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.

Note

TiDB Vector Search is currently in beta and only available for TiDB Serverless clusters.

Prerequisites

To complete this tutorial, you need:

Python 3.8 or higher installed.
Git installed.
A TiDB Serverless cluster. Follow creating a TiDB Serverless cluster to create your own TiDB Cloud cluster if you don't have one.

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

For your existing project, you can install the following packages:

pip install peewee pymysql python-dotenv tidb-vector

Step 4. Configure the environment variables

Navigate to the Clusters page, and then click the name of your target cluster to go to its overview page.
Click Connect in the upper-right corner. A connection dialog is displayed.
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.
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.
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
```

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)

Define a vector column optimized with index

Define a 3-dimensional vector column and optimize it with a vector search index (HNSW index).

class DocumentWithIndex(Model):
    class Meta:
        database = db
        table_name = 'peewee_demo_documents_with_index'

    content = TextField()
    embedding = VectorField(3, constraints=[SQL("COMMENT 'hnsw(distance=cosine)'")])

TiDB will use this index to accelerate vector search queries based on the cosine distance function.

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)