LifeSync

LifeSync is a multi-system repository that brings together a Backend, Frontend, LLM service, and Model artifacts to provide an integrated personal assistant / life-management platform. This README explains the repository layout, features, and how to get each subsystem running locally.

Repository: DevSharma03/LifeSync

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Table of contents

• About
• Project Features
• Repository Structure
• Quick Start (all 4 systems)
  • Prerequisites
  • Backend (API)
  • Frontend (UI)
  • LLM Service
  • Models (artifacts/storage)
• Environment variables (examples)
• Development notes & workflow
• Contributing
• License
• Contact

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About

LifeSync is designed to integrate a backend API, a user-facing frontend, and an LLM-based service layer that uses model artifacts stored in the Models/ folder. The goal is to provide an extendable foundation for building an intelligent personal assistant and life-management toolset.

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Project Features

• Centralized Backend API for user data, authentication, synchronization, scheduling, and integrations.
• Modern Frontend for user interactions (dashboard, calendar, reminders, chat with LLM).
• LLM service to handle natural language understanding, suggestions, and conversational workflows.
• Models directory to store model artifacts (local or mounted) for offline inference or fine-tuning.
• Extensible architecture — independent services that can be deployed separately (or in containers).
• Developer-friendly: clear separation of concerns and environment-based configuration.

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Repository structure

Top-level folders present in this repository:

• Backend/ — API server (Node.js / Express or similar)
• Frontend/ — Client application (React / Next.js or similar)
• LLM/ — LLM service (Python, FastAPI/Flask or Node-based wrapper)
• Models/ — Model files, checkpoints and related artifacts

Suggested in-repo tree (typical example):

LifeSync/
├─ Backend/
│  ├─ package.json
│  ├─ src/
│  │  ├─ index.js
│  │  ├─ routes/
│  │  └─ controllers/
│  └─ .env.example
├─ Frontend/
│  ├─ package.json
│  ├─ src/
│  │  ├─ App.jsx
│  │  ├─ pages/
│  │  └─ components/
│  └─ .env.example
├─ LLM/
│  ├─ requirements.txt
│  ├─ app.py          # or main.py / server.py
│  ├─ handlers/
│  └─ .env.example
├─ Models/
│  └─ README.md       # instructions for downloading/placing model files
└─ README.md

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Quick Start (all 4 systems)

This section gives step-by-step instructions to get each subsystem running locally. Adjust commands to match your project's scripts if they differ.

Prerequisites

• Node.js v16+ (or as required by project)
• npm or yarn
• Python 3.8+ (for LLM service)
• pip
• Git
• (Optional) Docker & Docker Compose if you prefer containerized setup
• A running database (MongoDB, Postgres, etc.) if the backend requires persistence

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Backend (API)

1. Open a terminal:

   cd Backend

2. Copy environment template:

   cp .env.example .env
   # Edit .env to set DATABASE_URL, JWT_SECRET, PORT, etc.

3. Install dependencies:

   npm install

4. Start the dev server:

   npm run dev
   # or
   npm start

5. API should be available at http://localhost:PORT (check your .env PORT).

Notes:

• If the backend uses a database, ensure DATABASE_URL points to your running DB and migrations are applied.
• Typical env values: DATABASE_URL, JWT_SECRET, PORT, REDIS_URL.

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Frontend (UI)

1. Open a new terminal:

   cd Frontend

2. Copy environment template:

   cp .env.example .env
   # Edit .env to set REACT_APP_API_URL or equivalent

3. Install dependencies:

   npm install

4. Start the frontend:

   npm start
   # or for Next.js: npm run dev

5. Open http://localhost:3000 (or the port shown by the dev server).

Environment:

• Set API base URL env variable (e.g., REACT_APP_API_URL=http://localhost:4000).

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LLM Service

The LLM folder contains the service that interacts with local or remote models. Commonly built in Python (FastAPI / Flask), but adjust commands to match your implementation.

1. Create & activate a Python virtual environment:

   cd LLM
   python -m venv .venv
   source .venv/bin/activate   # macOS / Linux
   .venv\Scripts\activate      # Windows

2. Install requirements:

   pip install -r requirements.txt

3. Configure environment:

   cp .env.example .env
   # Edit .env to set MODEL_DIR, MODEL_NAME, API_PORT, OPENAI_API_KEY (if using remote), etc.

4. Run the service:

   • If FastAPI / Uvicorn:

     uvicorn app:app --reload --host 0.0.0.0 --port 8000

   • If Flask/simple script:

     python app.py

5. The LLM service will typically expose an endpoint like http://localhost:8000/api/llm which the Backend or Frontend can call.

Notes:

• If using large local models, ensure Models/ contains the required files and that the LLM service has sufficient GPU/CPU resources configured.

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Models (artifacts / storage)

The Models/ directory holds model checkpoints, tokenizers, or binary files used by the LLM service.

• Place models in Models/<model-name>/ and set MODEL_DIR or MODEL_PATH in LLM/.env.
• For example:

  Models/
  └─ llama-2-7b/
     ├─ config.json
     ├─ pytorch_model.bin
     └─ tokenizer.json
  

• If models are large, store them outside of Git (use .gitignore) and include instructions in Models/README.md to download from the appropriate source.

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Environment variables (examples)

Example variables you may find across subsystems:

Backend (.env)

PORT=4000
DATABASE_URL=mongodb://localhost:27017/lifesync
JWT_SECRET=your_jwt_secret
LLM_SERVICE_URL=http://localhost:8000

Frontend (.env)

REACT_APP_API_URL=http://localhost:4000

LLM (.env)

MODEL_DIR=../Models/llama-2-7b
API_PORT=8000
OPENAI_API_KEY= # if you use remote APIs

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Development notes & workflow

• Each subsystem runs independently; use the Backend to centralize business logic and the LLM for NLP tasks.
• Frontend communicates with Backend via REST or GraphQL. Backend may forward LLM-specific requests to the LLM service.
• Use environment-based configuration and .env templates to make local setup reproducible.
• Add a docker-compose.yml if you want a one-command local startup for all services and dependencies (db, redis, etc.).

Suggested branch/workflow:

• main — stable production-ready code
• feature/* — feature branches
• fix/* — bug fixes
• PRs should include tests and a description of changes.

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Contributing

Thanks for considering contributing! A suggested contribution flow:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature/my-feature.
3. Make changes & add tests where appropriate.
4. Open a Pull Request describing your changes and the rationale.

Add a CONTRIBUTING.md to outline style, linting, testing, and commit message guidelines.

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License

No license is specified in the repository currently. If you'd like to open-source it, consider adding a license (MIT, Apache-2.0, etc.). Add a LICENSE file at the repo root.

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Contact

Repository: DevSharma03/LifeSync

For questions or help setting up, open an issue in the repo or contact the maintainer (DevSharma03) via GitHub.

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Thank you — this README is a template tailored to the current repo layout (Backend, Frontend, LLM, Models). If you want, I can:

• Commit this README.md to the repository,
• Generate .env.example templates for each subsystem,
• Create a Docker Compose example to run all 4 systems together.

Tell me which action you’d like me to do next and I’ll proceed.