LexiSharp 0.6.0

LexiSharp

A composable information retrieval toolkit for .NET — build, measure and inspect search pipelines, from lexical BM25 to hybrid and reranked retrieval.

Index, retrieve, rank and judge a search pipeline: an in-memory inverted index, four ranking strategies and their BM25 variants, rank fusion, reranking, optional PostgreSQL backends, and model-agnostic seams for dense, learned-sparse and neural scoring — the models stay in your application. The core package references no NuGet package at all.

📖 Full documentation → — the guide, the reference, and every measurement with the command that reproduces it. Published from docs/; this README is the short version and the details are delegated to those pages.

Install

dotnet add package LexiSharp   # the core: index, scorers, engines, decorators — no dependencies

net10.0, MIT. Optional: LexiSharp.MessagePack (binary index persistence), LexiSharp.AspNetCore (a GET /search minimal-API endpoint), LexiSharp.Postgres (lexical, vector, sparse, fuzzy and true BM25 backends) — packages.

Use it

using LexiSharp.Core;
using LexiSharp.Indexing;
using LexiSharp.Ranking;

ITextSearchEngine engine = new RankedTextSearchEngine(
    new InMemoryTextIndex(),
    new Bm25Scorer());

engine.Index(new[]
{
    new SearchDocument("1", "The search engine uses BM25 to rank the results"),
    new SearchDocument("2", "TF-IDF is a classic method of textual search"),
    new SearchDocument("3", "Italian cuisine is renowned in Rome"),
});

foreach (var result in engine.Search("textual search"))
    Console.WriteLine($"{result.DocumentId} - {result.Score:0.###}: {result.Document.Text}");

LexiSharpIndex<T> is the typed facade over the same engine if you would rather hand it your own objects — Getting started.

See it running

dotnet run --project samples/LexiSharp.Demo    # → http://localhost:5000

Five retrieval strategies over one corpus, compared live — BM25, corpus-derived semantic expansion, dense hashing embeddings, RRF fusion and a term-overlap rerank — with per-lane latency, highlighting and a click-through "why did this rank here?" panel. No model, no external service.

What it does not do

Stated plainly, so nothing is implied. The full list, with the measurement behind each claim, is Scope and limits.

• It does not beat a published BM25. On three public BEIR corpora the plain BM25 scorer reaches nDCG@10 0.308 (NFCorpus), 0.662 (SciFact) and 0.289 (ArguAna) against BEIR's published 0.325 / 0.665 / 0.315 — 5.2 %, 0.5 % and 8.3 % below. Corpora are md5-verified on download (evaluation).
• No scorer here has a measured win over a tuned BM25. BM25+ and BM25L, tuned on their own δ, tie a tuned BM25 on the reference corpus and NFCorpus and edge it by 0.002–0.004 on SciFact — an in-sample margin, so an upper bound rather than a result. On ArguAna, untuned, they lose, and no δ-tuned ArguAna row exists, so whether tuning closes that gap is unmeasured (ranking).
• The SQL backends' retrieval quality is unmeasured. The BEIR numbers come from the in-memory engines; the live integration tests cover schema, query paths and cosine behaviour, not relevance (backends).
• Not every combination is tested. Engines, scorers, rerankers and mergers are tested individually and in the combinations described, but not every pairing — treat an unusual one as supported but unproven until you test it on your data.
• Version 0.6.0, one maintainer. The public API may still change between minor versions — pin a version and read the release notes.

Development

dotnet build LexiSharp.slnx
dotnet test  tests/LexiSharp.Tests   # xUnit suite; the Postgres suites need POSTGRES_TEST_CONNECTION

Benchmarks, the evaluation harness and the behavioural gate: Reference and Benchmarks.

License

MIT — see LICENSE. The ParadeDB pg_search extension used by the BM25 backend is licensed separately, under AGPL-3.