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ailine/tools/test/check_qdrant.py

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2026-05-04 04:28:32 +08:00
#!/usr/bin/env python3
"""
检查 Qdrant 集合里的数据结构
"""
import asyncio
import os
import sys
from backend.rag_core import QdrantHybridStore
from backend.app.model_services import get_embedding_service
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def check_qdrant_data():
"""检查 Qdrant 中的数据结构"""
print("="*70)
print("检查 Qdrant 中的数据结构...")
print("="*70)
embeddings = get_embedding_service()
vs = QdrantHybridStore(collection_name="rag_documents", embeddings=embeddings)
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client = vs.get_qdrant_client()
# 先获取几个点看看 payload 结构
print("\n获取 5 个随机文档:")
results = client.scroll(
collection_name="rag_documents",
limit=5,
with_payload=True,
with_vectors=True
)
for i, point in enumerate(results[0], 1):
print(f"\n{i}. ID: {point.id}")
print(f" Payload: {point.payload}")
print(f" Payload 键: {list(point.payload.keys())}")
if "text" in point.payload:
text = point.payload["text"]
print(f" Text 长度: {len(text)}")
print(f" Text 预览: {text[:150]}...")
if "page_content" in point.payload:
print(f" page_content: {point.payload['page_content'][:150]}...")
# 看看向量
if point.vector:
print(f" 向量存在: {type(point.vector)}")
if isinstance(point.vector, dict):
print(f" 向量键: {list(point.vector.keys())}")
def check_sparse_embedder():
"""检查稀疏嵌入器"""
from backend.rag_core import get_sparse_embedder
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print("\n" + "="*70)
print("检查稀疏嵌入器...")
print("="*70)
sparse_embedder = get_sparse_embedder()
print(f"\n稀疏嵌入器: {sparse_embedder}")
print(f"Vocabulary 大小: {len(sparse_embedder.model.vocab)}")
print(f"示例查询: '冬天 食物'")
# 用中文试试
sparse_vec = sparse_embedder.embed_query("冬天 食物")
print(f"\n生成的稀疏向量:")
print(f" 索引数量: {len(sparse_vec['indices'])}")
print(f" 索引: {sparse_vec['indices'][:10]}")
print(f" 值: {sparse_vec['values'][:10]}")
if __name__ == "__main__":
check_qdrant_data()
check_sparse_embedder()