Python SDK for TiDB AI: A unified data platform empowering developers to build next-generation AI applications.
Note
This Python package is under rapid development and its API may change. It is recommended to use a fixed version when installing, e.g., pytidb==0.0.12.
pip install pytidb # To use built-in embedding functions and rerankers: pip install "pytidb[models]" # To convert query results to pandas DataFrame: pip install pandas
Create a free TiDB cluster at tidbcloud.com.
import os
from pytidb import TiDBClient
tidb_client = TiDBClient.connect(
host=os.getenv("TIDB_HOST"),
port=int(os.getenv("TIDB_PORT")),
username=os.getenv("TIDB_USERNAME"),
password=os.getenv("TIDB_PASSWORD"),
database=os.getenv("TIDB_DATABASE"),
ensure_db=True,
)
PyTiDB automatically embeds text fields (e.g., text) and stores the vector embedding in a vector field (e.g., text_vec).
Create a table with an embedding function:
from pytidb.schema import TableModel, Field, FullTextField
from pytidb.embeddings import EmbeddingFunction
# Set API key for embedding provider.
tidb_client.configure_embedding_provider("openai", api_key=os.getenv("OPENAI_API_KEY"))
class Chunk(TableModel):
__tablename__ = "chunks"
id: int = Field(primary_key=True)
text: str = FullTextField()
text_vec: list[float] = EmbeddingFunction(
"openai/text-embedding-3-small"
).VectorField(source_field="text") # 👈 Defines the vector field.
user_id: int = Field()
table = tidb_client.create_table(schema=Chunk, if_exists="skip")
Bulk insert data:
table.bulk_insert([
Chunk(id=2, text="bar", user_id=2), # 👈 The text field is embedded and saved to text_vec automatically.
Chunk(id=3, text="baz", user_id=3),
Chunk(id=4, text="qux", user_id=4),
])
Vector Search
Vector search finds the most relevant records based on semantic similarity, so you don't need to include all keywords explicitly in your query.
df = (
table.search("<query>") # 👈 The query is embedded automatically.
.filter({"user_id": 2})
.limit(2)
.to_list()
)
# Output: A list of dicts.
See the Vector Search example for more details.
Full-text Search
Full-text search tokenizes the query and finds the most relevant records by matching exact keywords.
df = (
table.search("<query>", search_type="fulltext")
.limit(2)
.to_pydantic()
)
# Output: A list of pydantic model instances.
See the Full-text Search example for more details.
Hybrid Search
Hybrid search combines exact matching from full-text search with semantic understanding from vector search, delivering more relevant and reliable results.
df = (
table.search("<query>", search_type="hybrid")
.limit(2)
.to_pandas()
)
# Output: A pandas DataFrame.
See the Hybrid Search example for more details.
Image Search
Image search lets you find visually similar images using natural language descriptions or another image as a reference.
from PIL import Image
from pytidb.schema import TableModel, Field
from pytidb.embeddings import EmbeddingFunction
# Define a multi-modal embedding model.
jina_embed_fn = EmbeddingFunction("jina_ai/jina-embeddings-v4") # Using multi-modal embedding model.
class Pet(TableModel):
__tablename__ = "pets"
id: int = Field(primary_key=True)
image_uri: str = Field()
image_vec: list[float] = jina_embed_fn.VectorField(
source_field="image_uri",
source_type="image"
)
table = tidb_client.create_table(schema=Pet, if_exists="skip")
# Insert sample images ...
table.insert(Pet(image_uri="path/to/shiba_inu_14.jpg"))
# Search for images using natural language
results = table.search("shiba inu dog").limit(1).to_list()
# Search for images using an image ...
query_image = Image.open("shiba_inu_15.jpg")
results = table.search(query_image).limit(1).to_pydantic()
See the Image Search example for more details.
PyTiDB supports a variety of operators for flexible filtering:
Operator Description Example$eq Equal to {"field": {"$eq": "hello"}} $gt Greater than {"field": {"$gt": 1}} $gte Greater than or equal {"field": {"$gte": 1}} $lt Less than {"field": {"$lt": 1}} $lte Less than or equal {"field": {"$lte": 1}} $in In array {"field": {"$in": [1, 2, 3]}} $nin Not in array {"field": {"$nin": [1, 2, 3]}} $and Logical AND {"$and": [{"field1": 1}, {"field2": 2}]} $or Logical OR {"$or": [{"field1": 1}, {"field2": 2}]} ⛓ Join Structured and Unstructured Data
from pytidb import Session
from pytidb.sql import select
# Create a table to store user data:
class User(TableModel):
__tablename__ = "users"
id: int = Field(primary_key=True)
name: str = Field(max_length=20)
with Session(engine) as session:
query = (
select(Chunk).join(User, Chunk.user_id == User.id).where(User.name == "Alice")
)
chunks = session.exec(query).all()
[(c.id, c.text, c.user_id) for c in chunks]
PyTiDB supports transaction management, helping you avoid race conditions and ensure data consistency.
with tidb_client.session() as session:
initial_total_balance = tidb_client.query("SELECT SUM(balance) FROM players").scalar()
# Transfer 10 coins from player 1 to player 2
tidb_client.execute("UPDATE players SET balance = balance - 10 WHERE id = 1")
tidb_client.execute("UPDATE players SET balance = balance + 10 WHERE id = 2")
session.commit()
# or session.rollback()
final_total_balance = tidb_client.query("SELECT SUM(balance) FROM players").scalar()
assert final_total_balance == initial_total_balance
Tip
Click the button below to install TiDB MCP Server in Cursor. Then, confirm by clicking Install when prompted.
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