Docpick MCP server

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

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

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

Shortened. The full README is on GitHub.