使用 lift-pdf 設計基於綱領引導的發票智慧管線：應付帳款提取、驗證與分類帳生成

In this tutorial, we build an end-to-end accounts-payable extraction pipeline with lift-pdf, using synthetic invoice PDFs as controlled test documents and a structured JSON schema as the target output format. Instead of treating invoice parsing as a simple OCR task, we frame it as schema-guided document understanding: we generate realistic invoices, define fields such as vendor identity, billing party, PO number, line items, tax, total amount, balance due, and payment status, and then ask the model to extract those values directly from the rendered PDF layout. We also include practical extraction traps that appear in real finance workflows, such as distinguishing bill-to from ship-to, separating subtotal from after-tax total, returning null for absent values, and correctly marking partially paid invoices as unpaid when a balance remains. Through GPU-aware model loading, optional 4-bit quantization, PDF generation and extraction, scoring, and ledger construction, we turn this tutorial into a compact yet realistic demonstration of document intelligence for invoice mining. Copy CodeCopiedUse a different BrowserN_DOCS = 3 FORCE_FULL_PRECISION = False FORCE_4BIT = False SHOW_FIRST_PAGE = True RUN_ON_REAL_PDF = False REAL_PDF_URL = "" REAL_PDF_PAGES = "0-1" PIN_PILLOW = True PILLOW_VERSION = "11.3.0" import os, sys, subprocess, json, re, time, warnings warnings.filterwarnings("ignore") os.environ["TOKENIZERS_PARALLELISM"] = "false" def pip(*pkgs, upgrade=False): """Install without invoking a shell (so '[hf]' is never glob-expanded).""" args = [sys.executable, "-m", "pip", "install", "-q"] + (["-U"] if upgrade else []) + list(pkgs) print(" pip install", *pkgs) subprocess.run(args, check=False) print("STEP 1/7 · Installing lift + light dependencies (first run is the slow one)…") pip("reportlab", "pypdfium2", "pandas", "matplotlib") pip("lift-pdf[hf]") pip("bitsandbytes", "accelerate", upgrade=True) if PIN_PILLOW: pip(f"pillow=={PILLOW_VERSION}") if "PIL" in sys.modules: import PIL if getattr(PIL, "__version__", "") != PILLOW_VERSION: print(f" Pinned Pillow {PILLOW_VERSION} on disk, but a stale " f"{getattr(PIL, '__version__', '?')} is loaded in memory — restarting runtime.") print(" Just re-run the cell(s) after Colab reconnects.") os.kill(os.getpid(), 9) print(" …install finished.\n") import torch We begin by defining the runtime controls that decide how many invoices we process, whether we use 4-bit loading, whether we preview the generated PDF, and whether we later test a real invoice. We install the core dependencies for PDF generation, rendering, tabular analysis, plotting, and lift-pdf inference. We also pin Pillow to a stable version because the tutorial addresses a known Colab compatibility issue among Pillow, torchvision, and Transformers. This setup gives us a reproducible environment before we load any model or generate any document. Copy CodeCopiedUse a different Browserdef detect_gpu(): if not torch.cuda.is_available(): raise SystemExit( "\n✗ No CUDA GPU found. In Colab: Runtime ▸ Change runtime type ▸ GPU " "(A100 is best; L4/T4 also work).\n" ) p = torch.cuda.get_device_properties(0) cc = torch.cuda.get_device_capability(0) return p.name, p.total_memory / 1e9, cc def enable_4bit(compute_dtype): """Load lift's weights in 4-bit NF4 whatever transformers Auto* class it uses internally.""" import inspect, functools, transformers from transformers import BitsAndBytesConfig bnb = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=compute_dtype, ) def patch(cls): try: cm = inspect.getattr_static(cls, "from_pretrained") orig = cm.__func__ if isinstance(cm, (classmethod, staticmethod)) else cm except Exception: return @functools.wraps(orig) def inner(cls_, *args, **kwargs): kwargs.setdefault("quantization_config", bnb) kwargs.setdefault("device_map", {"": 0}) model = orig(cls_, *args, **kwargs) try: model.to = lambda *a, **k: model model.cuda = lambda *a, **k: model except Exception: pass return model cls.from_pretrained = classmethod(inner) for name in ["AutoModelForImageTextToText", "AutoModelForMultimodalLM", "AutoModelForVision2Seq", "AutoModelForCausalLM", "AutoModel"]: c = getattr(transformers, name, None) if c is not None: patch(c) try: from transformers.modeling_utils import PreTrainedModel patch(PreTrainedModel) except Exception: pass print("STEP 2/7 · Preparing the model backend…") gpu_name, vram, cc = detect_gpu() use_4bit = FORCE_4BIT or (vram < 34 and not FORCE_FULL_PRECISION) compute_dtype = torch.bfloat16 if cc[0] >= 8 else torch.float16 print(f" GPU: {gpu_name} | ~{vram:.0f} GB | compute capability {cc[0]}.{cc[1]}") print(f" Load mode: {'4-bit NF4' if use_4bit else 'full bf16'} (compute dtype {compute_dtype})") os.environ.setdefault("TORCH_DEVICE", "cuda:0") os.environ.setdefault("MODEL_CHECKPOINT", "datalab-to/lift") if use_4bit: enable_4bit(compute_dtype) from lift import extract from lift.model import InferenceManager print(" Loading lift weights (≈20 GB download on first run)…") _t = time.time() MODEL = InferenceManager(method="hf") print(f" ✓ model ready in {time.time() - _t:.0f}s\n") def run_lift(pdf_path, schema, page_range=None): kw = {"model": MODEL} if page_range: kw["page_range"] = page_range result = extract(pdf_path, schema, **kw) return getattr(result, "extraction", None) We prepare the GPU-aware inference backend and decide whether the model should run in full precision or 4-bit NF4 quantization based on available VRAM. We patch the Hugging Face model-loading path so lift can transparently load the checkpoint with a BitsAndBytes quantization configuration when needed. We initialize the InferenceManager once and reuse it across all invoices, avoiding repeated model-loading overhead. Finally, we wrap lift.extract() inside a small helper so each PDF can be mined with the same schema and optional page range. Copy CodeCopiedUse a different BrowserDOCS = [ dict( invoice_number="INV-2026-0412", invoice_date="2026-05-04", due_date="2026-06-03", vendor_name="Cloudworks Inc.", vendor_address="500 Market St, Suite 900, San Francisco, CA 94105, USA", bill_to_name="Acme Robotics LLC", bill_to_address="12 Foundry Rd, Pittsburgh, PA 15222, USA", ship_to_name="Acme Robotics — Warehouse 4", ship_to_address="88 Dockside Blvd, Newark, NJ 07114, USA", po_number=None, discount_amount=None, currency_code="USD", currency_symbol="$", tax_rate=0.085, amount_paid=0.00, line_items=[ ("Cloud Compute — Standard tier (monthly)", 3, 240.00), ("Object Storage — 2 TB", 1, 46.00), ("Priority Support add-on", 1, 99.00), ], notes="Payment due within 30 days. Late payments accrue 1.5% monthly interest.", ), dict( invoice_number="INV-ND-2026-118", invoice_date="2026-04-18", due_date="2026-05-18", vendor_name="Nordic Design Studio Oy", vendor_address="Eteläranta 12, 00130 Helsinki, Finland", bill_to_name="Helsinki Media Oy", bill_to_address="Mannerheimintie 4, 00100 Helsinki, Finland", ship_to_name=None, ship_to_address=None, po_number="PO-HM-5589", discount_amount=785.00, currency_code="EUR", currency_symbol="€", tax_rate=0.24, amount_paid=8760.60, line_items=[ ("Brand identity design package", 1, 4200.00), ("Web UI design — 12 screens", 12, 180.00), ("Custom illustration set", 1, 850.00), ("Design-system documentation", 1, 640.00), ], notes="Paid in full — thank you. All amounts in EUR.", ), dict( invoice_number="INV-BR-4471", invoice_date="2026-06-01", due_date="2026-07-15", vendor_name="BuildRight Contractors Inc.", vendor_address="740 Industrial Way, Austin, TX 78744, USA", bill_to_name="Sunrise Property Group", bill_to_address="9 Lakeview Terrace, Austin, TX 78703, USA", ship_to_name="Sunrise Property Group — Lot 14 site office", ship_to_address="Parcel 14, Mesa Ridge Development, Austin, TX 78737, USA", po_number="PO-SPG-2211", discount_amount=None, currency_code="USD", currency_symbol="$", tax_rate=0.07, amount_paid=15000.00, line_items=[ ("Site preparation and grading", 1, 18500.0