Semantic Search on Wikipedia with Upstash Vector
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Semantic Search On Wikipedia With Upstash Vector
Overview :
This project, built with Next.js, leverages Upstash Vector to provide semantic search capabilities for Wikipedia. It features optimized loading of the custom Google font, Inter, to enable efficient search and retrieval of Wikipedia content.
Target Users :
This product is suitable for programmers, data scientists, and anyone in need of deep search and analysis of Wikipedia content.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 47.2K
Use Cases
Developers can use this tool to quickly search for relevant technical documentation in Wikipedia.
Data scientists can utilize the tool for large-scale semantic analysis of text data.
Educational institutions can use it as a teaching aid, helping students better understand complex concepts.
Features
Supports the Next.js framework, offering rapid development and deployment capabilities.
Integrates Upstash Vector for efficient vector search.
Automatically optimizes and loads the Inter font to enhance page display.
Provides semantic search functionality, improving the accuracy of user queries.
Supports custom environment configurations to accommodate various development needs.
Offers detailed development documentation and resource links for ease of learning and usage.
How to Use
1. Visit the project page and clone or download the project code.
2. Set up your local development environment according to the project documentation.
3. Run the development server using the command `yarn dev`, `pnpm dev`, or `bun dev`.
4. Open `http://localhost:3000` in your browser to view the results.
5. Modify the `app/page.tsx` file to customize the page content.
6. Develop and test semantic search functionality using Upstash Vector.
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