ArchiveNET

Where AI memories live forever - A decentralized semantic memory platform powered by blockchain and vector search

ArchiveNET is a revolutionary decentralized memory management platform that combines the power of AI embeddings with blockchain permanence. Built on Arweave, it provides enterprise-grade semantic search capabilities through advanced vector database technology, enabling applications to store, search, and retrieve contextual information with unprecedented permanence and accuracy.

Key Features

• Semantic Memory: AI-powered contextual memory storage and retrieval
• Blockchain Persistence: Permanent storage on Arweave blockchain
• Vector Search: State-of-the-art HNSW algorithm for similarity search
• High Performance: O(log N) search complexity for millions of vectors
• Decentralized: No single point of failure or censorship
• Rich Metadata: Comprehensive metadata support for enhanced search
• Enterprise-Ready: Production-grade API with authentication and monitoring

Architecture

ArchiveNET is a comprehensive monorepo consisting of four main components:

Eizen - Vector Database Engine

The world's first decentralized vector engine built on Arweave blockchain, implementing the Hierarchical Navigable Small Worlds (HNSW) algorithm for approximate nearest neighbor search.

Key Features:

• HNSW algorithm with O(log N) complexity
• Blockchain-based persistence via HollowDB
• Protobuf encoding for efficient storage
• Database-agnostic interface
• Handles millions of high-dimensional vectors

API - Backend Service

A robust Express.js API service providing semantic memory management with AI-powered search capabilities.

Stack:

• Express.js with TypeScript
• Neon PostgreSQL with Drizzle ORM
• EizenDB for vector operations
• Redis for caching
• JWT authentication
• Comprehensive validation with Zod

Frontend - Web Interface

A modern Next.js application providing an intuitive interface for memory management and search operations.

Features:

• React-based UI with TypeScript
• Real-time search capabilities
• Memory visualization
• User dashboard
• Responsive design

Eva - MCP Agent

The central Model Context Protocol (MCP) server that orchestrates memory operations and provides intelligent context management.

Documentation

• Developer Guide - Comprehensive API documentation
• Backend Integration - Backend implementation examples
• Eizen Engine - Vector database engine details
• HNSW Guide - Algorithm implementation details

Quick Start

Prerequisites

• Node.js 18+
• Docker & Docker Compose
• PostgreSQL database
• Redis server
• Arweave wallet

Installation

1. Clone the repository:

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   git clone https://github.com/s9swata/archivenet.git
   cd ArchiveNET

2. Start with Docker Compose:

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   docker-compose up -d

3. Manual setup (alternative):

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   # API Setup
   cd API
   npm install
   npm run build
   npx drizzle-kit push
   npm run dev
   
   # Frontend Setup
   cd ../client
   npm install
   npm run dev
   

Configuration

Create .env files in respective directories:

API/.env:

DATABASE_URL=your_postgres_url
REDIS_URL=redis://localhost:6379
JWT_SECRET=your_jwt_secret
ARWEAVE_WALLET_PATH=./data/wallet.json

API Integration

// Store a memory
const response = await fetch("/api/memories", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    content: "Project discussion about AI integration",
    metadata: { project: "AI-Platform", priority: "high" },
  }),
});

// Search memories
const searchResults = await fetch("/api/memories/search", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    query: "AI project discussions",
    limit: 10,
  }),
});

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Made with ❤️ for the decentralized AI future