FluxIndex.Core 0.5.4

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
• Local-First AI - Built-in LMSupply (ONNX-based), 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 (LMSupply embedding - no API key required)
var context = FluxIndexContext.CreateBuilder()
    .UseSQLite("fluxindex.db")
    .UseLMSupplyEmbedding()  // Built-in ONNX-based embedding
    .UseResilientLocalReranker()  // Auto fallback to algorithmic
    .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. Implement IEmbeddingService for your preferred provider:

// Example: Custom OpenAI embedding service
public class MyOpenAIEmbeddingService : IEmbeddingService
{
    public async Task<float[]> GetEmbeddingAsync(string text, CancellationToken ct = default)
    {
        // Your OpenAI API call here
    }
}

// Register your implementation
services.AddSingleton<IEmbeddingService, MyOpenAIEmbeddingService>();

var context = FluxIndexContext.CreateBuilder()
    .UseSQLite("fluxindex.db")
    .UseEmbeddingService<MyOpenAIEmbeddingService>()
    .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

Full benchmarks: BENCHMARK_RESULTS.md

Package Structure

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.