Awesome
lunr-core
A port of lunr.js to .NET Core. Lunr is a bit like Solr, but much smaller and not as bright.
TODO / up for grabs
- Multilingual support (lunr has optional support that remains to be ported)
- Documentation (adapted from lunr docs)
Example
A very simple search index can be created using the following:
var index = await Index.Build(async builder =>
{
builder
.AddField("title")
.AddField("body");
await builder.Add(new Document
{
{ "title", "Twelfth-Night" },
{ "body", "If music be the food of love, play on: Give me excess of it…" },
{ "author", "William Shakespeare" },
{ "id", "1" },
});
});
Then searching is as simple as:
await foreach (Result result in index.Search("love"))
{
// do something with that result
}
This returns a list of matching documents with a score of how closely they match, the search query as well as any associated metadata about the match:
new List<Result>
{
new Result(
documentReference: "1",
score: 0.3535533905932737,
matchData: new MatchData(
term: "love",
field: "body"
)
)
}
<!--[API documentation](https://lunrjs.com/docs/index.html) is available, as well as a [full working example](https://olivernn.github.io/moonwalkers/).-->
Description
Lunr-core is a small, full-text search library for use in small applications. It indexes documents and provides a simple search interface for retrieving documents that best match text queries. It is 100% compatible with lunr.js, meaning that an index file prepared on the server with lunr-core can be used on the client using lunr.js.
Why
Lunr-core is suitable for small applications that require a simple search engine but without the overhead of a full-scale search engine such as Lucene. Its compatibility with lunr.js also opens up some interesting client-side search scenarios.
<!-- ## Installation Simply include the lunr-core package in your application. Lunr-core supports all .NET Standard 2.0 platforms, including .NET Core and .NET Framework 4.6. -->Features
- Soon: Full text search support for 14 languages
- Boost terms at query time or boost entire documents at index time
- Scope searches to specific fields
- Fuzzy term matching with wildcards or edit distance
- No runtime dependencies beyond SDK, BCL AsyncInterfaces and System.Text.Json
Credits
- Original code by Oliver Nightingale and contributors, ported to .NET Core by Bertrand Le Roy.
- Icon adapted from https://commons.wikimedia.org/wiki/File:Internal_Structure_of_the_Moon.JPG by Iqbal Mahmud under Creative Commons Attribution Share Alike 4.0 International.
- Perf tests use a word list by Sindre Sorhus.