5ab1c29c
 tangwang
first commit
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from typing import Dict, List, Optional, Any
from dataclasses import dataclass
import json
from src.services.user_profile import UserProfile
from config.logging_config import get_app_logger
from google.protobuf.json_format import MessageToDict
import logging
from config.app_config import BOOST_CONFIGS, FRESH_BOOST_CONFIG, BOOST_WEIGHTS_CONFIG, FUNCTIONS_SCORE__SCORE_MODE__WHEN_NO_QUERY, FUNCTIONS_SCORE__SCORE_MODE__WHEN_HAS_QUERY
logger = get_app_logger(__name__)
@dataclass
class BoostConfig:
tag_id: int
tag_name: str
tag_type: Optional[str]
boost_value: float
es_intent_boost_value: float
reranker_intent_boost_value: float
intent_names: List[str]
platform: List[str]
# 标签ID 标签名称 标签类型 提权幅度
# 156 行业新品 销售属性 1.1
# 157 爆品/时货 销售属性 1.1
# 158 常年热销 销售属性 1.1
# 159 质量好 销售属性 1.1
# 162 小惠商品 null 1.05
# 163 优惠商品 null 1.1
# 164 特惠商品 null 1.3
# 165 超惠商品 null 1.15
# 3 一箱快出 null
# 5 推荐 null
# 10 人气热销 null
# 14 特色精选 null
# 17 赠品（新）（补柜专区） null
# 20 新品首发 null
# 21 0316-首发新品【新品页面专用】 null
# 25 0316essa新品-【新品页面专用】 null
# 26 essaone新品 null
# 27 0316最近上架(专区) null
# 40 一箱 null
# 41 快出 null
# 42 上市新品（报表）&（专区） null
# 43 9.20内销（专区） null
# 82 半箱拼团 null
# # 季节性，打入到 关键词字段 做匹配
# 149 年货 销售时节
# 150 万圣节 销售时节
# 151 圣诞节 销售时节
# 152 开学季 销售时节
# 153 复活节 销售时节
# 154 三八节 销售时节
# 155 情人节 销售时节
# TODO 根据 前端参数 客户类型 销售区域 做提权
# 标签ID 标签名称 标签类型
# 137 东欧市场 销售区域
# 138 欧美市场 销售区域
# 139 南美市场 销售区域
# 140 中东市场 销售区域
# 141 东南亚市场 销售区域
# 142 综合商超 客户类型
# 143 专业商超 客户类型
# 144 品牌商 客户类型
# 145 公司批发商 客户类型
# 146 市场批发商 客户类型
# 147 电商 客户类型
# 148 赠品商 客户类型
class SearchBoostStrategy:
def __init__(self):
# Initialize boost configurations from config file
self.boost_configs: List[BoostConfig] = [
BoostConfig(
config["tag_id"],
config["tag_name"],
config["tag_type"],
config["boost_value"],
config["es_intent_boost_value"],
config["reranker_intent_boost_value"],
config["intent_names"],
config["platform"]
) for config in BOOST_CONFIGS
]
# Create lookup dictionaries for faster access
self.tag_id_to_boost: Dict[int, float] = {
config.tag_id: config.boost_value for config in self.boost_configs
}
self.tag_name_to_boost: Dict[str, float] = {
config.tag_name: config.boost_value for config in self.boost_configs
}
# Create intent-based boost lookup for ES search
self.intent_to_boost: Dict[str, float] = {}
for config in self.boost_configs:
for intent_name in config.intent_names:
self.intent_to_boost[intent_name] = config.es_intent_boost_value
logger.debug(f"Initialized boost configs: {json.dumps([vars(c) for c in self.boost_configs], ensure_ascii=False)}")
def _get_platform_boost_configs(self, business_platform: Optional[str]) -> List[BoostConfig]:
"""
Filters boost configurations based on the business platform.
Returns a list of BoostConfig objects that match the platform.
"""
if not business_platform:
return self.boost_configs
return [
config for config in self.boost_configs
if business_platform in config.platform
]
def get_boost_query(self, user_profile: Optional[UserProfile] = None, label_field_name: Optional[str] = None, query_intents: Optional[List[str]] = None, business_platform: Optional[str] = None, search_context: Optional[Any] = None) -> dict:
"""
Generate the Elasticsearch boost query based on configured boost values and user profiles.
Returns a function_score query that only affects scoring without impacting recall.
Args:
user_profile: User profile for behavior-based boosting
label_field_name: Field name for label-based boosting
query_intents: Detected query intents for intent-based boosting
business_platform: Business platform for platform-based filtering
search_context: Search context containing business platform and sale category information
"""
log_prefix = search_context.format_log_prefix() if search_context else ""
functions = []
# Initialize boost query counters using int array for better performance
# boost_cnt[0]: tag_functions, boost_cnt[1]: fresh_functions, boost_cnt[2]: behavior_functions
# boost_cnt[3]: brand_functions, boost_cnt[4]: category_functions, boost_cnt[5]: price_range_functions
# boost_cnt[6]: video_functions, boost_cnt[7]: platform_category_functions
boost_cnt = [0] * 8
# Get platform-filtered boost configs
platform_boost_configs = self._get_platform_boost_configs(business_platform)
# Add boost for tag IDs - use dynamic field name and platform filtering
if label_field_name:
for config in platform_boost_configs:
tag_id = config.tag_id
boost_value = config.boost_value
# Check if this tag should get intent-based boost
final_boost_value = boost_value
if query_intents:
# Check if any detected intent matches this tag's intent_names
for intent in query_intents:
if intent in config.intent_names:
final_boost_value = config.es_intent_boost_value
logger.debug(f"{log_prefix} Intent-based boost for tag_id {tag_id}: {boost_value} -> {final_boost_value} (intent: {intent})")
break
functions.append({
"filter": {
"term": {
label_field_name: tag_id
}
},
"weight": final_boost_value
})
boost_cnt[0] += 1 # tag_functions
logger.debug(f"{log_prefix} Added {boost_cnt[0]} tag-based boost functions using field: {label_field_name} for platform: {business_platform}")
if query_intents:
logger.info(f"{log_prefix} Applied intent-based boost for intents: {query_intents}")
else:
logger.warning(f"{log_prefix} Label field name is empty, cannot apply tag boost")
logger.warning(f"{log_prefix} Tag boost functions will be skipped - label_field_name is required for dynamic field name")
# Add fresh boost using exact sigmoid formula
# Check if new product intent is detected and apply power factor
fresh_factor = FRESH_BOOST_CONFIG["default_factor"]
if query_intents:
for intent in query_intents:
if intent == FRESH_BOOST_CONFIG["new_product_intent"]:
fresh_factor = FRESH_BOOST_CONFIG["es_intent_factor"]
logger.debug(f"{log_prefix} New product intent detected: {intent}, applying ES fresh boost factor: {fresh_factor}")
break
functions.append({
"field_value_factor": {
"field": "on_sell_days_boost",
"missing": 1.0,
"factor": fresh_factor
}
})
boost_cnt[1] += 1 # fresh_functions
logger.debug(f"{log_prefix} Added fresh boost function with factor: {fresh_factor}")
# Add video boost
functions.append({
"filter": {
"term": {
"is_video": True
}
},
"weight": BOOST_WEIGHTS_CONFIG["video_boost_weight"]
})
boost_cnt[6] += 1 # video_functions
logger.debug(f"{log_prefix} Added video boost function with weight: {BOOST_WEIGHTS_CONFIG['video_boost_weight']}")
# ===== 平台类目排名提权 =====
if search_context and hasattr(search_context, 'businessPlatform') and hasattr(search_context, 'sale_category_id'):
if search_context.businessPlatform and search_context.sale_category_id:
platform_cate_top_keyword = f"{search_context.businessPlatform}_{search_context.sale_category_id}"
logger.debug(f"{log_prefix} Adding platform category ranking boost for keyword: {platform_cate_top_keyword}")
functions.append({
"filter": {
"term": {
"op_ranking_platform_cate_list": platform_cate_top_keyword
}
},
"weight": BOOST_WEIGHTS_CONFIG["platform_category_ranking_weight"]
})
boost_cnt[7] += 1 # platform_category_functions
logger.debug(f"{log_prefix} Added platform category ranking boost function for: {platform_cate_top_keyword}")
else:
logger.debug(f"{log_prefix} Skipping platform category boost - businessPlatform: {getattr(search_context, 'businessPlatform', 'None')}, sale_category_id: {getattr(search_context, 'sale_category_id', 'None')}")
else:
logger.debug(f"{log_prefix} Skipping platform category boost - search_context not provided or missing required fields")
# ===== 用户画像个性化提权 =====
# 基于用户画像信息进行个性化商品推荐，提高搜索结果的个性化匹配度
# 包括：用户行为、品牌偏好、类目偏好、价格偏好、客户商品结构等维度
if user_profile:
logger.debug(f"{log_prefix} Adding biz boosting based on user profile")
logger.debug(f"{log_prefix} User profile base info: {MessageToDict(user_profile.base_info)}")
# logger.debug(f"User profile statistics: {MessageToDict(user_profile.statistics)}")
# Add detailed debug logging for statistics
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f"{log_prefix} User profile statistics:")
stats_dict = MessageToDict(user_profile.statistics)
for key, value in stats_dict.items():
if isinstance(value, list):
logger.debug(f"{log_prefix} Statistics {key}: {len(value)} items, first item: {value[0] if value else 'None'}")
else:
logger.debug(f"{log_prefix} Statistics {key}: {value}")
# ===== 用户行为提权 =====
# 逻辑：从用户画像中提取行为记录（点击、加购、收藏、购买）
# 限制：最多使用前N个行为记录，避免过多记录影响性能
behavior_map = user_profile.behavior_map
# logger.debug(f"User behavior map: {MessageToDict(behavior_map)}")
# Add detailed debug logging for behavior map
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f"{log_prefix} User behavior map:")
behavior_dict = MessageToDict(behavior_map)
for behavior_type, behaviors in behavior_dict.items():
if isinstance(behaviors, list):
logger.debug(f"{log_prefix} Behavior {behavior_type}: {len(behaviors)} items, first item: {behaviors[0] if behaviors else 'None'}")
else:
logger.debug(f"{log_prefix} Behavior {behavior_type}: {behaviors}")
max_behavior_count_for_boost = BOOST_WEIGHTS_CONFIG["max_behavior_count_for_boost"]
for behavior_type in ['click', 'add_cart', 'collect', 'purchase']:
behaviors = getattr(behavior_map, behavior_type, [])
if behaviors:
sku_ids = [b.skuId for b in behaviors[:max_behavior_count_for_boost]]
logger.debug(f"{log_prefix} Adding boost for {behavior_type} behaviors with {len(sku_ids)} SKUs: {sku_ids[:10]}")
functions.append({
"filter": {
"terms": {
"sku_id": sku_ids
}
},
"weight": BOOST_WEIGHTS_CONFIG["user_behavior_weight"]
})
boost_cnt[2] += 1 # behavior_functions
# ===== 品牌偏好提权 =====
# 目的：基于用户偏好的品牌推荐商品，提高个性化匹配度
# 逻辑：从用户画像base_info中提取brandCategoryIds，对相关品牌商品进行提权
# 权重：从配置文件读取，默认1.1倍
if user_profile.base_info.brandCategoryIds:
brand_ids = [x for x in user_profile.base_info.brandCategoryIds]
logger.debug(f"{log_prefix} Adding boost for brand preferences with {len(brand_ids)} brand_ids {brand_ids[:10]}")
functions.append({
"filter": {
"terms": {
"brand_id": brand_ids
}
},
"weight": BOOST_WEIGHTS_CONFIG["brand_preference_weight"]
})
boost_cnt[3] += 1 # brand_functions
# ===== 类目偏好提权 =====
# 目的：基于用户偏好的商品类目推荐相关商品，提高个性化匹配度
# 逻辑：从用户画像statistics中提取category_group，对相关类目商品进行提权
# 权重：从配置文件读取，默认1.08倍
# 注意：当前功能已禁用，如需启用请将if False改为if True
if False:
if user_profile.statistics.category_group:
category_ids = [stat.keyId for stat in user_profile.statistics.category_group]
category_stats = [MessageToDict(stat) for stat in user_profile.statistics.category_group]
logger.debug(f"{log_prefix} Category preferences stats with {len(category_ids)} category_ids {category_ids[:10]}")
logger.debug(f"{log_prefix} Adding boost for category preferences with {len(category_ids)} category_ids {category_ids[:10]}")
functions.append({
"filter": {
"terms": {
"category_id": category_ids
}
},
"weight": BOOST_WEIGHTS_CONFIG["category_preference_weight"]
})
boost_cnt[4] += 1 # category_functions
# ===== 价格区间偏好提权 =====
# 目的：基于用户偏好的价格区间推荐相关商品，提高个性化匹配度
# 逻辑：从用户画像statistics中提取price_group，对相关价格区间商品进行提权
# 权重：从配置文件读取，默认1.1倍
# 注意：当前功能已禁用，如需启用请将if False改为if True
if False:
if user_profile.statistics.price_group:
price_ranges = [stat.keyId for stat in user_profile.statistics.price_group]
price_stats = [MessageToDict(stat) for stat in user_profile.statistics.price_group]
logger.debug(f"{log_prefix} Price range preferences stats: {price_stats}")
logger.debug(f"{log_prefix} Adding boost for price range preferences: {price_ranges}")
functions.append({
"filter": {
"terms": {
"price_range": price_ranges
}
},
"weight": BOOST_WEIGHTS_CONFIG["price_range_preference_weight"]
})
boost_cnt[5] += 1 # price_range_functions
# ===== 客户商品结构类目提权 =====
# 目的：基于客户商品结构分析，推荐符合客户业务模式的类目商品
# 逻辑：从用户画像base_info中提取customerGoodsStructure，分析客户的类目偏好
# 权重：从配置文件读取，默认1.08倍
# 注意：categoryIds对应前端类目，不是ES的category_id字段
if user_profile.base_info.customerGoodsStructure:
structure_list = [MessageToDict(s) for s in user_profile.base_info.customerGoodsStructure]
logger.debug(f"{log_prefix} Customer goods structure details: {structure_list}")
for structure in user_profile.base_info.customerGoodsStructure:
if structure.categoryIds:
logger.debug(f"{log_prefix} Adding boost for category IDs in structure length {len(structure.categoryIds)} category_ids {structure.categoryIds[:10]}")
functions.append({
"filter": {
"terms": {
# 注意： user_profile.base_info.customerGoodsStructure.categoryIds 对应的是前端类目 而不是 ES 的 category_id
"sale_category_all": [x for x in structure.categoryIds]
}
},
"weight": BOOST_WEIGHTS_CONFIG["customer_structure_category_weight"]
})
boost_cnt[4] += 1 # category_functions
if structure.priceBetween:
# logger.debug(f"Adding boost for price range in structure: {structure.priceBetween}")
# not support yet
pass
# Calculate total functions count
total_functions = len(functions)
# Log boost query statistics
logger.info(f"{log_prefix} ===== ES查询提权函数统计 =====")
logger.info(f"{log_prefix} 总提权函数数量: {total_functions}")
logger.info(f"{log_prefix} 标签提权函数: {boost_cnt[0]}")
logger.info(f"{log_prefix} 新品提权函数: {boost_cnt[1]}")
logger.info(f"{log_prefix} 行为提权函数: {boost_cnt[2]}")
logger.info(f"{log_prefix} 品牌提权函数: {boost_cnt[3]}")
logger.info(f"{log_prefix} 类目提权函数: {boost_cnt[4]}")
logger.info(f"{log_prefix} 价格区间提权函数: {boost_cnt[5]}")
logger.info(f"{log_prefix} 视频提权函数: {boost_cnt[6]}")
logger.info(f"{log_prefix} 平台类目排名提权函数: {boost_cnt[7]}")
logger.info(f"{log_prefix} ===== ES查询提权函数统计结束 =====")
if not functions:
logger.debug(f"{log_prefix} No boost functions generated")
return {}
score_mode = FUNCTIONS_SCORE__SCORE_MODE__WHEN_HAS_QUERY if search_context.search_query or search_context.query else FUNCTIONS_SCORE__SCORE_MODE__WHEN_NO_QUERY
boost_query = {
"function_score": {
"functions": functions,
"score_mode": score_mode,
"boost_mode": "multiply"
}
}
# logger.debug(f"Generated boost query: {json.dumps(boost_query, ensure_ascii=False)}")
return boost_query
def get_boost_value(self, tag_id: Optional[int] = None, tag_name: Optional[str] = None, platform: Optional[str] = None) -> float:
"""
Get the boost value for a given tag ID or name.
Returns 1.0 if no boost is configured or if platform doesn't match.
Args:
tag_id: Tag ID to look up
tag_name: Tag name to look up
platform: Business platform for filtering
"""
if tag_id is not None:
for config in self.boost_configs:
if config.tag_id == tag_id:
# Check platform compatibility
if platform and config.platform != platform:
logger.debug(f"Platform mismatch for tag_id {tag_id}: requested platform {platform}, tag platform {config.platform}")
return 1.0
logger.debug(f"Found boost value {config.boost_value} for tag_id {tag_id}")
return config.boost_value
if tag_name is not None:
for config in self.boost_configs:
if config.tag_name == tag_name:
# Check platform compatibility
if platform and config.platform != platform:
logger.debug(f"Platform mismatch for tag_name {tag_name}: requested platform {platform}, tag platform {config.platform}")
return 1.0
logger.debug(f"Found boost value {config.boost_value} for tag_name {tag_name}")
return config.boost_value
logger.debug(f"No boost value found for tag_id={tag_id}, tag_name={tag_name}, platform={platform}")
return 1.0
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