Search Wikipedia with AI — Queryra Playground

You know that feeling when you can describe a movie perfectly but can't remember the title? "The one where a jury has to decide if a young man is guilty, and one juror tries to convince the others he's innocent."

Keyword search can't help with that. But semantic search can.

The Queryra Wiki Playground lets you search 3,000+ Wikipedia articles using natural language descriptions. It's the clearest demonstration of the difference between matching words and matching meaning — and it's powered by the same Queryra REST API you can use in your own applications.

Go to queryra.com/playground/wiki and try these:

Movies by plot description:
- "jury decides if young man is guilty" → finds *12 Angry Men*
- "a ring that makes you invisible and corrupts everyone" → finds *The Lord of the Rings*
- "computer hacker discovers reality is a simulation" → finds *The Matrix*

Historical events by description:
- "wall dividing a city fell down in 1989" → finds *Berlin Wall*
- "first person to walk on the moon" → finds *Apollo 11* and *Neil Armstrong*
- "ship that hit an iceberg and sank" → finds *Titanic*

Concepts by explanation:
- "the study of how living things inherit traits" → finds *Genetics*
- "energy from splitting atoms" → finds *Nuclear fission*

People by description:
- "physicist who developed the theory of relativity" → finds *Albert Einstein*
- "South African leader who fought apartheid from prison" → finds *Nelson Mandela*

None of these queries contain the exact title of the article they find. The AI understands what you're describing and matches it to the right content.

We loaded the playground with over 3,000 Wikipedia articles across dozens of topics — history, science, movies, geography, technology, biography, and more.

This isn't a cherry-picked demo with 10 articles designed to make semantic search look good. It's a real-scale test with thousands of diverse documents. Some articles are short (200 words), others are long (5,000+ words). Topics range from ancient history to modern technology.

The point: semantic search scales. It works just as well on 3,000 articles as it does on 50. The AI builds an understanding of each document and matches queries by meaning, regardless of catalog size.

Unlike the WooCommerce demo which uses the Queryra WordPress plugin, the Wiki Playground runs entirely on the Queryra REST API.

The architecture is simple:

1. Indexing: We sent 3,000+ Wikipedia articles to the Queryra API. Each article's title and content were converted into vector embeddings by the AI.

1. Search: When you type a query, the frontend sends a GET request to the search endpoint. The API converts your query into a vector, finds the nearest matches, and returns ranked results with relevance scores.

1. Display: The frontend renders the results with titles, snippets, and scores. No WordPress, no WooCommerce — just HTML/JS talking to a REST API.

This is the same API available to any developer. If you can make an HTTP request, you can add semantic search to your application. Documentation, code examples, and authentication details are at queryra.com/docs.

The Wiki Playground demonstrates semantic search on knowledge content. But the same API powers completely different use cases:

Documentation search. Index your product docs, user guides, or technical manuals. Customers find answers by describing their problem instead of guessing the right keyword. "How do I change my password" finds the article titled "Account Security Settings."

Internal knowledge base. Company wikis, onboarding docs, process guides — searchable by natural language. New employees find what they need without knowing the exact document title.

Content recommendation. Feed your blog posts, articles, or courses into the API. Search for "beginner guide to investing" and surface the most semantically relevant content from your library.

Research tools. Index papers, reports, or datasets. Search by concept instead of citation. "Studies about the effect of sleep on memory" finds relevant papers even if they use different terminology.

Every use case works the same way: index your content once, search by meaning forever. The API handles the AI — you handle the frontend.

The playground uses Wikipedia, but you can index any text content through the Queryra API.

The free tier includes 100 records — enough to test semantic search on your documentation, FAQ, blog, or any text-based content. Create an account at queryra.com/signup, get your API key, and start indexing.

For WordPress/WooCommerce content, the WordPress plugin handles everything automatically — install, connect, sync. For everything else, the REST API gives you full control.