检索优化技巧
引言
检索优化是RAG系统性能提升的关键环节。通过合理的优化策略，可以在保持检索质量的同时显著提升系统性能。本文将深入探讨各种检索优化技巧，包括索引优化、查询优化、缓存策略和系统调优等方面。
检索优化概述
检索优化的目标
检索优化的层次
- 算法层面：优化检索算法本身
- 系统层面：优化系统架构和配置
- 数据层面：优化数据存储和索引
- 应用层面：优化查询处理和结果返回
索引优化
1. 索引结构优化
向量索引优化
python
import numpy as np
from typing import List, Dict, Tuple
import faiss
class VectorIndexOptimizer:
def __init__(self, dimension: int = 384):
self.dimension = dimension
self.index = None
self.index_type = None
def create_optimized_index(self, vectors: List[List[float]],
index_type: str = 'auto') -> faiss.Index:
"""创建优化的向量索引"""
vectors_array = np.array(vectors).astype('float32')
num_vectors = len(vectors)
if index_type == 'auto':
index_type = self._select_optimal_index_type(num_vectors)
if index_type == 'flat':
# 精确搜索，适合小规模数据
self.index = faiss.IndexFlatIP(self.dimension)
elif index_type == 'ivf':
# IVF索引，适合中等规模数据
nlist = min(4096, num_vectors // 100)
quantizer = faiss.IndexFlatIP(self.dimension)
self.index = faiss.IndexIVFFlat(quantizer, self.dimension, nlist)
self.index.train(vectors_array)
elif index_type == 'hnsw':
# HNSW索引，适合大规模数据
self.index = faiss.IndexHNSWFlat(self.dimension, 32)
else:
raise ValueError(f"Unsupported index type: {index_type}")
# 添加向量到索引
self.index.add(vectors_array)
# 优化索引参数
self._optimize_index_parameters()
return self.index
def _select_optimal_index_type(self, num_vectors: int) -> str:
"""根据数据规模选择最优索引类型"""
if num_vectors < 10000:
return 'flat'
elif num_vectors < 1000000:
return 'ivf'
else:
return 'hnsw'
def _optimize_index_parameters(self):
"""优化索引参数"""
if isinstance(self.index, faiss.IndexIVFFlat):
# 优化IVF参数
self.index.nprobe = min(64, self.index.nlist // 4)
elif isinstance(self.index, faiss.IndexHNSWFlat):
# 优化HNSW参数
self.index.hnsw.efSearch = 64
self.index.hnsw.efConstruction = 200
def search_optimized(self, query_vector: List[float], k: int = 10) -> Tuple[np.ndarray, np.ndarray]:
"""优化的搜索"""
query_array = np.array([query_vector]).astype('float32')
# 执行搜索
scores, indices = self.index.search(query_array, k)
return scores[0], indices[0]倒排索引优化
python
class InvertedIndexOptimizer:
def __init__(self):
self.index = {}
self.doc_freq = {}
self.collection_size = 0
def build_optimized_index(self, documents: List[str]) -> Dict:
"""构建优化的倒排索引"""
self.collection_size = len(documents)
# 构建基础索引
for doc_id, document in enumerate(documents):
terms = self._tokenize(document)
term_freq = {}
for term in terms:
if term not in self.index:
self.index[term] = {}
self.doc_freq[term] = 0
if doc_id not in self.index[term]:
self.index[term][doc_id] = 0
self.doc_freq[term] += 1
self.index[term][doc_id] += 1
term_freq[term] = term_freq.get(term, 0) + 1
# 优化索引
self._optimize_index_structure()
return self.index
def _tokenize(self, text: str) -> List[str]:
"""分词"""
import re
# 简单的分词实现
words = re.findall(r'\b\w+\b', text.lower())
return words
def _optimize_index_structure(self):
"""优化索引结构"""
# 移除低频词
min_doc_freq = max(1, self.collection_size // 1000)
terms_to_remove = [term for term, freq in self.doc_freq.items()
if freq < min_doc_freq]
for term in terms_to_remove:
del self.index[term]
del self.doc_freq[term]
# 压缩索引
self._compress_index()
def _compress_index(self):
"""压缩索引"""
# 使用更紧凑的数据结构
for term in self.index:
doc_ids = list(self.index[term].keys())
freqs = list(self.index[term].values())
# 使用numpy数组存储
self.index[term] = {
'doc_ids': np.array(doc_ids, dtype=np.uint32),
'freqs': np.array(freqs, dtype=np.uint16)
}
def search_optimized(self, query: str, k: int = 10) -> List[Tuple[int, float]]:
"""优化的搜索"""
query_terms = self._tokenize(query)
if not query_terms:
return []
# 计算文档分数
doc_scores = {}
for term in query_terms:
if term not in self.index:
continue
# 计算TF-IDF分数
term_data = self.index[term]
doc_ids = term_data['doc_ids']
freqs = term_data['freqs']
idf = np.log(self.collection_size / self.doc_freq[term])
for doc_id, freq in zip(doc_ids, freqs):
tf = freq
tf_idf = tf * idf
if doc_id not in doc_scores:
doc_scores[doc_id] = 0
doc_scores[doc_id] += tf_idf
# 排序并返回top-k
results = [(doc_id, score) for doc_id, score in doc_scores.items()]
results.sort(key=lambda x: x[1], reverse=True)
return results[:k]2. 索引更新优化
增量索引更新
python
class IncrementalIndexUpdater:
def __init__(self, base_index):
self.base_index = base_index
self.delta_index = {}
self.deleted_docs = set()
def add_documents(self, new_documents: List[str]) -> List[int]:
"""增量添加文档"""
new_doc_ids = []
for document in new_documents:
doc_id = self._get_next_doc_id()
new_doc_ids.append(doc_id)
# 添加到增量索引
terms = self._tokenize(document)
for term in terms:
if term not in self.delta_index:
self.delta_index[term] = {}
if doc_id not in self.delta_index[term]:
self.delta_index[term][doc_id] = 0
self.delta_index[term][doc_id] += 1
return new_doc_ids
def delete_documents(self, doc_ids: List[int]):
"""删除文档"""
self.deleted_docs.update(doc_ids)
def search_with_delta(self, query: str, k: int = 10) -> List[Tuple[int, float]]:
"""使用增量索引搜索"""
# 基础索引搜索
base_results = self.base_index.search_optimized(query, k * 2)
# 增量索引搜索
delta_results = self._search_delta_index(query, k * 2)
# 合并结果
combined_results = self._merge_results(base_results, delta_results)
# 过滤已删除的文档
filtered_results = [(doc_id, score) for doc_id, score in combined_results
if doc_id not in self.deleted_docs]
return filtered_results[:k]
def _search_delta_index(self, query: str, k: int) -> List[Tuple[int, float]]:
"""搜索增量索引"""
query_terms = self._tokenize(query)
doc_scores = {}
for term in query_terms:
if term not in self.delta_index:
continue
for doc_id, freq in self.delta_index[term].items():
if doc_id not in doc_scores:
doc_scores[doc_id] = 0
doc_scores[doc_id] += freq
results = [(doc_id, score) for doc_id, score in doc_scores.items()]
results.sort(key=lambda x: x[1], reverse=True)
return results[:k]
def _merge_results(self, base_results: List[Tuple[int, float]],
delta_results: List[Tuple[int, float]]) -> List[Tuple[int, float]]:
"""合并搜索结果"""
doc_scores = {}
# 添加基础索引结果
for doc_id, score in base_results:
doc_scores[doc_id] = score
# 添加增量索引结果
for doc_id, score in delta_results:
if doc_id in doc_scores:
doc_scores[doc_id] += score
else:
doc_scores[doc_id] = score
# 排序
combined_results = [(doc_id, score) for doc_id, score in doc_scores.items()]
combined_results.sort(key=lambda x: x[1], reverse=True)
return combined_results查询优化
1. 查询预处理优化
python
class QueryPreprocessor:
def __init__(self):
self.stop_words = self._load_stop_words()
self.synonym_dict = self._load_synonyms()
def optimize_query(self, query: str) -> str:
"""优化查询"""
# 1. 清理查询
cleaned_query = self._clean_query(query)
# 2. 扩展查询
expanded_query = self._expand_query(cleaned_query)
# 3. 重写查询
rewritten_query = self._rewrite_query(expanded_query)
return rewritten_query
def _clean_query(self, query: str) -> str:
"""清理查询"""
import re
# 移除特殊字符
cleaned = re.sub(r'[^\w\s]', ' ', query)
# 移除多余空格
cleaned = re.sub(r'\s+', ' ', cleaned)
return cleaned.strip()
def _expand_query(self, query: str) -> str:
"""扩展查询"""
words = query.split()
expanded_words = []
for word in words:
expanded_words.append(word)
# 添加同义词
if word in self.synonym_dict:
expanded_words.extend(self.synonym_dict[word][:2]) # 最多添加2个同义词
return ' '.join(expanded_words)
def _rewrite_query(self, query: str) -> str:
"""重写查询"""
# 移除停用词
words = query.split()
filtered_words = [word for word in words if word not in self.stop_words]
return ' '.join(filtered_words)
def _load_stop_words(self) -> set:
"""加载停用词"""
# 简单的停用词列表
return {'的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这'}
def _load_synonyms(self) -> dict:
"""加载同义词词典"""
return {
'人工智能': ['AI', '机器学习', '深度学习'],
'算法': ['算法', '方法', '技术'],
'数据': ['信息', '资料', '数据'],
'系统': ['平台', '系统', '框架']
}2. 查询缓存优化
python
class QueryCache:
def __init__(self, cache_size: int = 10000, ttl: int = 3600):
self.cache_size = cache_size
self.ttl = ttl
self.cache = {}
self.access_times = {}
self.access_counts = {}
def get_cached_result(self, query: str) -> List[Tuple[int, float]]:
"""获取缓存结果"""
query_hash = hash(query)
current_time = time.time()
if query_hash in self.cache:
cache_time, result = self.cache[query_hash]
# 检查TTL
if current_time - cache_time < self.ttl:
self.access_times[query_hash] = current_time
self.access_counts[query_hash] = self.access_counts.get(query_hash, 0) + 1
return result
else:
# 过期，删除
del self.cache[query_hash]
del self.access_times[query_hash]
del self.access_counts[query_hash]
return None
def cache_result(self, query: str, result: List[Tuple[int, float]]):
"""缓存结果"""
query_hash = hash(query)
current_time = time.time()
# 如果缓存已满，移除最少使用的项
if len(self.cache) >= self.cache_size:
self._evict_least_used()
self.cache[query_hash] = (current_time, result)
self.access_times[query_hash] = current_time
self.access_counts[query_hash] = 1
def _evict_least_used(self):
"""移除最少使用的缓存项"""
if not self.cache:
return
# 找到最少使用的项
least_used_hash = min(self.access_counts.items(), key=lambda x: x[1])[0]
del self.cache[least_used_hash]
del self.access_times[least_used_hash]
del self.access_counts[least_used_hash]
def get_cache_stats(self) -> Dict[str, int]:
"""获取缓存统计信息"""
return {
'cache_size': len(self.cache),
'max_size': self.cache_size,
'hit_rate': self._calculate_hit_rate()
}
def _calculate_hit_rate(self) -> float:
"""计算命中率"""
total_accesses = sum(self.access_counts.values())
if total_accesses == 0:
return 0.0
return len(self.cache) / total_accesses3. 查询路由优化
python
class QueryRouter:
def __init__(self, retrievers: Dict[str, object]):
self.retrievers = retrievers
self.routing_rules = self._build_routing_rules()
def route_query(self, query: str) -> str:
"""路由查询到合适的检索器"""
query_features = self._extract_query_features(query)
# 应用路由规则
for rule in self.routing_rules:
if self._matches_rule(query_features, rule):
return rule['retriever']
# 默认路由
return 'default'
def _extract_query_features(self, query: str) -> Dict[str, any]:
"""提取查询特征"""
features = {}
# 查询长度
features['length'] = len(query.split())
# 查询类型
features['type'] = self._classify_query_type(query)
# 语言
features['language'] = self._detect_language(query)
# 专业术语
features['technical_terms'] = self._count_technical_terms(query)
return features
def _classify_query_type(self, query: str) -> str:
"""分类查询类型"""
if '?' in query or query.startswith(('什么', '如何', '为什么')):
return 'question'
elif len(query.split()) < 3:
return 'keyword'
else:
return 'semantic'
def _detect_language(self, query: str) -> str:
"""检测语言"""
chinese_chars = sum(1 for char in query if '\u4e00' <= char <= '\u9fff')
if chinese_chars > len(query) * 0.3:
return 'chinese'
else:
return 'english'
def _count_technical_terms(self, query: str) -> int:
"""计算专业术语数量"""
technical_terms = ['算法', '机器学习', '深度学习', '人工智能', '数据']
return sum(1 for term in technical_terms if term in query)
def _build_routing_rules(self) -> List[Dict]:
"""构建路由规则"""
return [
{
'condition': lambda features: features['type'] == 'question',
'retriever': 'semantic'
},
{
'condition': lambda features: features['type'] == 'keyword',
'retriever': 'keyword'
},
{
'condition': lambda features: features['language'] == 'chinese',
'retriever': 'chinese'
},
{
'condition': lambda features: features['technical_terms'] > 0,
'retriever': 'technical'
}
]
def _matches_rule(self, features: Dict, rule: Dict) -> bool:
"""检查是否匹配规则"""
try:
return rule['condition'](features)
except:
return False缓存策略优化
1. 多级缓存
python
class MultiLevelCache:
def __init__(self, l1_size: int = 1000, l2_size: int = 10000):
self.l1_cache = {} # 内存缓存
self.l2_cache = {} # 磁盘缓存
self.l1_size = l1_size
self.l2_size = l2_size
self.l1_access_times = {}
self.l2_access_times = {}
def get(self, key: str) -> any:
"""获取缓存数据"""
# 首先检查L1缓存
if key in self.l1_cache:
self.l1_access_times[key] = time.time()
return self.l1_cache[key]
# 然后检查L2缓存
if key in self.l2_cache:
self.l2_access_times[key] = time.time()
# 提升到L1缓存
self._promote_to_l1(key, self.l2_cache[key])
return self.l2_cache[key]
return None
def put(self, key: str, value: any):
"""存储缓存数据"""
# 存储到L1缓存
self._put_to_l1(key, value)
def _put_to_l1(self, key: str, value: any):
"""存储到L1缓存"""
# 如果L1缓存已满，移除最少使用的项
if len(self.l1_cache) >= self.l1_size:
self._evict_from_l1()
self.l1_cache[key] = value
self.l1_access_times[key] = time.time()
def _promote_to_l1(self, key: str, value: any):
"""提升到L1缓存"""
# 如果L1缓存已满，移除最少使用的项
if len(self.l1_cache) >= self.l1_size:
self._evict_from_l1()
self.l1_cache[key] = value
self.l1_access_times[key] = time.time()
# 从L2缓存中移除
if key in self.l2_cache:
del self.l2_cache[key]
del self.l2_access_times[key]
def _evict_from_l1(self):
"""从L1缓存中移除最少使用的项"""
if not self.l1_cache:
return
# 找到最少使用的项
least_used_key = min(self.l1_access_times.items(), key=lambda x: x[1])[0]
# 移动到L2缓存
self._move_to_l2(least_used_key, self.l1_cache[least_used_key])
# 从L1缓存中移除
del self.l1_cache[least_used_key]
del self.l1_access_times[least_used_key]
def _move_to_l2(self, key: str, value: any):
"""移动到L2缓存"""
# 如果L2缓存已满，移除最少使用的项
if len(self.l2_cache) >= self.l2_size:
self._evict_from_l2()
self.l2_cache[key] = value
self.l2_access_times[key] = time.time()
def _evict_from_l2(self):
"""从L2缓存中移除最少使用的项"""
if not self.l2_cache:
return
# 找到最少使用的项
least_used_key = min(self.l2_access_times.items(), key=lambda x: x[1])[0]
# 从L2缓存中移除
del self.l2_cache[least_used_key]
del self.l2_access_times[least_used_key]2. 预取缓存
python
class PrefetchCache:
def __init__(self, cache_size: int = 1000, prefetch_size: int = 10):
self.cache_size = cache_size
self.prefetch_size = prefetch_size
self.cache = {}
self.access_patterns = {}
self.prefetch_queue = []
def get(self, key: str) -> any:
"""获取缓存数据"""
if key in self.cache:
# 记录访问模式
self._record_access_pattern(key)
# 触发预取
self._trigger_prefetch(key)
return self.cache[key]
return None
def put(self, key: str, value: any):
"""存储缓存数据"""
# 如果缓存已满，移除最少使用的项
if len(self.cache) >= self.cache_size:
self._evict_least_used()
self.cache[key] = value
def _record_access_pattern(self, key: str):
"""记录访问模式"""
current_time = time.time()
if key not in self.access_patterns:
self.access_patterns[key] = []
self.access_patterns[key].append(current_time)
# 只保留最近的访问记录
if len(self.access_patterns[key]) > 100:
self.access_patterns[key] = self.access_patterns[key][-100:]
def _trigger_prefetch(self, key: str):
"""触发预取"""
# 分析访问模式，预测下一个可能访问的键
predicted_keys = self._predict_next_keys(key)
# 添加到预取队列
for pred_key in predicted_keys:
if pred_key not in self.cache and pred_key not in self.prefetch_queue:
self.prefetch_queue.append(pred_key)
def _predict_next_keys(self, key: str) -> List[str]:
"""预测下一个可能访问的键"""
# 简单的预测逻辑：基于历史访问模式
if key in self.access_patterns:
# 分析访问时间间隔
access_times = self.access_patterns[key]
if len(access_times) > 1:
intervals = [access_times[i] - access_times[i-1] for i in range(1, len(access_times))]
avg_interval = sum(intervals) / len(intervals)
# 如果访问间隔较短，预测相关键
if avg_interval < 60: # 1分钟内
return self._get_related_keys(key)
return []
def _get_related_keys(self, key: str) -> List[str]:
"""获取相关键"""
# 简单的相关键生成逻辑
related_keys = []
# 基于键的相似性生成相关键
if 'query' in key:
# 生成相似的查询键
base_query = key.replace('query_', '')
for i in range(3):
related_keys.append(f'query_{base_query}_{i}')
return related_keys[:self.prefetch_size]
def _evict_least_used(self):
"""移除最少使用的项"""
if not self.cache:
return
# 找到最少使用的项
least_used_key = min(self.access_patterns.items(),
key=lambda x: len(x[1]) if x[0] in self.cache else float('inf'))[0]
if least_used_key in self.cache:
del self.cache[least_used_key]系统调优
1. 并发优化
python
import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict, Any
class ConcurrentRetriever:
def __init__(self, max_workers: int = 10):
self.max_workers = max_workers
self.executor = ThreadPoolExecutor(max_workers=max_workers)
self.semaphore = asyncio.Semaphore(max_workers)
async def concurrent_search(self, queries: List[str],
documents: List[str]) -> List[List[Tuple[int, float]]]:
"""并发搜索"""
tasks = []
for query in queries:
task = self._search_with_semaphore(query, documents)
tasks.append(task)
results = await asyncio.gather(*tasks)
return results
async def _search_with_semaphore(self, query: str,
documents: List[str]) -> List[Tuple[int, float]]:
"""使用信号量控制并发"""
async with self.semaphore:
return await self._async_search(query, documents)
async def _async_search(self, query: str,
documents: List[str]) -> List[Tuple[int, float]]:
"""异步搜索"""
# 在线程池中执行CPU密集型任务
loop = asyncio.get_event_loop()
result = await loop.run_in_executor(
self.executor,
self._cpu_intensive_search,
query,
documents
)
return result
def _cpu_intensive_search(self, query: str,
documents: List[str]) -> List[Tuple[int, float]]:
"""CPU密集型搜索"""
# 实际的搜索逻辑
results = []
for i, doc in enumerate(documents):
# 计算相似度
similarity = self._calculate_similarity(query, doc)
results.append((i, similarity))
# 排序
results.sort(key=lambda x: x[1], reverse=True)
return results
def _calculate_similarity(self, query: str, document: str) -> float:
"""计算相似度"""
# 简单的相似度计算
query_words = set(query.lower().split())
doc_words = set(document.lower().split())
intersection = query_words.intersection(doc_words)
union = query_words.union(doc_words)
if len(union) == 0:
return 0.0
return len(intersection) / len(union)2. 内存优化
python
class MemoryOptimizedRetriever:
def __init__(self, max_memory_mb: int = 1024):
self.max_memory_mb = max_memory_mb
self.current_memory_mb = 0
self.memory_usage = {}
def optimize_memory_usage(self, vectors: List[List[float]]) -> np.ndarray:
"""优化内存使用"""
vectors_array = np.array(vectors)
# 使用更紧凑的数据类型
if vectors_array.dtype == np.float64:
vectors_array = vectors_array.astype(np.float32)
# 压缩稀疏向量
if self._is_sparse(vectors_array):
vectors_array = self._compress_sparse_vectors(vectors_array)
# 分块处理
if vectors_array.nbytes > self.max_memory_mb * 1024 * 1024:
vectors_array = self._chunk_vectors(vectors_array)
return vectors_array
def _is_sparse(self, vectors: np.ndarray) -> bool:
"""检查是否为稀疏向量"""
zero_count = np.sum(vectors == 0)
total_elements = vectors.size
sparsity = zero_count / total_elements
return sparsity > 0.5
def _compress_sparse_vectors(self, vectors: np.ndarray) -> np.ndarray:
"""压缩稀疏向量"""
from scipy.sparse import csr_matrix
# 转换为稀疏矩阵
sparse_vectors = csr_matrix(vectors)
return sparse_vectors
def _chunk_vectors(self, vectors: np.ndarray) -> np.ndarray:
"""分块处理向量"""
chunk_size = self.max_memory_mb * 1024 * 1024 // vectors.itemsize
num_chunks = (len(vectors) + chunk_size - 1) // chunk_size
chunks = []
for i in range(num_chunks):
start_idx = i * chunk_size
end_idx = min((i + 1) * chunk_size, len(vectors))
chunk = vectors[start_idx:end_idx]
chunks.append(chunk)
return chunks性能监控
1. 检索性能监控
python
import time
import psutil
from typing import Dict, List
import logging
class RetrievalPerformanceMonitor:
def __init__(self):
self.metrics = {}
self.logger = logging.getLogger(__name__)
def monitor_search_performance(self, search_func, query: str,
documents: List[str]) -> Dict[str, float]:
"""监控搜索性能"""
# 记录开始时间
start_time = time.time()
# 记录开始时的系统资源
start_memory = psutil.Process().memory_info().rss / 1024 / 1024
start_cpu = psutil.Process().cpu_percent()
# 执行搜索
try:
results = search_func(query, documents)
success = True
except Exception as e:
self.logger.error(f"Search failed: {e}")
results = []
success = False
# 记录结束时间
end_time = time.time()
# 记录结束时的系统资源
end_memory = psutil.Process().memory_info().rss / 1024 / 1024
end_cpu = psutil.Process().cpu_percent()
# 计算性能指标
metrics = {
'search_time': end_time - start_time,
'memory_usage': end_memory - start_memory,
'cpu_usage': end_cpu - start_cpu,
'result_count': len(results),
'success': success
}
# 记录指标
self._record_metrics(metrics)
return metrics
def _record_metrics(self, metrics: Dict[str, float]):
"""记录性能指标"""
for key, value in metrics.items():
if key not in self.metrics:
self.metrics[key] = []
self.metrics[key].append(value)
def get_performance_summary(self) -> Dict[str, float]:
"""获取性能摘要"""
summary = {}
for key, values in self.metrics.items():
if values:
summary[f'{key}_avg'] = sum(values) / len(values)
summary[f'{key}_max'] = max(values)
summary[f'{key}_min'] = min(values)
return summary
def detect_performance_issues(self) -> List[str]:
"""检测性能问题"""
issues = []
# 检查搜索时间
if 'search_time' in self.metrics:
avg_time = sum(self.metrics['search_time']) / len(self.metrics['search_time'])
if avg_time > 1.0: # 超过1秒
issues.append(f"Average search time too high: {avg_time:.2f}s")
# 检查内存使用
if 'memory_usage' in self.metrics:
avg_memory = sum(self.metrics['memory_usage']) / len(self.metrics['memory_usage'])
if avg_memory > 100: # 超过100MB
issues.append(f"Average memory usage too high: {avg_memory:.2f}MB")
# 检查成功率
if 'success' in self.metrics:
success_rate = sum(self.metrics['success']) / len(self.metrics['success'])
if success_rate < 0.95: # 成功率低于95%
issues.append(f"Success rate too low: {success_rate:.2%}")
return issues最佳实践
1. 优化策略选择
python
def select_optimization_strategy(requirements: dict) -> List[str]:
"""选择优化策略"""
strategies = []
if requirements['latency'] == 'critical':
strategies.extend(['query_cache', 'index_optimization', 'concurrent_processing'])
if requirements['memory'] == 'limited':
strategies.extend(['memory_optimization', 'chunked_processing'])
if requirements['accuracy'] == 'high':
strategies.extend(['reranking', 'query_expansion'])
if requirements['scalability'] == 'high':
strategies.extend(['distributed_indexing', 'load_balancing'])
return strategies2. 性能调优指南
python
class PerformanceTuningGuide:
def __init__(self):
self.tuning_recommendations = {}
def get_tuning_recommendations(self, performance_metrics: Dict[str, float]) -> List[str]:
"""获取调优建议"""
recommendations = []
# 基于性能指标提供建议
if performance_metrics.get('search_time', 0) > 1.0:
recommendations.append("Consider using faster indexing algorithms like HNSW")
recommendations.append("Implement query caching to reduce repeated computations")
if performance_metrics.get('memory_usage', 0) > 500:
recommendations.append("Use memory-efficient data structures")
recommendations.append("Implement vector compression techniques")
if performance_metrics.get('cpu_usage', 0) > 80:
recommendations.append("Optimize CPU-intensive operations")
recommendations.append("Consider using GPU acceleration")
return recommendations总结
检索优化是RAG系统性能提升的关键环节。本文介绍了索引优化、查询优化、缓存策略和系统调优等多个方面的优化技巧。
关键要点：
- 索引优化：选择合适的索引结构和参数
- 查询优化：预处理查询、缓存结果、路由查询
- 缓存策略：多级缓存、预取缓存
- 系统调优：并发优化、内存优化、性能监控
通过合理应用这些优化技巧，可以显著提升RAG系统的性能和用户体验。
下一步学习建议：
- 阅读《生成与优化》系列，了解RAG系统的生成阶段优化
- 实践不同的优化技巧，测量性能提升效果
- 关注检索优化技术的最新发展和创新方案