feat: add model reload endpoint and forge CLI

src/companion/forge/cli.py

@@ -0,0 +1,133 @@
+"""CLI for model forge operations."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import typer
+
+from companion.config import load_config
+from companion.forge.extract import TrainingDataExtractor
+from companion.forge.reload import get_model_status, reload_model
+from companion.forge.train import train as train_model
+
+app = typer.Typer(help="Companion model forge - training pipeline")
+
+
+@app.command()
+def extract(
+    output: Path = typer.Option(
+        Path("~/.companion/training_data/extracted.jsonl"),
+        help="Output JSONL file path",
+    ),
+) -> None:
+    """Extract training examples from vault."""
+    config = load_config()
+
+    typer.echo("Scanning vault for reflection examples...")
+
+    extractor = TrainingDataExtractor(config)
+    examples = extractor.extract()
+
+    if not examples:
+        typer.echo("No reflection examples found in vault.")
+        typer.echo(
+            "Try adding tags like #reflection, #insight, or #learning to your notes."
+        )
+        raise typer.Exit(1)
+
+    # Save to JSONL
+    output = output.expanduser()
+    output.parent.mkdir(parents=True, exist_ok=True)
+    count = extractor.save_to_jsonl(output)
+
+    stats = extractor.get_stats()
+
+    typer.echo(f"\nExtracted {count} training examples:")
+    typer.echo(f"  - Average length: {stats.get('avg_length', 0)} chars")
+    if stats.get("top_tags"):
+        typer.echo(
+            f"  - Top tags: {', '.join(f'{tag}({cnt})' for tag, cnt in stats['top_tags'][:5])}"
+        )
+    typer.echo(f"\nSaved to: {output}")
+
+
+@app.command()
+def status() -> None:
+    """Check model status."""
+    config = load_config()
+
+    model_status = get_model_status(config)
+
+    typer.echo(f"Model Status:")
+    typer.echo(f"  Path: {model_status['path']}")
+    typer.echo(f"  Exists: {'Yes' if model_status['exists'] else 'No'}")
+    if model_status["exists"]:
+        typer.echo(f"  Type: {model_status['type']}")
+        typer.echo(f"  Size: {model_status['size_mb']} MB")
+
+
+@app.command()
+def reload(
+    model_path: Path = typer.Argument(
+        ...,
+        help="Path to new model directory or GGUF file",
+    ),
+    no_backup: bool = typer.Option(
+        False,
+        "--no-backup",
+        help="Skip backing up current model",
+    ),
+) -> None:
+    """Reload model with a new fine-tuned version."""
+    config = load_config()
+
+    model_path = model_path.expanduser()
+
+    try:
+        active_path = reload_model(config, model_path, backup=not no_backup)
+        typer.echo(f"Model reloaded successfully: {active_path}")
+    except FileNotFoundError as e:
+        typer.echo(f"Error: {e}")
+        raise typer.Exit(1)
+
+
+@app.command()
+def train(
+    data: Path = typer.Option(
+        Path("~/.companion/training_data/extracted.jsonl"),
+        help="Path to training data JSONL",
+    ),
+    output: Path = typer.Option(
+        Path("~/.companion/training"),
+        help="Output directory for checkpoints",
+    ),
+    epochs: int = typer.Option(3, help="Number of training epochs"),
+    lr: float = typer.Option(2e-4, help="Learning rate"),
+) -> None:
+    """Train model using QLoRA fine-tuning."""
+    data = data.expanduser()
+    output = output.expanduser()
+
+    if not data.exists():
+        typer.echo(f"Training data not found: {data}")
+        typer.echo("Run 'forge extract' first to generate training data.")
+        raise typer.Exit(1)
+
+    try:
+        final_path = train_model(
+            data_path=data,
+            output_dir=output,
+            num_epochs=epochs,
+            learning_rate=lr,
+        )
+        typer.echo(f"\nTraining complete! Model saved to: {final_path}")
+        typer.echo("\nTo use this model:")
+        typer.echo(f"  forge reload {final_path}")
+    except Exception as e:
+        typer.echo(f"Training failed: {e}")
+        raise typer.Exit(1)
+
+
+if __name__ == "__main__":
+    app()