🖐️Welcome!

LikAI is an AI-driven biosecurity coach designed for small and medium-sized shrimp farmers. It transforms complex GAqP (Good Aquaculture Practices) into personalized, adaptive, and affordable action plans, empowering farmers to build resilient operations and secure profitable harvests.

Mainnet Deployment

• Frontend Canister: https://hgn4e-xyaaa-aaaas-qbh6a-cai.icp0.io/

• Backend Canister: https://a4gq6-oaaaa-aaaab-qaa4q-cai.raw.icp0.io/?id=htknj-wqaaa-aaaas-qbh5q-cai

GitBooks Documentation

We also created our full project documentation using GitBooks Link: LikAI Project Documentation

Contents

• ✅ Project Overview

• ✅ Features

• ✅ High Level Architecture

• ✅ Detailed Architecture

• ✅ Retrieval Augmented Generation (RAG) Architecture

• ✅ User Flow Diagram

• ✅ ICP Features used

• ✅ Deployment Guide

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Features

• Personalized Farm Assessment:

  • Step-by-step onboarding flow collects farm data and generates a custom biosecurity report.

• Actionable Recommendations:

  • AI-powered suggestions for pond care, stock sourcing, farm access control, and disease readiness.

• Interactive How-To Guides:

  • Visual and checklist-based guides for key farm tasks, with integrated AI chat support.

• Progress Tracking:

  • Dashboard to monitor completion of biosecurity tasks and overall farm health.

• Compliance & Certification:

  • Automated compliance reports for BFAR accreditation and export standards.

• Offline Access:

  • Downloadable PDF reports for field reference.

• AI-Powered Assessment and Chatbot:

  • Personalized biosecurity plans and 24/7 AI coaching. See docs/ai-features.md for details.

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Demo Pictures

High Level Architecture

This diagram illustrates Likai's robust, hybrid architecture, combining the power of the Internet Computer Protocol (ICP) for secure, decentralized backend operations with client-side capabilities for enhanced user experience, including offline access, and external AI integration.

How Likai's Features Leverage ICP

• Personalized Biosecurity & GAqP Action Plans:

  • The plan generation logic and the storage of the farmer's assessment data and the resulting dynamic plan would be in canisters. The plan's evolution is tamper-proof.

• AI Coach & Knowledge Assistant:

  • The chat interface, chat history, and the logic for contextualizing prompts for external LLMs (via HTTPs outcalls) would be canister-based. The "Practical Biosecurity Library" content itself could be hosted in canisters for immutability.

• Smart Investment Guidance:

  • The logic for calculating ROI, generating investment recommendations, and storing the farmer's investment plans would run in canisters.

• Compliance & Market Access (Future/Enterprise):

  • The generation of compliance reports and the storage of certification data would leverage canisters for immutable record-keeping and verifiable outputs.

Getting Started

Prerequisites

• Node.js (v18+ recommended)

• npm or yarn

Installation

1. Clone the repository:

   git clone https://github.com/your-org/likai.git
   cd likai

2. Install dependencies:

   • Use the DevContainer to create a container with the preconfigured installation. There should be a prompt in the editor in which you can install a Dev Container and open the project in the Container

   • Wait for the installation to finish.

3. Configure environment variables:

   • Copy .env.example to .env.local and set your API keys (e.g., OPENAI_API_KEY).

4. Run the development server:

   • the project is configured to rely on the local dfx network to enable the icp internet identity authorization. Please head over to the Running on the Local DFX NETWORK section.

   # to be able to run the development server without errors, please use the commands below.
   
   npm run dfx:start # to start a local replica of the dfx network
   npm run dfx:deploy # to deploy the canisters on local
   # or
   yarn dfx:start
   yarn dfx:deploy
   
   # after the canisters are deployed, you can start the development server by using the commands below:
   
   npm run dev
   # or
   yarn dev

5. Open http://localhost:3000 in your browser.

Running on the Local DFX NETWORK

To run the project in a local dfx network, you can use the command below

• The project container is pre-configured to run the installation scripts, and the local links are not accessible if the codespaces are ran in the web.

• To Start, create a codespace of this repository, and make sure you run it on the VS Code Desktop

npm run deploy:local

Modules

• Farm Setup Basics: Legal, environmental, and infrastructure requirements.

• Pond & Water Care: Water quality management, pond preparation, aeration, and effluent handling.

• Healthy Stock Sourcing: Accredited hatchery sourcing, quarantine, and stocking protocols.

• Farm Access Control: Visitor management, disinfection stations, and equipment hygiene.

• Disease Readiness: Health monitoring, emergency response, and veterinary compliance.

See docs/modules.md and docs/ai-features.md for full module and AI details.

Data Model

The system uses a multi-entity data model for farm, assessment, planning, and reporting. See docs/onboarding-erd.md for the ERD and entity descriptions.

API Endpoints

• /api/generate-assessment-plan – Generates biosecurity tasks based on farm data.

• /api/generate-plan – Creates detailed action plans for risk factors.

• /api/generate-how-to – Produces step-by-step guides for farm tasks.

• /api/chat-how-to – AI chat support for how-to guides.

• /api/submit - Allows submission of onboarding form data to be sent towards the AI

Contributing

1. Fork the repository.

2. Create your feature branch (git checkout -b feature/my-feature).

3. Commit your changes (git commit -am 'Add new feature').

4. Push to the branch (git push origin feature/my-feature).

5. Open a pull request.

License

This project is licensed under the MIT LICENSE.