CMeta is a compact metadata format for describing data lakes, tables, and columns in a way that is:

• 🪶 Token-efficient: compresses schema/metadata for LLMs with narrow context windows
• 🧑‍💻 Human-readable: easy to read and edit manually
• 🔄 Reversible: can be converted to and from JSON (compact or verbose)

This package provides Python utilities to:

• Parse CMeta text ⇄ JSON
• Convert to/from a compact JSON structure
• Convert to/from a flat, extended JSON structure (very verbose for some specific Nexus use cases)

uv add nuklai-cmeta

Uses uv. You can also install with pip install nuklai-cmeta if you prefer.

• Hierarchy:

  • Lake → Tables → Columns
  • : denotes containers
  • * denotes columns

• Descriptions: in [ ... ]

• Types: in < ... > using full SQL types (string, int, boolean, date, timestamp, etc.)

• Nested fields: use dot notation (car.engine.horsepower)

• Escape rules:

  • ] → \]
  • < → \<
  • > → \>

Example:

Webshop[Contains all webshop data]:
  users[Contains all registered users]:
    * user_id<int>[Unique ID of a user]
    * name<string>[Full name of a user]
    * email<string>[Email address]
  orders[Customer orders]:
    * id<int>[Order id]
    * total<double>[Total amount]
    * created_at<timestamp>[When created]

from cmeta import parse_cmeta

text = """
Webshop[Contains all webshop data]:
  users[Contains all registered users]:
    * user_id<int>[Unique ID]
    * name<string>[Full name]
"""

model = parse_cmeta(text)
print(model.lakes[0].tables[0].columns[0].name)
# "user_id"

from cmeta import to_cmeta

cmeta_str = to_cmeta(model)
print(cmeta_str)

from cmeta import model_to_compact_json, compact_json_to_model

cj = model_to_compact_json(model)
print(cj[0]["tables"][0]["columns"][0])
# {'name': 'user_id', 'type': 'int', 'description': 'Unique ID'}

m2 = compact_json_to_model(cj)
assert to_cmeta(m2) == to_cmeta(model)

Compact JSON format:

[
  {
    "name": "Webshop",
    "description": "Contains all webshop data",
    "tables": [
      {
        "name": "users",
        "description": "Contains all registered users",
        "columns": [
          {"name": "user_id", "type": "int", "description": "Unique ID"}
        ]
      }
    ]
  }
]

from cmeta import model_to_extended_json, extended_json_to_model

ej = model_to_extended_json(model)
print(ej[0])
# {
#   'columnName': 'user_id',
#   'columnDescription': 'Unique ID',
#   'dataType': 'int',
#   'sourceDescription': 'Contains all webshop data',
#   'sourceName': 'Webshop',
#   'tableDescription': 'Contains all registered users',
#   'tableName': 'users'
# }

m3 = extended_json_to_model(ej)

Extended JSON format:

[
  {
    "columnName": "user_id",
    "columnDescription": "Unique ID",
    "dataType": "int",
    "sourceDescription": "Contains all webshop data",
    "sourceName": "Webshop",
    "tableDescription": "Contains all registered users",
    "tableName": "users"
  }
]

CMeta supports common datatypes:

• string
• int, bigint
• float, double, decimal
• boolean
• date, timestamp
• json, array, map

Clone and set up:

uv venv && source .venv/bin/activate
make install

Run checks:

make ci     # lint + typecheck + test
make lint   # ruff
make format # autoformat
make test   # pytest

Dev workflow for trusted contributors:

1. Bump the version in pyproject.toml.

2. Commit & push to main.

3. Tag the release:

   git tag v0.1.0 && git push origin v0.1.0

4. The GitHub Actions workflow will:

   • run tests
   • build wheels/sdist
   • publish to PyPI

MIT — free for personal and commercial use.