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Shipped

2026

MNEMOS

#Vector Database#Semantic Search#Java#HNSW#Algorithms

Live System

MN

Project Overview

Overview

MNEMOS is a Java-based vector database engine built from scratch to show how semantic retrieval systems work under the hood. Instead of wrapping Pinecone, Chroma, FAISS, or a managed vector store, it implements the core search layer directly.

Retrieval Engine

Exact Baseline: Brute Force search scans every vector and provides a correctness reference for other indexes.
Spatial Indexing: KD-Tree search demonstrates dimensional splitting, nearest-neighbor pruning, and the limits of tree-based search in higher dimensions.
Approximate Search: An HNSW-style graph index models layered proximity traversal for low-latency approximate nearest-neighbor retrieval.
Runtime Metrics: The API reports distance scores and latency measurements so retrieval strategies can be compared during demos.

Interactive System

MNEMOS ships with a browser UI that projects 16-dimensional vectors into a 2D canvas. Users can search seeded profile/project vectors, insert new text, compare algorithms, switch distance metrics, and inspect benchmark behavior.

Why It Matters

Most AI search demos stop at calling an embedding API. MNEMOS goes one layer deeper: vector storage, indexing, distance functions, top-k retrieval, live insertion, benchmark endpoints, and deployment-ready Java packaging with Docker.

Deployment & Repos

Live Source API Health

Tech Stack

Java 17JavalinMavenDockerHNSWKD-TreeVector SearchANNHTML5 CanvasTailwind CSSChart.jsRender

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