Wikipedia Semantic Search
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Wikipedia Semantic Search
Overview :
Wikipedia Semantic Search is an experimental project showcasing the scalability of Upstash Vector when handling large datasets. The project vectorized 23 million Wikipedia articles in 11 languages and stored 14.4 million vectors in a single Upstash Vector index. This enables users to explore Wikipedia's content through semantic search rather than traditional keyword searches.
Target Users :
This product is ideal for researchers, students, and anyone looking to gain deeper insights into specific topics. It enhances the user experience by providing semantic search capabilities, allowing users to discover more relevant information rather than relying solely on keyword-based results.
Total Visits: 96
Top Region: HK(100.00%)
Website Views : 50.5K
Use Cases
Researchers use this tool to explore literature within specific scientific fields.
Students leverage it to find in-depth information about historical events.
Language learners utilize it to search for Wikipedia articles in different languages for comparative learning.
Features
Supports multilingual vectorized searches for Wikipedia articles.
Provides semantic search results instead of simple keyword matching.
Capable of processing massive amounts of data, demonstrating large-scale data handling capabilities.
Offers more project details and background information through blog posts.
Users can input questions or keywords to retrieve related articles.
How to Use
1. Visit the Wikipedia Semantic Search website.
2. Choose the language you wish to search in.
3. Enter your question or keywords in the search box.
4. Click the search button or press the Enter key to submit your query.
5. Browse the search results and click on articles of interest to read.
6. If further searches are needed, you can return to the search page to continue.
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