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Integrate TiDB Vector Search with peewee
Learn how to integrate TiDB Vector Search with peewee to store embeddings and perform semantic searches.

Integrate TiDB Vector Search with peewee

This tutorial walks you through how to use peewee to interact with the TiDB Vector Search, store embeddings, and perform vector search queries.

Warning:

The vector search feature is experimental. It is not recommended that you use it in the production environment. This feature might be changed without prior notice. If you find a bug, you can report an issue on GitHub.

Note:

The vector search feature is only available for TiDB Self-Managed clusters and TiDB Cloud Serverless clusters.

Prerequisites

To complete this tutorial, you need:

If you don't have a TiDB cluster, you can create one as follows:

If you don't have a TiDB cluster, you can create one as follows:

Run the sample app

You can quickly learn about how to integrate TiDB Vector Search with peewee by following the steps below.

Step 1. Clone the repository

Clone the tidb-vector-python repository to your local machine:

git clone https://github.com/pingcap/tidb-vector-python.git

Step 2. Create a virtual environment

Create a virtual environment for your project:

cd tidb-vector-python/examples/orm-peewee-quickstart
python3 -m venv .venv
source .venv/bin/activate

Step 3. Install required dependencies

Install the required dependencies for the demo project:

pip install -r requirements.txt

Alternatively, you can install the following packages for your project:

pip install peewee pymysql python-dotenv tidb-vector

Step 4. Configure the environment variables

Configure the environment variables depending on the TiDB deployment option you've selected.

For a TiDB Cloud Serverless cluster, take the following steps to obtain the cluster connection string and configure environment variables:

  1. Navigate to the Clusters page, and then click the name of your target cluster to go to its overview page.

  2. Click Connect in the upper-right corner. A connection dialog is displayed.

  3. Ensure the configurations in the connection dialog match your operating environment.

    • Connection Type is set to Public.
    • Branch is set to main.
    • Connect With is set to General.
    • Operating System matches your environment.

    Tip:

    If your program is running in Windows Subsystem for Linux (WSL), switch to the corresponding Linux distribution.

  4. Copy the connection parameters from the connection dialog.

    Tip:

    If you have not set a password yet, click Generate Password to generate a random password.

  5. In the root directory of your Python project, create a .env file and paste the connection parameters to the corresponding environment variables.

    • TIDB_HOST: The host of the TiDB cluster.
    • TIDB_PORT: The port of the TiDB cluster.
    • TIDB_USERNAME: The username to connect to the TiDB cluster.
    • TIDB_PASSWORD: The password to connect to the TiDB cluster.
    • TIDB_DATABASE: The database name to connect to.
    • TIDB_CA_PATH: The path to the root certificate file.

    The following is an example for macOS:

    TIDB_HOST=gateway01.****.prod.aws.tidbcloud.com
TIDB_PORT=4000
TIDB_USERNAME=********.root
TIDB_PASSWORD=********
TIDB_DATABASE=test
TIDB_CA_PATH=/etc/ssl/cert.pem

For a TiDB Self-Managed cluster, create a .env file in the root directory of your Python project. Copy the following content into the .env file, and modify the environment variable values according to the connection parameters of your TiDB cluster:

TIDB_HOST=127.0.0.1
TIDB_PORT=4000
TIDB_USERNAME=root
TIDB_PASSWORD=
TIDB_DATABASE=test

If you are running TiDB on your local machine, TIDB_HOST is 127.0.0.1 by default. The initial TIDB_PASSWORD is empty, so if you are starting the cluster for the first time, you can omit this field.

The following are descriptions for each parameter:

  • TIDB_HOST: The host of the TiDB cluster.
  • TIDB_PORT: The port of the TiDB cluster.
  • TIDB_USERNAME: The username to connect to the TiDB cluster.
  • TIDB_PASSWORD: The password to connect to the TiDB cluster.
  • TIDB_DATABASE: The name of the database you want to connect to.

Step 5. Run the demo

python peewee-quickstart.py

Example output:

Get 3-nearest neighbor documents:
  - distance: 0.00853986601633272
    document: fish
  - distance: 0.12712843905603044
    document: dog
  - distance: 0.7327387580875756
    document: tree
Get documents within a certain distance:
  - distance: 0.00853986601633272
    document: fish
  - distance: 0.12712843905603044
    document: dog

Sample code snippets

You can refer to the following sample code snippets to develop your application.

Create vector tables

Connect to TiDB cluster

import os
import dotenv

from peewee import Model, MySQLDatabase, SQL, TextField
from tidb_vector.peewee import VectorField

dotenv.load_dotenv()

# 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': os.environ.get('TIDB_CA_PATH', '/path/to/ca.pem'),
#     },
# }

db = MySQLDatabase(
    database=os.environ.get('TIDB_DATABASE', 'test'),
    user=os.environ.get('TIDB_USERNAME', 'root'),
    password=os.environ.get('TIDB_PASSWORD', ''),
    host=os.environ.get('TIDB_HOST', 'localhost'),
    port=int(os.environ.get('TIDB_PORT', '4000')),
    **connect_kwargs,
)

Define a vector column

Create a table with a column named peewee_demo_documents that stores a 3-dimensional vector.

class Document(Model):
    class Meta:
        database = db
        table_name = 'peewee_demo_documents'

    content = TextField()
    embedding = VectorField(3)

Store documents with embeddings

Document.create(content='dog', embedding=[1, 2, 1])
Document.create(content='fish', embedding=[1, 2, 4])
Document.create(content='tree', embedding=[1, 0, 0])

Search the nearest neighbor documents

Search for the top-3 documents that are semantically closest to the query vector [1, 2, 3] based on the cosine distance function.

distance = Document.embedding.cosine_distance([1, 2, 3]).alias('distance')
results = Document.select(Document, distance).order_by(distance).limit(3)

Search documents within a certain distance

Search for the documents whose cosine distance from the query vector [1, 2, 3] is less than 0.2.

distance_expression = Document.embedding.cosine_distance([1, 2, 3])
distance = distance_expression.alias('distance')
results = Document.select(Document, distance).where(distance_expression < 0.2).order_by(distance).limit(3)

See also