247 lines
8.3 KiB
Python
247 lines
8.3 KiB
Python
|
"""
|
|||
|
|
详细分析完整参数查询的耗时
|
|||
|
|
对比带 metadata_filter 和不带的区别
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
import asyncio
|
|||
|
|
import sys
|
|||
|
|
import time
|
|||
|
|
from pathlib import Path
|
|||
|
|
|
|||
|
|
sys.path.insert(0, str(Path(__file__).parent.parent))
|
|||
|
|
|
|||
|
|
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
|
|||
|
|
from sqlalchemy.orm import sessionmaker
|
|||
|
|
from app.services.mid.kb_search_dynamic_tool import KbSearchDynamicTool, KbSearchDynamicConfig
|
|||
|
|
from app.core.config import get_settings
|
|||
|
|
from app.core.qdrant_client import get_qdrant_client
|
|||
|
|
from qdrant_client.models import FieldCondition, Filter, MatchValue
|
|||
|
|
|
|||
|
|
|
|||
|
|
async def profile_step_by_step():
|
|||
|
|
"""逐步分析完整参数查询的耗时"""
|
|||
|
|
settings = get_settings()
|
|||
|
|
|
|||
|
|
print("=" * 80)
|
|||
|
|
print("完整参数查询耗时分析")
|
|||
|
|
print("=" * 80)
|
|||
|
|
|
|||
|
|
query = "三年级语文学习"
|
|||
|
|
tenant_id = "szmp@ash@2026"
|
|||
|
|
metadata_filter = {"grade": "三年级", "subject": "语文"}
|
|||
|
|
|
|||
|
|
# 1. Embedding 生成
|
|||
|
|
print("\n📊 1. Embedding 生成")
|
|||
|
|
print("-" * 80)
|
|||
|
|
from app.services.embedding import get_embedding_provider
|
|||
|
|
|
|||
|
|
start = time.time()
|
|||
|
|
embedding_service = await get_embedding_provider()
|
|||
|
|
init_time = time.time() - start
|
|||
|
|
|
|||
|
|
start = time.time()
|
|||
|
|
embedding_result = await embedding_service.embed_query(query)
|
|||
|
|
embed_time = (time.time() - start) * 1000
|
|||
|
|
|
|||
|
|
# 获取 embedding 向量
|
|||
|
|
if hasattr(embedding_result, 'embedding_full'):
|
|||
|
|
query_vector = embedding_result.embedding_full
|
|||
|
|
elif hasattr(embedding_result, 'embedding'):
|
|||
|
|
query_vector = embedding_result.embedding
|
|||
|
|
else:
|
|||
|
|
query_vector = embedding_result
|
|||
|
|
|
|||
|
|
print(f" 初始化时间: {init_time * 1000:.2f} ms")
|
|||
|
|
print(f" Embedding 生成: {embed_time:.2f} ms")
|
|||
|
|
print(f" 向量维度: {len(query_vector)}")
|
|||
|
|
|
|||
|
|
# 2. 获取 collections 列表
|
|||
|
|
print("\n📊 2. 获取 collections 列表")
|
|||
|
|
print("-" * 80)
|
|||
|
|
client = await get_qdrant_client()
|
|||
|
|
qdrant_client = await client.get_client()
|
|||
|
|
|
|||
|
|
start = time.time()
|
|||
|
|
collections = await qdrant_client.get_collections()
|
|||
|
|
safe_tenant_id = tenant_id.replace('@', '_')
|
|||
|
|
prefix = f"kb_{safe_tenant_id}"
|
|||
|
|
tenant_collections = [
|
|||
|
|
c.name for c in collections.collections
|
|||
|
|
if c.name.startswith(prefix)
|
|||
|
|
]
|
|||
|
|
list_time = (time.time() - start) * 1000
|
|||
|
|
|
|||
|
|
print(f" 获取 collections: {list_time:.2f} ms")
|
|||
|
|
print(f" Collections: {tenant_collections}")
|
|||
|
|
|
|||
|
|
# 3. 构建 metadata filter
|
|||
|
|
print("\n📊 3. 构建 metadata filter")
|
|||
|
|
print("-" * 80)
|
|||
|
|
start = time.time()
|
|||
|
|
|
|||
|
|
must_conditions = []
|
|||
|
|
for key, value in metadata_filter.items():
|
|||
|
|
field_path = f"metadata.{key}"
|
|||
|
|
condition = FieldCondition(
|
|||
|
|
key=field_path,
|
|||
|
|
match=MatchValue(value=value),
|
|||
|
|
)
|
|||
|
|
must_conditions.append(condition)
|
|||
|
|
qdrant_filter = Filter(must=must_conditions) if must_conditions else None
|
|||
|
|
|
|||
|
|
filter_time = (time.time() - start) * 1000
|
|||
|
|
print(f" 构建 filter: {filter_time:.2f} ms")
|
|||
|
|
print(f" Filter: {qdrant_filter}")
|
|||
|
|
|
|||
|
|
# 4. 逐个 collection 搜索（带 filter）
|
|||
|
|
print("\n📊 4. Qdrant 搜索（带 metadata filter）")
|
|||
|
|
print("-" * 80)
|
|||
|
|
|
|||
|
|
total_search_time = 0
|
|||
|
|
total_results = 0
|
|||
|
|
|
|||
|
|
for collection_name in tenant_collections:
|
|||
|
|
print(f"\n Collection: {collection_name}")
|
|||
|
|
|
|||
|
|
# 检查是否存在
|
|||
|
|
start = time.time()
|
|||
|
|
exists = await qdrant_client.collection_exists(collection_name)
|
|||
|
|
check_time = (time.time() - start) * 1000
|
|||
|
|
print(f" 检查存在: {check_time:.2f} ms")
|
|||
|
|
|
|||
|
|
if not exists:
|
|||
|
|
print(f" ❌ 不存在")
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# 搜索（带 filter）
|
|||
|
|
start = time.time()
|
|||
|
|
try:
|
|||
|
|
results = await qdrant_client.query_points(
|
|||
|
|
collection_name=collection_name,
|
|||
|
|
query=query_vector,
|
|||
|
|
using="full",
|
|||
|
|
limit=5,
|
|||
|
|
score_threshold=0.5,
|
|||
|
|
query_filter=qdrant_filter,
|
|||
|
|
)
|
|||
|
|
search_time = (time.time() - start) * 1000
|
|||
|
|
total_search_time += search_time
|
|||
|
|
total_results += len(results.points)
|
|||
|
|
|
|||
|
|
print(f" 搜索时间: {search_time:.2f} ms")
|
|||
|
|
print(f" 结果数: {len(results.points)}")
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f" ❌ 搜索失败: {e}")
|
|||
|
|
|
|||
|
|
print(f"\n 总搜索时间: {total_search_time:.2f} ms")
|
|||
|
|
print(f" 总结果数: {total_results}")
|
|||
|
|
|
|||
|
|
# 5. 对比：不带 filter 的搜索
|
|||
|
|
print("\n📊 5. Qdrant 搜索（不带 metadata filter）对比")
|
|||
|
|
print("-" * 80)
|
|||
|
|
|
|||
|
|
total_search_time_no_filter = 0
|
|||
|
|
total_results_no_filter = 0
|
|||
|
|
|
|||
|
|
for collection_name in tenant_collections:
|
|||
|
|
start = time.time()
|
|||
|
|
try:
|
|||
|
|
results = await qdrant_client.query_points(
|
|||
|
|
collection_name=collection_name,
|
|||
|
|
query=query_vector,
|
|||
|
|
using="full",
|
|||
|
|
limit=5,
|
|||
|
|
score_threshold=0.5,
|
|||
|
|
# 不带 filter
|
|||
|
|
)
|
|||
|
|
search_time = (time.time() - start) * 1000
|
|||
|
|
total_search_time_no_filter += search_time
|
|||
|
|
total_results_no_filter += len(results.points)
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f" {collection_name}: 失败 {e}")
|
|||
|
|
|
|||
|
|
print(f" 总搜索时间（无 filter）: {total_search_time_no_filter:.2f} ms")
|
|||
|
|
print(f" 总结果数（无 filter）: {total_results_no_filter}")
|
|||
|
|
|
|||
|
|
# 6. 完整 KB Search 流程
|
|||
|
|
print("\n📊 6. 完整 KB Search 流程（带 context）")
|
|||
|
|
print("-" * 80)
|
|||
|
|
|
|||
|
|
engine = create_async_engine(settings.database_url)
|
|||
|
|
async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
|
|||
|
|
|
|||
|
|
async with async_session() as session:
|
|||
|
|
config = KbSearchDynamicConfig(
|
|||
|
|
enabled=True,
|
|||
|
|
top_k=5,
|
|||
|
|
timeout_ms=30000,
|
|||
|
|
min_score_threshold=0.5,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
tool = KbSearchDynamicTool(session=session, config=config)
|
|||
|
|
|
|||
|
|
start = time.time()
|
|||
|
|
result = await tool.execute(
|
|||
|
|
query=query,
|
|||
|
|
tenant_id=tenant_id,
|
|||
|
|
scene="学习方案",
|
|||
|
|
top_k=5,
|
|||
|
|
context=metadata_filter,
|
|||
|
|
)
|
|||
|
|
total_time = (time.time() - start) * 1000
|
|||
|
|
|
|||
|
|
print(f" 总耗时: {total_time:.2f} ms")
|
|||
|
|
print(f" 工具内部耗时: {result.duration_ms} ms")
|
|||
|
|
print(f" 结果数: {len(result.hits)}")
|
|||
|
|
print(f" 应用的 filter: {result.applied_filter}")
|
|||
|
|
|
|||
|
|
# 7. 对比：不带 context 的完整流程
|
|||
|
|
print("\n📊 7. 完整 KB Search 流程（不带 context）")
|
|||
|
|
print("-" * 80)
|
|||
|
|
|
|||
|
|
async with async_session() as session:
|
|||
|
|
tool = KbSearchDynamicTool(session=session, config=config)
|
|||
|
|
|
|||
|
|
start = time.time()
|
|||
|
|
result = await tool.execute(
|
|||
|
|
query=query,
|
|||
|
|
tenant_id=tenant_id,
|
|||
|
|
scene="学习方案",
|
|||
|
|
top_k=5,
|
|||
|
|
# 不带 context
|
|||
|
|
)
|
|||
|
|
total_time_no_context = (time.time() - start) * 1000
|
|||
|
|
|
|||
|
|
print(f" 总耗时: {total_time_no_context:.2f} ms")
|
|||
|
|
print(f" 工具内部耗时: {result.duration_ms} ms")
|
|||
|
|
print(f" 结果数: {len(result.hits)}")
|
|||
|
|
|
|||
|
|
# 8. 总结
|
|||
|
|
print("\n" + "=" * 80)
|
|||
|
|
print("📈 耗时分析总结")
|
|||
|
|
print("=" * 80)
|
|||
|
|
|
|||
|
|
print(f"\n带 metadata filter:")
|
|||
|
|
print(f" Embedding: {embed_time:.2f} ms")
|
|||
|
|
print(f" 获取 collections: {list_time:.2f} ms")
|
|||
|
|
print(f" Qdrant 搜索: {total_search_time:.2f} ms")
|
|||
|
|
print(f" 完整流程: {total_time:.2f} ms")
|
|||
|
|
|
|||
|
|
print(f"\n不带 metadata filter:")
|
|||
|
|
print(f" Qdrant 搜索: {total_search_time_no_filter:.2f} ms")
|
|||
|
|
print(f" 完整流程: {total_time_no_context:.2f} ms")
|
|||
|
|
|
|||
|
|
print(f"\nMetadata filter 额外开销:")
|
|||
|
|
print(f" Qdrant 搜索: {total_search_time - total_search_time_no_filter:.2f} ms")
|
|||
|
|
print(f" 完整流程: {total_time - total_time_no_context:.2f} ms")
|
|||
|
|
|
|||
|
|
if total_search_time > total_search_time_no_filter:
|
|||
|
|
print(f"\n⚠️ 带 filter 的搜索更慢，可能原因:")
|
|||
|
|
print(f" - Filter 增加了索引查找的复杂度")
|
|||
|
|
print(f" - 需要匹配 metadata 字段")
|
|||
|
|
print(f" - 建议: 检查 Qdrant 的 payload 索引配置")
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
asyncio.run(profile_step_by_step())
|