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Python MD-Models

Tests PyPI - Version PyPI - Python Version

Build metadata-first Python apps from Markdown-defined models. mdmodels is the Python package for the MDModels Rust crate, with batteries included for data modeling, AI workflows, SQL/graph backends, and API generation. 🚀

Why MD-Models?

  • 🧩 Model once in Markdown, then generate strongly typed Python models
  • 🤖 Work with AI for extraction, mapping, Q&A, and similarity search
  • 🗃️ Persist and query with SQL, vectors, and graph databases
  • 🌐 Ship interfaces fast via REST, GraphQL, and MCP helpers

What's in the bag? 🎒

  • 🧱 Core model tooling - Load, inspect, and work with metadata models
  • 🐍 Pydantic generation - Generate rich Python model classes from MD-Models
  • 🤖 LLM workflows - Extract, map, search, and answer questions over metadata
  • 🗄️ SQL and vector search - Build SQL-backed stores and pgvector-style embedding workflows
  • 🕸️ Graph databases - Build and query graph representations of your models
  • 🌐 API generation - Expose model-backed services through REST and GraphQL helpers
  • 🔌 MCP integrations - Create MCP-compatible interfaces for model and SQL workflows

Note: This package is actively evolving and APIs may change. Feedback and contributions are welcome. 🙌

Installation

We recommend using uv for a fast, reproducible Python workflow.

Install uv (if needed):

curl -LsSf https://astral.sh/uv/install.sh | sh

Install the base package:

uv pip install mdmodels

Install optional feature sets:

# LLM tools
uv pip install "mdmodels[chat]"

# Graph database tools
uv pip install "mdmodels[graph]"

# SQL backends
uv pip install "mdmodels[postgres]"
uv pip install "mdmodels[mysql]"
uv pip install "mdmodels[sqlserver]"

# Vector search (pgvector + embeddings)
uv pip install "mdmodels[pgvector]"

# API integrations
uv pip install "mdmodels[rest]"
uv pip install "mdmodels[graphql]"

# MCP integration
uv pip install "mdmodels[mcp]"

Documentation 📚

Guides, tutorials, and API usage:

Development

Run all tests:

uv run pytest

Run tests with coverage report:

uv run pytest --cov=mdmodels --cov-report=html

Run tests in Docker:

docker build --build-arg PYTHON_VERSION=3.12 -t mdmodels .
docker run -v $(pwd):/app mdmodels

Use the helper script:

./run-tests.sh --python=3.12

Skip expensive tests:

uv run pytest -m "not expensive"