docpick 0.1.1

Document in, Structured JSON out. Schema-driven document extraction with local OCR + LLM.

Docpick

Document in, Structured JSON out. Locally. With your schema.

docpick is a lightweight, schema-driven document extraction pipeline that combines local OCR engines with local LLMs to extract structured JSON from any document — invoices, receipts, bills of lading, tax forms, and more.

• Zero cloud dependency — runs entirely on your machine (CPU or GPU)
• Custom schemas — define your own Pydantic models or use 8 built-in document schemas
• Validation built-in — checkdigit verification, cross-field rules, cross-document consistency
• Apache 2.0 — no GPL/AGPL dependencies

Install

pip install docpick            # core (LLM extraction only)
pip install docpick[paddle]    # + PaddleOCR (recommended)
pip install docpick[easyocr]   # + EasyOCR (Korean-optimized)
pip install docpick[got]       # + GOT-OCR2.0 (GPU, vision-language)
pip install docpick[all]       # all OCR backends

Requirements: Python 3.11+ / LLM endpoint (vLLM, Ollama, or OpenAI-compatible)

Quick Start

Python API

from docpick import DocpickPipeline
from docpick.schemas import InvoiceSchema

pipeline = DocpickPipeline()
result = pipeline.extract("invoice.pdf", schema=InvoiceSchema)

print(result.data)           # Structured dict matching schema
print(result.validation)     # Validation errors/warnings
print(result.confidence)     # Per-field confidence scores

CLI

# Extract structured data
docpick extract invoice.pdf --schema invoice --output result.json

# OCR only (no LLM)
docpick ocr document.png --lang ko,en

# Validate extracted JSON
docpick validate result.json --schema invoice

# Batch process a directory
docpick batch ./documents/ --schema invoice --output ./results/ --concurrency 4

# List available schemas
docpick schemas list

# Show schema details
docpick schemas show invoice

Built-in Schemas

Schema	Document Type	Key Validations
invoice	Commercial invoices	Line item sums, tax ID checkdigit, date order
receipt	Retail/restaurant receipts	Total = subtotal + tax + tip
bill_of_lading	Ocean/air B/L	Container weight sums, ISO 6346, HS code format
purchase_order	Purchase orders	PO total = line items, delivery date order
kr_tax_invoice	Korean e-tax invoice (세금계산서)	Business number checkdigit (x2), supply/tax/total sums
bank_statement	Bank statements	IBAN mod97, period date order
id_document	Passport/ID (ICAO 9303)	MRZ, ISO 3166 country codes, date ranges
certificate_of_origin	Certificate of Origin	ISO 3166 alpha-2 country codes

Custom Schemas

Define your own schema with Pydantic:

from pydantic import BaseModel
from docpick import DocpickPipeline
from docpick.validation.rules import SumEqualsRule, RequiredFieldRule

class MyDocument(BaseModel):
    """Custom document schema."""
    company_name: str | None = None
    total_amount: float | None = None
    tax_amount: float | None = None
    net_amount: float | None = None
    items: list[dict] | None = None

    class ValidationRules:
        rules = [
            RequiredFieldRule("company_name"),
            SumEqualsRule(["net_amount", "tax_amount"], "total_amount"),
        ]

pipeline = DocpickPipeline()
result = pipeline.extract("my_document.pdf", schema=MyDocument)

Or use a JSON Schema file:

docpick extract document.pdf --schema my_schema.json

Validation

Check Digit Algorithms

Algorithm	Use Case
kr_business_number	Korean business registration number (10 digits)
luhn	Credit card numbers
iso_6346	Shipping container numbers
iban_mod97	International bank account numbers
awb_mod7	Air waybill numbers
mrz	Machine Readable Zone (passport/ID)

Cross-Field Rules

Rule	Description
SumEqualsRule	Sum of fields equals target (with tolerance)
DateBeforeRule	Date A must precede Date B
RequiredFieldRule	Field must be non-null and non-empty
FieldEqualsRule	Two fields must be equal
RangeRule	Numeric field within min/max bounds
RegexRule	Field matches regex pattern

Cross-Document Validation

Validate consistency across related documents (e.g., Invoice + B/L + Packing List):

from docpick.validation.cross_document import create_trade_document_validator

validator = create_trade_document_validator()
result = validator.validate({
    "invoice": invoice_data,
    "bl": bl_data,
    "packing_list": packing_list_data,
    "certificate": certificate_data,
})
print(result.is_valid)

OCR Engines

Engine	Type	GPU	Languages	Best For
PaddleOCR	Traditional OCR	Optional	111	General documents (default)
EasyOCR	Traditional OCR	Optional	80+	Korean text
GOT-OCR2.0	Vision-Language	Required	Multi	Complex layouts
VLM	Vision-Language	Required	Multi	Direct image → JSON

2-Tier Auto Engine

The default auto engine uses confidence-based fallback:

1. Tier 1 (CPU): PaddleOCR → EasyOCR
2. Tier 2 (GPU): GOT-OCR2.0 → VLM

If Tier 1 average confidence falls below threshold (default 0.7), automatically escalates to Tier 2.

LLM Providers

Provider	Endpoint	Default Model
vLLM	http://localhost:30000/v1	Qwen/Qwen3.5-32B-AWQ
Ollama	http://localhost:11434	qwen3.5:7b

Configure via CLI or YAML:

docpick config set llm.provider ollama
docpick config set llm.base_url http://localhost:11434
docpick config set llm.model qwen3.5:7b

Error Handling

The pipeline is designed to be resilient:

• OCR failure → automatic fallback to next available engine
• LLM JSON parse failure → automatic retry with correction prompt (up to 1 retry)
• Partial results → returns whatever was extracted, with errors logged in result.errors
• Document load failure → returns empty result with error message

result = pipeline.extract("damaged.pdf", schema=InvoiceSchema)
if result.errors:
    print("Pipeline warnings:", result.errors)
if result.data:
    print("Partial extraction:", result.data)

Batch Processing

Process entire directories with parallel workers:

from docpick.batch import BatchProcessor
from docpick.schemas import InvoiceSchema

processor = BatchProcessor(concurrency=4)
result = processor.process_directory(
    "./invoices/",
    schema=InvoiceSchema,
    recursive=True,
)

print(f"Processed {result.succeeded}/{result.total} files")
for path, extraction in result.results.items():
    print(f"{path}: {extraction.data.get('total_amount')}")

Architecture

Document (PDF/Image)
  → DocumentLoader (pypdfium2)
  → Tier 1: OCR (PaddleOCR/EasyOCR, CPU)
    → [confidence < threshold] → Tier 2: VLM (GOT/VLM, GPU)
  → LLM Extractor (vLLM/Ollama, schema prompt)
  → Pydantic Validation (checkdigit, cross-field, cross-document)
  → ExtractionResult (structured JSON + confidence + validation)

License

Apache 2.0 — all dependencies are Apache 2.0 or MIT licensed.