https://github.com/hgschandeepa/recommendation-model-using-milvus-vector-db
Recommendation Model using Milvus vector db
https://github.com/hgschandeepa/recommendation-model-using-milvus-vector-db
docker knn-classification milvus vector-database
Last synced: 5 months ago
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Recommendation Model using Milvus vector db
- Host: GitHub
- URL: https://github.com/hgschandeepa/recommendation-model-using-milvus-vector-db
- Owner: HGSChandeepa
- Created: 2024-03-12T23:01:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-05T13:27:23.000Z (about 1 year ago)
- Last Synced: 2025-04-07T12:49:26.812Z (6 months ago)
- Topics: docker, knn-classification, milvus, vector-database
- Language: Jupyter Notebook
- Homepage: https://zilliz.com/authors/Samin_Chandeepa
- Size: 32.9 MB
- Stars: 12
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
**Title: Recommendation Model Using Milvus Vector Database with Docker**
**Read About Vector Databases** :https://zilliz.com/authors/Samin_Chandeepa
## Overview
This repository contains an implementation of a recommendation model using Milvus, a vector database designed to store and search high-dimensional vectors efficiently, deployed with Docker. The recommendation model leverages the capabilities of Milvus to perform fast similarity searches, enabling efficient retrieval of similar items based on user preferences or item attributes.
## Features
- Utilizes Milvus for efficient storage and retrieval of high-dimensional vectors.
- Implements recommendation algorithms for personalized recommendations.
- Supports both user-based and item-based recommendation strategies.
- Provides easy-to-use APIs for integrating the recommendation model into existing applications.
- Supports scalability for handling large datasets and user bases.
- Dockerized deployment for easy setup and management.
## Installation
1. Clone this repository:
```bash
git clone https://github.com/HGSChandeepa/recommendation-model-milvus.git
```
2. Navigate to the project directory:
```bash
cd recommendation-model-milvus
```
3. Build and run the Docker container:
```bash
docker-compose up --build
```
## Usage
1. Prepare your dataset and generate embeddings for items or users using your preferred method.
2. Configure the Milvus instance:
- Set up a Milvus server and configure connection parameters in `config.py`.
3. Load the embeddings into Milvus:
- Use the provided scripts or implement your own to load vectors into Milvus.
4. Initialize the recommendation model:
- Use the provided classes or implement your own recommendation logic.
5. Interact with the recommendation model:
- Use the provided APIs to obtain recommendations based on user preferences or item attributes.
## Example
Check out the provided Jupyter notebooks (`examples/`) for a demonstration of how to use the recommendation model with sample datasets.
## Contributing
Contributions are welcome! If you have any ideas for improvement or find any issues, please open an issue or submit a pull request.
## License
This project is licensed under the [MIT License](LICENSE).
## Acknowledgements
- Thanks to the creators of Milvus for providing an efficient vector database solution.
- This project was inspired by various recommendation systems and libraries.
## Contact
For any questions or inquiries, feel free to contact saminchandeepa@gmail.com.
Happy recommending! 🚀