# Clone the repository
git clone https://github.com/janovincze/local_bi_assistant.git
cd local_bi_assistant

# Start the BI assistant (uses base Mistral model for testing)
docker compose up bi-assistant

# Or with development mode (hot reload)
docker compose --profile dev up

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and enter directory
git clone https://github.com/janovincze/local_bi_assistant.git
cd local_bi_assistant

# Create venv and install dependencies (with training support)
uv venv
uv sync --extra train-local

# Activate virtual environment
source .venv/bin/activate

# Build dbt models (creates sample database)
cd dbt_project && dbt build && cd ..

# Prepare training data
python -m training.scripts.prepare_data

# Train the model (QLoRA on Mac M4, ~2 hours)
python -m training.scripts.train_local

# Create Ollama model
ollama create bi-assistant -f output/mlx_lora/Modelfile

# Start MCP server
python -m mcp_server.server

# Clone and enter directory
git clone https://github.com/janovincze/local_bi_assistant.git
cd local_bi_assistant

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -e ".[train-local]"

# Build dbt models
cd dbt_project && dbt build && cd ..

# Prepare training data
python -m training.scripts.prepare_data

# Train the model (QLoRA on Mac M4)
python -m training.scripts.train_local

# Create Ollama model
ollama create bi-assistant -f output/mlx_lora/Modelfile

# Start MCP server
python -m mcp_server.server

{
  "mcpServers": {
    "bi-assistant": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "cwd": "/path/to/local_bi_assistant",
      "env": {
        "DUCKDB_PATH": "./dbt_project/jaffle_shop.duckdb",
        "BI_MODEL": "bi-assistant:latest"
      }
    }
  }
}

python -m training.scripts.train_local
ollama create bi-assistant -f output/mlx_lora/Modelfile

python -m training.scripts.train_sqlcoder
# Uses MLX with adapters - no Ollama model needed
# Automatically detected by MCP server

python -m training.scripts.prepare_data \
  --dbt-project ./dbt_project \
  --query-logs ./data/query_history \
  --output ./data/training

python scripts/benchmark_models.py