9e9b75e0b3
28 persona workspace with document upload, OCR pipeline, and vector embedding assignment via AnythingLLM API. Supports 5 clusters (intel, cyber, military, humanities, engineering) with batch processing and resume capability. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
681 lines
27 KiB
Python
681 lines
27 KiB
Python
#!/usr/bin/env python3
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"""
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AnythingLLM × Persona Library Integration Setup
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Three-phase pipeline:
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Phase A: Upload all text-based files
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Phase B: OCR all scanned PDFs in-place
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Phase C: Upload newly OCR'd files + assign to workspaces
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Usage:
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python3 setup.py --storage-setup # Symlink direct-uploads/ to HDD
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python3 setup.py --create-workspaces # Create workspaces + load persona prompts
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python3 setup.py --upload-documents # Full pipeline: upload → OCR → upload → assign
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python3 setup.py --persona frodo # Single persona
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python3 setup.py --cluster intel # Intel cluster (frodo,echo,ghost,oracle,wraith,scribe,polyglot)
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python3 setup.py --cluster cyber # Cyber cluster
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python3 setup.py --cluster military # Military cluster
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python3 setup.py --cluster humanities # Humanities cluster
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python3 setup.py --cluster engineering # Engineering cluster
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python3 setup.py --priority 1 # Only priority 1 (core) folders
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python3 setup.py --dry-run # Preview
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python3 setup.py --status # Show state
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"""
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import argparse
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import json
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import os
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import shutil
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import subprocess
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import sys
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import time
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from pathlib import Path
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import yaml
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try:
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import requests
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except ImportError:
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print("pip install requests pyyaml")
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sys.exit(1)
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CONFIG_PATH = Path(__file__).parent / "config.yaml"
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PROGRESS_PATH = Path(__file__).parent / "upload_progress.json"
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ANYTHINGLLM_STORAGE = Path.home() / ".config/anythingllm-desktop/storage"
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SKIP_EXT = set()
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CLUSTERS = {
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"intel": ["frodo", "echo", "ghost", "oracle", "wraith", "scribe", "polyglot"],
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"cyber": ["neo", "bastion", "sentinel", "specter", "phantom", "cipher", "vortex"],
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"military": ["marshal", "centurion", "corsair", "warden", "medic"],
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"humanities": ["chronos", "tribune", "arbiter", "ledger", "sage", "herald", "scholar", "gambit"],
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"engineering": ["forge", "architect"],
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}
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def load_config():
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with open(CONFIG_PATH) as f:
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cfg = yaml.safe_load(f)
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global SKIP_EXT
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SKIP_EXT = set(cfg["processing"]["skip_extensions"])
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return cfg
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def load_progress():
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if PROGRESS_PATH.exists():
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with open(PROGRESS_PATH) as f:
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return json.load(f)
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return {"uploaded_files": {}, "workspace_docs": {}, "ocr_done": [], "ocr_failed": []}
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def save_progress(progress):
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with open(PROGRESS_PATH, "w") as f:
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json.dump(progress, f, indent=2, ensure_ascii=False)
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# ──────────────────────────────────────────────────────────
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# API
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# ──────────────────────────────────────────────────────────
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def api_request(config, method, endpoint, **kwargs):
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url = f"{config['anythingllm']['base_url']}{endpoint}"
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headers = {"Authorization": f"Bearer {config['anythingllm']['api_key']}"}
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if "json" in kwargs:
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headers["Content-Type"] = "application/json"
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resp = getattr(requests, method)(url, headers=headers, **kwargs)
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if resp.status_code not in (200, 201):
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print(f" API error {resp.status_code}: {resp.text[:300]}")
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return None
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return resp.json()
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def api_upload(config, file_path, folder_name=None):
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endpoint = f"/document/upload/{folder_name}" if folder_name else "/document/upload"
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url = f"{config['anythingllm']['base_url']}{endpoint}"
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headers = {"Authorization": f"Bearer {config['anythingllm']['api_key']}"}
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size_mb = file_path.stat().st_size / (1024 * 1024)
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timeout = max(120, int(120 + (size_mb / 10) * 30))
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try:
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with open(file_path, "rb") as f:
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files = {"file": (file_path.name, f, "application/octet-stream")}
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resp = requests.post(url, headers=headers, files=files, timeout=timeout)
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if resp.status_code not in (200, 201):
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return None, resp.text[:200]
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data = resp.json()
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if data.get("success") and data.get("documents"):
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return data["documents"], None
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return None, data.get("error", "Unknown error")
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except requests.exceptions.Timeout:
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return None, f"timeout ({timeout}s)"
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except Exception as e:
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return None, str(e)
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def check_api(config):
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try:
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return api_request(config, "get", "/auth") is not None
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except Exception:
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return False
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def check_collector_alive():
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try:
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return requests.get("http://127.0.0.1:8888", timeout=3).status_code == 200
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except Exception:
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return False
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def wait_for_collector(max_wait=90):
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print(" ⏳ waiting for collector...", end="", flush=True)
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for i in range(max_wait):
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if check_collector_alive():
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print(" ✓")
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return True
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time.sleep(1)
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if i % 10 == 9:
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print(".", end="", flush=True)
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print(" ✗ timeout")
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return False
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def get_existing_workspaces(config):
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result = api_request(config, "get", "/workspaces")
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if result and "workspaces" in result:
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return {ws["name"]: ws for ws in result["workspaces"]}
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return {}
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# ──────────────────────────────────────────────────────────
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# PDF DETECTION & OCR
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# ──────────────────────────────────────────────────────────
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def is_scanned_pdf(file_path):
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"""Fast scan detection via pdffonts (~0.04s vs pdftotext ~2s).
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No fonts embedded = scanned/image-only PDF."""
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if file_path.suffix.lower() != ".pdf":
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return False
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try:
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proc = subprocess.Popen(
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["pdffonts", "-l", "3", str(file_path)],
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stdout=subprocess.PIPE, stderr=subprocess.PIPE,
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)
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try:
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stdout, _ = proc.communicate(timeout=3)
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lines = [l for l in stdout.decode(errors="ignore").strip().split("\n") if l.strip()]
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return len(lines) <= 2
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except subprocess.TimeoutExpired:
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proc.kill()
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proc.wait()
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return False # assume text if slow
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except Exception:
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return False
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def ocr_pdf(file_path, language="tur+eng", dpi=200):
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"""OCR a scanned PDF in-place. Returns True on success."""
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import tempfile
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tmp_fd, tmp_path = tempfile.mkstemp(suffix=".pdf", dir=file_path.parent)
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os.close(tmp_fd)
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tmp_path = Path(tmp_path)
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try:
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result = subprocess.run(
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["ocrmypdf", "--skip-text", "--rotate-pages", "--deskew",
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"--jobs", "4", "--image-dpi", str(dpi), "-l", language,
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"--output-type", "pdf", "--quiet",
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str(file_path), str(tmp_path)],
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capture_output=True, text=True, timeout=600,
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)
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if result.returncode in (0, 6) and tmp_path.exists() and tmp_path.stat().st_size > 0:
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tmp_path.replace(file_path)
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return True
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tmp_path.unlink(missing_ok=True)
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return False
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except Exception:
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tmp_path.unlink(missing_ok=True)
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return False
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# ──────────────────────────────────────────────────────────
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# STEP 1: Storage offload
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# ──────────────────────────────────────────────────────────
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def storage_setup(config, dry_run=False):
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hdd_base = Path(config["storage"]["hdd_storage"])
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src = ANYTHINGLLM_STORAGE / "direct-uploads"
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dst = hdd_base / "direct-uploads"
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print("═══ Storage: direct-uploads/ → HDD ═══\n")
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if src.is_symlink():
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print(f" ✓ already symlinked → {src.resolve()}")
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return
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if not dry_run:
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dst.mkdir(parents=True, exist_ok=True)
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if src.exists():
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shutil.copytree(str(src), str(dst), dirs_exist_ok=True)
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shutil.rmtree(src)
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src.symlink_to(dst)
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print(f" ✓ done\n")
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# ──────────────────────────────────────────────────────────
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# STEP 2: Create workspaces + load prompts
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# ──────────────────────────────────────────────────────────
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def extract_system_prompt(config, persona_file):
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personas_dir = Path(config["storage"]["personas_dir"])
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fp = personas_dir / persona_file
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if not fp.exists():
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alt = personas_dir / persona_file.replace("/general.md", "") / "_meta.yaml"
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fp = alt if alt.exists() else None
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if not fp:
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return None
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content = fp.read_text(encoding="utf-8")
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if fp.suffix == ".yaml":
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meta = yaml.safe_load(content)
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return meta.get("system_prompt", meta.get("description", ""))
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parts = content.split("---")
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if len(parts) >= 3:
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try:
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fm = yaml.safe_load(parts[1])
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except yaml.YAMLError:
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fm = {}
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tone = fm.get("tone", "")
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body = "---".join(parts[2:]).strip()
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return f"Tone: {tone}\n\n{body}" if tone else body
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return content
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def create_workspaces(config, persona_list=None, dry_run=False):
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print("═══ Creating Workspaces ═══\n")
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if not config["anythingllm"]["api_key"]:
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print(" ✗ API key not set!")
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return
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existing = get_existing_workspaces(config)
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created = skipped = 0
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for codename, ws_config in config["workspaces"].items():
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if persona_list and codename not in persona_list:
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continue
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name = ws_config["name"]
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persona_file = ws_config.get("persona_file", "")
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system_prompt = extract_system_prompt(config, persona_file) if persona_file else ""
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if name in existing:
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# Update prompt if workspace exists
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slug = existing[name].get("slug", "?")
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if system_prompt and not dry_run:
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api_request(config, "post", f"/workspace/{slug}/update",
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json={"openAiPrompt": system_prompt})
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print(f" ✓ {codename} (prompt: {len(system_prompt or '')} chars)")
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skipped += 1
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continue
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print(f" → {codename}: creating '{name}'")
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if not dry_run:
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result = api_request(config, "post", "/workspace/new", json={"name": name})
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if result:
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slug = result.get("workspace", {}).get("slug", "?")
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if system_prompt:
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api_request(config, "post", f"/workspace/{slug}/update",
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json={"openAiPrompt": system_prompt})
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print(f" ✓ created + prompt ({len(system_prompt)} chars)")
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created += 1
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else:
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print(f" ✗ failed")
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print(f"\n Created: {created}, Updated: {skipped}\n")
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# ──────────────────────────────────────────────────────────
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# STEP 3: Three-phase upload pipeline
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# ──────────────────────────────────────────────────────────
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def collect_all_files(config, book_library, persona_list=None, priority_filter=None, max_size_mb=100):
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"""Scan all folders and classify files as text-based or scanned."""
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text_files = {} # folder_name → [paths]
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scanned_files = {} # folder_name → [paths]
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persona_folders = {} # codename → [folder_names]
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folder_to_path = {} # folder_name → source path
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for codename, ws_config in config["workspaces"].items():
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if persona_list and codename not in persona_list:
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continue
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persona_folders[codename] = []
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for entry in ws_config.get("folders", []):
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folder_path = entry["path"]
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priority = entry.get("priority", 2)
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if priority_filter and priority > priority_filter:
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continue
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folder_name = folder_path.replace("/", "_")
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persona_folders[codename].append(folder_name)
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if folder_name in text_files:
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continue # already scanned
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src = book_library / folder_path
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folder_to_path[folder_name] = src
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text_files[folder_name] = []
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scanned_files[folder_name] = []
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if not src.exists():
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print(f" ⚠ {folder_path} not found")
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continue
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# Use find with -printf for speed on HDD (one syscall, no per-file stat)
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try:
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find_result = subprocess.run(
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["find", str(src), "-type", "f", "-not", "-empty",
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"-printf", "%s %p\n"],
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capture_output=True, text=True, timeout=120,
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)
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all_files = []
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max_bytes = (max_size_mb or 9999) * 1024 * 1024
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for line in find_result.stdout.strip().split("\n"):
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if not line:
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continue
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parts = line.split(" ", 1)
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if len(parts) != 2:
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continue
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size, path = int(parts[0]), Path(parts[1])
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if path.suffix.lower() not in SKIP_EXT and size <= max_bytes:
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all_files.append(path)
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all_files.sort()
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except Exception:
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all_files = sorted(
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f for f in src.rglob("*")
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if f.is_file() and f.suffix.lower() not in SKIP_EXT
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and f.stat().st_size > 0
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and (not max_size_mb or f.stat().st_size / (1024*1024) <= max_size_mb)
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)
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print(f" {folder_name}: {len(all_files)} files found", flush=True)
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# Classify every file (pdffonts is fast: ~0.04s per file)
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for i, f in enumerate(all_files):
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if is_scanned_pdf(f):
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scanned_files[folder_name].append(f)
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else:
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text_files[folder_name].append(f)
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if (i + 1) % 500 == 0:
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print(f" {folder_name}: {i+1}/{len(all_files)} classified...", flush=True)
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t = len(text_files[folder_name])
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s = len(scanned_files[folder_name])
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print(f" {folder_name}: {t} text, {s} scanned")
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return text_files, scanned_files, persona_folders, folder_to_path
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def upload_file_batch(config, folder_name, files, progress, batch_size, delay):
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"""Upload a list of files to a folder. Returns (uploaded, failed) counts."""
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uploaded = failed = 0
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new_files = [f for f in files if str(f) not in progress["uploaded_files"]]
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if not new_files:
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return 0, 0
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print(f" → {folder_name}: {len(new_files)} files")
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for i, fp in enumerate(new_files):
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if uploaded > 0 and uploaded % batch_size == 0:
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print(f" ⏸ batch pause ({delay}s)...")
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time.sleep(delay)
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size_mb = fp.stat().st_size / (1024 * 1024)
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print(f" [{i+1}/{len(new_files)}] {fp.name} ({size_mb:.1f}MB)", end="", flush=True)
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if not check_collector_alive():
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print(f" ⚠ collector down")
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if not wait_for_collector():
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print(" ✗ stopping — restart AnythingLLM and --resume")
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save_progress(progress)
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return uploaded, failed
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docs, error = None, None
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for attempt in range(3):
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docs, error = api_upload(config, fp, folder_name=folder_name)
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if docs:
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break
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if error and "not online" in str(error):
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print(f" ↻", end="", flush=True)
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time.sleep(5)
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if not wait_for_collector():
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save_progress(progress)
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return uploaded, failed
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elif attempt < 2:
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time.sleep(2)
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print(f" ↻", end="", flush=True)
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if docs:
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progress["uploaded_files"][str(fp)] = {
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"location": docs[0].get("location", ""),
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"folder": folder_name,
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"name": docs[0].get("name", ""),
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}
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uploaded += 1
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print(f" ✓")
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else:
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failed += 1
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progress.setdefault("failed_files", {})[str(fp)] = {
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"folder": folder_name, "error": str(error)[:200],
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}
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print(f" ✗ {error}")
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if uploaded % 10 == 0:
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save_progress(progress)
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save_progress(progress)
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return uploaded, failed
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def assign_to_workspaces(config, persona_folders, progress, batch_size, delay):
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"""Phase C2: assign uploaded docs to persona workspaces."""
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print("── Assigning to workspaces ──\n")
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existing_ws = get_existing_workspaces(config)
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for codename, folders in sorted(persona_folders.items()):
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ws_name = config["workspaces"][codename]["name"]
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ws_info = existing_ws.get(ws_name)
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if not ws_info:
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continue
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slug = ws_info["slug"]
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doc_locs = []
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for fn in folders:
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for fpath, info in progress["uploaded_files"].items():
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if info.get("folder") == fn and info.get("location"):
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doc_locs.append(info["location"])
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already = set(progress.get("workspace_docs", {}).get(codename, []))
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new_docs = [loc for loc in doc_locs if loc not in already]
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if not new_docs:
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if doc_locs:
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print(f" ✓ {codename}: {len(doc_locs)} docs assigned")
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continue
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print(f" → {codename} ({slug}): {len(new_docs)} docs")
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for bs in range(0, len(new_docs), batch_size):
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batch = new_docs[bs:bs + batch_size]
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result = api_request(config, "post", f"/workspace/{slug}/update-embeddings",
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json={"adds": batch, "deletes": []})
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if result:
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progress.setdefault("workspace_docs", {}).setdefault(codename, []).extend(batch)
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print(f" ✓ {len(batch)} docs embedded")
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else:
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print(f" ✗ batch failed")
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if bs + batch_size < len(new_docs):
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time.sleep(delay)
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save_progress(progress)
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print()
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def upload_documents(config, persona_list=None, priority_filter=None,
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dry_run=False, resume=False, max_size_mb=100):
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"""Three-phase pipeline: text upload → OCR → OCR upload → assign."""
|
||
print("═══ Upload Pipeline ═══\n")
|
||
|
||
if not check_api(config):
|
||
print(" ✗ AnythingLLM API not reachable.")
|
||
return
|
||
|
||
book_library = Path(config["storage"]["book_library"])
|
||
batch_size = config["processing"]["batch_size"]
|
||
delay = config["processing"]["delay_between_batches"]
|
||
progress = load_progress() if resume else {
|
||
"uploaded_files": {}, "workspace_docs": {}, "ocr_done": [], "ocr_failed": [],
|
||
}
|
||
|
||
# Scan & classify
|
||
print(" Scanning folders...\n")
|
||
text_files, scanned_files, persona_folders, _ = collect_all_files(
|
||
config, book_library, persona_list, priority_filter, max_size_mb,
|
||
)
|
||
|
||
total_text = sum(len(v) for v in text_files.values())
|
||
total_scanned = sum(len(v) for v in scanned_files.values())
|
||
already = len(progress["uploaded_files"])
|
||
|
||
print(f"\n Text-based files: {total_text}")
|
||
print(f" Scanned PDFs: {total_scanned}")
|
||
print(f" Already uploaded: {already}")
|
||
print(f" Personas: {len(persona_folders)}\n")
|
||
|
||
if dry_run:
|
||
for fn in sorted(set(list(text_files.keys()) + list(scanned_files.keys()))):
|
||
t = len([f for f in text_files.get(fn, []) if str(f) not in progress["uploaded_files"]])
|
||
s = len([f for f in scanned_files.get(fn, []) if str(f) not in progress["ocr_done"]])
|
||
if t or s:
|
||
print(f" {fn}: {t} text, {s} scanned")
|
||
print(f"\n Personas:")
|
||
for c, flds in sorted(persona_folders.items()):
|
||
print(f" {c}: {', '.join(flds)}")
|
||
return
|
||
|
||
# ── Phase A: Upload text-based files ──
|
||
print("══ Phase A: Upload text-based files ══\n")
|
||
total_up = total_fail = 0
|
||
for fn, files in sorted(text_files.items()):
|
||
up, fail = upload_file_batch(config, fn, files, progress, batch_size, delay)
|
||
total_up += up
|
||
total_fail += fail
|
||
print(f"\n Phase A done: {total_up} uploaded, {total_fail} failed\n")
|
||
|
||
# ── Phase B: OCR scanned PDFs ──
|
||
total_scanned_remaining = sum(
|
||
1 for files in scanned_files.values()
|
||
for f in files if str(f) not in progress.get("ocr_done", [])
|
||
)
|
||
if total_scanned_remaining > 0:
|
||
print(f"══ Phase B: OCR {total_scanned_remaining} scanned PDFs ══\n")
|
||
ocr_ok = ocr_fail = 0
|
||
for fn, files in sorted(scanned_files.items()):
|
||
pending = [f for f in files if str(f) not in progress.get("ocr_done", [])]
|
||
if not pending:
|
||
continue
|
||
print(f" → {fn}: {len(pending)} PDFs")
|
||
for i, pdf in enumerate(pending):
|
||
size_mb = pdf.stat().st_size / (1024 * 1024)
|
||
print(f" [{i+1}/{len(pending)}] {pdf.name} ({size_mb:.1f}MB)", end="", flush=True)
|
||
if ocr_pdf(pdf):
|
||
progress.setdefault("ocr_done", []).append(str(pdf))
|
||
ocr_ok += 1
|
||
print(f" ✓")
|
||
else:
|
||
progress.setdefault("ocr_failed", []).append(str(pdf))
|
||
ocr_fail += 1
|
||
print(f" ✗")
|
||
if (ocr_ok + ocr_fail) % 5 == 0:
|
||
save_progress(progress)
|
||
save_progress(progress)
|
||
print(f"\n Phase B done: {ocr_ok} OCR'd, {ocr_fail} failed\n")
|
||
|
||
# ── Phase C: Upload OCR'd files ──
|
||
ocr_to_upload = {fn: [f for f in files if str(f) in progress.get("ocr_done", [])]
|
||
for fn, files in scanned_files.items()}
|
||
total_ocr_upload = sum(
|
||
1 for files in ocr_to_upload.values()
|
||
for f in files if str(f) not in progress["uploaded_files"]
|
||
)
|
||
if total_ocr_upload > 0:
|
||
print(f"══ Phase C: Upload {total_ocr_upload} OCR'd files ══\n")
|
||
total_up2 = total_fail2 = 0
|
||
for fn, files in sorted(ocr_to_upload.items()):
|
||
if not files:
|
||
continue
|
||
up, fail = upload_file_batch(config, fn, files, progress, batch_size, delay)
|
||
total_up2 += up
|
||
total_fail2 += fail
|
||
print(f"\n Phase C done: {total_up2} uploaded, {total_fail2} failed\n")
|
||
|
||
# ── Assign to workspaces ──
|
||
assign_to_workspaces(config, persona_folders, progress, batch_size, delay)
|
||
|
||
|
||
# ──────────────────────────────────────────────────────────
|
||
# STATUS
|
||
# ──────────────────────────────────────────────────────────
|
||
|
||
def show_status(config):
|
||
print("═══ Integration Status ═══\n")
|
||
|
||
# Storage
|
||
du = ANYTHINGLLM_STORAGE / "direct-uploads"
|
||
if du.is_symlink():
|
||
print(f" ✓ direct-uploads/ → {du.resolve()} (HDD)")
|
||
elif du.exists():
|
||
print(f" ⚠ direct-uploads/ on SSD — run --storage-setup")
|
||
|
||
for d in ["documents", "lancedb", "vector-cache"]:
|
||
p = ANYTHINGLLM_STORAGE / d
|
||
if p.exists():
|
||
try:
|
||
sz = sum(f.stat().st_size for f in p.rglob("*") if f.is_file()) / (1024**2)
|
||
print(f" ● {d}/ ({sz:.0f} MB)")
|
||
except Exception:
|
||
print(f" ● {d}/")
|
||
|
||
api_ok = check_api(config) if config["anythingllm"]["api_key"] else False
|
||
collector_ok = check_collector_alive()
|
||
print(f"\n API: {'✓' if api_ok else '✗'} Collector: {'✓' if collector_ok else '✗'}")
|
||
|
||
progress = load_progress()
|
||
uploaded = len(progress.get("uploaded_files", {}))
|
||
ocr_done = len(progress.get("ocr_done", []))
|
||
assigned = len(progress.get("workspace_docs", {}))
|
||
|
||
if uploaded or ocr_done:
|
||
print(f"\n Uploaded: {uploaded} OCR'd: {ocr_done} Personas assigned: {assigned}")
|
||
folders = {}
|
||
for info in progress.get("uploaded_files", {}).values():
|
||
f = info.get("folder", "?")
|
||
folders[f] = folders.get(f, 0) + 1
|
||
for f, c in sorted(folders.items(), key=lambda x: -x[1])[:20]:
|
||
print(f" {c:4d} {f}")
|
||
|
||
print()
|
||
|
||
|
||
# ──────────────────────────────────────────────────────────
|
||
# MAIN
|
||
# ──────────────────────────────────────────────────────────
|
||
|
||
def resolve_persona_list(args, config):
|
||
"""Resolve --persona / --cluster to a list of codenames."""
|
||
if args.persona:
|
||
return [args.persona]
|
||
if args.cluster:
|
||
cl = CLUSTERS.get(args.cluster)
|
||
if not cl:
|
||
print(f"Unknown cluster: {args.cluster}")
|
||
print(f"Available: {', '.join(CLUSTERS.keys())}")
|
||
sys.exit(1)
|
||
return cl
|
||
return None # all
|
||
|
||
|
||
def main():
|
||
parser = argparse.ArgumentParser(description="AnythingLLM × Persona Integration")
|
||
parser.add_argument("--storage-setup", action="store_true")
|
||
parser.add_argument("--create-workspaces", action="store_true")
|
||
parser.add_argument("--upload-documents", action="store_true")
|
||
parser.add_argument("--all", action="store_true", help="Run all steps")
|
||
parser.add_argument("--status", action="store_true")
|
||
parser.add_argument("--persona", type=str, help="Single persona filter")
|
||
parser.add_argument("--cluster", type=str, help="Cluster filter: intel, cyber, military, humanities, engineering")
|
||
parser.add_argument("--priority", type=int, help="Max priority (1=core)")
|
||
parser.add_argument("--max-size", type=int, default=100, help="Max file MB (default: 100)")
|
||
parser.add_argument("--dry-run", action="store_true")
|
||
parser.add_argument("--resume", action="store_true")
|
||
|
||
args = parser.parse_args()
|
||
config = load_config()
|
||
|
||
if not any([args.storage_setup, args.create_workspaces,
|
||
args.upload_documents, args.all, args.status]):
|
||
parser.print_help()
|
||
return
|
||
|
||
persona_list = resolve_persona_list(args, config)
|
||
|
||
if args.status:
|
||
show_status(config)
|
||
return
|
||
if args.storage_setup or args.all:
|
||
storage_setup(config, dry_run=args.dry_run)
|
||
if args.create_workspaces or args.all:
|
||
create_workspaces(config, persona_list=persona_list, dry_run=args.dry_run)
|
||
if args.upload_documents or args.all:
|
||
upload_documents(config, persona_list=persona_list,
|
||
priority_filter=args.priority,
|
||
dry_run=args.dry_run,
|
||
resume=args.resume or args.all,
|
||
max_size_mb=args.max_size)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|