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Pet Image Search Demo

This example showcases how to build a powerful image search application by combining TiDB's vector search capabilities with multimodal embedding models.

With just a few lines of code, you can create an intelligent search system that understands both text and images.

  • 🔍 Text-to-Image Search: Find the perfect pet photos by describing what you're looking for in natural language - from "fluffy orange cat"
  • 🖼️ Image-to-Image Search: Upload a photo and instantly discover visually similar pets based on breed, color, pose and more

PyTiDB Image Search Demo

Pet image search via multimodal embeddings

Prerequisites

How to run

Step 1: Clone the repository to local

git clone https://github.com/pingcap/pytidb.git
cd pytidb/examples/image_search/

Step 2: Install the required packages

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r reqs.txt

Step 3: Set up environment variables

Go to TiDB Cloud console and get the connection parameters, then set up the environment variable like this:

cat > .env <<EOF
TIDB_HOST={gateway-region}.prod.aws.tidbcloud.com
TIDB_PORT=4000
TIDB_USERNAME={prefix}.root
TIDB_PASSWORD={password}
TIDB_DATABASE=test

JINA_AI_API_KEY={your-jina-api-key}
EOF

Step 3: Download and extract the dataset

In this demo, we will use the Oxford Pets dataset to load pet images to the database for search.

For Linux/MacOS:

# Download the dataset
curl -L -o oxford_pets.tar.gz "https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz"

# Extract the dataset
mkdir -p oxford_pets
tar -xzf oxford_pets.tar.gz -C oxford_pets

Step 4: Run the app

streamlit run app.py

Open http://localhost:8501 in your browser.

Step 5: Load data

In the sample app, you can click the Load Sample Data button to load some sample data to the database.

Or if you want to load all the data in the Oxford Pets dataset, click the Load All Data button.

Step 6: Search

  1. Select the Search type in the sidebar
  2. Input a text description of the pet you're looking for, or upload a photo of a dog or cat
  3. Click the Search button
  • Source Code: View on GitHub

  • Category: Search

  • Description: Build an image search application using multimodal embeddings for both text-to-image and image-to-image search.

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