As a grad student with ADHD, I developed a website that uses an embedding model to systematically find and retrieve similar research papers by generating vectors from abstracts, incorporating tools like Milvus for vector storage and Gradio for the frontend, and maintaining a weekly-updated database from Kaggle.
Embedding Model (MixedBread), Milvus, Gradio, Hamming Distance for Fast Search
2024-12-26
- Website
Highlight 1
The use of advanced embedding models and vector databases significantly improves the accuracy and relevance of paper suggestions, providing meaningful search results based on content similarity.
Highlight 2
Weekly updates of the vector database ensure the search results stay current and relevant, keeping up with the latest research in the field.
Highlight 3
The front-end interface built with Gradio is intuitive and easy to use, making it accessible to users with varying levels of technical expertise.
Improvement 1
The site could benefit from additional filtering options, such as by date range, topic, or research methodology, allowing users to narrow down results more effectively.
Improvement 2
The search speed, while optimized with Hamming distance, might still be slow for large datasets. Exploring further optimizations or better indexing techniques could enhance performance.
Improvement 3
While the site serves an important niche, there could be better integration with reference management tools (e.g., Zotero, EndNote) to directly export or organize relevant papers.
Product Functionality
Consider adding features like keyword-based search or advanced filtering options (e.g., by topic, citation count, date, etc.) to help users refine their search results.
UI & UX
Improving the visual appeal of the UI, such as adding more interactive elements or providing a clearer layout for the search results, could enhance user engagement.
SEO or Marketing
Implement SEO strategies to optimize search engine discoverability, including using appropriate keywords for literature reviews, research papers, and academic content.
MultiLanguage Support
Adding multi-language support would make the platform accessible to a wider global audience, especially in non-English-speaking academic communities.
- 1
How does the website find similar papers?
The website uses an embedding model to generate vector representations of research paper abstracts. It compares these vectors to find papers with similar content, returning the most relevant matches based on meaning rather than exact keyword matching.
- 2
How often is the database updated?
The vector database is updated on a weekly basis using metadata pulled from Kaggle's arXiv dataset, ensuring the search results reflect the latest research papers.
- 3
Is the website free to use?
Yes, the website is free to use, but it operates on a free oracle instance, which may impact the speed of searches, especially for large queries.