LeanCorpus

A fast, embeddable full-text search engine for .NET. Ships as a single library. Write an index, run a query, ship your app.

dotnet add package LeanCorpus

Get started  |  API reference

What it does

Area Details
Indexing Memory-mapped segments, BM25 scoring, index-time sort, schema validation, concurrent multi-thread indexing, CRC-protected commits
Mapping Roslyn source generator for typed LeanDocument mappers, schemas, field descriptors, and stored-field materialisers
Queries Term, boolean, phrase, prefix, wildcard, fuzzy, range, regexp, span, geo bounding box, geo distance, disjunction max
Advanced queries HNSW vector ANN (VectorQuery), filtered vector search, reciprocal rank fusion (RrfQuery), block-join, more-like-this, function score, constant score
Analysis Pluggable tokenisers (standard, n-gram, edge n-gram, CJK bigram), char filters, token filters, stemmers for 10+ languages
Search features Facets, aggregations, highlighting, spell-check, field collapsing, query cache
Concurrency SearcherManager for near-real-time search, snapshot backup, configurable commit retention
Operations IndexValidator.Check, leancorpus-cli.exe check, deep validation for DocValues, stored fields, postings, vectors, HNSW, and live docs
Observability ActivitySource traces, System.Diagnostics.Metrics, OpenTelemetry export, slow query log, search analytics

Quick start

using Rowles.LeanCorpus.Store;
using Rowles.LeanCorpus.Index.Indexer;
using Rowles.LeanCorpus.Document;
using Rowles.LeanCorpus.Document.Fields;
using Rowles.LeanCorpus.Search.Searcher;
using Rowles.LeanCorpus.Search.Queries;

// Index
using var writer = new IndexWriter(new MMapDirectory("./index"), new IndexWriterConfig());

var doc = new LeanDocument();
doc.Add(new TextField("title", "The quick brown fox"));
doc.Add(new StringField("id", "1"));
writer.AddDocument(doc);
writer.Commit();

// Search
using var searcher = new IndexSearcher(new MMapDirectory("./index"));
var results = searcher.Search(new TermQuery("title", "fox"), topN: 10);

foreach (var hit in results.ScoreDocs)
    Console.WriteLine($"doc {hit.DocId}: {fields["id"][0]} score {hit.Score}");

Full walkthrough

Example: typed mapping with the source generator

using Rowles.LeanCorpus.Mapping.Attributes;

[LeanDocument]
public partial class Product
{
    [LeanString("id", Required = true)]
    public required string Id { get; init; }

    [LeanText("title")]
    public string? Title { get; init; }

    [LeanNumeric("price")]
    public double Price { get; init; }
}

// Index
var config = new IndexWriterConfig { Schema = ProductIndex.CreateSchema() };
using var writer = new IndexWriter(new MMapDirectory("./index"), config);
writer.AddDocument(ProductIndex.ToDocument(new Product { Id = "p1", Title = "Widget", Price = 9.99 }));
writer.Commit();

// Search with typed results
using var searcher = new IndexSearcher(new MMapDirectory("./index"));
var hits = searcher.Search(new TermQuery("title", "widget"), topN: 10);
foreach (var hit in hits.ScoreDocs)
{
    var stored = StoredDocument.Create(searcher.GetStoredFields(hit.DocId), null);
    var product = ProductIndex.FromStoredDocument(stored);
    Console.WriteLine($"{product.Id}: {product.Title} £{product.Price}");
}

Example: vector search with HNSW

using Rowles.LeanCorpus.Document.Fields;
using Rowles.LeanCorpus.Search.Queries;

var doc = new LeanDocument();
doc.Add(new StringField("id", "v1"));
doc.Add(new VectorField("embedding", new float[] { 0.1f, 0.2f, 0.3f, 0.4f }));
writer.AddDocument(doc);
writer.Commit();

var query = new VectorQuery("embedding", new float[] { 0.15f, 0.25f, 0.35f, 0.45f }, topK: 10);
var hits = searcher.Search(query, topN: 10);
// Results ranked by cosine similarity

Why native .NET?

The index format is built for memory-mapped I/O. The query engine uses SIMD posting intersection and BlockMax WAND for early termination on large result sets. Targets net10.0 and net11.0.

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