RootAI 🌿

A plug-and-play semantic reasoning pipeline that runs 100 % locally and speaks to any LLM — OpenAI, Ollama, LM Studio, or any OpenAI-compatible endpoint including local browser-accessible models.

Your query is automatically routed through 6 specialised layers before a final answer is synthesised:

Prompt Analyzer → Knowledge Graph → RAG Engine →
Reasoning Engine → Constraint Checker → LLM Synthesis

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⚡ Quick start (1 click)

Prerequisites

• Python 3.10 or newer — https://www.python.org/downloads/
  (No Node.js, no Docker, no other tools needed.)

Steps

# 1. Clone the repo (or download the ZIP and unzip it)
git clone https://github.com/tattoosonmyskin/rootai1.0.git
cd rootai1.0

# 2. Run — this installs everything automatically
python run.py        # macOS / Linux / Windows
# or on macOS/Linux: bash run.sh
# or on Windows:     double-click run.bat

The launcher will:

1. Create a local Python virtual environment (.venv/)
2. Install dependencies from requirements.txt
3. Copy .env.example → .env if no .env exists
4. Start the server on http://localhost:8000
5. Open your browser automatically

On first launch, a Settings panel will pop up so you can enter your LLM API key.

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⚙️ LLM Configuration

Edit .env (created automatically on first run) or use the ⚙ Settings button inside the app.

Provider	API Key	Base URL	Model
OpenAI	your OpenAI key	https://api.openai.com/v1	gpt-4o-mini
Ollama (local)	ollama	http://localhost:11434/v1	llama3 / mistral / …
LM Studio (local)	lmstudio	http://localhost:1234/v1	your loaded model
Any OpenAI-compatible	your key	your endpoint	your model

Local models (no API key required)

Ollama is the easiest way to run models locally:

# Install Ollama from https://ollama.com, then:
ollama pull llama3       # or mistral, phi3, gemma, etc.
ollama serve             # starts on http://localhost:11434

Then in RootAI settings: key = ollama, base URL = http://localhost:11434/v1.

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📁 Project structure

rootai1.0/
├── app.py              ← FastAPI server (API + static file serving)
├── run.py              ← 1-click launcher (auto-installs, opens browser)
├── run.sh              ← Shell launcher for macOS/Linux
├── run.bat             ← Batch launcher for Windows
├── requirements.txt    ← Python dependencies
├── .env.example        ← Config template (copy to .env)
├── core/
│   ├── llm_client.py   ← Async LLM invocation (any OpenAI-compatible API)
│   ├── pipeline.py     ← 6-layer reasoning pipeline with SSE streaming
│   └── store.py        ← JSON-file session/entity storage (no database needed)
├── static/
│   └── index.html      ← Full SPA frontend (Tailwind CDN, vanilla JS, no build)
└── data/               ← Auto-created; stores session history as JSON files

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🛠 Manual / developer setup

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env        # then edit .env
uvicorn app:app --reload    # hot-reload during development

API docs available at http://localhost:8000/api/docs.

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🔌 API

Method	Path	Description
POST	/api/run	Stream pipeline (SSE)
GET	/api/sessions	List past sessions
GET	/api/sessions/{id}	Get session
POST	/api/sessions/{id}/review	Approve / flag answer
DELETE	/api/sessions/{id}	Delete session
GET	/api/settings	Read LLM settings
POST	/api/settings	Save LLM settings to .env

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📜 Legacy React source

The original React/Vite code is preserved in the full boy/ folder for reference.