🧩 Installation

Whether you're running it locally as a Python dev or deploying in containers as an ops engineer—Locentra OS installs cleanly, boots fast, and stays under your control.

This guide covers both manual setup and Docker-based deployment.

🧪 System Requirements

Minimum:

Python 3.10+
Node.js 18+
PostgreSQL 13+
Docker & Docker Compose (for containerized setup)

Recommended:

Unix-like system (Linux/macOS)
8GB+ RAM for live inference and training
GPU (CUDA) if using large models locally

🧱 1. Clone the Repository

git clone https://github.com/Locentra/OS.git
cd OS

🐍 2. Manual Installation (Local Python Setup)

Ideal for devs who want to step through the system layer by layer.

🔹 Backend Setup

cd backend
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

🔹 Environment Variables

Copy the default .env and adjust settings:

cp .env.example .env

Example config:

MODEL_NAME=tiiuae/falcon-rw-1b
DATABASE_URL=postgresql://user:pass@localhost:5432/Locentra
DEBUG=true

🔹 Frontend Setup

cd ../web
npm install

🔹 Start Services

Backend:

cd ../backend
bash ../scripts/run_server.sh

Frontend:
```
cd ../web
npm run dev
```
Initialize Database:
```
python ../database/db_init.py
```

🐳 3. Docker Deployment (Recommended)

Fastest way to run the full stack locally. Clean, reproducible, no system conflicts.

🔹 One-Liner Boot

cp .env.example .env
docker-compose up --build

🔹 Access Points

Logs are streamed to console by default. Extend docker-compose.yml to route logs or mount volumes as needed.

📁 4. Backend Structure Overview

Locentra-os/backend/
├── api/         # FastAPI routes (REST API layer)
├── core/        # Engine bootstrapping, registry, settings
├── models/      # Inference, model loader, fine-tuning, adapter
├── agents/      # AutoTrainer, FeedbackAgent (self-learning logic)
├── db/          # SQLAlchemy models, migrations, connectors
├── services/    # Logic layer: memory, analytics, user mgmt
├── data/        # Tokenizer, vector embedding, dataset prep
├── utils/       # Metrics, crypto helpers, evaluation

The backend is modular—every service, agent, or component can be extended or replaced.

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Last updated 1 day ago

Locentra-os/backend/ ├── api/ # FastAPI routes (REST API layer) ├── core/ # Engine bootstrapping, registry, settings ├── models/ # Inference, model loader, fine-tuning, adapter ├── agents/ # AutoTrainer, FeedbackAgent (self-learning logic) ├── db/ # SQLAlchemy models, migrations, connectors ├── services/ # Logic layer: memory, analytics, user mgmt ├── data/ # Tokenizer, vector embedding, dataset prep ├── utils/ # Metrics, crypto helpers, evaluation