Question 1

How to implement fuzzy search with lunr-core in .NET?

Accepted Answer

Lunr-core supports fuzzy matching through wildcards and edit distance in queries. Use the search interface with terms like 'lov*' for wildcards or enable edit distance in the index builder, similar to lunr.js's approach.

Question 2

Lunr-core vs Elasticsearch for a small .NET app?

Accepted Answer

Lunr-core is better for lightweight, integrated search without external dependencies, while Elasticsearch offers more features but requires setup and scaling. Choose based on your app's size and search complexity.

Question 3

Can I use lunr-core for server-side search in ASP.NET Core?

Accepted Answer

Yes, lunr-core is designed for .NET Core and works well in ASP.NET Core applications. It indexes documents server-side and provides async search, but ensure datasets are small for optimal performance.

Question 4

What are the performance limits of lunr-core with large datasets?

Accepted Answer

Lunr-core is optimized for small applications, so indexing or searching thousands of documents might slow down. For larger datasets, consider batching indexes or evaluating a more robust engine like Lucene.NET.

Question 5

How to boost document relevance in lunr-core queries?

Accepted Answer

You can boost terms at query time or documents at index time using the builder API. For example, set boosts on specific fields when adding documents to tune relevance scores in search results.

Question 6

Is lunr-core compatible with Blazor for client-side search?

Accepted Answer

Yes, due to its lunr.js compatibility, you can prepare indexes on the server with lunr-core and load them in Blazor using lunr.js, enabling efficient client-side search without server round-trips.

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