LexiSharp.AspNetCore

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. This README is the short version; the details live in docs/.

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).

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 — see 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

• An inverted index — term positions, document frequencies, corpus statistics, incremental Add/Remove, and named text fields per document (indexing).
• Four scorers and their variants — BM25, TF-IDF, query likelihood, a boolean filter, plus BM25+, BM25L and field-weighted BM25F, each with a per-term IScoreExplainer breakdown (ranking).- The usual query operators — metadata filters, phrases, prefix and fuzzy terms, synonyms, facets, highlighting, pagination (querying).
• Composable pipelines — rerankers (MMR, cascade, cross-encoder, MaxSim), hybrid federation with five merge strategies, cost-based and intent-based routing (pipelines).
• Embeddings as seams, not as features — IEmbeddingProvider, ISparseEmbeddingProvider, ITokenEmbeddingProvider, ICrossEncoderScorer: you supply the model, the library wires the retrieval. HashingEmbeddingProvider makes the whole stack testable with no model at all (embeddings).
• Persistence and scale as separate packages — MessagePack, ASP.NET Core, and PostgreSQL engines over tsvector, pgvector, sparsevec, pg_trgm and ParadeDB's Tantivy BM25 (backends).
• Instrumentation — SearchTrace walks one ranking back stage by stage; RetrievalTelemetry measures every search in production; RetrievalAgreementAnalyzer says whether a fused page was agreed on or insisted upon (observability).
• The instruments to distrust your own ranking — IR metrics, four parameter tuners (BM25, BM25F, BM25+, BM25L) that report whether the extra knob earned its place, a benchmark CLI over your corpus, and a per-query diff that names which queries a change rescued and which it lost (evaluation).

What is measured, and what is not

A library that quotes only its good numbers is not worth reading twice. Every figure below is reproducible with a command on the evaluation page.

• Against published baselines. On three public BEIR corpora, BM25 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.
• The composed stack, where it is measured. On NFCorpus the dense lane alone lands at BM25 level (0.304), BM25+dense RRF reaches 0.333 and a cross-encoder rerank 0.346. Those lanes are the only ones that use a real model, it comes from the harness rather than the library, and this is the one corpus where all of them run.
• Zero runtime dependencies in the core. Inverted index, BM25, every scorer and every decorator are BCL only.
• Tests, not assertions. Everything documented is covered by the xUnit suite; the Postgres and ParadeDB integration tests run live on every build and self-skip without POSTGRES_TEST_CONNECTION. Ranking outputs are gated by a committed golden master, so a change to scoring shows up as a reviewable diff.
• Named, not glossed over. SearchTrace's time cost is unmeasured (its allocation is pinned; no timing is quoted because the baseline itself varied 2.3× between identical runs). The SQL backends' retrieval quality is unmeasured — the BEIR harness runs the in-memory engines. The learned-sparse lane has no model wired into that harness, and corpus-derived expansion is a measured loss where it has been measured (0.8469 against BM25's 0.8751 on the reference corpus). BM25F has no measured win over a tuned BM25, and proximity hurts at full strength. 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 on the queries that chose it, so an upper bound rather than a result. ArguAna is untuned and is their worst showing anywhere: 0.243 and 0.249 against BM25's 0.289 at a fixed δ, with no δ-tuned row to say whether tuning closes it. Not every combination of engine, scorer, reranker and merger is exercised by a test.- Version 0.4.0, one maintainer. The public API may still change between minor versions — pin a version.

Full scope and limits: docs/reference.md.

Documentation

Hosted at https://manuc66.github.io/LexiSharp/ (published from docs/).

Build & test

dotnet build LexiSharp.slnx
dotnet test  tests/LexiSharp.Tests                # xUnit suite; Postgres suites need POSTGRES_TEST_CONNECTION
dotnet run  --project bench/LexiSharp.Benchmarks  # BenchmarkDotNet (published numbers: docs/benchmarks.md)
dotnet run  --project bench/LexiSharp.Cli -c Release -- verify bench/reference-corpus/corpus \
    --queries bench/reference-corpus/queries.json --qrels bench/reference-corpus/qrels.tsv \
    --configs bm25,bm25-semantic,bm25f,bm25+,bm25l,bm25-proximity-full --top-k 5 \
    --against bench/reference-corpus/golden/rankings.txt

License

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