0.6 + (overlap_ratio * 0.3)
return min(base_confidence, 0.95)
# ── Main Classification Engine ────────────────────────────────────────
class HSClassificationEngine:
"""Main HS code classification interface with hybrid approach.
Combines pyhscodes database search with LLM refinement for optimal
accuracy. pyhscodes handles 80% of cases locally; LLM is used only
for edge cases requiring deeper reasoning.
Attributes:
_pyhscodes: The pyhscodes classifier instance, or None if unavailable.
"""
def __init__(self) -> None:
"""Initialize the engine with pyhscodes and semantic cache."""
self._pyhscodes: Optional[PyHSCodesClassifier] = None
self._cache: ClassificationCache = ClassificationCache()
self._semaphore: Optional[asyncio.Semaphore] = None
try:
self._pyhscodes = PyHSCodesClassifier()
except ImportError:
logger.warning("pyhscodes not available, LLM-only mode")
def _get_semaphore(self) -> asyncio.Semaphore:
"""Get or create the async semaphore (lazy, thread-safe)."""
if self._semaphore is None:
self._semaphore = asyncio.Semaphore(5)
return self._semaphore
def classify(
self,
description: str,
country_origin: str = "",
country_destination: str = "",
use_llm: bool = False,
target_digits: TargetDigits = 6,
) -> ClassificationResponse:
"""Classify a product description to HS code.
Args:
description: Product description to classify.
country_origin: Country of origin (ISO 3166-1 alpha-2).
country_destination: Destination country (ISO 3166-1 alpha-2).
use_llm: Whether to use LLM for refinement.
target_digits: Target code length (6, 10, or 12).
Returns:
ClassificationResponse with primary code and alternatives.
"""
start_time: float = _time.time()
# ── Cache check (in-memory first, zero-latency) ────────────────
cached: Optional[ClassificationResponse] = self._cache.get(description)
if cached is not None:
logger.info(
"Cache HIT (memory) for '%s' → HS %s (%.0f%%, %.1fms)",
description[:60],
cached.primary.hs_code,
cached.primary.confidence * 100,
(_time.time() - start_time) * 1000,
)
cached.processing_time_ms = (_time.time() - start_time) * 1000
return cached
request = ClassificationRequest(
description=description,
country_origin=country_origin,
country_destination=country_destination,
)
# Step 0: Keyword pre-filter (fastest path for common products)
keyword_match: Optional[HSClassification] = self._keyword_pre_filter(description)
if keyword_match and keyword_match.confidence >= 0.90:
logger.info(
"Keyword pre-filter match for '%s' → HS %s (95%%)",
description[:60],
keyword_match.hs_code,
)
processing_time = (_time.time() - start_time) * 1000
response = ClassificationResponse(
request=request,
primary=keyword_match,
alternatives=[],
processing_time_ms=processing_time,
model_used="keyword-match",
)
self._cache.put(description, response)
return response
# Step 1: pyhscodes classification (always try first)
pyhscodes_results: list[HSClassification] = []
if self._pyhscodes:
pyhscodes_results = self._pyhscodes.search(description, max_results=5)
# Step 2: If LLM requested and pyhscodes didn't give high confidence
llm_result: Optional[HSClassification] = None
if use_llm and (
not pyhscodes_results
or pyhscodes_results[0].confidence < HIGH_CONFIDENCE_THRESHOLD
):
llm_result = self._classify_with_llm(
description, country_origin, country_destination, target_digits
)
# Step 3: Select best result with cross-validation
primary: Optional[HSClassification] = None
alternatives: list[HSClassification] = []
if llm_result and (
not pyhscodes_results
or llm_result.confidence > pyhscodes_results[0].confidence
):
if pyhscodes_results:
llm_chapter: str = (
llm_result.hs_code[:2] if len(llm_result.hs_code) >= 2 else ""
)
pyh_chapter: str = (
pyhscodes_results[0].hs_code[:2]
if len(pyhscodes_results[0].hs_code) >= 2
else ""
)
if llm_chapter and pyh_chapter and llm_chapter != pyh_chapter:
# LLM wins if its confidence is significantly higher
confidence_gap: float = (
llm_result.confidence - pyhscodes_results[0].confidence
)
if confidence_gap > 0.15:
# LLM is much more confident — trust it
llm_result.needs_human_review = True
llm_result.reasoning += (
f" [CROSS-VALIDATION: LLM chapter {llm_chapter} differs from"
f" pyhscodes chapter {pyh_chapter} — LLM preferred due to higher"
f" confidence ({llm_result.confidence:.0%} vs"
f" {pyhscodes_results[0].confidence:.0%})]"
)
primary = llm_result
alternatives = pyhscodes_results[:3]
else:
# Close confidence — prefer pyhscodes, flag for review
llm_result.needs_human_review = True
llm_result.reasoning += (
f" [CROSS-VALIDATION WARNING: LLM chapter {llm_chapter} differs from"
f" pyhscodes chapter {pyh_chapter} — human review required]"
)
primary = pyhscodes_results[0]
alternatives = [llm_result] + pyhscodes_results[1:3]
else:
primary = llm_result
alternatives = pyhscodes_results[:3]
else:
primary = llm_result
elif pyhscodes_results:
primary = pyhscodes_results[0]
alternatives = pyhscodes_results[1:3]
if llm_result:
alternatives.append(llm_result)
else:
primary = HSClassification(
hs_code="000000",
description="Unable to classify",
confidence=0.0,
source="none",
needs_human_review=True,
reasoning="No matching HS codes found",
)
# Step 4: Validate and flag for review
if primary:
primary = self._post_process_validation(primary)
primary.needs_human_review = self._needs_human_review(
primary, description
)
# Additional check: if confidence below NEEDS_REVIEW_THRESHOLD, flag it
if primary.confidence < NEEDS_REVIEW_THRESHOLD:
primary.needs_human_review = True
if primary.needs_human_review:
primary.reasoning += " [Flagged for human review]"
processing_time: float = (_time.time() - start_time) * 1000
response = ClassificationResponse(
request=request,
primary=primary,
alternatives=alternatives,
processing_time_ms=processing_time,
model_used="pyhscodes" if not llm_result else "pyhscodes+llm",
)
# ── Cache store ────────────────────────────────────────────────
try:
self._cache.put(description, response)
except Exception as cache_exc:
logger.debug("Cache store failed: %s", cache_exc)
return response
async def classify_batch(
self,
items: list[dict[str, str]],
use_llm: bool = False,
target_digits: TargetDigits = 6,
) -> list[ClassificationResponse]:
"""Classify a batch of products concurrently with semaphore control.
Uses asyncio.gather with a Semaphore(5) to limit concurrent LLM
API calls. Checks in-memory cache first, then DB cache, then
fires the LLM.
Args:
items: List of dicts with keys: description, country_origin (opt),
country_destination (opt).
use_llm: Whether to use LLM for refinement.
target_digits: Target code length (6, 10, or 12).
Returns:
List of ClassificationResponse objects, one per input item.
"""
if not items:
return []
sem: asyncio.Semaphore = self._get_semaphore()
async def _classify_one(item: dict[str, str]) -> ClassificationResponse:
desc: str = item.get("description", "")
origin: str = item.get("country_origin", "")
dest: str = item.get("country_destination", "")
# Tier 1: In-memory cache (zero latency)
cached: Optional[ClassificationResponse] = self._cache.get(desc)
if cached is not None:
return cached
# Tier 2: DB cache (async, ~5ms)
db_cached: Optional[ClassificationResponse] = await self._cache.get_from_db(desc)
if db_cached is not None:
self._cache.put(desc, db_cached)
return db_cached
# Tier 3: Live classification with semaphore-controlled concurrency
async with sem:
# Run the synchronous classify in a thread pool
loop = asyncio.get_running_loop()
result: ClassificationResponse = await loop.run_in_executor(
None,
lambda: self.classify(
description=desc,
country_origin=origin,
country_destination=dest,
use_llm=use_llm,
target_digits=target_digits,
),
)
return result
tasks = [_classify_one(item) for item in items]
results: list[ClassificationResponse] = await asyncio.gather(
*tasks, return_exceptions=False
)
# Cross-validate batch results
results = self._cross_validate_batch(results)
logger.info(
"Batch classified %d items (%d LLM, %d cached)",
len(results),
sum(1 for r in results if r.model_used in ("pyhscodes+llm",)),
sum(1 for r in results if r.model_used.startswith("cache")),
)
return list(results)
def _classify_with_llm(
self,
description: str,
country_origin: str,
country_destination: str,
target_digits: int,
) -> Optional[HSClassification]:
"""Use LLM for HS code classification.
Supports OpenAI, Anthropic, Google Gemini, and OpenCode-compatible
providers (DeepSeek, etc.) via ModelConfig.
Args:
description: Product description.
country_origin: Country of origin.
country_destination: Destination country.
target_digits: Target code length.
Returns:
HSClassification from LLM, or None if unavailable/failed.
"""
import os
try:
from hermes.config.settings import get_settings
settings = get_settings()
prompt: str = self._build_classification_prompt(
description, country_origin, country_destination, target_digits
)
# Determine provider: explicit config > env vars > fallback
provider = settings.model.provider.lower()
api_key = settings.model.api_key
base_url = settings.model.base_url
model_name = settings.model.name
# Also check env vars as fallback
openai_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
anthropic_key: Optional[str] = os.environ.get("ANTHROPIC_API_KEY")
google_key: Optional[str] = os.environ.get("GOOGLE_API_KEY") or os.environ.get("GEMINI_API_KEY")
# Priority: opencode/deepseek > google/gemini > openai > anthropic
if provider in ("opencode", "deepseek") and api_key:
return self._call_opencode(prompt, target_digits, api_key, base_url, model_name)
elif provider in ("google", "gemini") and api_key:
return self._call_gemini(prompt, target_digits, api_key)
elif google_key:
return self._call_gemini(prompt, target_digits, google_key)
elif openai_key:
return self._call_openai(prompt, target_digits)
elif anthropic_key:
return self._call_anthropic(prompt, target_digits)
else:
logger.warning("No LLM API keys available for classification")
return None
except Exception as exc:
logger.error("LLM classification failed: %s", exc)
return None
def _build_classification_prompt(
self,
description: str,
country_origin: str,
country_destination: str,
target_digits: int,
) -> str:
"""Build prompt for LLM classification with chapter context and examples.
Args:
description: Product description.
country_origin: Country of origin.
country_destination: Destination country.
target_digits: Target code length.
Returns:
Formatted prompt string.
"""
# Build chapter context section (condensed for token efficiency)
chapter_guide_lines: list[str] = []
for ch, desc in sorted(CHAPTER_DESCRIPTIONS.items()):
chapter_guide_lines.append(f" {ch}: {desc}")
chapter_guide = "\n".join(chapter_guide_lines)
prompt: str = (
"You are a world-class trade compliance officer and HS code classification expert.\n\n"
"## 4-Pillar Classification Framework\n"
"Analyze the product using these 4 pillars:\n"
"1. MATERIAL COMPOSITION — What is the product made of? (e.g., cotton, steel, plastic, wood)\n"
"2. FUNCTION/USE — What does the product do? What is it used for?\n"
"3. ESSENTIAL CHARACTER — What is the most important feature or component?\n"
"4. MANUFACTURING PROCESS — How was it made? (e.g., knitted, woven, cast, machined)\n\n"
"## HS Code Structure\n"
"HS codes follow a hierarchical structure:\n"
"- 2-digit Chapter (e.g., 09 = Coffee, tea, spices)\n"
"- 4-digit Heading (e.g., 0902 = Tea, whether or not flavored)\n"
"- 6-digit Subheading (e.g., 090210 = Green tea, in containers ≤3kg)\n"
f"- {target_digits}-digit National code (HTSUS/TARIC extension)\n\n"
"## HS Chapter Reference\n"
f"{chapter_guide}\n\n"
"## Classification Examples (with 4-Pillar Analysis)\n"
"Example 1: \"Arabica coffee beans, roasted, 500g bags\"\n"
"- Material: Coffee beans (agricultural product)\n"
"- Function: Beverage ingredient\n"
"- Essential: Roasted coffee\n"
"- Process: Roasted\n"
'→ hs_code: "090121" (Coffee, roasted, not decaffeinated)\n\n'
"Example 2: \"Laptop computer, Intel i7 processor, 15.6 inch screen\"\n"
"- Material: Electronic components, plastic/metal casing\n"
"- Function: Data processing, computing\n"
"- Essential: Portable digital computer\n"
"- Process: Assembled electronic device\n"
'→ hs_code: "847130" (Portable digital automatic data processing machines)\n\n'
"Example 3: \"Cotton t-shirt, men's, 100% cotton\"\n"
"- Material: 100% cotton\n"
"- Function: Clothing, upper body wear\n"
"- Essential: Knitted T-shirt\n"
"- Process: Knitted/crocheted\n"
'→ hs_code: "610910" (T-shirts, singlets, knitted/crocheted, of cotton)\n\n'
"Example 4: \"Stainless steel screws, M6 x 20mm, box of 100\"\n"
"- Material: Stainless steel\n"
"- Function: Fastening\n"
"- Essential: Screw (threaded fastener)\n"
"- Process: Machined/forged\n"
'→ hs_code: "731815" (Screws, of stainless steel)\n\n'
"Example 5: \"Extra virgin olive oil, 500ml glass bottle\"\n"
"- Material: Olive oil (vegetable oil)\n"
"- Function: Cooking/food ingredient\n"
"- Essential: Virgin olive oil\n"
"- Process: First cold pressing\n"
'→ hs_code: "150910" (Olive oil, virgin, in containers ≤18kg)\n\n'
"Example 6: \"Silk women's dress, evening wear, embroidered\"\n"
"- Material: Silk\n"
"- Function: Women's clothing, formal wear\n"
"- Essential: Silk dress (woven, not knitted)\n"
"- Process: Woven + embroidered\n"
'→ hs_code: "620449" (Women\'s dresses of silk, woven)\n\n'
"Example 7: \"CNC milling machine, 3-axis, 10kW motor\"\n"
"- Material: Steel/iron construction\n"
"- Function: Metal cutting/milling\n"
"- Essential: Machine tool for removing material\n"
"- Process: CNC controlled, machined\n"
'→ hs_code: "845710" (Machining centres for working metal)\n\n'
"Example 8: \"Leather travel bag, brown, with shoulder strap\"\n"
"- Material: Leather\n"
"- Function: Carrying personal items\n"
"- Essential: Travel goods/suitcase\n"
"- Process: Sewn leather construction\n"
'→ hs_code: "420212" (Trunks, suitcases with outer surface of plastics or textile)\n\n'
"Example 9: \"Wooden dining table, oak, extendable\"\n"
"- Material: Oak wood\n"
"- Function: Dining surface, furniture\n"
"- Essential: Table (wooden furniture)\n"
"- Process: Joinery, finished wood\n"
'→ hs_code: "940360" (Other wooden furniture)\n\n'
"Example 10: \"Wool carpet, hand-tufted, 2m x 3m\"\n"
"- Material: Wool\n"
"- Function: Floor covering\n"
"- Essential: Tufted carpet\n"
"- Process: Hand-tufted\n"
'→ hs_code: "570110" (Carpets of wool/fine animal hair, knotted)\n\n'
)
if country_origin:
prompt += f"Country of Origin: {country_origin}\n"
if country_destination:
prompt += f"Destination Country: {country_destination}\n"
prompt += (
f"\n## Task\n"
f"Classify the following product to a {target_digits}-digit HS code:\n\n"
f"Product Description: {description}\n\n"
"## Response Format\n"
"Return ONLY valid JSON (no markdown, no explanation outside JSON):\n"
"{\n"
f' "hs_code": "{target_digits}-digit numeric code",\n'
' "description": "Brief description of what this HS code covers",\n'
' "confidence": 0.0 to 1.0 (your confidence in this classification),\n'
' "reasoning": "Why this code was chosen - key factors",\n'
' "chapter": "2-digit chapter code",\n'
' "heading": "4-digit heading code",\n'
' "alternatives": [\n'
' {"hs_code": "code", "description": "desc", "confidence": 0.0, "reason": "why"}\n'
" ]\n"
"}\n\n"
"## Rules\n"
"1. Use WCO HS nomenclature - classify by material composition and essential character\n"
"2. Primary classification should be 6-digit international HS code\n"
f"3. Provide {target_digits}-digit code (pad with 0s if only 6-digit is certain)\n"
"4. ALWAYS provide top 3 alternative codes with confidence scores\n"
"5. If unsure about the exact code, provide the most likely chapter and heading\n"
"6. Consider: What is the product made of? What is it used for?\n"
"7. Consider: What is the essential character or principal function?\n"
"8. Be precise - avoid broad catch-all codes unless truly applicable\n"
)
return prompt
def _call_openai(
self, prompt: str, target_digits: int
) -> Optional[HSClassification]:
"""Call OpenAI API for classification.
Args:
prompt: The classification prompt.
target_digits: Target code length.
Returns:
HSClassification from OpenAI, or None on failure.
"""
try:
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.1,
max_tokens=500,
)
content: str = response.choices[0].message.content or ""
return self._parse_llm_response(content, "openai", target_digits)
except Exception as exc:
raise LLMClassificationError("openai", str(exc)) from exc
def _call_anthropic(
self, prompt: str, target_digits: int
) -> Optional[HSClassification]:
"""Call Anthropic API for classification.
Args:
prompt: The classification prompt.
target_digits: Target code length.
Returns:
HSClassification from Anthropic, or None on failure.
"""
try:
import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=500,
messages=[{"role": "user", "content": prompt}],
)
content: str = response.content[0].text
return self._parse_llm_response(content, "anthropic", target_digits)
except Exception as exc:
raise LLMClassificationError("anthropic", str(exc)) from exc
def _call_opencode(
self,
prompt: str,
target_digits: int,
api_key: str,
base_url: str,
model_name: str,
) -> Optional[HSClassification]:
"""Call OpenCode-compatible API (DeepSeek, Qwen, etc.) for classification.
Uses the OpenAI SDK with a custom base_url for OpenAI-compatible
endpoints. Reasoning models (deepseek-v4-flash) use reasoning_content
for thinking tokens; non-reasoning models (qwen3.7-plus) return
directly in content.
Args:
prompt: The classification prompt.
target_digits: Target code length.
api_key: API key for the provider.
base_url: Base URL for the API.
model_name: Model name to use.
Returns:
HSClassification from the provider, or None on failure.
"""
try:
import openai
client = openai.OpenAI(api_key=api_key, base_url=base_url)
# Reasoning models need more tokens for thinking
is_reasoning: bool = "deepseek" in model_name.lower()
max_tokens: int = 8192 if is_reasoning else 1024
response = client.chat.completions.create(
model=model_name,
messages=[{"role": "user", "content": prompt}],
temperature=0.1,
max_tokens=max_tokens,
)
choice = response.choices[0]
content: str = choice.message.content or ""
# Reasoning models may put the answer in reasoning_content
# when content is empty (truncated by max_tokens)
if not content.strip():
reasoning = getattr(choice.message, "reasoning_content", None)
if reasoning:
content = reasoning
logger.info(
"LLM content empty, extracting from reasoning_content (%d chars)",
len(reasoning),
)
return self._parse_llm_response(content, "opencode", target_digits)
except Exception as exc:
raise LLMClassificationError("opencode", str(exc)) from exc
def _call_gemini(
self, prompt: str, target_digits: int, api_key: str
) -> Optional[HSClassification]:
"""Call Google Gemini API for classification.
Uses the google-genai SDK (successor to google-generativeai).
Args:
prompt: The classification prompt.
target_digits: Target code length.
api_key: Google API key.
Returns:
HSClassification from Gemini, or None on failure.
"""
try:
from google import genai
client = genai.Client(api_key=api_key)
response = client.models.generate_content(
model="gemini-2.0-flash",
contents=prompt,
config=genai.types.GenerateContentConfig(
temperature=0.1,
max_output_tokens=500,
),
)
content: str = response.text
return self._parse_llm_response(content, "gemini", target_digits)
except Exception as exc:
raise LLMClassificationError("gemini", str(exc)) from exc
def _parse_llm_response(
self, content: str, source: str, target_digits: int
) -> Optional[HSClassification]:
"""Parse LLM response into HSClassification.
LLM self-reported confidence is capped at LLM_MAX_CONFIDENCE (0.85)
because LLMs are calibrationally poor and may output inflated confidence
for hallucinated codes.
Args:
content: Raw LLM response text.
source: LLM provider name (openai, anthropic).
target_digits: Expected code length.
Returns:
HSClassification if parsing succeeds, None otherwise.
"""
try:
import json
# Strip markdown code blocks (```json ... ```) if present
cleaned: str = content.strip()
if cleaned.startswith("```"):
# Remove opening fence (```json or ```)
first_newline: int = cleaned.find("\n")
if first_newline != -1:
cleaned = cleaned[first_newline + 1 :]
# Remove closing fence
if cleaned.rstrip().endswith("```"):
cleaned = cleaned.rstrip()[: -len("```")].rstrip()
# Extract outermost JSON block by matching brace depth
start: int = cleaned.find("{")
if start == -1:
logger.error("No JSON found in LLM response")
return None
depth: int = 0
end: int = -1
for i in range(start, len(cleaned)):
if cleaned[i] == "{":
depth += 1
elif cleaned[i] == "}":
depth -= 1
if depth == 0:
end = i
break
if end == -1:
logger.error("Unbalanced JSON braces in LLM response")
return None
data: dict[str, Any] = json.loads(cleaned[start : end + 1])
hs_code: str = data.get("hs_code", "")
# Normalize: strip dots, dashes, spaces
hs_code = re.sub(r"[.\-\s]", "", hs_code)
if len(hs_code) != target_digits:
logger.warning(
"LLM returned %d-digit code, expected %d",
len(hs_code),
target_digits,
)
if len(hs_code) > target_digits:
hs_code = hs_code[:target_digits]
else:
hs_code = hs_code.ljust(target_digits, "0")
raw_confidence: float = data.get("confidence", 0.5)
capped_confidence: float = min(raw_confidence, LLM_MAX_CONFIDENCE)
if raw_confidence > LLM_MAX_CONFIDENCE:
logger.warning(
"LLM self-reported confidence %.2f capped to %.2f",
raw_confidence,
LLM_MAX_CONFIDENCE,
)
# Check if code exists in pyhscodes database
in_database: bool = False
if PYHSCODES_AVAILABLE:
try:
db_result = _pyhscodes.lookup(hs_code)
if db_result and db_result.hscode:
in_database = True
except Exception:
pass
if not in_database:
try:
db_result = _pyhscodes.lookup(hs_code[:6])
if db_result and db_result.hscode:
in_database = True
except Exception:
pass
# Compute final confidence: LLM confidence × database_match × format_validity
format_valid: bool = bool(HS6_PATTERN.match(hs_code[:6]))
db_match_factor: float = 1.0 if in_database else 0.85
format_factor: float = 1.0 if format_valid else 0.8
final_confidence: float = min(
capped_confidence * db_match_factor * format_factor,
LLM_MAX_CONFIDENCE,
)
alternatives: list[HSClassification] = []
for alt in data.get("alternatives", []):
alt_code: str = alt.get("hs_code", "")
alt_code = re.sub(r"[.\-\s]", "", alt_code)
if len(alt_code) > target_digits:
alt_code = alt_code[:target_digits]
alt_conf: float = min(alt.get("confidence", 0.5), LLM_MAX_CONFIDENCE)
alternatives.append(
HSClassification(
hs_code=alt_code,
description=alt.get("description", ""),
confidence=alt_conf,
source=f"llm-{source}",
)
)
return HSClassification(
hs_code=hs_code,
description=data.get("description", ""),
confidence=final_confidence,
source=f"llm-{source}",
reasoning=data.get("reasoning", ""),
alternatives=alternatives,
)
except Exception as exc:
logger.error("Failed to parse LLM response: %s", exc)
return None
def _needs_human_review(
self, classification: HSClassification, query: str
) -> bool:
"""Determine if classification needs human review.
Args:
classification: The classification to evaluate.
query: The original product query.
Returns:
True if human review is recommended.
"""
if classification.confidence < MEDIUM_CONFIDENCE_THRESHOLD:
return True
if not classification.is_valid:
return True
if classification.source.startswith("llm-"):
return True
query_lower: str = query.lower()
if any(term in query_lower for term in _AMBIGUOUS_TERMS):
return True
return False
@staticmethod
def _keyword_pre_filter(description: str) -> Optional[HSClassification]:
"""Look up product in the keyword→HS code mapping for instant classification.
Uses longest-match-first to handle multi-word terms (e.g., "olive oil"
matches before "oil"). Uses word boundary matching to avoid false positives.
Args:
description: Product description to match.
Returns:
HSClassification with high confidence if exact keyword match found,
None otherwise.
"""
desc_lower: str = description.lower().strip()
for keyword in _KEYWORD_MATCH_ORDER:
# Use word boundary regex to avoid substring matches
# e.g., "ring" should not match "Bamboo flooring planks"
pattern = re.compile(r'\b' + re.escape(keyword) + r'\b')
if pattern.search(desc_lower):
code: str = KEYWORD_HS_MAPPING[keyword]
# Look up the full code description from pyhscodes if available
desc_from_db: str = ""
if PYHSCODES_AVAILABLE:
try:
result = _pyhscodes.lookup(code)
if result:
desc_from_db = result.description or ""
except Exception:
pass
if not desc_from_db:
# Fallback: use chapter description
chapter: str = code[:2]
desc_from_db = CHAPTER_DESCRIPTIONS.get(chapter, "HS code")
return HSClassification(
hs_code=code,
description=desc_from_db,
confidence=0.95,
source="keyword-match",
reasoning=f"Exact keyword match: '{keyword}' → {code}",
)
return None
@staticmethod
def _post_process_validation(
classification: HSClassification,
) -> HSClassification:
"""Validate and adjust a classification result.
Checks format, database existence, and applies confidence adjustments.
Args:
classification: The classification to validate.
Returns:
Validated (possibly adjusted) classification.
"""
if classification.source == "none":
return classification
# Format validation
code: str = classification.hs_code
format_valid: bool = bool(HS6_PATTERN.match(code[:6])) if len(code) >= 6 else False
# Database existence check
in_database: bool = False
if PYHSCODES_AVAILABLE:
try:
result = _pyhscodes.lookup(code)
if result and result.hscode:
in_database = True
except Exception:
pass
# Also try 6-digit prefix
if not in_database and len(code) >= 6:
try:
result = _pyhscodes.lookup(code[:6])
if result and result.hscode:
in_database = True
except Exception:
pass
# Confidence adjustment based on validation
adjusted_conf: float = classification.confidence
if not format_valid:
adjusted_conf *= 0.8 # Penalize invalid format
if not in_database and classification.source.startswith("llm-"):
adjusted_conf *= 0.85 # Penalize LLM codes not in database
classification.confidence = min(adjusted_conf, LLM_MAX_CONFIDENCE)
# Flag if code not in database
if not in_database and classification.source == "pyhscodes":
classification.needs_human_review = True
classification.reasoning += " [Code not found in pyhscodes database]"
return classification
@staticmethod
def _find_closest_database_code(
description: str, max_candidates: int = 5
) -> list[HSClassification]:
"""Find closest matching codes in pyhscodes database using fuzzy matching.
Args:
description: Product description.
max_candidates: Maximum number of candidates to return.
Returns:
List of HSClassification candidates sorted by relevance.
"""
if not PYHSCODES_AVAILABLE:
return []
candidates: list[HSClassification] = []
# Split description into keywords and search each
words: list[str] = re.findall(r"[a-zA-Z]{3,}", description.lower())
search_terms: list[str] = [w for w in words if w not in _LLM_STOP_WORDS][:5]
for term in search_terms:
try:
results = _pyhscodes.search_fuzzy(term)
if results:
for r in results[:2]:
# Calculate relevance using SequenceMatcher
ratio: float = SequenceMatcher(
None, description.lower(), r.description.lower()
).ratio()
candidates.append(HSClassification(
hs_code=r.hscode,
description=r.description,
confidence=min(0.6 + ratio * 0.3, 0.9),
source="pyhscodes-fuzzy",
section=r.section if hasattr(r, "section") else "",
))
except Exception:
continue
# Deduplicate by code and sort by confidence
seen: set[str] = set()
unique: list[HSClassification] = []
for c in sorted(candidates, key=lambda x: x.confidence, reverse=True):
if c.hs_code not in seen:
seen.add(c.hs_code)
unique.append(c)
return unique[:max_candidates]
def _cross_validate_batch(
self, results: list[ClassificationResponse]
) -> list[ClassificationResponse]:
"""Cross-validate batch results: flag similar descriptions with different codes.
Args:
results: List of classification responses.
Returns:
Same list with needs_human_review flags updated.
"""
if len(results) < 2:
return results
for i, r1 in enumerate(results):
for j, r2 in enumerate(results):
if i >= j:
continue
# Compare descriptions using SequenceMatcher
d1: str = r1.request.description.lower()
d2: str = r2.request.description.lower()
similarity: float = SequenceMatcher(None, d1, d2).ratio()
# If descriptions are very similar but codes differ, flag both
if similarity > 0.75:
code1: str = r1.primary.hs_code[:4] # Compare at heading level
code2: str = r2.primary.hs_code[:4]
if code1 != code2:
r1.primary.needs_human_review = True
r2.primary.needs_human_review = True
r1.primary.reasoning += (
f" [BATCH: Similar to '{r2.request.description[:40]}' "
f"but different classification]"
)
r2.primary.reasoning += (
f" [BATCH: Similar to '{r1.request.description[:40]}' "
f"but different classification]"
)
return results
# ── Convenience Functions ─────────────────────────────────────────────
_engine: Optional[HSClassificationEngine] = None
_engine_lock: threading.Lock = threading.Lock()
def get_engine() -> HSClassificationEngine:
"""Get or create the global HS classification engine (thread-safe).
Returns:
The singleton HSClassificationEngine instance.
"""
global _engine
if _engine is None:
with _engine_lock:
if _engine is None:
_engine = HSClassificationEngine()
return _engine
def classify_product(
description: str,
country_origin: str = "",
country_destination: str = "",
use_llm: bool = False,
target_digits: TargetDigits = 6,
) -> ClassificationResponse:
"""Classify a product to HS code.
Args:
description: Product description.
country_origin: Country of origin.
country_destination: Destination country.
use_llm: Whether to use LLM refinement.
target_digits: Target code length.
Returns:
ClassificationResponse with primary code and alternatives.
"""
engine: HSClassificationEngine = get_engine()
return engine.classify(
description, country_origin, country_destination, use_llm, target_digits
)
def lookup_code(code: str) -> Optional[HSClassification]:
"""Lookup a specific HS code.
Args:
code: The HS code to lookup.
Returns:
HSClassification if found, None otherwise.
"""
if not PYHSCODES_AVAILABLE:
return None
classifier: PyHSCodesClassifier = PyHSCodesClassifier()
return classifier.lookup(code)
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