Neuracore Types

Shared type definitions for the Neuracore platform. This package maintains a single source of truth for data types in Python (Pydantic models) and automatically generates TypeScript types.

Overview

  • Python Package: neuracore-types - Pydantic models for Python backend
  • NPM Package: @neuracore/types - TypeScript types for frontend

Installation

Python

pip install neuracore-types

TypeScript/JavaScript

npm install @neuracore/types
# or
yarn add @neuracore/types
# or
pnpm add @neuracore/types

To track unreleased types from main (used by staging deploys and tests), install the main dist-tag instead. It is republished on every push to main that changes the generated types:

npm install @neuracore/types@main

Development

Setup

# Clone the repository
git clone https://github.com/neuracoreai/neuracore_types.git
cd neuracore_types

# Install Python dependencies
pip install -e ".[dev]"

# Install Node dependencies
npm install

Generate TypeScript Types

The TypeScript types are automatically generated from the Python Pydantic models:

npm install json-schema-to-typescript
python scripts/generate_types.py

This will:

  1. Read the Pydantic models from neuracore_types/neuracore_types.py
  2. Generate TypeScript definitions in typescript/neuracore_types.ts
  3. Create an index file at typescript/index.ts

Build TypeScript Package

npm run build

This compiles the TypeScript files to JavaScript and generates type declarations in the dist/ directory.

Release Process

Creating PRs

All PRs must follow these conventions:

  1. Version Label: Add exactly one version label to your PR:

    • version:major - Breaking changes
    • version:minor - New features
    • version:patch - Bug fixes
    • version:none - No release (docs, chores, etc.)

  2. Commit Format: PR title and all commits must use conventional commit format:

    <prefix>: <description>

    Valid prefixes: feat, fix, chore, docs, ci, test, refactor, style, perf

    Examples:

    • feat: add new data type for robot state
    • fix: resolve serialization issue in TypeScript types
    • chore: update dependencies

Pending Changelog

For significant changes (version:major or version:minor), update changelogs/pending-changelog.md:

## Summary

This release adds support for new sensor data types and improves TypeScript type generation.

Simply append your summary to the existing content. This will appear at the top of the release notes.

Triggering a Release

Releases are manual and triggered via GitHub Actions:

  1. Go to Actions → Release → Run workflow
  2. Optional: Check dry_run to preview without publishing
  3. The workflow will:
    • Analyze all PRs since last release
    • Determine version bump (highest priority across all PRs)
    • Generate changelog with all PRs grouped by type
    • Bump version in pyproject.toml, package.json, and __init__.py
    • Generate TypeScript types from Python models
    • Publish Python package to PyPI
    • Build and publish npm package to npm registry
    • Create GitHub release

Dry run shows what would happen without making any changes - useful for testing before a real release.

CI/CD

The repository includes GitHub Actions workflows:

  1. PR Checks:

    • Validates version labels
    • Enforces conventional commit format
    • Runs pre-commit hooks
    • Suggests changelog updates for major/minor changes

  2. Release (manual trigger):

    • Generates TypeScript types from Python models
    • Builds and validates both packages
    • Publishes to PyPI and npm registry
    • Creates GitHub release with changelog

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (11.10.0)

Owner

PyPI data
Data sourced directly from PyPI's database.

Credits

Author: Neuracore

License

MIT

Requires

Python >=3.10

Provides Extra

dev ml

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