# Mintoak RAG Assistant - macOS Setup Guide

This guide describes how to set up and run the Mintoak RAG Knowledge Assistant locally on macOS (optimized for Apple Silicon Apple M1/M2/M3/M4 chips using MLX).

---

## Prerequisites
1. **macOS**: Ventura (13.0) or higher recommended.
2. **Python**: Python 3.9, 3.10, or 3.11 installed. (Check with `python3 --version`).
3. **Git LFS**: Install Git Large File Storage to manage weights/vectors:
   ```bash
   brew install git-lfs
   git lfs install
   ```

---

## 1. Environment Setup

It is highly recommended to use a dedicated virtual environment for MLX:

```bash
# Create a virtual environment named 'mlx-env'
python3 -m venv mlx-env

# Activate the virtual environment
source mlx-env/bin/activate

# Upgrade pip
pip install --upgrade pip
```

---

## 2. Installation of Dependencies

Install the requirements from `requirements.txt` along with the Apple-specific MLX inference framework (`mlx-lm`):

```bash
# Install core dependencies
pip install -r requirements.txt

# Install MLX framework (Apple Silicon acceleration)
pip install mlx-lm
```

---

## 3. Running the Assistant

There are two ways to run the assistant locally on macOS:

### Option A: Local MLX Chat Server (Recommended for Apple Silicon)
This runs the local MLX-optimized version of the chat interface using 4-bit quantized Qwen 2.5 weights.

```bash
# Ensure your environment is active
source mlx-env/bin/activate

# Start the local server
python3 scripts/mintoak/chat_server.py
```
* Once started, open **`http://localhost:5001`** in your browser.
* The local database will automatically populate with 900+ chunks on first run.

### Option B: PyTorch/Transformers Server (Standard App)
This runs the CPU/GPU PyTorch version of the application (matching the Hugging Face Spaces deployment):

```bash
# Start the production Flask app
python3 app.py
```
* Access the interface at **`http://localhost:7860`**.

---

## 4. Running the Evaluation Suite

To test the RAG grounding performance, compliance filters (e.g. banned phrase checking), and out-of-scope refusals, run the evaluation script:

```bash
# Run tests on a batch of queries
python3 scripts/mintoak/evaluate_rag.py --max_cases 25
```

---

## Troubleshooting
* **`ModuleNotFoundError: No module named 'mlx_lm'`**: Make sure you have activated the virtual environment (`source mlx-env/bin/activate`) before running scripts.
* **ChromaDB Issues**: If you encounter SQLite or Chroma database conflicts, reset the local DB cache:
  ```bash
  rm -rf data/mintoak/chroma_db
  ```
  The database will automatically rebuild from `mintoak_chunks.json` on the next server start.