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Flux.Abstractions 0.14.0

FluxIndex

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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 for integration details.

Performance

Full benchmarks:

Package Structure

Which package do I need?

Minimal setup — bring your own embedding service, no native binaries:

dotnet add package FluxIndex.Core
dotnet add package FluxIndex.Storage.SQLite

Full SDK — includes document processing (PDF, DOCX, HWP, web crawling):

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

Documentation

• - Quick start and configuration
• - Architecture and API reference
• - HyDE, Contextual Retrieval, Query Expansion
• - Core principles and design philosophy

Examples

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

Requirements

• .NET 10.0 or later
• SQLite or PostgreSQL

License

MIT License - see file.

Contributing

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