📖 Read the Deep-Dive: I built a zero-dependency C# vector database that saturates DDR5 RAM bandwidth.

Glacier.Vector is a high-performance, SIMD-accelerated vector search engine and index for .NET 10. Built for speed and efficiency, it provides a zero-copy architecture for storing and querying high-dimensional embeddings, commonly used in LLM-powered applications and semantic search.

• 🚀 SIMD-Accelerated Search: Utilizes hardware intrinsics (AVX2, AVX-512) for lightning-fast brute-force vector comparisons (Cosine Similarity, Dot Product, Euclidean Distance).
• 🧠 LLM Optimized: Specifically designed to handle standard embedding dimensions (e.g., 1536 for OpenAI text-embedding-3-small).
• 📥 Zero-Copy Memory Model: Efficiently manages large vector datasets in-memory using Span<T> and Memory<T>, minimizing allocations and GC pressure.
• 🤖 MCP Server Support: Built-in support for the Model Context Protocol, allowing seamless integration with AI agents and tools.
• 🛠️ Extensible Storage: Pluggable storage backends, starting with high-performance InMemoryVectorStorage.

Glacier.Vector is available as a NuGet package. Install it using the .NET CLI:

dotnet add package Glacier.Vector

using Glacier.Vector.Index;
using Glacier.Vector.Storage;

// Initialize storage for 1536-dimensional vectors
using var storage = new InMemoryVectorStorage(dimensions: 1536);
using var index = new VectorIndex(storage);

float[] vector = GetEmbeddings("Hello, world!");
index.Add(vector, id: "doc_1", metadata: "Greeting message");

float[] query = GetEmbeddings("Hi there");
var results = index.Search(query, topK: 5);

foreach (var hit in results)
{
    Console.WriteLine($"ID: {hit.Id}, Score: {hit.Score:F4}, Metadata: {hit.Metadata}");
}

Glacier.Vector includes a built-in MCP (Model Context Protocol) server host, allowing you to use your vector database as a tool for AI agents (like Claude or Antigravity).

First, build the host application in Release mode:

dotnet build src/Glacier.Vector.Host/Glacier.Vector.Host.csproj -c Release

Add the following entry to your mcp_config.json (usually located in %AppData%\Roaming\Claude\claude_desktop_config.json or your agent's config directory):

{
  "mcpServers": {
    "glacier-vector": {
      "command": "dotnet",
      "args": [
        "ABS_PATH_TO_REPO/src/Glacier.Vector.Host/bin/Release/net10.0/Glacier.Vector.Host.dll"
      ]
    }
  }
}

• add_vector: Adds a 1536-dimensional vector and associated metadata to the index.
• search_vectors: Performs a semantic search for the closest matches to a query vector.
• ping: Simple health check to verify the server is responding.

The server defaults to 1536 dimensions, optimized for modern LLM embedding models.

Glacier.Vector is designed to saturate your CPU's memory bandwidth during search operations. On modern hardware, it can scan millions of vectors per second using SIMD-optimized kernels.

1. Compute Kernels: Static, SIMD-optimized methods for distance calculations.
2. Vector Storage: Manages the raw memory layouts of vectors and metadata.
3. Vector Index: Coordinates search operations and manages IDs/metadata mappings.
4. MCP Integration: Exposes the vector search capabilities via a standard Model Context Protocol interface.

We welcome contributions! Please see CONTRIBUTING.md for details.

Developed by Ian Cowley and Antigravity (Google DeepMind).

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