https://github.com/rishav-dahal/modular-query-refinement
Modular Query Refinement for Search Enhancement is a dynamic web-based application that improves search efficiency and accuracy. Using natural language processing (NLP), it refines user queries by analyzing intent, context, and semantics. The modular components optimize search results, making queries more relevant and personalized.
https://github.com/rishav-dahal/modular-query-refinement
django docker nextjs nginx nlp query
Last synced: 3 months ago
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Modular Query Refinement for Search Enhancement is a dynamic web-based application that improves search efficiency and accuracy. Using natural language processing (NLP), it refines user queries by analyzing intent, context, and semantics. The modular components optimize search results, making queries more relevant and personalized.
- Host: GitHub
- URL: https://github.com/rishav-dahal/modular-query-refinement
- Owner: rishav-dahal
- Created: 2025-06-04T08:20:17.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-25T19:01:49.000Z (11 months ago)
- Last Synced: 2025-07-25T22:46:55.087Z (11 months ago)
- Topics: django, docker, nextjs, nginx, nlp, query
- Language: Python
- Homepage:
- Size: 184 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Modular Query Refinement for Search Enhancement
**Modular Query Refinement for Search Enhancement** is a dynamic web-based application designed to improve the efficiency and accuracy of search. Using natural language processing (NLP) techniques, the system refines user queries by analyzing intent, context, and semantics to generate more relevant search results. Its modular components personalize and optimize queries, enhancing the overall search experience.
This application is particularly useful where traditional keyword-based searches fall short in capturing nuanced user intent. By streamlining the search process, it delivers more precise results, reduces irrelevant searches, and improves user satisfaction.
Its modular design allows flexibility and adaptability across various domains, making it suitable for industries such as e-commerce, academic research, and beyond. By refining queries based on contextual relevance, this tool makes search engines more intuitive and effective for diverse users.
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## Features
- **NLP-Powered Query Refinement:** Analyzes user intent, context, and semantics to improve query accuracy.
- **Modular Architecture:** Flexible components that can be adapted to different domains and industries.
- **Personalized Adjustments:** Tailors search refinements based on user context for better relevance.
- **Enhanced Search Results:** Provides more precise and meaningful search outcomes.
- **Cross-Domain Utility:** Useful in e-commerce, research, and any domain requiring advanced search.
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## Technologies Used
- Frontend: Next.js, HTML, CSS
- Backend: Django
- Database: PostgreSQL
- API: RESTful API
- Deployment: Docker, Nginx
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## Contributing
As this project is proprietary, contributions are currently not being accepted.
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## License
This project is licensed under the Contributor License Agreement (CLA).
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## Contributors
- **Rishav Dahal** - Initial development - [rishav-dahal](https://github.com/rishav-dahal)
- **Sandhya Gotame** - Initial development - [Sandhya-Gautam](https://github.com/Sandhya-Gautam)
- **Dhiraj Poudel** - Initial development - [DhirazX](https://github.com/DhirazX)
- **Binit Bikram KC** - Initial development - [beinit7799](https://github.com/beinit7799)
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## Contact
For any questions or inquiries, please contact [contact@rishavdahal.com.np](mailto:contact@rishavdahal.com.np).