| |
| """ |
| main.py — CLI entry point for the Pluto pipeline. |
| |
| Usage: |
| python main.py --query "Your question" --corpus ./corpus |
| python main.py --serve # start web UI |
| python main.py --serve --query "..." --corpus ... # both |
| """ |
|
|
| import argparse |
| import json |
| import sys |
| from pathlib import Path |
|
|
| from dotenv import load_dotenv |
|
|
| load_dotenv() |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser( |
| description="Pluto — Real Mode-Switching Extraction Pipeline", |
| ) |
| parser.add_argument("--query", "-q", type=str, help="User query to process") |
| parser.add_argument("--corpus", "-c", type=str, default="./corpus", help="Path to corpus directory") |
| parser.add_argument("--output", "-o", type=str, default="./output", help="Path to output directory") |
| parser.add_argument("--serve", action="store_true", help="Start the web dashboard server") |
| parser.add_argument("--port", type=int, default=8000, help="Server port (default: 8000)") |
| args = parser.parse_args() |
|
|
| |
| corpus = Path(args.corpus) |
| if not corpus.exists(): |
| corpus.mkdir(parents=True, exist_ok=True) |
| print(f"[!] Created empty corpus directory: {corpus}") |
|
|
| if args.serve: |
| _start_server(args.port) |
| elif args.query: |
| _run_cli(args.query, str(corpus), args.output) |
| else: |
| parser.print_help() |
| sys.exit(1) |
|
|
|
|
| def _run_cli(query: str, corpus_dir: str, output_dir: str): |
| """Run the pipeline from CLI and print structured output.""" |
| from pluto.pipeline import PipelineRunner |
|
|
| print("=" * 60) |
| print(" Pluto Pipeline v1.0") |
| print("=" * 60) |
| print(f" Query: {query}") |
| print(f" Corpus: {corpus_dir}") |
| print("=" * 60) |
| print() |
|
|
| def progress(stage, data): |
| status = data.get("status", "") |
| if status == "running": |
| print(f" > S{_stage_num(stage)} {stage.upper()} ... running") |
| elif status == "complete": |
| extras = {k: v for k, v in data.items() if k != "status"} |
| print(f" [OK] S{_stage_num(stage)} {stage.upper()} -- done {extras}") |
|
|
| runner = PipelineRunner(corpus_dir=corpus_dir, output_dir=output_dir) |
| runner.on_progress(progress) |
|
|
| result = runner.run(query) |
|
|
| print() |
| print("-" * 60) |
| print(" FINAL OUTPUT") |
| print("-" * 60) |
| print(json.dumps(result.model_dump(), indent=2, ensure_ascii=True)) |
| print() |
| print(f" Confidence: {result.confidence}") |
| print(f" Real switching: {result.trace_summary.real_switching}") |
| print(f" Modes used: {result.trace_summary.modes_used_counts}") |
| print(f" Models used: {result.trace_summary.models_used}") |
| print(f" Chunks processed: {result.trace_summary.chunks_processed}") |
| print("=" * 60) |
|
|
|
|
| def _start_server(port: int): |
| """Start the FastAPI web dashboard.""" |
| import uvicorn |
| print(f" Starting Pluto Dashboard on http://localhost:{port}") |
| uvicorn.run("pluto.server:app", host="0.0.0.0", port=port, reload=False) |
|
|
|
|
| def _stage_num(stage: str) -> int: |
| return {"route": 0, "extract": 1, "merge": 2, "evidence_check": 3, "finish": 4}.get(stage, -1) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
