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Integrate TiDB Vector Search with Django ORM
Learn how to integrate TiDB Vector Search with Django ORM to store embeddings and perform semantic search.

Integrate TiDB Vector Search with Django ORM

This tutorial walks you through how to use Django ORM 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 Cloud Serverless clusters.

Prerequisites

To complete this tutorial, you need:

Run the sample app

You can quickly learn about how to integrate TiDB Vector Search with Django ORM 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-django-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 Django django-tidb mysqlclient numpy python-dotenv

If you encounter installation issues with mysqlclient, refer to the mysqlclient official documentation.

What is django-tidb

django-tidb is a TiDB dialect for Django that enhances the Django ORM to support TiDB-specific features (For example, Vector Search) and resolves compatibility issues between TiDB and Django.

To install django-tidb, choose a version that matches your Django version. For example, if you are using django==4.2.*, install django-tidb==4.2.*. The minor version does not need to be the same. It is recommended to use the latest minor version.

For more information, refer to django-tidb repository.

Step 4. Configure the 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

Step 5. Run the demo

Migrate the database schema:

python manage.py migrate

Run the Django development server:

python manage.py runserver

Open your browser and visit http://127.0.0.1:8000 to try the demo application. Here are the available API paths:

API Path Description
POST: /insert_documents Insert documents with embeddings.
GET: /get_nearest_neighbors_documents Get the 3-nearest neighbor documents.
GET: /get_documents_within_distance Get documents within a certain distance.

Sample code snippets

You can refer to the following sample code snippets to complete your own application development.

Connect to the TiDB cluster

In the file sample_project/settings.py, add the following configurations:

dotenv.load_dotenv()

DATABASES = {
    "default": {
        # https://github.com/pingcap/django-tidb
        "ENGINE": "django_tidb",
        "HOST": os.environ.get("TIDB_HOST", "127.0.0.1"),
        "PORT": int(os.environ.get("TIDB_PORT", 4000)),
        "USER": os.environ.get("TIDB_USERNAME", "root"),
        "PASSWORD": os.environ.get("TIDB_PASSWORD", ""),
        "NAME": os.environ.get("TIDB_DATABASE", "test"),
        "OPTIONS": {
            "charset": "utf8mb4",
        },
    }
}

TIDB_CA_PATH = os.environ.get("TIDB_CA_PATH", "")
if TIDB_CA_PATH:
    DATABASES["default"]["OPTIONS"]["ssl_mode"] = "VERIFY_IDENTITY"
    DATABASES["default"]["OPTIONS"]["ssl"] = {
        "ca": TIDB_CA_PATH,
    }

You can create a .env file in the root directory of your project and set up the environment variables TIDB_HOST, TIDB_PORT, TIDB_USERNAME, TIDB_PASSWORD, TIDB_DATABASE, and TIDB_CA_PATH with the actual values of your TiDB cluster.

Create vector tables

Define a vector column

tidb-django provides a VectorField to store vector embeddings in a table.

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

class Document(models.Model):
   content = models.TextField()
   embedding = VectorField(dimensions=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(models.Model):
   content = models.TextField()
   # Note:
   #   - Using comment to add hnsw index is a temporary solution. In the future it will use `CREATE INDEX` syntax.
   #   - Currently the HNSW index cannot be changed after the table has been created.
   #   - Only Django >= 4.2 supports `db_comment`.
   embedding = VectorField(dimensions=3, db_comment="hnsw(distance=cosine)")

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

Store documents with embeddings

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

Search the nearest neighbor documents

TiDB Vector support below distance functions:

  • L1Distance
  • L2Distance
  • CosineDistance
  • NegativeInnerProduct

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

results = Document.objects.annotate(
   distance=CosineDistance('embedding', [1, 2, 3])
).order_by('distance')[: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.

results = Document.objects.annotate(
   distance=CosineDistance('embedding', [1, 2, 3])
).filter(distance__lt=0.2).order_by('distance')[:3]

See also