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🗣 Wd提问: 高级搜索引擎

🤖 Ai回答:
高级搜索引擎技术详解

1、搜索引擎核心架构

1.1 基本工作流程

用户查询 → 查询处理 → 索引检索 → 排序算法 → 结果返回

1.2 核心组件
爬虫系统：网页抓取和索引
索引系统：倒排索引构建
查询处理器：查询解析和优化
排序引擎：相关性评分和排名
结果展示：用户界面呈现

2、高级搜索技术

2.1 深度学习在搜索中的应用

神经网络模型
python
示例：BERT用于搜索理解
from transformers import BertTokenizer, BertModel
import torch

class SearchQueryEncoder:
def __init__(self):
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.model = BertModel.from_pretrained('bert-base-uncased')

def encode_query(self, query):
inputs = self.tokenizer(query, return_tensors='pt',
padding=True, truncation=True)
with torch.no_grad():
outputs = self.model(**inputs)
return outputs.last_hidden_state.mean(dim=1)

端到端搜索系统
python
class AdvancedSearchEngine:
def __init__(self):
self.query_encoder = SearchQueryEncoder()
self.document_index = {}
self.similarity_model = None

def search(self, query, top_k=10):
查询编码
query_vector = self.query_encoder.encode_query(query)

相似度计算
similarities = []
for doc_id, doc_vector in self.document_index.items():
similarity = self.cosine_similarity(query_vector, doc_vector)
similarities.append((doc_id, similarity))

排序返回
return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]

2.2 多模态搜索

文本+图像搜索
python
class MultimodalSearchEngine:
def __init__(self):
self.text_encoder = TextEncoder()
self.image_encoder = ImageEncoder()
self.cross_modal_model = CrossModalMatcher()

def search_multimodal(self, query_text, query_image):
text_embedding = self.text_encoder.encode(query_text)
image_embedding = self.image_encoder.encode(query_image)

跨模态匹配
scores = self.cross_modal_model.match(text_embedding, image_embedding)
return scores

3、高级查询处理

3.1 查询理解与扩展

查询意图识别
python
class QueryIntentClassifier:
def __init__(self):
self.intents = {
'informational': ['what is', 'how to', 'define'],
'navigational': ['site:', 'www.'],
'transactional': ['buy', 'order', 'price']
}

def classify_intent(self, query):
query_lower = query.lower()
for intent, keywords in self.intents.items():
if any(keyword in query_lower for keyword in keywords):
return intent
return 'general'

查询扩展
python
class QueryExpander:
def __init__(self):
self.synonym_dict = load_synonyms()
self.related_terms = load_related_terms()

def expand_query(self, original_query):
expanded_terms = set([original_query])

同义词扩展
for term in original_query.split():
if term in self.synonym_dict:
expanded_terms.update(self.synonym_dict[term])

相关词扩展
for term in original_query.split():
if term in self.related_terms:
expanded_terms.update(self.related_terms[term])

return ' '.join(expanded_terms)

3.2 自然语言处理技术

实体识别与链接
python
import spacy

class NERProcessor:
def __init__(self):
self.nlp = spacy.load(en_core_web_sm)

def extract_entities(self, text):
doc = self.nlp(text)
entities = []
for ent in doc.ents:
entities.append({
'text': ent.text,
'label': ent.label_,
'start': ent.start_char,
'end': ent.end_char
})
return entities

def resolve_entities(self, entities):
resolved = []
for entity in entities:
实体消歧
resolved_entity = self.disambiguate(entity['text'])
resolved.append(resolved_entity)
return resolved

4、高级排序算法

4.1 机器学习排序

Learning to Rank (LTR)
python
from sklearn.ensemble import GradientBoostingRegressor
import numpy as np

class LTRRanker:
def __init__(self):
self.model = GradientBoostingRegressor(n_estimators=100)
self.feature_extractor = FeatureExtractor()

def train(self, training_data):
X = []
y = []

for sample in training_data:
features = self.feature_extractor.extract(sample)
X.append(features)
y.append(sample['relevance_score'])

self.model.fit(X, y)

def rank_documents(self, query, documents):
scores = []
for doc in documents:
features = self.feature_extractor.extract({
'query': query,
'document': doc
})
score = self.model.predict([features])[0]
scores.append((doc, score))

return sorted(scores, key=lambda x: x[1], reverse=True)

4.2 多维度排序

综合评分系统
python
class MultiDimensionalRanker:
def __init__(self):
self.weights = {
'relevance': 0.4,
'popularity': 0.2,
'freshness': 0.2,
'authority': 0.2
}

def calculate_combined_score(self, document, query_context):
relevance_score = self.calculate_relevance(document, query_context)
popularity_score = self.calculate_popularity(document)
freshness_score = self.calculate_freshness(document)
authority_score = self.calculate_authority(document)

combined_score = (
relevance_score * self.weights['relevance'] +
popularity_score * self.weights['popularity'] +
freshness_score * self.weights['freshness'] +
authority_score * self.weights['authority']
)

return combined_score

5、实时搜索优化

5.1 缓存策略
python
import redis
import json
from datetime import timedelta

class SmartCache:
def __init__(self):
self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
self.cache_ttl = 3600 1小时

def get_cached_result(self, query_key):
cached = self.redis_client.get(query_key)
if cached:
return json.loads(cached)
return None

def cache_result(self, query_key, result):
self.redis_client.setex(
query_key,
self.cache_ttl,
json.dumps(result)
)

def invalidate_cache(self, pattern):
keys = self.redis_client.keys(pattern)
if keys:
self.redis_client.delete(*keys)

5.2 流式搜索
python
import asyncio
import aiohttp

class StreamingSearchEngine:
def __init__(self):
self.session = aiohttp.ClientSession()

async def stream_search_results(self, query, max_results=100):
异步获取部分结果
partial_results = await self.fetch_partial_results(query, max_results)

流式返回
for result in partial_results:
yield result

后续处理
await self.process_remaining_results(query)

async def fetch_partial_results(self, query, limit):
实现具体的异步搜索逻辑
pass

6、搜索质量评估

6.1 A/B测试框架
python
class ABTestFramework:
def __init__(self):
self.experiments = {}

def run_experiment(self, experiment_name, variants, test_duration):
随机分配用户到不同变体
收集指标数据
分析统计显著性
pass

def analyze_results(self, experiment_name):
计算置信区间
进行假设检验
pass

6.2 搜索质量指标
python
class SearchQualityMetrics:
def __init__(self):
self.metrics = {}

def calculate_precision_at_k(self, relevant_docs, retrieved_docs, k):
计算Precision@K
if len(retrieved_docs) == 0:
return 0.0

relevant_at_k = len(set(relevant_docs) & set(retrieved_docs[:k]))
return relevant_at_k / min(k, len(retrieved_docs))

def calculate_ndcg(self, relevant_docs, retrieved_docs, k):
计算NDCG@K
dcg = self._calculate_dcg(relevant_docs, retrieved_docs, k)
idcg = self._calculate_idcg(relevant_docs, k)
return

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