FluxIndex

RAG library for .NET 10.0 - Build semantic search and retrieval systems with vector + keyword hybrid search.

Key Features

• Hybrid Search - Vector (semantic) + Keyword (BM25) with automatic strategy selection
• High Performance - Embedding cache (100% faster), batch indexing (24ms/1K chunks)
• Local Reranking - Cross-encoder neural reranking with automatic algorithmic fallback
• Graph Traversal - BFS/DFS, Dijkstra shortest path, PageRank-style importance
• Vector Quantization - Scalar (Int8/Int4), Product Quantization, Binary (32x compression)
• Multiple Storage - SQLite, PostgreSQL with pgvector
• AI Provider Agnostic - Core provides abstract base classes, bring your own embedding service
• Document Processing - PDF/DOCX/TXT via FileFlux, web crawling via WebFlux
• MCP Server - Model Context Protocol for AI assistant integration
• Production Ready - Redis caching, clean architecture, .NET 10.0

Quick Start

dotnet add package FluxIndex.SDK
dotnet add package FluxIndex.Storage.SQLite

using FluxIndex.SDK;

// 1. Setup (InMemory embedding for testing)
var context = FluxIndexContext.CreateBuilder()
    .UseSQLite("fluxindex.db")
    .Build();

// 2. Index
await context.Indexer.IndexDocumentAsync(
    "FluxIndex is a RAG library for .NET", "doc-001");

// 3. Search
var results = await context.Retriever.SearchAsync("RAG library", maxResults: 5);

Using Custom Embedding Service

FluxIndex is AI provider-agnostic. Extend EmbeddingServiceBase for your preferred provider:

// Example: LMSupply embedding (local ONNX-based, no API key)
public class LMSupplyEmbedder : EmbeddingServiceBase, IAsyncDisposable
{
    private readonly IEmbeddingModel _model;
    private LMSupplyEmbedder(IEmbeddingModel model) => _model = model;

    public static async Task<LMSupplyEmbedder> CreateAsync(string modelId = "default")
    {
        var model = await LocalEmbedder.LoadAsync(modelId);
        return new LMSupplyEmbedder(model);
    }

    protected override async Task<float[]> EmbedCoreAsync(string text, CancellationToken ct)
        => await _model.EmbedAsync(text, ct);

    public override int GetEmbeddingDimension() => _model.Dimensions;
    public override string GetModelName() => _model.ModelId;
    public ValueTask DisposeAsync() => _model.DisposeAsync();
}

// Register and use
var context = FluxIndexContext.CreateBuilder()
    .UseSQLite("fluxindex.db")
    .ConfigureServices(s => s.AddSingleton<IEmbeddingService>(
        LMSupplyEmbedder.CreateAsync().GetAwaiter().GetResult()))
    .Build();

MCP Server

FluxIndex provides Model Context Protocol (MCP) server for AI assistant integration.

Available Tools: search, memorize, unmemorize, status

See FluxIndex.MCP for integration details.

Performance

Operation	Performance	Notes
Batch Indexing	24ms/1K chunks	8-thread parallelism
Vector Search	0.6ms/query	In-memory embeddings
Embedding Cache	100% faster	Eliminates API calls
Semantic Cache	<5ms	Redis, 95% similarity

Full benchmarks: BENCHMARK_RESULTS.md

Package Structure

Package	NuGet	Description
FluxIndex.Core		Interfaces, abstract base classes, and core logic
FluxIndex.SDK		All-in-one SDK with FileFlux, WebFlux, FluxCurator, FluxImprover
FluxIndex.Storage.SQLite		SQLite vector store
FluxIndex.Storage.PostgreSQL		PostgreSQL with pgvector
FluxIndex.Storage.Neo4j		Neo4j graph database
FluxIndex.Storage.Qdrant		Qdrant vector database
FluxIndex.Cache.Redis		Redis semantic cache
FluxIndex.Extensions.FileVault		Git-like file tracking for RAG indexing with folder monitoring

Documentation

• Guide - Quick start and configuration
• Reference - Architecture and API reference
• Advanced RAG - HyDE, Contextual Retrieval, Query Expansion
• Philosophy - Core principles and design philosophy

Examples

• RealQualityTest - LMSupply + SQLite integration
• WebFluxSample - Web crawling with WebFlux
• ChunkingQualityTest - FileFlux chunking analysis
• FileFluxIndexSample - Document indexing workflow

Requirements

• .NET 10.0 or later
• SQLite or PostgreSQL

License

MIT License - see LICENSE file.

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.