Docpick

한국어 문서 · llms.txt

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

Terminal

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

server.ts

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

bash

# 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:

server.ts

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:

bash

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):

server.ts

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:

Tier 1 (CPU): PaddleOCR → EasyOCR
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:8000/v1`	Qwen/Qwen3.5-32B-AWQ
Ollama	`http://localhost:11434`	qwen3.5:7b

Configure via CLI or YAML:

bash

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

python

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:

server.ts

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

mermaid

flowchart TD
    A["📄 Document\n(PDF / Image)"] --> B["DocumentLoader\n(pypdfium2)"]
    B --> C["Tier 1: OCR\n(PaddleOCR / EasyOCR)\nCPU"]
    C --> D{"Confidence\n≥ threshold?"}
    D -->|"yes"| F["LLM Extractor\n(vLLM / Ollama)\nSchema prompt"]
    D -->|"no"| E["Tier 2: VLM\n(GOT / VLM)\nGPU"]
    E --> F
    F --> G["Pydantic Validation"]
    G --> H["✅ ExtractionResult"]

License

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

_{Part of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.}