276 lines
9.0 KiB
Python
276 lines
9.0 KiB
Python
import os
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import time
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import multiprocessing
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from typing import Optional
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from utils import (
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get_global_connection_pool, init_global_connection_pool,
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get_global_file_cache, init_global_file_cache,
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setup_system_optimizations
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)
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from agent.sharded_agent_manager import init_global_sharded_agent_manager
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try:
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from utils.system_optimizer import apply_optimization_profile
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except ImportError:
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def apply_optimization_profile(profile):
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return {"profile": profile, "status": "system_optimizer not available"}
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from utils.fastapi_utils import get_content_from_messages
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from embedding import get_model_manager
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from pydantic import BaseModel
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import logging
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logger = logging.getLogger('app')
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router = APIRouter()
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class EncodeRequest(BaseModel):
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texts: list[str]
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batch_size: int = 32
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class EncodeResponse(BaseModel):
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success: bool
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embeddings: list[list[float]]
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shape: list[int]
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processing_time: float
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total_texts: int
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error: Optional[str] = None
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# 系统优化设置初始化
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logger.info("正在初始化系统优化...")
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system_optimizer = setup_system_optimizations()
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# 全局助手管理器配置（使用优化后的配置）
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max_cached_agents = int(os.getenv("MAX_CACHED_AGENTS", "50")) # 增加缓存大小
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shard_count = int(os.getenv("SHARD_COUNT", "16")) # 分片数量
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# 初始化优化的全局助手管理器
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agent_manager = init_global_sharded_agent_manager(
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max_cached_agents=max_cached_agents,
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shard_count=shard_count
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)
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# 初始化连接池
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connection_pool = init_global_connection_pool(
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max_connections_per_host=int(os.getenv("MAX_CONNECTIONS_PER_HOST", "100")),
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max_connections_total=int(os.getenv("MAX_CONNECTIONS_TOTAL", "500")),
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keepalive_timeout=int(os.getenv("KEEPALIVE_TIMEOUT", "30")),
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connect_timeout=int(os.getenv("CONNECT_TIMEOUT", "10")),
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total_timeout=int(os.getenv("TOTAL_TIMEOUT", "60"))
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)
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# 初始化文件缓存
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file_cache = init_global_file_cache(
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cache_size=int(os.getenv("FILE_CACHE_SIZE", "1000")),
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ttl=int(os.getenv("FILE_CACHE_TTL", "300"))
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)
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logger.info("系统优化初始化完成")
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logger.info(f"- 分片Agent管理器: {shard_count} 个分片，最多缓存 {max_cached_agents} 个agent")
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logger.info(f"- 连接池: 每主机100连接，总计500连接")
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logger.info(f"- 文件缓存: 1000个文件，TTL 300秒")
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@router.get("/api/health")
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async def health_check():
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"""Health check endpoint"""
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return {"message": "Database Assistant API is running"}
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@router.get("/api/v1/system/performance")
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async def get_performance_stats():
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"""获取系统性能统计信息"""
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try:
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# 获取agent管理器统计
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agent_stats = agent_manager.get_cache_stats()
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# 获取连接池统计（简化版）
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pool_stats = {
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"connection_pool": "active",
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"max_connections_per_host": 100,
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"max_connections_total": 500,
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"keepalive_timeout": 30
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}
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# 获取文件缓存统计
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file_cache_stats = {
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"cache_size": len(file_cache._cache) if hasattr(file_cache, '_cache') else 0,
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"max_cache_size": file_cache.cache_size if hasattr(file_cache, 'cache_size') else 1000,
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"ttl": file_cache.ttl if hasattr(file_cache, 'ttl') else 300
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}
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# 系统资源信息
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try:
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import psutil
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system_stats = {
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"cpu_count": multiprocessing.cpu_count(),
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"memory_total_gb": round(psutil.virtual_memory().total / (1024**3), 2),
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"memory_available_gb": round(psutil.virtual_memory().available / (1024**3), 2),
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"memory_percent": psutil.virtual_memory().percent,
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"disk_usage_percent": psutil.disk_usage('/').percent
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}
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except ImportError:
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system_stats = {
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"cpu_count": multiprocessing.cpu_count(),
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"memory_info": "psutil not available"
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}
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return {
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"success": True,
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"timestamp": int(time.time()),
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"performance": {
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"agent_manager": agent_stats,
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"connection_pool": pool_stats,
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"file_cache": file_cache_stats,
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"system": system_stats
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}
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}
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except Exception as e:
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logger.error(f"Error getting performance stats: {str(e)}")
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raise HTTPException(status_code=500, detail=f"获取性能统计失败: {str(e)}")
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@router.post("/api/v1/system/optimize")
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async def optimize_system(profile: str = "balanced"):
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"""应用系统优化配置"""
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try:
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# 应用优化配置
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config = apply_optimization_profile(profile)
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return {
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"success": True,
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"message": f"已应用 {profile} 优化配置",
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"config": config
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}
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except Exception as e:
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logger.error(f"Error applying optimization profile: {str(e)}")
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raise HTTPException(status_code=500, detail=f"应用优化配置失败: {str(e)}")
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@router.post("/api/v1/system/clear-cache")
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async def clear_system_cache(cache_type: Optional[str] = None):
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"""清理系统缓存"""
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try:
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cleared_counts = {}
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if cache_type is None or cache_type == "agent":
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# 清理agent缓存
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agent_count = agent_manager.clear_cache()
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cleared_counts["agent_cache"] = agent_count
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if cache_type is None or cache_type == "file":
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# 清理文件缓存
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if hasattr(file_cache, '_cache'):
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file_count = len(file_cache._cache)
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file_cache._cache.clear()
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cleared_counts["file_cache"] = file_count
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return {
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"success": True,
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"message": f"已清理指定类型的缓存",
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"cleared_counts": cleared_counts
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}
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except Exception as e:
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logger.error(f"Error clearing cache: {str(e)}")
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raise HTTPException(status_code=500, detail=f"清理缓存失败: {str(e)}")
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@router.get("/api/v1/system/config")
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async def get_system_config():
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"""获取当前系统配置"""
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try:
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return {
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"success": True,
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"config": {
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"max_cached_agents": max_cached_agents,
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"shard_count": shard_count,
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"tokenizer_parallelism": os.getenv("TOKENIZERS_PARALLELISM", "true"),
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"max_connections_per_host": os.getenv("MAX_CONNECTIONS_PER_HOST", "100"),
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"max_connections_total": os.getenv("MAX_CONNECTIONS_TOTAL", "500"),
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"file_cache_size": os.getenv("FILE_CACHE_SIZE", "1000"),
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"file_cache_ttl": os.getenv("FILE_CACHE_TTL", "300")
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}
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}
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except Exception as e:
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logger.error(f"Error getting system config: {str(e)}")
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raise HTTPException(status_code=500, detail=f"获取系统配置失败: {str(e)}")
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@router.post("/system/remove-project-cache")
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async def remove_project_cache(dataset_id: str):
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"""移除特定项目的缓存"""
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try:
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removed_count = agent_manager.remove_cache_by_unique_id(dataset_id)
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if removed_count > 0:
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return {"message": f"项目缓存移除成功: {dataset_id}", "removed_count": removed_count}
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else:
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return {"message": f"未找到项目缓存: {dataset_id}", "removed_count": 0}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"移除项目缓存失败: {str(e)}")
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@router.post("/api/v1/embedding/encode", response_model=EncodeResponse)
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async def encode_texts(request: EncodeRequest):
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"""
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文本编码 API
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Args:
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request: 包含 texts 和 batch_size 的编码请求
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Returns:
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编码结果
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"""
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try:
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model_manager = get_model_manager()
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if not request.texts:
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return EncodeResponse(
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success=False,
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embeddings=[],
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shape=[0, 0],
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processing_time=0.0,
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total_texts=0,
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error="texts 不能为空"
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)
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start_time = time.time()
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# 使用模型管理器编码文本
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embeddings = await model_manager.encode_texts(
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request.texts,
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batch_size=request.batch_size
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)
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processing_time = time.time() - start_time
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# 转换为列表格式
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embeddings_list = embeddings.tolist()
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return EncodeResponse(
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success=True,
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embeddings=embeddings_list,
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shape=list(embeddings.shape),
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processing_time=processing_time,
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total_texts=len(request.texts)
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)
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except Exception as e:
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logger.error(f"文本编码 API 错误: {e}")
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return EncodeResponse(
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success=False,
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embeddings=[],
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shape=[0, 0],
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processing_time=0.0,
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total_texts=len(request.texts) if request else 0,
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error=f"编码失败: {str(e)}"
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)
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