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TiDBVectorClient
✓ from tidb_vector import TiDBVectorClient
✗ from tidb_vector.integrations import TiDBVectorClient
Initialize TiDBVectorClient, insert vectors, and perform similarity search.
import os
from tidb_vector.integrations import TiDBVectorClient
# TiDB connection parameters (use env vars for auth)
host = os.environ.get('TIDB_HOST', '127.0.0.1')
port = int(os.environ.get('TIDB_PORT', '4000'))
user = os.environ.get('TIDB_USER', 'root')
password = os.environ.get('TIDB_PASSWORD', '')
database = os.environ.get('TIDB_DATABASE', 'test')
# Create vector table and client
connection_string = f'mysql+pymysql://{user}:{password}@{host}:{port}/{database}'
client = TiDBVectorClient(
table_name='vector_demo',
connection_string=connection_string,
vector_dimension=3,
distance_strategy='cosine'
)
# Insert vectors
ids = ['id1', 'id2', 'id3']
vectors = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
metadata = [
{'text': 'first'},
{'text': 'second'},
{'text': 'third'}
]
client.insert(ids=ids, vectors=vectors, metadata=metadata)
# Similarity search
results = client.query(query_vector=[1.0, 2.0, 3.0], top_k=2)
for r in results:
print(r.id, r.distance, r.metadata)
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Version history
0.0.15latest on PyPI · released Jul 15, 2025
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Dependencies
pymysqlrequiredRequired for MySQL connection to TiDB
sqlalchemyoptionalUsed for ORM-style operations