title	emoji	colorFrom	colorTo	sdk	pinned	license	short_description
Qdrant Webinar: Cloud Inference	🖼️	blue	yellow	docker	false	apache-2.0	Personal Image Catalog

webinar-cloud-inference

This repository contains materials from the hands-on webinar "How to Build a Multimodal Search Stack with One API". It implements a personal image catalog that can be searched by image or text, with additional object detection capabilities.

Software Stack

The project is built using Python and JavaScript, integrating with external services through their APIs. The multimodal search capabilities are implemented using Qdrant Cloud Inference, with FastAPI serving as the backend API and a modern frontend built with Vite and DaisyUI.

Prerequisites

This project requires Qdrant Cloud as it uses Cloud Inference features that are not available in local Qdrant instances.

You'll need to sign up for a free account on Qdrant Cloud to get:

• A cluster URL to connect to your instance
• An API key for authentication
• Access to Cloud Inference capabilities

You'll need Python 3.10 or higher installed, and we recommend using uv for dependency management. For the frontend, you'll need Node.js 18 or higher.

Since we'll be working with cloud inference, no GPU access is required. Install all necessary libraries with a single command:

# Backend dependencies
cd backend-app
uv sync

# Frontend dependencies
cd ../frontend-app
npm install

The project uses different models for different tasks, specifically:

• YOLO for object detection
• CLIP for vision-language embeddings
• Qdrant Cloud Inference for vector operations

While you can swap these models with alternatives in the code, we cannot guarantee the system will maintain the same performance level.

Configuration

Create a .env file in the backend-app directory with the following entries:

# Qdrant configuration
QDRANT_URL=your_qdrant_cluster_url
QDRANT_API_KEY=your_qdrant_api_key
QDRANT_COLLECTION_NAME=your_collection_name

# YOLO Model Configuration (optional)
YOLO_MODEL=yolo11s.pt

# Feature Flags (optional)
ENABLE_INGEST=true

You can rename .env.example to .env and fill in the values, or set these as environment variables.

Feature Flags

The application supports feature flags to control functionality:

• ENABLE_INGEST: Controls whether the ingest functionality is available. When set to false, both the frontend and backend will hide the ingest features. Defaults to true.

Qdrant Cloud Setup

Since this project uses Cloud Inference features, you must use Qdrant Cloud. After signing up:

1. Create a new cluster in your Qdrant Cloud dashboard
2. Note your cluster URL and API key
3. Use these credentials in your .env file

The Cloud Inference feature allows creating vectors from raw data by sending it directly to the Qdrant cloud instance, eliminating the need for local model inference.

Usage

Development Setup

For development, you can run the services separately:

Backend Setup

1. Navigate to backend directory

   cd backend-app

2. Install dependencies

   uv sync

3. Configure environment variables Create a .env file in the backend-app directory with your Qdrant credentials.

4. Setup Qdrant collection

   uv run python setup.py

5. Start the backend server

   # For development with uvicorn
   uv run uvicorn main:app --reload --host 0.0.0.0 --port 7860
   
   # For production with gunicorn
   uv run gunicorn main:app -c gunicorn.conf.py

Frontend Setup

1. Navigate to frontend directory

   cd frontend-app

2. Install dependencies

   npm install

3. Start the development server

   npm run dev

Production Deployment

For production, use the unified Docker container (see Docker Deployment section above).

API Documentation

Once the backend is running, visit:

• Interactive API docs: http://localhost:7860/docs
• ReDoc documentation: http://localhost:7860/redoc

Usage Examples

Ingesting Images

Via API:

curl -X POST "http://localhost:7860/api/v1/ingest" \
     -H "Content-Type: application/json" \
     -d '{"url": "https://example.com/image.jpg"}'

Searching Images

Via API:

curl "http://localhost:7860/api/v1/search?query=cat%20sitting%20on%20a%20chair&limit=5"

Via Frontend:

Open http://localhost:7860 and use the web interface.

Docker Deployment

The project uses a unified Docker container that combines both the backend API and frontend application. This simplifies deployment and reduces resource usage.

Quick Start with Docker

1. Build the unified container:

   # Using the build script
   ./build.sh
   
   # Or manually
   docker build -t webinar-app .

2. Run the application:

   # With environment variables
   docker run -p 7860:7860 --env-file .env webinar-app
   
   # Or with inline environment variables
   docker run -p 7860:7860 \
     -e QDRANT_URL=your_qdrant_cluster_url \
     -e QDRANT_API_KEY=your_qdrant_api_key \
     -e QDRANT_COLLECTION_NAME=your_collection_name \
     webinar-app

3. Access the application:

   • Frontend: http://localhost:7860
   • API Documentation: http://localhost:7860/docs
   • Health Check: http://localhost:7860/health

Environment Variables

Create a .env file in the root directory with your Qdrant credentials:

QDRANT_URL=your_qdrant_cluster_url
QDRANT_API_KEY=your_qdrant_api_key
QDRANT_COLLECTION_NAME=your_collection_name

For a detailed explanation of the system and its components, refer to the webinar recording and the source code in this repository.