Qdrant Vector Search API with Flask

This project provides a simple REST API built with Flask for storing and retrieving string data using vector embeddings in a Qdrant vector database. It allows users to store textual data and retrieve the most relevant matches based on partial input queries using cosine similarity.

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🚀 Features

• Store strings with their vector embeddings
• Retrieve original strings using partial matches
• Uses Qdrant as the vector search backend
• Embeddings generated using SentenceTransformer
• Fully containerized using Docker and Docker Compose
• Easy-to-use API endpoints

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static image of web page:

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Setup Instructions

1. Clone the Repository

git clone https://github.com/theashishmavii/Qdrant-API-project.git

2. Make sure docker is running

docker-compose up --build

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📁 Project Structure

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├── app/
│ ├── init.py
│ ├── main.py
| ├──qdrant_client.py
| ├──embedding.py
│ └── static/
│ └── index.html # (Optional UI for testing)
├── .env # Environment variables
├── requirements.txt # Python dependencies
├── Dockerfile # Image for Flask service
├── docker-compose.yml # Setup Flask + Qdrant services
└── README.md # You’re here!

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