性能优化策略
引言
RAG系统的性能优化是确保系统高效运行的关键。随着数据量的增长和用户需求的提升，性能优化变得越来越重要。本文将深入探讨RAG系统的性能优化策略，包括检索优化、生成优化、缓存策略、并发处理和系统调优等方面。
性能优化概述
什么是RAG系统性能优化
RAG系统性能优化是指通过技术手段提升系统的响应速度、吞吐量和资源利用率，确保系统能够高效处理用户查询和生成回答。
性能优化的目标
性能优化的挑战
- 检索性能：大规模向量检索的延迟
- 生成性能：LLM调用的延迟和成本
- 并发处理：多用户同时查询的处理
- 资源限制：CPU、内存、存储的限制
- 数据增长：随着数据量增长的性能下降
检索性能优化
1. 向量索引优化
索引结构优化
python
class VectorIndexOptimizer:
def __init__(self):
self.index_types = {
'flat': FlatIndex(),
'ivf': IVFIndex(),
'hnsw': HNSWIndex(),
'pq': PQIndex()
}
self.current_index = None
def optimize_index(self, vectors: List[List[float]],
query_pattern: str = 'random') -> str:
"""优化向量索引"""
# 根据数据特征选择最佳索引类型
if len(vectors) < 1000:
# 小数据集使用Flat索引
index_type = 'flat'
elif query_pattern == 'random':
# 随机查询使用IVF索引
index_type = 'ivf'
elif query_pattern == 'similar':
# 相似查询使用HNSW索引
index_type = 'hnsw'
else:
# 默认使用PQ索引
index_type = 'pq'
# 创建优化索引
self.current_index = self.index_types[index_type]
self.current_index.build(vectors)
return index_type
def search_optimized(self, query_vector: List[float],
top_k: int = 5) -> List[Dict[str, any]]:
"""优化搜索"""
if self.current_index is None:
raise Exception('索引未初始化')
# 使用优化索引搜索
results = self.current_index.search(query_vector, top_k)
return results
class FlatIndex:
def __init__(self):
self.vectors = []
self.ids = []
def build(self, vectors: List[List[float]]):
"""构建Flat索引"""
self.vectors = vectors
self.ids = list(range(len(vectors)))
def search(self, query_vector: List[float], top_k: int) -> List[Dict[str, any]]:
"""Flat搜索"""
similarities = []
for i, vector in enumerate(self.vectors):
similarity = self._calculate_similarity(query_vector, vector)
similarities.append((i, similarity))
# 排序并返回top_k
similarities.sort(key=lambda x: x[1], reverse=True)
results = []
for i, (idx, similarity) in enumerate(similarities[:top_k]):
results.append({
'id': self.ids[idx],
'similarity': similarity,
'rank': i + 1
})
return results
def _calculate_similarity(self, vec1: List[float], vec2: List[float]) -> float:
"""计算相似度"""
import numpy as np
vec1_np = np.array(vec1)
vec2_np = np.array(vec2)
# 计算余弦相似度
dot_product = np.dot(vec1_np, vec2_np)
norm1 = np.linalg.norm(vec1_np)
norm2 = np.linalg.norm(vec2_np)
if norm1 == 0 or norm2 == 0:
return 0.0
return dot_product / (norm1 * norm2)
class IVFIndex:
def __init__(self, n_clusters: int = 100):
self.n_clusters = n_clusters
self.clusters = []
self.cluster_centers = []
self.vectors = []
self.ids = []
def build(self, vectors: List[List[float]]):
"""构建IVF索引"""
self.vectors = vectors
self.ids = list(range(len(vectors)))
# 使用K-means聚类
self._cluster_vectors(vectors)
def _cluster_vectors(self, vectors: List[List[float]]):
"""聚类向量"""
import numpy as np
from sklearn.cluster import KMeans
vectors_np = np.array(vectors)
# 执行K-means聚类
kmeans = KMeans(n_clusters=self.n_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(vectors_np)
# 存储聚类中心和向量分配
self.cluster_centers = kmeans.cluster_centers_
self.clusters = [[] for _ in range(self.n_clusters)]
for i, label in enumerate(cluster_labels):
self.clusters[label].append(i)
def search(self, query_vector: List[float], top_k: int) -> List[Dict[str, any]]:
"""IVF搜索"""
import numpy as np
query_np = np.array(query_vector)
# 找到最相关的聚类
cluster_similarities = []
for i, center in enumerate(self.cluster_centers):
similarity = self._calculate_similarity(query_vector, center.tolist())
cluster_similarities.append((i, similarity))
# 排序聚类
cluster_similarities.sort(key=lambda x: x[1], reverse=True)
# 在相关聚类中搜索
candidates = []
for cluster_idx, _ in cluster_similarities[:5]: # 搜索前5个聚类
for vector_idx in self.clusters[cluster_idx]:
vector = self.vectors[vector_idx]
similarity = self._calculate_similarity(query_vector, vector)
candidates.append((vector_idx, similarity))
# 排序并返回top_k
candidates.sort(key=lambda x: x[1], reverse=True)
results = []
for i, (idx, similarity) in enumerate(candidates[:top_k]):
results.append({
'id': self.ids[idx],
'similarity': similarity,
'rank': i + 1
})
return results
def _calculate_similarity(self, vec1: List[float], vec2: List[float]) -> float:
"""计算相似度"""
import numpy as np
vec1_np = np.array(vec1)
vec2_np = np.array(vec2)
dot_product = np.dot(vec1_np, vec2_np)
norm1 = np.linalg.norm(vec1_np)
norm2 = np.linalg.norm(vec2_np)
if norm1 == 0 or norm2 == 0:
return 0.0
return dot_product / (norm1 * norm2)
class HNSWIndex:
def __init__(self, m: int = 16, ef_construction: int = 200):
self.m = m
self.ef_construction = ef_construction
self.ef_search = 50
self.vectors = []
self.ids = []
self.graph = {}
def build(self, vectors: List[List[float]]):
"""构建HNSW索引"""
self.vectors = vectors
self.ids = list(range(len(vectors)))
# 构建HNSW图
self._build_hnsw_graph(vectors)
def _build_hnsw_graph(self, vectors: List[List[float]]):
"""构建HNSW图"""
# 简化的HNSW实现
# 实际应用中可以使用专业的HNSW库
self.graph = {}
for i, vector in enumerate(vectors):
self.graph[i] = []
# 找到最近的邻居
neighbors = self._find_nearest_neighbors(vector, vectors, self.m)
for neighbor_idx in neighbors:
if neighbor_idx != i:
self.graph[i].append(neighbor_idx)
if neighbor_idx not in self.graph:
self.graph[neighbor_idx] = []
self.graph[neighbor_idx].append(i)
def _find_nearest_neighbors(self, query_vector: List[float],
vectors: List[List[float]], k: int) -> List[int]:
"""找到最近的邻居"""
similarities = []
for i, vector in enumerate(vectors):
similarity = self._calculate_similarity(query_vector, vector)
similarities.append((i, similarity))
similarities.sort(key=lambda x: x[1], reverse=True)
return [idx for idx, _ in similarities[:k]]
def search(self, query_vector: List[float], top_k: int) -> List[Dict[str, any]]:
"""HNSW搜索"""
# 简化的HNSW搜索
# 实际应用中可以使用专业的HNSW库
# 从随机节点开始搜索
import random
start_node = random.randint(0, len(self.vectors) - 1)
# 使用贪心搜索
current_node = start_node
visited = set()
candidates = []
while current_node not in visited and len(candidates) < self.ef_search:
visited.add(current_node)
# 计算当前节点的相似度
similarity = self._calculate_similarity(
query_vector, self.vectors[current_node]
)
candidates.append((current_node, similarity))
# 移动到最相似的邻居
best_neighbor = None
best_similarity = -1
for neighbor in self.graph.get(current_node, []):
if neighbor not in visited:
neighbor_similarity = self._calculate_similarity(
query_vector, self.vectors[neighbor]
)
if neighbor_similarity > best_similarity:
best_similarity = neighbor_similarity
best_neighbor = neighbor
if best_neighbor is not None:
current_node = best_neighbor
else:
break
# 排序并返回top_k
candidates.sort(key=lambda x: x[1], reverse=True)
results = []
for i, (idx, similarity) in enumerate(candidates[:top_k]):
results.append({
'id': self.ids[idx],
'similarity': similarity,
'rank': i + 1
})
return results
def _calculate_similarity(self, vec1: List[float], vec2: List[float]) -> float:
"""计算相似度"""
import numpy as np
vec1_np = np.array(vec1)
vec2_np = np.array(vec2)
dot_product = np.dot(vec1_np, vec2_np)
norm1 = np.linalg.norm(vec1_np)
norm2 = np.linalg.norm(vec2_np)
if norm1 == 0 or norm2 == 0:
return 0.0
return dot_product / (norm1 * norm2)
class PQIndex:
def __init__(self, n_subvectors: int = 8):
self.n_subvectors = n_subvectors
self.codebooks = []
self.codes = []
self.vectors = []
self.ids = []
def build(self, vectors: List[List[float]]):
"""构建PQ索引"""
self.vectors = vectors
self.ids = list(range(len(vectors)))
# 训练PQ码本
self._train_codebooks(vectors)
# 编码向量
self._encode_vectors(vectors)
def _train_codebooks(self, vectors: List[List[float]]):
"""训练PQ码本"""
import numpy as np
from sklearn.cluster import KMeans
vectors_np = np.array(vectors)
vector_dim = vectors_np.shape[1]
subvector_dim = vector_dim // self.n_subvectors
self.codebooks = []
for i in range(self.n_subvectors):
start_idx = i * subvector_dim
end_idx = start_idx + subvector_dim
# 提取子向量
subvectors = vectors_np[:, start_idx:end_idx]
# 训练码本
kmeans = KMeans(n_clusters=256, random_state=42)
kmeans.fit(subvectors)
self.codebooks.append(kmeans.cluster_centers_)
def _encode_vectors(self, vectors: List[List[float]]):
"""编码向量"""
import numpy as np
vectors_np = np.array(vectors)
vector_dim = vectors_np.shape[1]
subvector_dim = vector_dim // self.n_subvectors
self.codes = []
for vector in vectors_np:
code = []
for i in range(self.n_subvectors):
start_idx = i * subvector_dim
end_idx = start_idx + subvector_dim
subvector = vector[start_idx:end_idx]
# 找到最近的码本中心
distances = np.linalg.norm(
self.codebooks[i] - subvector, axis=1
)
closest_center = np.argmin(distances)
code.append(closest_center)
self.codes.append(code)
def search(self, query_vector: List[float], top_k: int) -> List[Dict[str, any]]:
"""PQ搜索"""
import numpy as np
# 编码查询向量
query_code = self._encode_query(query_vector)
# 计算距离
distances = []
for i, code in enumerate(self.codes):
distance = self._calculate_pq_distance(query_code, code)
distances.append((i, distance))
# 排序并返回top_k
distances.sort(key=lambda x: x[1])
results = []
for i, (idx, distance) in enumerate(distances[:top_k]):
results.append({
'id': self.ids[idx],
'similarity': 1.0 / (1.0 + distance), # 转换为相似度
'rank': i + 1
})
return results
def _encode_query(self, query_vector: List[float]) -> List[int]:
"""编码查询向量"""
import numpy as np
query_np = np.array(query_vector)
vector_dim = query_np.shape[0]
subvector_dim = vector_dim // self.n_subvectors
code = []
for i in range(self.n_subvectors):
start_idx = i * subvector_dim
end_idx = start_idx + subvector_dim
subvector = query_np[start_idx:end_idx]
# 找到最近的码本中心
distances = np.linalg.norm(
self.codebooks[i] - subvector, axis=1
)
closest_center = np.argmin(distances)
code.append(closest_center)
return code
def _calculate_pq_distance(self, query_code: List[int],
vector_code: List[int]) -> float:
"""计算PQ距离"""
distance = 0.0
for i in range(len(query_code)):
# 计算子向量距离
subvector_distance = np.linalg.norm(
self.codebooks[i][query_code[i]] -
self.codebooks[i][vector_code[i]]
)
distance += subvector_distance ** 2
return np.sqrt(distance)2. 检索策略优化
多阶段检索
python
class MultiStageRetrievalOptimizer:
def __init__(self):
self.stages = [
CoarseRetrievalStage(),
FineRetrievalStage(),
RerankingStage()
]
self.cache = RetrievalCache()
def optimize_retrieval(self, query: str, top_k: int = 5) -> List[Dict[str, any]]:
"""多阶段检索优化"""
# 检查缓存
cached_result = self.cache.get(query, top_k)
if cached_result:
return cached_result
# 执行多阶段检索
candidates = []
# 第一阶段：粗检索
coarse_results = self.stages[0].retrieve(query, top_k * 10)
candidates.extend(coarse_results)
# 第二阶段：精检索
fine_results = self.stages[1].retrieve(query, candidates, top_k * 3)
candidates = fine_results
# 第三阶段：重排序
final_results = self.stages[2].rerank(query, candidates, top_k)
# 缓存结果
self.cache.set(query, top_k, final_results)
return final_results
class CoarseRetrievalStage:
def __init__(self):
self.index = None
self.vectorizer = Vectorizer()
def retrieve(self, query: str, top_k: int) -> List[Dict[str, any]]:
"""粗检索"""
# 向量化查询
query_vector = self.vectorizer.vectorize_query(query)
# 使用快速索引检索
results = self.index.search(query_vector, top_k)
return results
class FineRetrievalStage:
def __init__(self):
self.vectorizer = Vectorizer()
self.similarity_calculator = SimilarityCalculator()
def retrieve(self, query: str, candidates: List[Dict[str, any]],
top_k: int) -> List[Dict[str, any]]:
"""精检索"""
# 向量化查询
query_vector = self.vectorizer.vectorize_query(query)
# 重新计算相似度
refined_results = []
for candidate in candidates:
# 获取文档向量
doc_vector = self.vectorizer.vectorize_document(candidate['document'])
# 计算精确相似度
similarity = self.similarity_calculator.calculate(
query_vector, doc_vector
)
refined_results.append({
'document': candidate['document'],
'similarity': similarity,
'doc_id': candidate['doc_id']
})
# 排序并返回top_k
refined_results.sort(key=lambda x: x['similarity'], reverse=True)
return refined_results[:top_k]
class RerankingStage:
def __init__(self):
self.reranker = CrossEncoderReranker()
self.feature_extractor = FeatureExtractor()
def rerank(self, query: str, candidates: List[Dict[str, any]],
top_k: int) -> List[Dict[str, any]]:
"""重排序"""
# 提取特征
features = []
for candidate in candidates:
feature = self.feature_extractor.extract_features(
query, candidate['document']
)
features.append(feature)
# 重排序
reranked_results = self.reranker.rerank(query, candidates, features)
return reranked_results[:top_k]
class RetrievalCache:
def __init__(self, max_size: int = 1000):
self.cache = {}
self.max_size = max_size
self.access_times = {}
def get(self, query: str, top_k: int) -> List[Dict[str, any]]:
"""获取缓存结果"""
key = f"{query}_{top_k}"
if key in self.cache:
# 更新访问时间
self.access_times[key] = time.time()
return self.cache[key]
return None
def set(self, query: str, top_k: int, results: List[Dict[str, any]]):
"""设置缓存结果"""
key = f"{query}_{top_k}"
# 检查缓存大小
if len(self.cache) >= self.max_size:
self._evict_oldest()
# 存储结果
self.cache[key] = results
self.access_times[key] = time.time()
def _evict_oldest(self):
"""驱逐最旧的缓存项"""
oldest_key = min(self.access_times.keys(), key=lambda k: self.access_times[k])
del self.cache[oldest_key]
del self.access_times[oldest_key]3. 并行检索优化
并行处理
python
import asyncio
import concurrent.futures
from typing import List, Dict, Any
class ParallelRetrievalOptimizer:
def __init__(self, max_workers: int = 4):
self.max_workers = max_workers
self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)
def parallel_retrieve(self, queries: List[str], top_k: int = 5) -> List[List[Dict[str, any]]]:
"""并行检索多个查询"""
# 提交所有任务
futures = []
for query in queries:
future = self.executor.submit(self._retrieve_single, query, top_k)
futures.append(future)
# 收集结果
results = []
for future in concurrent.futures.as_completed(futures):
try:
result = future.result()
results.append(result)
except Exception as e:
print(f"检索失败: {e}")
results.append([])
return results
def _retrieve_single(self, query: str, top_k: int) -> List[Dict[str, any]]:
"""检索单个查询"""
# 实现单个查询的检索逻辑
pass
async def async_parallel_retrieve(self, queries: List[str],
top_k: int = 5) -> List[List[Dict[str, any]]]:
"""异步并行检索"""
# 创建异步任务
tasks = []
for query in queries:
task = asyncio.create_task(self._async_retrieve_single(query, top_k))
tasks.append(task)
# 等待所有任务完成
results = await asyncio.gather(*tasks, return_exceptions=True)
# 处理异常
processed_results = []
for result in results:
if isinstance(result, Exception):
print(f"异步检索失败: {result}")
processed_results.append([])
else:
processed_results.append(result)
return processed_results
async def _async_retrieve_single(self, query: str, top_k: int) -> List[Dict[str, any]]:
"""异步检索单个查询"""
# 实现异步单个查询的检索逻辑
pass
class DistributedRetrievalOptimizer:
def __init__(self, nodes: List[str]):
self.nodes = nodes
self.load_balancer = LoadBalancer()
self.client = DistributedClient()
def distributed_retrieve(self, query: str, top_k: int = 5) -> List[Dict[str, any]]:
"""分布式检索"""
# 选择节点
selected_nodes = self.load_balancer.select_nodes(self.nodes, 3)
# 并行检索
futures = []
for node in selected_nodes:
future = self.client.retrieve_async(node, query, top_k)
futures.append(future)
# 收集结果
results = []
for future in futures:
try:
result = future.result()
results.extend(result)
except Exception as e:
print(f"分布式检索失败: {e}")
# 合并和排序结果
merged_results = self._merge_results(results, top_k)
return merged_results
def _merge_results(self, results: List[List[Dict[str, any]]],
top_k: int) -> List[Dict[str, any]]:
"""合并结果"""
# 合并所有结果
all_results = []
for result_list in results:
all_results.extend(result_list)
# 去重
unique_results = {}
for result in all_results:
doc_id = result['doc_id']
if doc_id not in unique_results or result['similarity'] > unique_results[doc_id]['similarity']:
unique_results[doc_id] = result
# 排序并返回top_k
sorted_results = sorted(unique_results.values(),
key=lambda x: x['similarity'], reverse=True)
return sorted_results[:top_k]
class LoadBalancer:
def __init__(self):
self.node_weights = {}
self.node_loads = {}
def select_nodes(self, nodes: List[str], count: int) -> List[str]:
"""选择节点"""
# 根据负载选择节点
sorted_nodes = sorted(nodes, key=lambda x: self.node_loads.get(x, 0))
return sorted_nodes[:count]
def update_load(self, node: str, load: float):
"""更新节点负载"""
self.node_loads[node] = load
class DistributedClient:
def __init__(self):
self.http_client = HttpClient()
def retrieve_async(self, node: str, query: str, top_k: int):
"""异步检索"""
# 实现异步HTTP请求
pass生成性能优化
1. LLM调用优化
批量处理
python
class LLMCallOptimizer:
def __init__(self):
self.batch_size = 8
self.request_queue = asyncio.Queue()
self.response_cache = ResponseCache()
async def batch_generate(self, requests: List[Dict[str, any]]) -> List[Dict[str, any]]:
"""批量生成"""
# 检查缓存
cached_responses = []
uncached_requests = []
for request in requests:
cached_response = self.response_cache.get(request)
if cached_response:
cached_responses.append(cached_response)
else:
uncached_requests.append(request)
# 批量处理未缓存的请求
if uncached_requests:
batch_responses = await self._process_batch(uncached_requests)
# 缓存响应
for request, response in zip(uncached_requests, batch_responses):
self.response_cache.set(request, response)
# 合并结果
all_responses = cached_responses + batch_responses
else:
all_responses = cached_responses
return all_responses
async def _process_batch(self, requests: List[Dict[str, any]]) -> List[Dict[str, any]]:
"""处理批量请求"""
# 分批处理
batches = [requests[i:i + self.batch_size]
for i in range(0, len(requests), self.batch_size)]
all_responses = []
for batch in batches:
batch_response = await self._call_llm_batch(batch)
all_responses.extend(batch_response)
return all_responses
async def _call_llm_batch(self, batch: List[Dict[str, any]]) -> List[Dict[str, any]]:
"""调用LLM批量处理"""
# 实现LLM批量调用
pass
class ResponseCache:
def __init__(self, max_size: int = 1000):
self.cache = {}
self.max_size = max_size
self.access_times = {}
def get(self, request: Dict[str, any]) -> Dict[str, any]:
"""获取缓存响应"""
key = self._generate_key(request)
if key in self.cache:
self.access_times[key] = time.time()
return self.cache[key]
return None
def set(self, request: Dict[str, any], response: Dict[str, any]):
"""设置缓存响应"""
key = self._generate_key(request)
if len(self.cache) >= self.max_size:
self._evict_oldest()
self.cache[key] = response
self.access_times[key] = time.time()
def _generate_key(self, request: Dict[str, any]) -> str:
"""生成缓存键"""
# 基于请求内容生成键
import hashlib
content = json.dumps(request, sort_keys=True)
return hashlib.md5(content.encode()).hexdigest()
def _evict_oldest(self):
"""驱逐最旧的缓存项"""
oldest_key = min(self.access_times.keys(), key=lambda k: self.access_times[k])
del self.cache[oldest_key]
del self.access_times[oldest_key]流式生成
python
class StreamingGenerationOptimizer:
def __init__(self):
self.streaming_client = StreamingClient()
self.buffer_size = 1024
async def stream_generate(self, query: str, context: str) -> AsyncGenerator[str, None]:
"""流式生成"""
# 构建提示
prompt = self._build_prompt(query, context)
# 流式调用LLM
async for chunk in self.streaming_client.stream_generate(prompt):
yield chunk
def _build_prompt(self, query: str, context: str) -> str:
"""构建提示"""
return f"""
基于以下上下文回答问题：
上下文：
{context}
问题：
{query}
回答：
"""
class StreamingClient:
def __init__(self):
self.http_client = HttpClient()
async def stream_generate(self, prompt: str) -> AsyncGenerator[str, None]:
"""流式生成"""
# 实现流式HTTP请求
pass2. 提示优化
提示模板优化
python
class PromptTemplateOptimizer:
def __init__(self):
self.templates = {
'qa': QATemplate(),
'summarization': SummarizationTemplate(),
'translation': TranslationTemplate(),
'code_generation': CodeGenerationTemplate()
}
self.optimizer = TemplateOptimizer()
def optimize_prompt(self, query: str, context: str,
template_type: str = 'qa') -> str:
"""优化提示"""
# 选择模板
template = self.templates.get(template_type, self.templates['qa'])
# 优化模板
optimized_template = self.optimizer.optimize_template(template, query, context)
# 生成提示
prompt = optimized_template.generate(query, context)
return prompt
class QATemplate:
def __init__(self):
self.base_template = """
基于以下上下文回答问题：
上下文：
{context}
问题：
{query}
回答：
"""
def generate(self, query: str, context: str) -> str:
"""生成提示"""
return self.base_template.format(context=context, query=query)
class SummarizationTemplate:
def __init__(self):
self.base_template = """
请总结以下内容：
内容：
{context}
总结：
"""
def generate(self, query: str, context: str) -> str:
"""生成提示"""
return self.base_template.format(context=context)
class TranslationTemplate:
def __init__(self):
self.base_template = """
请将以下内容翻译成{target_language}：
原文：
{context}
译文：
"""
def generate(self, query: str, context: str) -> str:
"""生成提示"""
# 从查询中提取目标语言
target_language = self._extract_target_language(query)
return self.base_template.format(
context=context,
target_language=target_language
)
def _extract_target_language(self, query: str) -> str:
"""提取目标语言"""
# 简单的语言提取逻辑
if '英文' in query or 'English' in query:
return '英文'
elif '中文' in query or 'Chinese' in query:
return '中文'
else:
return '英文'
class CodeGenerationTemplate:
def __init__(self):
self.base_template = """
请根据以下需求生成代码：
需求：
{query}
代码：
"""
def generate(self, query: str, context: str) -> str:
"""生成提示"""
return self.base_template.format(query=query)
class TemplateOptimizer:
def __init__(self):
self.optimization_rules = [
self._optimize_length,
self._optimize_clarity,
self._optimize_structure
]
def optimize_template(self, template, query: str, context: str):
"""优化模板"""
optimized_template = template
for rule in self.optimization_rules:
optimized_template = rule(optimized_template, query, context)
return optimized_template
def _optimize_length(self, template, query: str, context: str):
"""优化长度"""
# 根据查询和上下文长度调整模板
if len(query) > 100:
# 长查询使用简化模板
template.base_template = template.base_template.replace(
'基于以下上下文回答问题：', '请回答：'
)
return template
def _optimize_clarity(self, template, query: str, context: str):
"""优化清晰度"""
# 添加清晰度指示
template.base_template = template.base_template.replace(
'回答：', '请提供清晰、准确的回答：'
)
return template
def _optimize_structure(self, template, query: str, context: str):
"""优化结构"""
# 添加结构指示
template.base_template = template.base_template.replace(
'回答：', '请按以下结构回答：\n1. 主要观点\n2. 详细说明\n3. 总结\n\n回答：'
)
return template缓存策略
1. 多级缓存
缓存层次
python
class MultiLevelCache:
def __init__(self):
self.l1_cache = L1Cache() # 内存缓存
self.l2_cache = L2Cache() # Redis缓存
self.l3_cache = L3Cache() # 数据库缓存
def get(self, key: str) -> any:
"""获取缓存"""
# L1缓存
value = self.l1_cache.get(key)
if value is not None:
return value
# L2缓存
value = self.l2_cache.get(key)
if value is not None:
# 回填L1缓存
self.l1_cache.set(key, value)
return value
# L3缓存
value = self.l3_cache.get(key)
if value is not None:
# 回填L1和L2缓存
self.l1_cache.set(key, value)
self.l2_cache.set(key, value)
return value
return None
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
# 设置所有级别的缓存
self.l1_cache.set(key, value, ttl)
self.l2_cache.set(key, value, ttl)
self.l3_cache.set(key, value, ttl)
class L1Cache:
def __init__(self, max_size: int = 1000):
self.cache = {}
self.max_size = max_size
self.access_times = {}
self.ttl = {}
def get(self, key: str) -> any:
"""获取缓存"""
if key in self.cache:
# 检查TTL
if time.time() > self.ttl.get(key, 0):
del self.cache[key]
del self.access_times[key]
del self.ttl[key]
return None
# 更新访问时间
self.access_times[key] = time.time()
return self.cache[key]
return None
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
# 检查缓存大小
if len(self.cache) >= self.max_size:
self._evict_oldest()
# 设置缓存
self.cache[key] = value
self.access_times[key] = time.time()
self.ttl[key] = time.time() + ttl
def _evict_oldest(self):
"""驱逐最旧的缓存项"""
oldest_key = min(self.access_times.keys(), key=lambda k: self.access_times[k])
del self.cache[oldest_key]
del self.access_times[oldest_key]
del self.ttl[oldest_key]
class L2Cache:
def __init__(self):
self.redis_client = RedisClient()
def get(self, key: str) -> any:
"""获取缓存"""
return self.redis_client.get(key)
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
self.redis_client.set(key, value, ttl)
class L3Cache:
def __init__(self):
self.db_client = DatabaseClient()
def get(self, key: str) -> any:
"""获取缓存"""
return self.db_client.get_cache(key)
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
self.db_client.set_cache(key, value, ttl)2. 智能缓存
缓存策略
python
class IntelligentCache:
def __init__(self):
self.cache_policies = {
'frequent': FrequentAccessPolicy(),
'recent': RecentAccessPolicy(),
'predictive': PredictiveAccessPolicy()
}
self.current_policy = 'frequent'
self.access_patterns = AccessPatternAnalyzer()
def get(self, key: str) -> any:
"""获取缓存"""
# 分析访问模式
self.access_patterns.record_access(key)
# 根据策略获取缓存
policy = self.cache_policies[self.current_policy]
return policy.get(key)
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
# 根据策略设置缓存
policy = self.cache_policies[self.current_policy]
policy.set(key, value, ttl)
def adapt_policy(self):
"""自适应策略"""
# 分析访问模式
patterns = self.access_patterns.analyze_patterns()
# 选择最佳策略
if patterns['frequency'] > 0.8:
self.current_policy = 'frequent'
elif patterns['recency'] > 0.8:
self.current_policy = 'recent'
else:
self.current_policy = 'predictive'
class FrequentAccessPolicy:
def __init__(self):
self.cache = {}
self.access_counts = {}
self.max_size = 1000
def get(self, key: str) -> any:
"""获取缓存"""
if key in self.cache:
self.access_counts[key] = self.access_counts.get(key, 0) + 1
return self.cache[key]
return None
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
if len(self.cache) >= self.max_size:
self._evict_least_frequent()
self.cache[key] = value
self.access_counts[key] = 1
def _evict_least_frequent(self):
"""驱逐最少访问的缓存项"""
least_frequent_key = min(self.access_counts.keys(),
key=lambda k: self.access_counts[k])
del self.cache[least_frequent_key]
del self.access_counts[least_frequent_key]
class RecentAccessPolicy:
def __init__(self):
self.cache = {}
self.access_times = {}
self.max_size = 1000
def get(self, key: str) -> any:
"""获取缓存"""
if key in self.cache:
self.access_times[key] = time.time()
return self.cache[key]
return None
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
if len(self.cache) >= self.max_size:
self._evict_oldest()
self.cache[key] = value
self.access_times[key] = time.time()
def _evict_oldest(self):
"""驱逐最旧的缓存项"""
oldest_key = min(self.access_times.keys(), key=lambda k: self.access_times[k])
del self.cache[oldest_key]
del self.access_times[oldest_key]
class PredictiveAccessPolicy:
def __init__(self):
self.cache = {}
self.predictor = AccessPredictor()
self.max_size = 1000
def get(self, key: str) -> any:
"""获取缓存"""
if key in self.cache:
# 更新预测模型
self.predictor.update_model(key, True)
return self.cache[key]
else:
# 更新预测模型
self.predictor.update_model(key, False)
return None
def set(self, key: str, value: any, ttl: int = 3600):
"""设置缓存"""
if len(self.cache) >= self.max_size:
self._evict_least_likely()
self.cache[key] = value
def _evict_least_likely(self):
"""驱逐最不可能访问的缓存项"""
least_likely_key = min(self.cache.keys(),
key=lambda k: self.predictor.predict_probability(k))
del self.cache[least_likely_key]
class AccessPatternAnalyzer:
def __init__(self):
self.access_history = []
self.patterns = {}
def record_access(self, key: str):
"""记录访问"""
self.access_history.append({
'key': key,
'timestamp': time.time()
})
def analyze_patterns(self) -> Dict[str, float]:
"""分析访问模式"""
if not self.access_history:
return {'frequency': 0.0, 'recency': 0.0}
# 分析频率模式
frequency_score = self._analyze_frequency()
# 分析时间模式
recency_score = self._analyze_recency()
return {
'frequency': frequency_score,
'recency': recency_score
}
def _analyze_frequency(self) -> float:
"""分析频率模式"""
# 计算访问频率
access_counts = {}
for access in self.access_history:
key = access['key']
access_counts[key] = access_counts.get(key, 0) + 1
# 计算频率分数
if access_counts:
max_count = max(access_counts.values())
avg_count = sum(access_counts.values()) / len(access_counts)
return avg_count / max_count
return 0.0
def _analyze_recency(self) -> float:
"""分析时间模式"""
# 计算最近访问时间
recent_accesses = [access for access in self.access_history
if time.time() - access['timestamp'] < 3600]
return len(recent_accesses) / len(self.access_history) if self.access_history else 0.0
class AccessPredictor:
def __init__(self):
self.model = None
self.features = {}
self.labels = {}
def update_model(self, key: str, accessed: bool):
"""更新预测模型"""
# 提取特征
features = self._extract_features(key)
# 存储特征和标签
self.features[key] = features
self.labels[key] = accessed
# 训练模型
self._train_model()
def predict_probability(self, key: str) -> float:
"""预测访问概率"""
if key not in self.features:
return 0.0
features = self.features[key]
# 使用模型预测
if self.model:
return self.model.predict_proba([features])[0][1]
else:
return 0.5
def _extract_features(self, key: str) -> List[float]:
"""提取特征"""
# 简单的特征提取
features = [
len(key),
hash(key) % 100,
time.time() % 1000
]
return features
def _train_model(self):
"""训练预测模型"""
if len(self.features) < 10:
return
# 准备训练数据
X = list(self.features.values())
y = list(self.labels.values())
# 训练模型
from sklearn.ensemble import RandomForestClassifier
self.model = RandomForestClassifier(n_estimators=10, random_state=42)
self.model.fit(X, y)并发处理优化
1. 异步处理
异步架构
python
import asyncio
from typing import List, Dict, Any, AsyncGenerator
class AsyncRAGSystem:
def __init__(self):
self.document_processor = AsyncDocumentProcessor()
self.vectorizer = AsyncVectorizer()
self.retriever = AsyncRetriever()
self.generator = AsyncGenerator()
self.cache = AsyncCache()
async def process_query_async(self, query: str, user_id: str) -> Dict[str, any]:
"""异步处理查询"""
try:
# 并行执行检索和用户信息获取
retrieval_task = asyncio.create_task(
self.retriever.retrieve_async(query)
)
user_task = asyncio.create_task(
self.get_user_info_async(user_id)
)
# 等待任务完成
retrieval_result, user_info = await asyncio.gather(
retrieval_task, user_task
)
# 生成回答
generation_result = await self.generator.generate_async(
query, retrieval_result['documents']
)
# 并行执行记录历史和发送通知
history_task = asyncio.create_task(
self.record_query_history_async(user_id, query, generation_result['response'])
)
notification_task = asyncio.create_task(
self.send_notification_async(user_id, generation_result['response'])
)
# 等待任务完成
await asyncio.gather(history_task, notification_task)
return {
'response': generation_result['response'],
'relevant_docs': retrieval_result['documents'],
'timestamp': time.time()
}
except Exception as e:
return {'error': f'处理查询失败: {str(e)}'}
async def get_user_info_async(self, user_id: str) -> Dict[str, any]:
"""异步获取用户信息"""
# 实现异步用户信息获取
pass
async def record_query_history_async(self, user_id: str, query: str, response: str):
"""异步记录查询历史"""
# 实现异步历史记录
pass
async def send_notification_async(self, user_id: str, response: str):
"""异步发送通知"""
# 实现异步通知发送
pass
class AsyncDocumentProcessor:
def __init__(self):
self.processors = {
'pdf': AsyncPDFProcessor(),
'docx': AsyncDocxProcessor(),
'txt': AsyncTextProcessor(),
'html': AsyncHTMLProcessor()
}
async def process_document_async(self, document: Dict[str, any]) -> Dict[str, any]:
"""异步处理文档"""
doc_type = document.get('type', 'txt')
processor = self.processors.get(doc_type, self.processors['txt'])
return await processor.process_async(document)
async def process_documents_async(self, documents: List[Dict[str, any]]) -> List[Dict[str, any]]:
"""异步批量处理文档"""
tasks = []
for document in documents:
task = asyncio.create_task(self.process_document_async(document))
tasks.append(task)
results = await asyncio.gather(*tasks)
return results
class AsyncVectorizer:
def __init__(self):
self.embedding_model = AsyncEmbeddingModel()
self.batch_size = 32
async def vectorize_document_async(self, document: Dict[str, any]) -> List[float]:
"""异步向量化文档"""
text = document['content']
return await self.embedding_model.encode_async(text)
async def vectorize_documents_async(self, documents: List[Dict[str, any]]) -> List[List[float]]:
"""异步批量向量化文档"""
# 分批处理
batches = [documents[i:i + self.batch_size]
for i in range(0, len(documents), self.batch_size)]
all_vectors = []
for batch in batches:
batch_vectors = await self._vectorize_batch_async(batch)
all_vectors.extend(batch_vectors)
return all_vectors
async def _vectorize_batch_async(self, batch: List[Dict[str, any]]) -> List[List[float]]:
"""异步批量向量化"""
texts = [doc['content'] for doc in batch]
vectors = await self.embedding_model.encode_batch_async(texts)
return vectors
class AsyncRetriever:
def __init__(self):
self.vectorizer = AsyncVectorizer()
self.index = AsyncVectorIndex()
self.similarity_calculator = AsyncSimilarityCalculator()
async def retrieve_async(self, query: str, top_k: int = 5) -> Dict[str, any]:
"""异步检索"""
# 向量化查询
query_vector = await self.vectorizer.vectorize_query_async(query)
# 搜索相似文档
similar_docs = await self.index.search_async(query_vector, top_k)
return {
'documents': similar_docs,
'query_vector': query_vector
}
async def hybrid_retrieve_async(self, query: str, filters: Dict[str, any] = None) -> Dict[str, any]:
"""异步混合检索"""
# 并行执行语义检索和关键词检索
semantic_task = asyncio.create_task(
self.semantic_retrieve_async(query)
)
keyword_task = asyncio.create_task(
self.keyword_retrieve_async(query, filters)
)
semantic_result, keyword_result = await asyncio.gather(
semantic_task, keyword_task
)
# 合并结果
merged_result = self._merge_results(semantic_result, keyword_result)
return merged_result
async def semantic_retrieve_async(self, query: str) -> List[Dict[str, any]]:
"""异步语义检索"""
# 实现异步语义检索
pass
async def keyword_retrieve_async(self, query: str, filters: Dict[str, any] = None) -> List[Dict[str, any]]:
"""异步关键词检索"""
# 实现异步关键词检索
pass
def _merge_results(self, semantic_result: List[Dict[str, any]],
keyword_result: List[Dict[str, any]]) -> List[Dict[str, any]]:
"""合并结果"""
# 实现结果合并逻辑
pass
class AsyncGenerator:
def __init__(self):
self.llm = AsyncLLM()
self.prompt_builder = AsyncPromptBuilder()
async def generate_async(self, query: str, relevant_docs: List[Dict[str, any]]) -> Dict[str, any]:
"""异步生成回答"""
# 构建提示
prompt = await self.prompt_builder.build_prompt_async(query, relevant_docs)
# 生成回答
response = await self.llm.generate_async(prompt)
return {
'response': response,
'prompt': prompt
}
async def stream_generate_async(self, query: str, relevant_docs: List[Dict[str, any]]) -> AsyncGenerator[str, None]:
"""异步流式生成"""
# 构建提示
prompt = await self.prompt_builder.build_prompt_async(query, relevant_docs)
# 流式生成
async for chunk in self.llm.stream_generate_async(prompt):
yield chunk
class AsyncCache:
def __init__(self):
self.redis_client = AsyncRedisClient()
self.memory_cache = {}
async def get_async(self, key: str) -> any:
"""异步获取缓存"""
# 先检查内存缓存
if key in self.memory_cache:
return self.memory_cache[key]
# 检查Redis缓存
value = await self.redis_client.get_async(key)
if value:
# 回填内存缓存
self.memory_cache[key] = value
return value
async def set_async(self, key: str, value: any, ttl: int = 3600):
"""异步设置缓存"""
# 设置内存缓存
self.memory_cache[key] = value
# 设置Redis缓存
await self.redis_client.set_async(key, value, ttl)
async def invalidate_async(self, key: str):
"""异步失效缓存"""
# 删除内存缓存
if key in self.memory_cache:
del self.memory_cache[key]
# 删除Redis缓存
await self.redis_client.delete_async(key)2. 线程池优化
线程池管理
python
import threading
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
from typing import List, Dict, Any
class ThreadPoolOptimizer:
def __init__(self, max_workers: int = 4):
self.max_workers = max_workers
self.thread_pool = ThreadPoolExecutor(max_workers=max_workers)
self.process_pool = ProcessPoolExecutor(max_workers=max_workers)
self.task_queue = asyncio.Queue()
self.result_cache = {}
def submit_task(self, func, *args, **kwargs):
"""提交任务"""
future = self.thread_pool.submit(func, *args, **kwargs)
return future
def submit_cpu_intensive_task(self, func, *args, **kwargs):
"""提交CPU密集型任务"""
future = self.process_pool.submit(func, *args, **kwargs)
return future
def batch_submit_tasks(self, tasks: List[Dict[str, any]]) -> List[any]:
"""批量提交任务"""
futures = []
for task in tasks:
func = task['func']
args = task.get('args', [])
kwargs = task.get('kwargs', {})
if task.get('cpu_intensive', False):
future = self.submit_cpu_intensive_task(func, *args, **kwargs)
else:
future = self.submit_task(func, *args, **kwargs)
futures.append(future)
# 等待所有任务完成
results = []
for future in futures:
try:
result = future.result()
results.append(result)
except Exception as e:
print(f"任务执行失败: {e}")
results.append(None)
return results
def optimize_thread_pool(self):
"""优化线程池"""
# 根据系统负载调整线程池大小
cpu_count = os.cpu_count()
current_load = self._get_system_load()
if current_load > 0.8:
# 高负载时减少线程数
optimal_workers = max(1, cpu_count // 2)
elif current_load < 0.3:
# 低负载时增加线程数
optimal_workers = min(cpu_count * 2, 16)
else:
# 中等负载时保持当前线程数
optimal_workers = cpu_count
if optimal_workers != self.max_workers:
self._resize_thread_pool(optimal_workers)
def _get_system_load(self) -> float:
"""获取系统负载"""
import psutil
return psutil.cpu_percent() / 100.0
def _resize_thread_pool(self, new_size: int):
"""调整线程池大小"""
# 关闭当前线程池
self.thread_pool.shutdown(wait=True)
# 创建新的线程池
self.thread_pool = ThreadPoolExecutor(max_workers=new_size)
self.max_workers = new_size
class ConcurrentRAGSystem:
def __init__(self):
self.thread_pool_optimizer = ThreadPoolOptimizer()
self.document_processor = DocumentProcessor()
self.vectorizer = Vectorizer()
self.retriever = Retriever()
self.generator = Generator()
def process_query_concurrent(self, query: str, user_id: str) -> Dict[str, any]:
"""并发处理查询"""
try:
# 并行执行检索和用户信息获取
retrieval_future = self.thread_pool_optimizer.submit_task(
self.retriever.retrieve, query
)
user_future = self.thread_pool_optimizer.submit_task(
self.get_user_info, user_id
)
# 等待任务完成
retrieval_result = retrieval_future.result()
user_info = user_future.result()
# 生成回答
generation_result = self.generator.generate(
query, retrieval_result['documents']
)
# 并行执行记录历史和发送通知
history_future = self.thread_pool_optimizer.submit_task(
self.record_query_history, user_id, query, generation_result['response']
)
notification_future = self.thread_pool_optimizer.submit_task(
self.send_notification, user_id, generation_result['response']
)
# 等待任务完成
history_future.result()
notification_future.result()
return {
'response': generation_result['response'],
'relevant_docs': retrieval_result['documents'],
'timestamp': time.time()
}
except Exception as e:
return {'error': f'处理查询失败: {str(e)}'}
def process_documents_concurrent(self, documents: List[Dict[str, any]]) -> List[Dict[str, any]]:
"""并发处理文档"""
# 创建任务列表
tasks = []
for document in documents:
task = {
'func': self.document_processor.process_document,
'args': [document],
'cpu_intensive': True
}
tasks.append(task)
# 批量提交任务
results = self.thread_pool_optimizer.batch_submit_tasks(tasks)
return results
def vectorize_documents_concurrent(self, documents: List[Dict[str, any]]) -> List[List[float]]:
"""并发向量化文档"""
# 创建任务列表
tasks = []
for document in documents:
task = {
'func': self.vectorizer.vectorize_document,
'args': [document],
'cpu_intensive': True
}
tasks.append(task)
# 批量提交任务
results = self.thread_pool_optimizer.batch_submit_tasks(tasks)
return results
def get_user_info(self, user_id: str) -> Dict[str, any]:
"""获取用户信息"""
# 实现用户信息获取
pass
def record_query_history(self, user_id: str, query: str, response: str):
"""记录查询历史"""
# 实现历史记录
pass
def send_notification(self, user_id: str, response: str):
"""发送通知"""
# 实现通知发送
pass系统调优
1. 资源优化
内存优化
python
class MemoryOptimizer:
def __init__(self):
self.memory_monitor = MemoryMonitor()
self.garbage_collector = GarbageCollector()
self.object_pool = ObjectPool()
def optimize_memory(self):
"""优化内存使用"""
# 监控内存使用
memory_usage = self.memory_monitor.get_memory_usage()
if memory_usage > 0.8:
# 高内存使用率时进行优化
self._perform_memory_optimization()
def _perform_memory_optimization(self):
"""执行内存优化"""
# 清理缓存
self._clear_caches()
# 垃圾回收
self.garbage_collector.collect()
# 压缩对象池
self.object_pool.compress()
def _clear_caches(self):
"""清理缓存"""
# 清理各种缓存
pass
class MemoryMonitor:
def __init__(self):
self.memory_threshold = 0.8
self.monitoring_interval = 30
def get_memory_usage(self) -> float:
"""获取内存使用率"""
import psutil
return psutil.virtual_memory().percent / 100.0
def start_monitoring(self):
"""开始监控"""
while True:
memory_usage = self.get_memory_usage()
if memory_usage > self.memory_threshold:
self._trigger_optimization()
time.sleep(self.monitoring_interval)
def _trigger_optimization(self):
"""触发优化"""
# 触发内存优化
pass
class GarbageCollector:
def __init__(self):
self.collection_threshold = 1000
self.object_count = 0
def collect(self):
"""垃圾回收"""
import gc
gc.collect()
def track_object(self, obj):
"""跟踪对象"""
self.object_count += 1
if self.object_count > self.collection_threshold:
self.collect()
self.object_count = 0
class ObjectPool:
def __init__(self):
self.pools = {}
self.max_pool_size = 100
def get_object(self, obj_type: str):
"""获取对象"""
if obj_type in self.pools and self.pools[obj_type]:
return self.pools[obj_type].pop()
else:
return self._create_object(obj_type)
def return_object(self, obj_type: str, obj):
"""返回对象"""
if obj_type not in self.pools:
self.pools[obj_type] = []
if len(self.pools[obj_type]) < self.max_pool_size:
self.pools[obj_type].append(obj)
def _create_object(self, obj_type: str):
"""创建对象"""
# 根据类型创建对象
pass
def compress(self):
"""压缩对象池"""
for obj_type in self.pools:
if len(self.pools[obj_type]) > self.max_pool_size:
self.pools[obj_type] = self.pools[obj_type][:self.max_pool_size]CPU优化
python
class CPUOptimizer:
def __init__(self):
self.cpu_monitor = CPUMonitor()
self.task_scheduler = TaskScheduler()
self.process_optimizer = ProcessOptimizer()
def optimize_cpu(self):
"""优化CPU使用"""
# 监控CPU使用
cpu_usage = self.cpu_monitor.get_cpu_usage()
if cpu_usage > 0.8:
# 高CPU使用率时进行优化
self._perform_cpu_optimization()
def _perform_cpu_optimization(self):
"""执行CPU优化"""
# 调整任务调度
self.task_scheduler.optimize_scheduling()
# 优化进程
self.process_optimizer.optimize_processes()
def set_cpu_affinity(self, process_id: int, cpu_cores: List[int]):
"""设置CPU亲和性"""
import psutil
process = psutil.Process(process_id)
process.cpu_affinity(cpu_cores)
class CPUMonitor:
def __init__(self):
self.cpu_threshold = 0.8
self.monitoring_interval = 30
def get_cpu_usage(self) -> float:
"""获取CPU使用率"""
import psutil
return psutil.cpu_percent() / 100.0
def get_cpu_cores(self) -> int:
"""获取CPU核心数"""
import psutil
return psutil.cpu_count()
def start_monitoring(self):
"""开始监控"""
while True:
cpu_usage = self.get_cpu_usage()
if cpu_usage > self.cpu_threshold:
self._trigger_optimization()
time.sleep(self.monitoring_interval)
def _trigger_optimization(self):
"""触发优化"""
# 触发CPU优化
pass
class TaskScheduler:
def __init__(self):
self.task_queue = asyncio.Queue()
self.priority_queue = PriorityQueue()
def optimize_scheduling(self):
"""优化任务调度"""
# 调整任务优先级
self._adjust_task_priorities()
# 重新调度任务
self._reschedule_tasks()
def _adjust_task_priorities(self):
"""调整任务优先级"""
# 根据系统负载调整任务优先级
pass
def _reschedule_tasks(self):
"""重新调度任务"""
# 重新调度任务
pass
class ProcessOptimizer:
def __init__(self):
self.process_monitor = ProcessMonitor()
self.process_manager = ProcessManager()
def optimize_processes(self):
"""优化进程"""
# 监控进程
processes = self.process_monitor.get_processes()
# 优化进程
for process in processes:
self._optimize_process(process)
def _optimize_process(self, process):
"""优化单个进程"""
# 设置进程优先级
self._set_process_priority(process)
# 设置CPU亲和性
self._set_cpu_affinity(process)
def _set_process_priority(self, process):
"""设置进程优先级"""
# 设置进程优先级
pass
def _set_cpu_affinity(self, process):
"""设置CPU亲和性"""
# 设置CPU亲和性
pass2. 网络优化
连接池优化
python
class ConnectionPoolOptimizer:
def __init__(self):
self.connection_pools = {}
self.pool_monitor = PoolMonitor()
self.pool_manager = PoolManager()
def optimize_connection_pools(self):
"""优化连接池"""
# 监控连接池
pool_stats = self.pool_monitor.get_pool_stats()
# 优化连接池
for pool_name, stats in pool_stats.items():
self._optimize_pool(pool_name, stats)
def _optimize_pool(self, pool_name: str, stats: Dict[str, any]):
"""优化单个连接池"""
# 调整池大小
self._adjust_pool_size(pool_name, stats)
# 调整超时设置
self._adjust_timeout(pool_name, stats)
# 清理空闲连接
self._cleanup_idle_connections(pool_name, stats)
def _adjust_pool_size(self, pool_name: str, stats: Dict[str, any]):
"""调整池大小"""
# 根据使用率调整池大小
usage_rate = stats['active_connections'] / stats['max_connections']
if usage_rate > 0.8:
# 高使用率时增加池大小
new_size = min(stats['max_connections'] * 2, 100)
self.pool_manager.resize_pool(pool_name, new_size)
elif usage_rate < 0.3:
# 低使用率时减少池大小
new_size = max(stats['max_connections'] // 2, 5)
self.pool_manager.resize_pool(pool_name, new_size)
def _adjust_timeout(self, pool_name: str, stats: Dict[str, any]):
"""调整超时设置"""
# 根据响应时间调整超时
avg_response_time = stats['avg_response_time']
if avg_response_time > 5.0:
# 响应时间长时增加超时
new_timeout = min(stats['timeout'] * 2, 60)
self.pool_manager.set_timeout(pool_name, new_timeout)
elif avg_response_time < 1.0:
# 响应时间短时减少超时
new_timeout = max(stats['timeout'] // 2, 5)
self.pool_manager.set_timeout(pool_name, new_timeout)
def _cleanup_idle_connections(self, pool_name: str, stats: Dict[str, any]):
"""清理空闲连接"""
# 清理长时间空闲的连接
idle_connections = stats['idle_connections']
if idle_connections > stats['max_connections'] * 0.5:
self.pool_manager.cleanup_idle_connections(pool_name)
class PoolMonitor:
def __init__(self):
self.pool_stats = {}
def get_pool_stats(self) -> Dict[str, Dict[str, any]]:
"""获取连接池统计信息"""
return self.pool_stats
def update_pool_stats(self, pool_name: str, stats: Dict[str, any]):
"""更新连接池统计信息"""
self.pool_stats[pool_name] = stats
class PoolManager:
def __init__(self):
self.pools = {}
def resize_pool(self, pool_name: str, new_size: int):
"""调整池大小"""
if pool_name in self.pools:
self.pools[pool_name].resize(new_size)
def set_timeout(self, pool_name: str, timeout: int):
"""设置超时"""
if pool_name in self.pools:
self.pools[pool_name].set_timeout(timeout)
def cleanup_idle_connections(self, pool_name: str):
"""清理空闲连接"""
if pool_name in self.pools:
self.pools[pool_name].cleanup_idle_connections()最佳实践
1. 性能优化策略
python
def select_performance_optimization_strategy(requirements: dict) -> List[str]:
"""选择性能优化策略"""
strategies = []
if requirements['response_time'] < 1.0:
strategies.append('implement_caching')
strategies.append('optimize_indexing')
if requirements['throughput'] > 1000:
strategies.append('implement_parallel_processing')
strategies.append('optimize_concurrent_handling')
if requirements['memory_usage'] > 0.8:
strategies.append('implement_memory_optimization')
strategies.append('optimize_object_pooling')
if requirements['cpu_usage'] > 0.8:
strategies.append('implement_cpu_optimization')
strategies.append('optimize_task_scheduling')
return strategies2. 性能监控建议
python
class PerformanceMonitoringAdvisor:
def __init__(self):
self.monitoring_metrics = {}
def get_monitoring_recommendations(self, performance_metrics: Dict[str, float]) -> List[str]:
"""获取监控建议"""
recommendations = []
# 基于性能指标提供建议
if performance_metrics.get('response_time', 0) > 5.0:
recommendations.append("监控响应时间，优化检索和生成过程")
if performance_metrics.get('throughput', 0) < 100:
recommendations.append("监控吞吐量，优化并发处理能力")
if performance_metrics.get('memory_usage', 0) > 0.8:
recommendations.append("监控内存使用，优化内存管理")
if performance_metrics.get('cpu_usage', 0) > 0.8:
recommendations.append("监控CPU使用，优化任务调度")
return recommendations总结
RAG系统性能优化是确保系统高效运行的关键技术。本文介绍了性能优化的核心概念、方法和最佳实践，包括检索优化、生成优化、缓存策略、并发处理和系统调优等方面。
关键要点：
- 多层次优化：从索引、检索到生成的全面优化
- 缓存策略：使用多级缓存和智能缓存提升性能
- 并发处理：通过异步和线程池优化提升并发能力
- 系统调优：优化内存、CPU和网络资源使用
在下一篇文章中，我们将探讨监控与运维，了解如何建立完善的RAG系统监控和运维体系。
下一步学习建议：
- 阅读《监控与运维》，了解RAG系统的监控和运维方法
- 实践不同的性能优化技术，比较它们的效果
- 关注RAG系统性能优化技术的最新发展和创新方案