205 lines
6.9 KiB
Python
205 lines
6.9 KiB
Python
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"""
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详细性能分析 - 确认每个环节的耗时
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"""
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import asyncio
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import sys
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import time
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
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from sqlalchemy.orm import sessionmaker
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from app.services.mid.kb_search_dynamic_tool import KbSearchDynamicTool, KbSearchDynamicConfig
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from app.core.config import get_settings
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from app.core.qdrant_client import get_qdrant_client
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from app.services.embedding import get_embedding_provider
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async def profile_detailed():
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"""详细分析每个环节的耗时"""
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settings = get_settings()
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print("=" * 80)
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print("详细性能分析")
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print("=" * 80)
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query = "三年级语文学习"
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tenant_id = "szmp@ash@2026"
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metadata_filter = {"grade": "三年级", "subject": "语文"}
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# 1. Embedding 生成（应该已预初始化）
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print("\n📊 1. Embedding 生成")
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print("-" * 80)
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start = time.time()
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embedding_service = await get_embedding_provider()
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init_time = (time.time() - start) * 1000
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start = time.time()
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embedding_result = await embedding_service.embed_query(query)
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embed_time = (time.time() - start) * 1000
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# 获取 embedding 向量
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if hasattr(embedding_result, 'embedding_full'):
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query_vector = embedding_result.embedding_full
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elif hasattr(embedding_result, 'embedding'):
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query_vector = embedding_result.embedding
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else:
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query_vector = embedding_result
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print(f" 获取服务实例: {init_time:.2f} ms")
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print(f" Embedding 生成: {embed_time:.2f} ms")
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print(f" 向量维度: {len(query_vector)}")
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# 2. 获取 collections 列表（带缓存）
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print("\n📊 2. 获取 collections 列表")
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print("-" * 80)
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client = await get_qdrant_client()
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qdrant_client = await client.get_client()
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start = time.time()
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from app.services.metadata_cache_service import get_metadata_cache_service
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cache_service = await get_metadata_cache_service()
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cache_key = f"collections:{tenant_id}"
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# 尝试从缓存获取
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redis_client = await cache_service._get_redis()
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cache_hit = False
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if redis_client and cache_service._enabled:
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cached = await redis_client.get(cache_key)
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if cached:
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import json
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tenant_collections = json.loads(cached)
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cache_hit = True
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cache_time = (time.time() - start) * 1000
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print(f" ✅ 缓存命中: {cache_time:.2f} ms")
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print(f" Collections: {tenant_collections}")
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if not cache_hit:
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import json
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# 从 Qdrant 查询
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start = time.time()
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collections = await qdrant_client.get_collections()
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safe_tenant_id = tenant_id.replace('@', '_')
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prefix = f"kb_{safe_tenant_id}"
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tenant_collections = [
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c.name for c in collections.collections
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if c.name.startswith(prefix)
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]
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tenant_collections.sort()
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db_time = (time.time() - start) * 1000
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print(f" ❌ 缓存未命中，从 Qdrant 查询: {db_time:.2f} ms")
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print(f" Collections: {tenant_collections}")
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# 缓存结果
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if redis_client and cache_service._enabled:
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await redis_client.setex(cache_key, 300, json.dumps(tenant_collections))
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print(f" 已缓存到 Redis")
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# 3. Qdrant 搜索（每个 collection）
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print("\n📊 3. Qdrant 搜索")
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print("-" * 80)
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from qdrant_client.models import FieldCondition, Filter, MatchValue
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# 构建 filter
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start = time.time()
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must_conditions = []
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for key, value in metadata_filter.items():
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field_path = f"metadata.{key}"
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condition = FieldCondition(
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key=field_path,
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match=MatchValue(value=value),
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)
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must_conditions.append(condition)
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qdrant_filter = Filter(must=must_conditions) if must_conditions else None
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filter_time = (time.time() - start) * 1000
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print(f" 构建 filter: {filter_time:.2f} ms")
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# 逐个 collection 搜索
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total_search_time = 0
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for collection_name in tenant_collections:
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print(f"\n Collection: {collection_name}")
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# 检查是否存在
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start = time.time()
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exists = await qdrant_client.collection_exists(collection_name)
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check_time = (time.time() - start) * 1000
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print(f" 检查存在: {check_time:.2f} ms")
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if not exists:
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print(f" ❌ 不存在")
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continue
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# 搜索
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start = time.time()
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try:
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results = await qdrant_client.query_points(
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collection_name=collection_name,
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query=query_vector,
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using="full",
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limit=5,
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score_threshold=0.5,
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query_filter=qdrant_filter,
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)
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search_time = (time.time() - start) * 1000
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total_search_time += search_time
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print(f" 搜索时间: {search_time:.2f} ms")
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print(f" 结果数: {len(results.points)}")
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except Exception as e:
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print(f" ❌ 搜索失败: {e}")
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print(f"\n 总搜索时间: {total_search_time:.2f} ms")
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# 4. 完整 KB Search 流程
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print("\n📊 4. 完整 KB Search 流程")
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print("-" * 80)
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engine = create_async_engine(settings.database_url)
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async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
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async with async_session() as session:
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config = KbSearchDynamicConfig(
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enabled=True,
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top_k=5,
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timeout_ms=30000,
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min_score_threshold=0.5,
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)
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tool = KbSearchDynamicTool(session=session, config=config)
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start = time.time()
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result = await tool.execute(
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query=query,
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tenant_id=tenant_id,
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scene="学习方案",
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top_k=5,
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context=metadata_filter,
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)
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total_time = (time.time() - start) * 1000
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print(f" 总耗时: {total_time:.2f} ms")
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print(f" 工具内部耗时: {result.duration_ms} ms")
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print(f" 结果数: {len(result.hits)}")
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# 5. 总结
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print("\n" + "=" * 80)
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print("📈 耗时总结")
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print("=" * 80)
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print(f"\n各环节耗时:")
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print(f" Embedding 获取服务: {init_time:.2f} ms")
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print(f" Embedding 生成: {embed_time:.2f} ms")
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print(f" Collections 获取: {cache_time if cache_hit else db_time:.2f} ms")
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print(f" Filter 构建: {filter_time:.2f} ms")
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print(f" Qdrant 搜索: {total_search_time:.2f} ms")
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print(f" 完整流程: {total_time:.2f} ms")
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other_time = total_time - embed_time - (cache_time if cache_hit else db_time) - filter_time - total_search_time
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print(f" 其他开销: {other_time:.2f} ms")
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print("\n" + "=" * 80)
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if __name__ == "__main__":
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asyncio.run(profile_detailed())
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