airalogy 0.0.12

The world's first universal framework for standardized data digitization

airalogy

The world's first universal framework for data digitization and automation

• Airalogy Platform
• Docs (English)
• Docs (Chinese)
• Good practices for documentation

Key Features

Airalogy lets you create fully custom protocols (Airalogy Protocols) for defining how data is collected, validated, and processed.

Area	Highlights
Airalogy Markdown (AIMD)	Define rich, custom data fields directly in Markdown—variables ({{var}}), procedural steps ({{step}}), checkpoints ({{check}}), and more.
Model-based Data Validation	Attach a model to every protocol for strict type checking—supports datetime, enums, nested models, lists, etc.; and Airalogy-specific built-in types (UserName, CurrentTime, AiralogyMarkdown, file IDs, ...).
Assigner for Auto-Computation	Use the declarative @assigner decorator to compute field values automatically.

Requirements

Python ≥ 3.13

Installation

pip install airalogy

Quick Start

Use one typed AIMD

protocol.aimd

# Serum sample collection
Participant: {{var|subject_name: UserName, title="Participant name"}}
Collection time: {{var|collected_at: CurrentTime}}
Serum volume (mL): {{var|serum_volume: float, gt=0}}
Ice-bath time (min): {{var|ice_time: int = 0, ge=0}}
Sample photo: {{var|sample_photo: FileIdPNG, description="Upload collection photo"}}

{{step|collect}} Collect serum sample as per standard procedure.
{{step|verify_labels, 2}} Verify labels and IDs.
{{step|ice_hold, 2}} Immediately place sample on ice.

{{check|info_confirmed}} Confirm details and metadata.

• Run airalogy check to validate the AIMD and use it directly.
• Need an explicit model file? airalogy generate_model protocol.aimd -o model.py auto-generates the Pydantic model that matches these types.

Extended: add model and assigner

protocol/
├─ protocol.aimd  # Airalogy Markdown
├─ model.py       # Optional: Define data validation model
└─ assigner.py    # Optional: Define auto-computation logic

protocol.aimd

# Reagent preparation
Solvent name: {{var|solvent_name}}
Target solution volume (L): {{var|target_solution_volume}}
Solute name: {{var|solute_name}}
Solute molar mass (g/mol): {{var|solute_molar_mass}}
Target molar concentration (mol/L): {{var|target_molar_concentration}}
Required solute mass (g): {{var|required_solute_mass}}

model.py

from pydantic import BaseModel, Field

class VarModel(BaseModel):
    solvent_name: str
    target_solution_volume: float = Field(gt=0)
    solute_name: str
    solute_molar_mass: float = Field(gt=0)
    target_molar_concentration: float = Field(gt=0)
    required_solute_mass: float = Field(gt=0)

assigner.py

from airalogy.assigner import AssignerResult, assigner


@assigner(
    assigned_fields=["required_solute_mass"],
    dependent_fields=[
        "target_solution_volume",
        "solute_molar_mass",
        "target_molar_concentration",
    ],
    mode="auto",
)
def calculate_required_solute_mass(dependent_fields: dict) -> AssignerResult:
    target_solution_volume = dependent_fields["target_solution_volume"]
    solute_molar_mass = dependent_fields["solute_molar_mass"]
    target_molar_concentration = dependent_fields["target_molar_concentration"]

    required_solute_mass = (
        target_solution_volume * target_molar_concentration * solute_molar_mass
    )

    return AssignerResult(
        assigned_fields={
            "required_solute_mass": required_solute_mass,
        },
    )

Command Line Interface

Airalogy provides a CLI tool for common operations. After installation, you can use the airalogy command:

$ airalogy --help
usage: airalogy [-h] [-v] {check,c,generate_model,gm} ...

Airalogy CLI - Tools for Airalogy

positional arguments:
  {check,c,generate_model,gm}
                        Available commands
    check (c)           Check AIMD syntax
    generate_model (gm)
                        Generate VarModel

options:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit

Syntax Checking

Check AIMD syntax:

# Check default protocol.aimd file
airalogy check

# Check specific AIMD file
airalogy check my_protocol.aimd

# Using alias
airalogy c my_protocol.aimd

Model Generation

Generate VarModel from AIMD file:

# Generate model.py from protocol.aimd
airalogy generate_model

# Generate with custom output file
airalogy generate_model my_protocol.aimd -o my_model.py

# Using alias
airalogy gm my_protocol.aimd -o custom_model.py

Development Setup

We use uv for environment management and build, ruff for lint/format.

setup project environment:

uv sync

Testing

uv run pytest

License

Apache 2.0

Cite This Framework

@misc{yang2025airalogyaiempowereduniversaldata,
      title={Airalogy: AI-empowered universal data digitization for research automation}, 
      author={Zijie Yang and Qiji Zhou and Fang Guo and Sijie Zhang and Yexun Xi and Jinglei Nie and Yudian Zhu and Liping Huang and Chou Wu and Yonghe Xia and Xiaoyu Ma and Yingming Pu and Panzhong Lu and Junshu Pan and Mingtao Chen and Tiannan Guo and Yanmei Dou and Hongyu Chen and Anping Zeng and Jiaxing Huang and Tian Xu and Yue Zhang},
      year={2025},
      eprint={2506.18586},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.18586}, 
}