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Cipher AI Vault

Developer Grant

Cipher AI Vault is an Azle-based proof of concept that seamlessly integrates:

  • In-memory VectorDB
  • In-memory LLM
  • Secure asset storage
  • Stable memory data storage
  • Cycles-distro top-up
  • ic-auth for authentication

This versatile platform showcases the Internet Computer's potential for secure, sandboxed AI development, offering adaptable tools for a wide range of AI-driven applications.

Note: This demo is a proof of concept and not intended for production use. It was developed as part of a Developer Grant from the DFINITY Foundation.

Table of Contents

Demo Canisters and Repositories

Main Demo

Standalone Demo Canisters

Grant Deliverables Repositories

Core Features

  1. Frontend Canister: Main entry point for user interactions
  2. In-memory VectorDB: Stores and manages embeddings for efficient retrieval
  3. In-memory LLM: Processes natural language queries and interacts with the VectorDB
  4. Secure Asset Storage: Dedicated module for secure asset storage
  5. Secure Data Store: Dedicated canister for storing data in stable memory
  6. Cycles Distro Canister: Manages cycles and top-ups
  7. ic-auth: Handles authentication with various wallets (Plug, Stoic, NFID, and Internet Identity)

Prerequisites

WebGPU Support

For the best experience, use a WebGPU enabled browser. We recommend Chrome Canary.

Required Software

  • DFX
  • Node.js
  • Azle development kit

For setup assistance, refer to:

Required Wallets

You will need one of the following wallets:

Quick Setup Instructions

  1. Clone the repository:

    git clone https://github.com/supaIC/Cipher-AI-Vault.git
    cd Cipher-AI-Vault
  2. Run the setup script:

    npm run setup

    Note: You may be prompted to enter your DFX identity password during setup.

Detailed Setup and Deployment

For detailed instructions, refer to the following README files within this repository:

Cipher AI Vault Roadmap

  • Data Store backup canister
  • Edit Data Store file entries
  • Multiple in-memory LLM support
  • Models stored in asset canisters
  • Embeddings backed up in Stable Memory
  • Generate a Data File from a document using in-memory LLM
  • Generate images to be stored in the Image Store using in-memory Stable Diffusion

License

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