Build Real-Time Image Search Powered By Natural Language AI ⭐

Tutorials & Methods
, , , , ,
1

Build Real-Time Image Search Powered by Natural Language AI :star:

Discover a Fast, Scalable Way to Build a Live Image Search Engine Using Multimodal AI

A groundbreaking method has emerged for constructing a live image search engine that allows users to search using natural language queries like “a cute dog” or “an airplane”. By combining state-of-the-art multimodal AI models, vector databases, and real-time indexing, this system delivers results with astonishing speed and semantic accuracy.

:gear: Core Technologies

:brain: CLIP (ViT-L/14)
CLIP is a vision-language model that encodes both images and text into the same semantic vector space. It enables semantic search where users can type natural language prompts to retrieve related images.

:high_voltage: CocoIndex
An ultra-performant real-time data transformation framework. It:

  • Monitors image folders
  • Embeds images using CLIP
  • Streams updates to a vector database every 60 seconds

:link: CocoIndex on GitHub

:package: Qdrant
A vector database used to store and search image embeddings with high performance.

:rocket: FastAPI
A modern Python API framework used to build the backend server that handles natural language queries and delivers results.

:puzzle_piece: System Architecture

  1. Ingest & Embed Images

    • Images from a local folder (img/) are monitored continuously.

    • Each image is passed through a CLIP embedding function:

      @cocoindex.op.function(cache=True, behavior_version=1, gpu=True)
def embed_image(img_bytes: bytes) -> Vector:
    ...

  2. Store Embeddings in Qdrant

    • Each embedded vector is saved to Qdrant:

      img_embeddings.export("img_embeddings", Qdrant(...))

  3. Query with Natural Language

    • Text input is embedded using CLIP:

      def embed_query(text: str) -> list[float]:
    ...

    • The FastAPI /search endpoint finds semantically similar images in Qdrant:

      @app.get("/search")
def search(q: str, limit: int = 5):
    ...

  4. Serve Images and API

    • Images are served from /img
    • Backend served via uvicorn, CORS-enabled, and real-time indexed

:hammer_and_wrench: Setup Instructions

Create the Qdrant Collection

curl -X PUT 'http://localhost:6333/collections/image_search' \
-H 'Content-Type: application/json' \
-d '{
  "vectors": {
    "embedding": {
      "size": 768,
      "distance": "Cosine"
    }
  }
}'

Initialize CocoIndex

cocoindex setup main.py

Run Backend

uvicorn main:app --reload --host 0.0.0.0 --port 8000

Launch Frontend

cd frontend
npm install
npm run dev

Visit: http://localhost:5174

:high_voltage: Live Updates in Action

  • Drop new images into the img/ folder.
  • CocoIndex detects changes and updates the index within 1 minute.
  • Instantly searchable via the web interface.

:hammer_and_wrench: Optional Monitoring:
To view indexing progress live:

cocoindex server -ci main.py

Downloadable Version & Frontend Customization Version:

Here’s what you need:

:package: Downloadable Version (Full Project Setup)

To replicate and run the live image search engine locally, follow this folder structure and instructions:

project-root/
├── main.py                  # Backend with FastAPI + CocoIndex logic
├── img/                     # Drop your searchable images here
├── .env                     # Your environment config (e.g., Qdrant URLs)
├── frontend/                # Minimal React/Next.js front-end
│   ├── package.json
│   ├── public/
│   ├── src/
│   └── ...
└── requirements.txt         # Python dependencies

:wrench: Requirements

Python
Create a requirements.txt:

fastapi
uvicorn
cocoindex
qdrant-client
torch
transformers
Pillow
python-dotenv

Install with:

pip install -r requirements.txt

Frontend
In /frontend/:

npm install
npm run dev

:link: Repo Initialization

Clone and adapt the CocoIndex template from:
https://github.com/cocoindex/cocoindex

:artist_palette: Frontend Customization Tips

The default frontend is intentionally minimal. Here’s how you can customize it:

1. Search Bar with Suggestions

Use libraries like react-autosuggest or @mui/material for autocomplete functionality.

2. Display Image Results in Grid

Enhance UX with a responsive masonry layout using:

npm install react-masonry-css

<Masonry
  breakpointCols={3}
  className="my-masonry-grid"
  columnClassName="my-masonry-grid_column"
>
  {images.map(img => (
    <img src={`/img/${img.filename}`} key={img.filename} />
  ))}
</Masonry>

3. Result Scoring

Include the relevance score returned from Qdrant:

<p>Match Score: {result.score.toFixed(2)}</p>

4. Drag-and-Drop Upload

Enable instant indexing via drag-n-drop:

  • Use react-dropzone
  • Upload to /img/ and trigger cocoindex auto-indexing

:rocket: Deployment Suggestions

  • Use Docker Compose to run Qdrant, backend, and frontend in containers.
  • Host on Render, Railway, or Vercel.
  • Monitor CocoIndex with cocoindex server -ci main.py.

Download ZIP Boilerplate:

:white_check_mark: Your Live Image Search Boilerplate is ready!

:package: Download ZIP File

This includes:

  • :brain: Backend with FastAPI, CLIP embedding, Qdrant integration
  • :framed_picture: Frontend (React + Vite)
  • :file_folder: Image folder
  • :gear: .env setup and requirements.txt

:unlocked: This method leverages cutting-edge AI models and real-time processing frameworks to create a highly responsive, intelligent image search system. Ideal for developers, AI enthusiasts, or enterprises needing semantic visual search capability.

ENJOY & HAPPY LEARNING! :heart:

Appreciate the share, Don’t be cheap!

I aim to provide the best of the best, trusted, reliable, and useful content that could!

10 Likes
2

Thanks SAM

1 Like
3

it’s really wonderful post.

1 Like