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.
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 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
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.
- Connection Type is set to
Copy the connection parameters from the connection dialog.
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]