fix: restore main runtime sources and cache consistency
This commit is contained in:
User
273
scripts/analyze_rag_performance.py
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273
scripts/analyze_rag_performance.py
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# -*- coding: utf-8 -*-
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"""
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RAG 性能分析脚本
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分析 RAG 流程各阶段耗时，帮助定位性能瓶颈
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"""
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import sys
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import os
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import json
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import time
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import argparse
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from datetime import datetime
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from typing import Dict
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# 添加项目根目录到路径
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, PROJECT_ROOT)
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def call_rag_stream_api(
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question: str,
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kb_name: str = "public_kb",
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base_url: str = "http://localhost:5001",
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) -> Dict:
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"""
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调用 RAG 流式 API 并收集各阶段事件
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Returns:
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包含各阶段耗时信息的字典
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"""
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import requests
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url = f"{base_url.rstrip('/')}/rag"
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer mock-token-admin"
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}
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data = {
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"message": question,
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"collections": [kb_name] if kb_name else []
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}
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events = []
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timing = {
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'total_duration_ms': 0,
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'search_time_ms': 0,
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'rerank_time_ms': 0,
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'llm_time_ms': 0,
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'stages': []
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}
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try:
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start_time = time.time()
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response = requests.post(url, json=data, headers=headers, stream=True, timeout=300)
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response.raise_for_status()
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for line in response.iter_lines():
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if line:
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line_str = line.decode('utf-8')
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if line_str.startswith('data: '):
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try:
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event = json.loads(line_str[6:])
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event['_received_at'] = time.time()
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events.append(event)
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# 分析 finish 事件中的耗时信息
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if event.get('type') == 'finish':
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timing['total_duration_ms'] = event.get('duration_ms', 0)
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if 'timing' in event:
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timing['search_time_ms'] = event['timing'].get('total_search_ms', 0)
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timing['rerank_time_ms'] = event['timing'].get('rerank_ms', 0)
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timing['rerank_cached'] = event['timing'].get('rerank_cached', False)
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except json.JSONDecodeError:
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continue
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end_time = time.time()
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timing['actual_elapsed_ms'] = int((end_time - start_time) * 1000)
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# 计算 LLM 生成时间
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if timing['total_duration_ms'] > 0 and timing['search_time_ms'] > 0:
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timing['llm_time_ms'] = timing['total_duration_ms'] - timing['search_time_ms']
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finish_event = next((event for event in events if event.get('type') == 'finish'), None)
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error_event = next((event for event in events if event.get('type') == 'error'), None)
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return {
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'success': finish_event is not None and error_event is None,
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'error': error_event.get('message', '') if error_event else (
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'' if finish_event else 'SSE 流结束但未收到 finish 事件'
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),
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'events': events,
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'timing': timing,
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'answer': finish_event.get('answer', '') if finish_event else '',
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}
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except requests.exceptions.RequestException as e:
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return {
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'success': False,
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'error': str(e),
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'events': events,
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'timing': timing
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}
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def analyze_performance(result: Dict, question: str) -> None:
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"""分析并打印性能数据"""
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print("\n" + "=" * 80)
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print(f"问题: {question}")
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print("=" * 80)
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if not result['success']:
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print(f"[X] 请求失败: {result.get('error', '未知错误')}")
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return
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timing = result['timing']
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events = result['events']
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# 打印各阶段事件时间线
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print("\n[事件时间线]")
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print("-" * 80)
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first_event_time = None
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for event in events:
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event_type = event.get('type', 'unknown')
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received_at = event.get('_received_at', 0)
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if first_event_time is None:
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first_event_time = received_at
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relative_time = 0
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else:
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relative_time = (received_at - first_event_time) * 1000
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if event_type == 'finish':
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print(f" {relative_time:>8.0f}ms | {event_type:20s} | 总耗时: {event.get('duration_ms', 0)}ms")
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elif event_type == 'sources':
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sources = event.get('sources', [])
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print(f" {relative_time:>8.0f}ms | {event_type:20s} | 找到 {len(sources)} 个来源")
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elif event_type == 'chunks_retrieved':
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chunks = event.get('data', {}).get('chunks', [])
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print(f" {relative_time:>8.0f}ms | {event_type:20s} | 召回 {len(chunks)} 个切片")
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elif event_type == 'chunk':
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# 流式输出，只显示第一个
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if not hasattr(analyze_performance, '_chunk_printed'):
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print(f" {relative_time:>8.0f}ms | {event_type:20s} | 开始流式输出...")
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analyze_performance._chunk_printed = True
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else:
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print(f" {relative_time:>8.0f}ms | {event_type:20s}")
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if hasattr(analyze_performance, '_chunk_printed'):
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delattr(analyze_performance, '_chunk_printed')
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# 打印耗时统计
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print("\n[耗时统计]")
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print("-" * 80)
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total = timing['total_duration_ms']
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search = timing['search_time_ms']
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rerank = timing['rerank_time_ms']
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llm = timing['llm_time_ms']
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actual = timing.get('actual_elapsed_ms', total)
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if total > 0:
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print(f" 总耗时 (API报告): {total:>8}ms ({total/1000:.2f}s)")
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print(f" 实际耗时 (本地测量): {actual:>8}ms ({actual/1000:.2f}s)")
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print()
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print(f" 检索阶段: {search:>8}ms ({search/total*100:.1f}%)")
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if rerank > 0:
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cached_flag = " [缓存]" if timing.get('rerank_cached') else ""
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print(f" 重排序阶段: {rerank:>8}ms ({rerank/total*100:.1f}%){cached_flag}")
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print(f" LLM生成阶段: {llm:>8}ms ({llm/total*100:.1f}%)")
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# 性能诊断
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print("\n[性能诊断]")
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print("-" * 80)
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if total > 10000:
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print(" [!] 总耗时超过 10 秒，需要优化")
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if search > 3000:
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print(f" [!] 检索耗时过长 ({search}ms)，可能原因:")
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print(" - 向量库数据量过大")
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print(" - 未命中查询缓存")
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print(" - BM25 索引加载慢")
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if rerank > 2000 and not timing.get('rerank_cached'):
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print(f" [!] 重排序耗时过长 ({rerank}ms)，可能原因:")
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print(" - Rerank 模型计算量大")
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print(" - 候选切片数量过多")
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if llm > 5000:
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print(f" [!] LLM生成耗时过长 ({llm}ms)，可能原因:")
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print(" - 模型生成速度慢")
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print(" - 输出 token 数量多")
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print(" - 网络延迟")
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print(" 建议: 检查 MiMo thinking 是否关闭、上下文长度及 max_tokens")
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if total < 5000:
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print(" [OK] 性能良好")
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else:
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print(" [!] 未能获取耗时数据")
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def run_performance_tests(
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base_url: str = "http://localhost:5001",
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kb_name: str = "public_kb",
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question: str = None,
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):
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"""执行性能测试"""
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# 测试问题（不同类型）
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test_questions = [
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{"question": "智启科技成立于哪一年？", "type": "精确匹配"},
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{"question": "公司的愿景是什么？", "type": "语义理解"},
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{"question": "公司有哪些分公司，分别在哪些城市？", "type": "跨文档关联"},
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{"question": "一个入职3年的员工，累计病假1个月，能拿到多少病假工资？", "type": "复杂推理"},
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{"question": "P4级工程师的年薪范围是多少？", "type": "表格数据"},
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]
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if question:
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test_questions = [{"question": question, "type": "自定义问题"}]
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print("=" * 80)
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print("RAG Performance Analysis")
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print(f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print("=" * 80)
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results = []
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for i, test in enumerate(test_questions):
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print(f"\n\n[{i+1}/{len(test_questions)}] 类型: {test['type']}")
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result = call_rag_stream_api(test['question'], kb_name=kb_name, base_url=base_url)
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analyze_performance(result, test['question'])
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results.append({
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'question': test['question'],
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'type': test['type'],
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'success': result['success'],
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'timing': result['timing']
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})
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# 汇总统计
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print("\n\n" + "=" * 80)
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print("[Performance Summary]")
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print("=" * 80)
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successful = [r for r in results if r['success']]
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if successful:
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total_times = [r['timing']['total_duration_ms'] for r in successful]
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search_times = [r['timing']['search_time_ms'] for r in successful]
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llm_times = [r['timing']['llm_time_ms'] for r in successful]
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print(f"\n成功请求数: {len(successful)}/{len(results)}")
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print(f"\n平均总耗时: {sum(total_times)/len(total_times):.0f}ms")
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print(f"平均检索耗时: {sum(search_times)/len(search_times):.0f}ms")
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print(f"平均LLM耗时: {sum(llm_times)/len(llm_times):.0f}ms")
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print(f"\n最快总耗时: {min(total_times)}ms")
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print(f"最慢总耗时: {max(total_times)}ms")
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# 保存详细报告
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report_dir = os.path.join(PROJECT_ROOT, ".data", "performance")
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os.makedirs(report_dir, exist_ok=True)
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report_file = os.path.join(
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report_dir,
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f"performance_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
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)
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with open(report_file, 'w', encoding='utf-8') as f:
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json.dump({
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'test_time': datetime.now().isoformat(),
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'results': results
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}, f, ensure_ascii=False, indent=2)
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print(f"\n详细报告已保存: {report_file}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="RAG SSE 链路耗时分析")
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parser.add_argument("--base-url", default="http://localhost:5001")
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parser.add_argument("--kb", default="public_kb", help="目标知识库名称")
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parser.add_argument("--question", help="只测试一个自定义问题")
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args = parser.parse_args()
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run_performance_tests(args.base_url, args.kb, args.question)
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82
scripts/download_models.py
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82
scripts/download_models.py
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@@ -0,0 +1,82 @@
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"""
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下载 BGE embedding 和 reranker 模型到本地 models/ 目录
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首次配置时运行：
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python scripts/download_models.py
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"""
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import os
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import sys
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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MODELS_DIR = os.path.join(PROJECT_ROOT, "models")
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EMBEDDING_MODEL_PATH = os.path.join(MODELS_DIR, "bge-base-zh-v1.5")
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RERANK_MODEL_PATH = os.path.join(MODELS_DIR, "bge-reranker-base")
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os.makedirs(MODELS_DIR, exist_ok=True)
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def download_embedding_model():
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"""下载 BGE 中文向量模型"""
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if os.path.exists(os.path.join(EMBEDDING_MODEL_PATH, "config.json")):
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print(f"[跳过] Embedding 模型已存在: {EMBEDDING_MODEL_PATH}")
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return True
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print(f"[下载] BGE 中文向量模型 (bge-base-zh-v1.5)...")
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print(f" 目标路径: {EMBEDDING_MODEL_PATH}")
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try:
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("BAAI/bge-base-zh-v1.5")
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model.save(EMBEDDING_MODEL_PATH)
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print(f"[完成] Embedding 模型下载成功!")
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return True
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except Exception as e:
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print(f"[失败] Embedding 模型下载失败: {e}")
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return False
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def download_reranker_model():
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"""下载 BGE reranker 模型"""
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if os.path.exists(os.path.join(RERANK_MODEL_PATH, "config.json")):
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print(f"[跳过] Reranker 模型已存在: {RERANK_MODEL_PATH}")
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return True
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print(f"[下载] BGE Reranker 模型 (bge-reranker-base)...")
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print(f" 目标路径: {RERANK_MODEL_PATH}")
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try:
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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os.makedirs(RERANK_MODEL_PATH, exist_ok=True)
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model = AutoModelForSequenceClassification.from_pretrained("BAAI/bge-reranker-base")
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tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-base")
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model.save_pretrained(RERANK_MODEL_PATH)
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tokenizer.save_pretrained(RERANK_MODEL_PATH)
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print(f"[完成] Reranker 模型下载成功!")
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return True
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except Exception as e:
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print(f"[失败] Reranker 模型下载失败: {e}")
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print(f" (项目启动时会自动尝试下载，或可稍后手动重试)")
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return False
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if __name__ == "__main__":
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print("=" * 60)
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print("RAG 项目模型下载脚本")
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print("=" * 60)
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ok1 = download_embedding_model()
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print()
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ok2 = download_reranker_model()
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print()
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print("=" * 60)
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if ok1:
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print("Embedding 模型: OK (项目可以启动)")
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else:
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print("Embedding 模型: FAILED (项目无法启动，请检查网络)")
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if ok2:
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print("Reranker 模型: OK")
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else:
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print("Reranker 模型: FAILED (不影响启动，首次使用 rerank 时自动下载)")
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print("=" * 60)
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