This is a Python client for TiDB Vector.
Now only TiDB Cloud Serverless cluster support vector data type, see this blog for more information.
pip install tidb-vector
TiDB vector supports below distance functions:
L1Distance
L2Distance
CosineDistance
NegativeInnerProduct
It also supports using hnsw index with l2 or cosine distance to speed up the search, for more details see Vector Search Indexes in TiDB
Supports following orm or framework:
Learn how to connect to TiDB Serverless in the TiDB Cloud documentation.
Define table with vector field
from sqlalchemy import Column, Integer, create_engine
from sqlalchemy.orm import declarative_base
from tidb_vector.sqlalchemy import VectorType
engine = create_engine('mysql://****.root:******@gateway01.xxxxxx.shared.aws.tidbcloud.com:4000/test')
Base = declarative_base()
class Test(Base):
__tablename__ = 'test'
id = Column(Integer, primary_key=True)
embedding = Column(VectorType(3))
# or add hnsw index when creating table
class TestWithIndex(Base):
__tablename__ = 'test_with_index'
id = Column(Integer, primary_key=True)
embedding = Column(VectorType(3), comment="hnsw(distance=l2)")
Base.metadata.create_all(engine)
Insert vector data
test = Test(embedding=[1, 2, 3])
session.add(test)
session.commit()
Get the nearest neighbors
session.scalars(select(Test).order_by(Test.embedding.l2_distance([1, 2, 3.1])).limit(5))
Get the distance
session.scalars(select(Test.embedding.l2_distance([1, 2, 3.1])))
Get within a certain distance
session.scalars(select(Test).filter(Test.embedding.l2_distance([1, 2, 3.1]) < 0.2))
To use vector field in Django, you need to use django-tidb
.
Define peewee table with vector field
from peewee import Model, MySQLDatabase
from tidb_vector.peewee import VectorField
# Using `pymysql` as the driver
connect_kwargs = {
'ssl_verify_cert': True,
'ssl_verify_identity': True,
}
# Using `mysqlclient` as the driver
connect_kwargs = {
'ssl_mode': 'VERIFY_IDENTITY',
'ssl': {
# Root certificate default path
# https://docs.pingcap.com/tidbcloud/secure-connections-to-serverless-clusters/#root-certificate-default-path
'ca': '/etc/ssl/cert.pem' # MacOS
},
}
db = MySQLDatabase(
'peewee_test',
user='xxxxxxxx.root',
password='xxxxxxxx',
host='xxxxxxxx.shared.aws.tidbcloud.com',
port=4000,
**connect_kwargs,
)
class TestModel(Model):
class Meta:
database = db
table_name = 'test'
embedding = VectorField(3)
# or add hnsw index when creating table
class TestModelWithIndex(Model):
class Meta:
database = db
table_name = 'test_with_index'
embedding = VectorField(3, constraints=[SQL("COMMENT 'hnsw(distance=l2)'")])
db.connect()
db.create_tables([TestModel, TestModelWithIndex])
Insert vector data
TestModel.create(embedding=[1, 2, 3])
Get the nearest neighbors
TestModel.select().order_by(TestModel.embedding.l2_distance([1, 2, 3.1])).limit(5)
Get the distance
TestModel.select(TestModel.embedding.cosine_distance([1, 2, 3.1]).alias('distance'))
Get within a certain distance
TestModel.select().where(TestModel.embedding.l2_distance([1, 2, 3.1]) < 0.5)
Within the framework, you can directly utilize the built-in TiDBVectorClient
, as demonstrated by integrations like Langchain and Llama index, to seamlessly interact with TiDB Vector. This approach abstracts away the need to manage the underlying ORM, simplifying your interaction with the vector store.
We provide TiDBVectorClient
which is based on sqlalchemy, you need to use pip install tidb-vector[client]
to install it.
Create a TiDBVectorClient
instance:
from tidb_vector.integrations import TiDBVectorClient
TABLE_NAME = 'vector_test'
CONNECTION_STRING = 'mysql+pymysql://<USER>:<PASSWORD>@<HOST>:4000/<DB>?ssl_verify_cert=true&ssl_verify_identity=true'
tidb_vs = TiDBVectorClient(
# the table which will store the vector data
table_name=TABLE_NAME,
# tidb connection string
connection_string=CONNECTION_STRING,
# the dimension of the vector, in this example, we use the ada model, which has 1536 dimensions
vector_dimension=1536,
# if recreate the table if it already exists
drop_existing_table=True,
)
Bulk insert:
ids = [
"f8e7dee2-63b6-42f1-8b60-2d46710c1971",
"8dde1fbc-2522-4ca2-aedf-5dcb2966d1c6",
"e4991349-d00b-485c-a481-f61695f2b5ae",
]
documents = ["foo", "bar", "baz"]
embeddings = [
text_to_embedding("foo"),
text_to_embedding("bar"),
text_to_embedding("baz"),
]
metadatas = [
{"page": 1, "category": "P1"},
{"page": 2, "category": "P1"},
{"page": 3, "category": "P2"},
]
tidb_vs.insert(
ids=ids,
texts=documents,
embeddings=embeddings,
metadatas=metadatas,
)
Query:
tidb_vs.query(text_to_embedding("foo"), k=3)
# query with filter
tidb_vs.query(text_to_embedding("foo"), k=3, filter={"category": "P1"})
Bulk delete:
tidb_vs.delete(["f8e7dee2-63b6-42f1-8b60-2d46710c1971"])
# delete with filter
tidb_vs.delete(["f8e7dee2-63b6-42f1-8b60-2d46710c1971"], filter={"category": "P1"})
There are some examples to show how to use the tidb-vector-python to interact with TiDB Vector in different scenarios.
- OpenAI Embedding: use the OpenAI embedding model to generate vectors for text data, store them in TiDB Vector, and search for similar text.
- Image Search: use the OpenAI CLIP model to generate vectors for image and text, store them in TiDB Vector, and search for similar images.
- LlamaIndex RAG with UI: use the LlamaIndex to build an RAG(Retrieval-Augmented Generation) application.
for more examples, see the examples directory.