type-bridge 1.4.3

A modern, Pythonic ORM for TypeDB with an Attribute-based API

TypeBridge

A modern, Pythonic ORM for TypeDB with an Attribute-based API that aligns with TypeDB's type system.

Features

• True TypeDB Semantics: Attributes are independent types that entities and relations own
• Complete Type Support: All TypeDB value types - String, Integer, Double, Decimal, Boolean, Date, DateTime, DateTimeTZ, Duration
• Flag System: Clean API for @key, @unique, and @card annotations
• Flexible Cardinality: Express any cardinality constraint with Card(min, max)
• Pydantic Integration: Built on Pydantic v2 for automatic validation, serialization, and type safety
• Type-Safe: Full Python type hints and IDE autocomplete support
• Declarative Models: Define entities and relations using Python classes
• Automatic Schema Generation: Generate TypeQL schemas from your Python models
• Code Generator: Generate Python models from TypeQL schema files (.tql)
• Schema Conflict Detection: Automatic detection of breaking schema changes to prevent data loss
• Data Validation: Automatic type checking and coercion via Pydantic, including keyword validation
• JSON Support: Seamless JSON serialization/deserialization
• CRUD Operations: Full CRUD with fetching API (get, filter, all, update) for entities and relations
• Lifecycle Hooks: Pre/post-operation hooks for audit logging, validation, cache invalidation, and async notifications
• Chainable Operations: Filter, delete, and bulk update with method chaining and lambda functions
• Query Builder: Pythonic interface for building TypeQL queries
• Multi-player Roles: A single role can accept multiple entity types via Role.multi(...)
• Transaction Context: Share transactions across multiple operations with TransactionContext
• Django-style Lookups: Filter with __contains, __gt, __in, __isnull and more
• Dict Helpers: to_dict() and from_dict() for easy serialization and API integration
• Bulk Operations: update_many() and delete_many() for efficient batch processing

Installation

# Install from PyPI
pip install type-bridge

# Or with uv
uv add type-bridge

From source:

git clone https://github.com/ds1sqe/type-bridge.git
cd type_bridge
uv sync

Quick Start

1. Define Attribute Types

TypeBridge supports all TypeDB value types:

from type_bridge import String, Integer, Double, Decimal, Boolean, Date, DateTime, DateTimeTZ, Duration

class Name(String):
    pass

class Age(Integer):
    pass

class Balance(Decimal):  # High-precision fixed-point numbers
    pass

class BirthDate(Date):  # Date-only values
    pass

class UpdatedAt(DateTimeTZ):  # Timezone-aware datetime
    pass

Configuring Attribute Type Names:

from type_bridge import AttributeFlags, TypeNameCase

# Option 1: Explicit name override
class Name(String):
    flags = AttributeFlags(name="person_name")
# TypeDB: attribute person_name, value string;

# Option 2: Case formatting
class UserEmail(String):
    flags = AttributeFlags(case=TypeNameCase.SNAKE_CASE)
# TypeDB: attribute user_email, value string;

2. Define Entities

from type_bridge import Entity, TypeFlags, Flag, Key, Card

class Person(Entity):
    flags = TypeFlags(name="person")  # Optional, defaults to lowercase class name

    # Use Flag() for key/unique markers and Card for cardinality
    name: Name = Flag(Key)                   # @key (implies @card(1..1))
    age: Age | None = None                   # @card(0..1) - optional field (explicit default)
    email: Email                             # @card(1..1) - default cardinality
    tags: list[Tag] = Flag(Card(min=2))      # @card(2..) - two or more (unordered set)

Note: list[Type] represents an unordered set in TypeDB. TypeDB has no list type - order is never preserved.

3. Create Instances

# Create entity instances with attribute values (keyword arguments required)
alice = Person(
    name=Name("Alice"),
    age=Age(30),
    email=Email("[e-mail]")
)

# Pydantic handles validation and type coercion automatically
print(alice.name.value)  # "Alice"

4. Work with Data

from type_bridge import Database, SchemaManager

# Connect to database
db = Database(address="localhost:1729", database="mydb")
db.connect()
db.create_database()

# Define schema
schema_manager = SchemaManager(db)
schema_manager.register(Person, Company, Employment)
schema_manager.sync_schema()

# Insert entities - use typed instances
alice = Person(
    name=Name("Alice"),
    age=Age(30),
    email=Email("[e-mail]")
)
Person.manager(db).insert(alice)

# Or use PUT for idempotent insert (safe to run multiple times!)
Person.manager(db).put(alice)  # Won't create duplicates

# Insert relations - use typed instances
employment = Employment(
    employee=alice,
    employer=techcorp,
    position=Position("Engineer"),
    salary=Salary(100000)
)
Employment.manager(db).insert(employment)

5. Cardinality Constraints

from type_bridge import Card, Flag

class Person(Entity):
    flags = TypeFlags(name="person")

    # Cardinality options:
    name: Name                              # @card(1..1) - exactly one (default)
    age: Age | None = None                  # @card(0..1) - zero or one (explicit default)
    tags: list[Tag] = Flag(Card(min=2))     # @card(2..) - two or more (unbounded)
    skills: list[Skill] = Flag(Card(max=5)) # @card(0..5) - zero to five
    jobs: list[Job] = Flag(Card(1, 3))      # @card(1..3) - one to three

6. Define Relations

from type_bridge import Relation, TypeFlags, Role

class Employment(Relation):
    flags = TypeFlags(name="employment")

    # Define roles with type-safe Role[T] syntax
    employee: Role[Person] = Role("employee", Person)
    employer: Role[Company] = Role("employer", Company)

    # Relations can own attributes
    position: Position                   # @card(1..1)
    salary: Salary | None = None         # @card(0..1) - explicit default

# Multi-player role example (one role, multiple entity types)
class Document(Entity):
    flags = TypeFlags(name="document")
    name: Name = Flag(Key)

class Email(Entity):
    flags = TypeFlags(name="email")
    name: Name = Flag(Key)

class Trace(Relation):
    flags = TypeFlags(name="trace")
    origin: Role[Document | Email] = Role.multi("origin", Document, Email)

7. Using Python Inheritance

class Animal(Entity):
    flags = TypeFlags(abstract=True)  # Abstract entity
    name: Name

class Dog(Animal):  # Automatically: dog sub animal in TypeDB
    breed: Breed

8. Generate Models from TypeQL Schema

Instead of writing Python classes manually, generate them from your TypeQL schema:

# Generate Python models from a schema file
python -m type_bridge.generator schema.tql -o ./myapp/models/

Or programmatically:

from type_bridge.generator import generate_models

generate_models("schema.tql", "./myapp/models/")

This generates a complete Python package:

myapp/models/
├── __init__.py      # Package exports, SCHEMA_VERSION, schema_text()
├── attributes.py    # Attribute class definitions
├── entities.py      # Entity class definitions
├── relations.py     # Relation class definitions
├── registry.py      # Schema metadata, JSON Schema fragments, lookup functions
└── schema.tql       # Copy of original schema

The generator supports:

• Entity/relation/attribute inheritance (sub keyword)
• @key, @unique, @card constraints (including on plays and relates)
• @regex and @values constraints
• @abstract and @independent types
• @range(min..max) constraints (integers, floats, dates, datetimes)
• Role overrides (relates X as Y)
• TypeDB function definitions with precise return type hints
• Registry module generation for schema metadata and JSON Schema fragments
• Both # and // comment styles

See the Code Generator guide for full documentation.

Documentation

https://ds1sqe.github.io/type-bridge/ — Full documentation site with user guide, API reference, and development guides.

• Getting Started — Installation and quick start
• User Guide — Attributes, entities, relations, CRUD, queries, and more
• API Reference — Auto-generated from source docstrings
• Development — Setup, testing, and internals

Pydantic Integration

TypeBridge is built on Pydantic v2, giving you powerful features:

class Person(Entity):
    flags = TypeFlags(name="person")
    name: Name = Flag(Key)
    age: Age

# Automatic validation and type coercion
alice = Person(name=Name("Alice"), age=Age(30))

# JSON serialization
json_data = alice.model_dump_json()

# JSON deserialization
bob = Person.model_validate_json('{"name": "Bob", "age": 25}')

# Model copying
alice_copy = alice.model_copy(update={"age": Age(31)})

Running Examples

TypeBridge includes comprehensive examples organized by complexity:

# Basic CRUD examples (start here!)
uv run python examples/basic/crud_01_define.py  # Schema definition
uv run python examples/basic/crud_02_insert.py  # Data insertion
uv run python examples/basic/crud_03_read.py    # Fetching API
uv run python examples/basic/crud_04_update.py  # Update operations

# Additional basic examples
uv run python examples/basic/crud_05_filter.py    # Advanced filtering
uv run python examples/basic/crud_06_aggregate.py # Aggregations
uv run python examples/basic/crud_07_delete.py    # Delete operations
uv run python examples/basic/crud_08_put.py       # Idempotent PUT operations

# Advanced examples
uv run python examples/advanced/schema_01_manager.py       # Schema operations
uv run python examples/advanced/schema_02_comparison.py    # Schema comparison
uv run python examples/advanced/schema_03_conflict.py      # Conflict detection
uv run python examples/advanced/features_01_pydantic.py    # Pydantic integration
uv run python examples/advanced/features_02_type_safety.py # Literal types
uv run python examples/advanced/query_01_expressions.py    # Query expressions
uv run python examples/advanced/validation_01_reserved_words.py  # Keyword validation

Running Tests

TypeBridge uses a two-tier testing approach with 100% test pass rate:

# Unit tests (fast, no external dependencies) - DEFAULT
uv run pytest                              # Run unit tests
uv run pytest tests/unit/attributes/ -v   # Test all 9 attribute types
uv run pytest tests/unit/core/ -v         # Test core functionality
uv run pytest tests/unit/flags/ -v        # Test flag system
uv run pytest tests/unit/expressions/ -v  # Test query expressions

# Integration tests (requires running TypeDB server)
# Option 1: Use Docker (recommended)
./test-integration.sh                     # Starts Docker, runs tests, stops Docker

# Option 2: Use existing TypeDB server
USE_DOCKER=false uv run pytest -m integration -v  # Run integration tests (~60s)

# Run specific integration test categories
uv run pytest tests/integration/crud/entities/ -v      # Entity CRUD tests
uv run pytest tests/integration/crud/relations/ -v    # Relation CRUD tests
uv run pytest tests/integration/queries/ -v           # Query expression tests
uv run pytest tests/integration/schema/ -v            # Schema operation tests

# All tests
uv run pytest -m "" -v                    # Run all tests
./test.sh                                 # Run full test suite with detailed output
./check.sh                                # Run linting and type checking

Rust Core

The project includes a Rust core (type-bridge-core/) that provides high-performance implementations of the query compiler, validation engine, and value coercer. When the native extension is installed, Python automatically delegates to Rust for:

• Validation — up to 40x faster schema-aware query validation
• Compilation — up to 2.5x faster AST-to-TypeQL compilation via serde bridge
• Value coercion — Type-safe value coercion and TypeQL literal formatting

The Rust core is a Cargo workspace with five crates:

Crate	Description
type-bridge-core-lib	Pure-Rust AST, schema parser, query compiler, and validation engine
type-bridge-orm	Async ORM with entity/relation managers, chainable queries, and batch operations
type-bridge-orm-derive	Derive macros for TypeBridgeEntity, TypeBridgeRelation, TypeBridgeAttribute
type-bridge-core	PyO3 bindings exposing the Rust core to Python
type-bridge-server	Transport-agnostic query pipeline with interceptor chain and HTTP API

See type-bridge-core/README.md for build instructions and architecture details.

Requirements

• Python 3.13+
• TypeDB 3.x server
• typedb-driver>=3.8.0
• pydantic>=2.12.4
• isodate==0.7.2 (for Duration type support)
• lark>=1.1.9 (for schema parsing)
• jinja2>=3.1.0 (for code generation)
• typer>=0.15.0 (for CLI)

Release Notes

See the CHANGELOG.md for detailed release notes and version history.

License

MIT License