ElBruno.LocalEmbeddings 1.0.8

LocalEmbeddings

A .NET library for generating text embeddings locally using ONNX Runtime and Microsoft.Extensions.AI abstractions — no external API calls required.

Features

• Local Embedding Generation — Run inference entirely on your machine using ONNX Runtime
• Microsoft.Extensions.AI Integration — Implements IEmbeddingGenerator<string, Embedding<float>>
• Kernel Memory Integration — Companion package ElBruno.LocalEmbeddings.KernelMemory provides a native ITextEmbeddingGenerator adapter for Microsoft Kernel Memory
• VectorData Integration — Companion package ElBruno.LocalEmbeddings.VectorData adds DI helpers for Microsoft.Extensions.VectorData (VectorStore and typed collections)
• HuggingFace Model Support — Use popular sentence transformer models from HuggingFace Hub
• Automatic Model Caching — Models are downloaded once and cached locally
• Dependency Injection Support — First-class IServiceCollection integration
• Single-String Convenience API — GenerateAsync("text") and GenerateEmbeddingAsync("text") — no array wrapping needed
• Similarity Helpers — Cosine similarity, all-pairs Similarity(...) matrix, and FindClosest(...) for semantic search
• Thread-Safe & Batched — Concurrent generation and efficient multi-text processing

Installation

dotnet add package ElBruno.LocalEmbeddings

For Kernel Memory integration, also install the companion package:

dotnet add package ElBruno.LocalEmbeddings.KernelMemory

For VectorData integration, install:

dotnet add package ElBruno.LocalEmbeddings.VectorData

Quick Start

using ElBruno.LocalEmbeddings;

// Create the generator (downloads model automatically on first run)
using var generator = new LocalEmbeddingGenerator();

// Generate a single embedding — no array wrapping needed
var embedding = await generator.GenerateEmbeddingAsync("Hello, world!");
Console.WriteLine($"Dimensions: {embedding.Vector.Length}"); // 384

// Or get the full result collection
var result = await generator.GenerateAsync("Hello, world!");
Console.WriteLine($"Embedding: [{string.Join(", ", result[0].Vector.ToArray().Take(3))}...]");

For custom models and runtime behavior, use the options-based constructor: new LocalEmbeddingGenerator(new LocalEmbeddingsOptions { ... }).

Want to go further? See the Getting Started guide for a step-by-step walkthrough — from cosine similarity to semantic search, dependency injection, and full RAG with a local LLM.

Prefer a containerized dev environment? See the Dev Container section in the Contributing guide.

Samples

See the samples README for prerequisites and run instructions.

Configuration

var options = new LocalEmbeddingsOptions
{
    ModelName = "sentence-transformers/all-MiniLM-L6-v2",  // HuggingFace model
    MaxSequenceLength = 512,                                // Max tokens
    CacheDirectory = null,                                  // Auto-detect per platform
    EnsureModelDownloaded = true,                           // Download if missing
    NormalizeEmbeddings = false                              // L2 normalize vectors
};

See Configuration docs for supported models, local model paths, and cache locations.

Documentation

Building from Source

git clone https://github.com/elbruno/elbruno.localembeddings.git
cd elbruno.localembeddings
dotnet build
dotnet test

Requirements

• .NET 10.0 SDK or later
• ONNX Runtime compatible platform (Windows, Linux, macOS)

License

This project is licensed under the MIT License — see the LICENSE file for details.