graph-api-python 0.3.1

A fluent graph query library for Python with ElementStore and graph operations

Graph Elements Python Library

A Python library for graph-based data structures and queries, providing TypeScript-equivalent functionality for managing BaseElement, Node, Edge, MetaNode, and ElementStore classes with a powerful fluent GraphQuery API.

Features

• 🧩 Complete Graph Elements: BaseElement, Node, Edge, MetaNode classes with full TypeScript parity
• 🔍 Powerful Query API: Fluent GraphQuery interface with comprehensive filtering operators
• � Clean API: Simple addNode(), createNode(), addEdge() methods with Props class
• 🔗 Shorter Query Syntax: classId(), where(), gte(), order_by(), first() aliases
• �🗄️ Flexible Storage: ElementStore with pluggable storage backends (memory, custom implementations)
• 📊 Rich Filtering: Support for EQ, GT, LT, GTE, LTE, BETWEEN, CONTAINS, STARTS_WITH, ENDS_WITH, REGEX, IN operators
• 🔄 Method Chaining: Intuitive fluent API for complex query composition
• 📈 Aggregation Functions: count, sum, mean/avg, median, min, max operations
• 🎯 Type Safe: Full typing support with TypeScript-equivalent interfaces
• ⚡ Async Support: Async versions of node and edge creation methods
• ✅ Well Tested: Comprehensive test suite with 297 test cases

Installation

For End Users

# Install from PyPI (recommended)
pip install graph-api-python

# Or install from GitHub (latest development version)
pip install git+https://github.com/damylen/graph-api-python.git@main

# Install specific version from GitHub
pip install git+https://github.com/damylen/graph-api-python.git@v0.1.0

For Development

This project uses uv for fast, reliable Python package management.

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

# Clone and set up the project
git clone <repository-url>
cd graph-api
uv sync

Quick Start

from graph_api import GraphQuery, ElementStore, Props

# Create an element store
store = ElementStore()

# Add nodes using the clean API
alice = store.addNode('person', name="Alice", age=30, tags=["developer", "senior"])
bob = store.addNode('person', name="Bob", age=28, tags=["designer"])

# Or create with Props object
props = Props(name="Carol", age=32, department="Engineering")
carol = store.addNode('person', props)

# Create edges between nodes
friendship = store.addEdge('friendship', alice, bob, strength=0.9, type="close_friends")

# Create queries with intuitive syntax
query = GraphQuery(store)

# Basic filtering
people = query.classId('person').r()

# Advanced filtering with method chaining
senior_devs = (query
    .classId('person')
    .gte('age', 25)
    .contains('tags', 'senior')
    .r())

# Aggregations
total_people = query.classId('person').count()
avg_age = query.classId('person').avg('age')

# Sorting and limiting
youngest = (query
    .classId('person')
    .order_by('age', 'asc')
    .first(5)
    .r())

API Usage

The library provides a clean, intuitive API for graph operations:

Node Operations

from graph_api import ElementStore, Props

store = ElementStore()

# Add nodes with keyword arguments
person = store.addNode('person', name="John", age=30, city="Boston")

# Add nodes with Props object
props = Props(name="Alice", age=28, job="Engineer")
alice = store.addNode('person', props)

# Create nodes without adding to store
node = store.createNode('person', name="Bob", age=25)
# Later add to store
store.addNode(node)

# Async versions available
async def add_user():
    user = await store.addNodeAsync('user', name="Async User", active=True)
    return user

Edge Operations

# Create relationships between nodes
alice = store.addNode('person', name="Alice")
bob = store.addNode('person', name="Bob")

# Add edge with properties
friendship = store.addEdge('friendship', alice, bob, 
                          strength=0.9, type="close_friends")

# Create edge without adding to store
edge = store.createEdge('relationship', alice, bob, 
                       Props(type="colleague", department="Engineering"))

# Async edge creation
async def create_relationship():
    rel = await store.addEdgeAsync('follows', alice, bob, since="2023")
    return rel

Props Class

The Props class provides a simple way to define element properties:

from graph_api import Props

# Create props with keyword arguments
props = Props(name="Alice", age=30, job="Engineer")

# Props behaves like a dictionary
props['department'] = "Engineering"
assert props['name'] == "Alice"
assert 'age' in props

Query Methods

Filtering Methods

• classId(class_id): Filter by element class ID
• where(key, operator, value): Filter by property with operator
• where(key, value): Filter by property equality (shorthand)
• prop(key, value): Alias for property equality
• gt(key, value), lt(key, value), gte(key, value), lte(key, value): Comparison operators
• contains(key, value): Check if property contains value
• startsWith(key, value), endsWith(key, value): String matching

Sorting and Limiting

• order_by(property, direction): Sort by property ('asc' or 'desc')
• first(n): Limit to first N results
• last(n): Limit to last N results

Aggregation Methods

• count(): Count results
• sum(property): Sum numeric property
• avg(property): Average of numeric property
• median(property): Median of numeric property
• min(property): Minimum of numeric property
• max(property): Maximum of numeric property

Example Usage

# Chain methods for complex queries
young_engineers = (query
    .classId('person')
    .where('department', 'Engineering')
    .lt('age', 35)
    .order_by('age', 'desc')
    .first(10)
    .r())

# Use comparison operators
seniors = query.classId('person').gte('age', 30).r()

# Aggregations
avg_age = query.classId('person').avg('age')
total_people = query.classId('person').count()

Development

This project uses uv for dependency management and development workflows.

Prerequisites

• Python 3.12+
• uv

Setup

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone <repository-url>
cd graph-api

# Create virtual environment and install dependencies
uv sync

# Install in development mode
uv pip install -e .

Running Tests

# Run all tests
uv run pytest tests/ -v

# Run tests with coverage
uv run pytest tests/ -v --cov=graph_api --cov-report=html

# Run specific test file
uv run pytest tests/test_graph_query.py -v

Code Quality

# Run linting
uv run ruff check .

# Format code
uv run black .

# Sort imports
uv run isort .

# Type checking
uv run mypy graph_api/

# All quality checks
uv run ruff check . && uv run black --check . && uv run isort --check-only . && uv run mypy graph_api/

Adding Dependencies

# Add runtime dependency
uv add requests

# Add development dependency
uv add --dev pytest-mock

# Update dependencies
uv lock --upgrade

Using Makefile (Optional)

For convenience, common development tasks are available via Makefile:

# Set up development environment
make dev

# Run all quality checks and tests
make qa

# Run tests with coverage
make test

# Format code and fix linting
make lint-fix

# Build package
make build

# See all available commands
make help

Test Coverage

• ✅ 297 tests passing
• ✅ All filtering operators tested
• ✅ Method chaining validated
• ✅ Edge cases covered
• ✅ Aggregation functions verified
• ✅ Clean API methods tested
• ✅ Async operations validated

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.