contextgraph 0.2.0

A powerful graph database with Cypher query support and advanced visualization capabilities

ContextGraph

A powerful graph database with Cypher query support and advanced visualization capabilities, built on igraph.

🚀 Features

• 🔍 Cypher Query Language: Full support for Cypher queries including MATCH, CREATE, WHERE, RETURN, and more
• 📊 Advanced Visualization: Multiple backends (matplotlib, plotly, graphviz) with interactive capabilities
• 🔗 Variable-Length Paths: Support for path queries with depth limits (*1..3, *2, *)
• 🔤 String Operations: Comprehensive string search and manipulation functions
• 💾 Data Import/Export: High-performance CSV import and multiple serialization formats
• ⚡ Transactions: ACID-compliant transaction support with rollback capabilities
• 🎯 High Performance: Built on igraph for efficient graph operations
• 🐍 Pythonic API: Clean, intuitive Python interface

📦 Installation

Basic Installation

pip install contextgraph

With Visualization Support

pip install contextgraph[visualization]

With All Optional Dependencies

pip install contextgraph[all]

Development Installation

git clone https://github.com/yourusername/contextgraph.git
cd contextgraph
pip install -e .[dev]

🏃‍♂️ Quick Start

from contextgraph import GraphDB

# Create a new graph database
db = GraphDB()

# Create nodes
alice_id = db.create_node(['Person'], {'name': 'Alice', 'age': 30})
bob_id = db.create_node(['Person'], {'name': 'Bob', 'age': 25})
company_id = db.create_node(['Company'], {'name': 'TechCorp'})

# Create relationships
db.create_relationship(alice_id, bob_id, 'KNOWS', {'since': 2020})
db.create_relationship(alice_id, company_id, 'WORKS_FOR')

# Query with Cypher
result = db.execute("""
    MATCH (p:Person)-[:KNOWS]->(friend:Person)
    WHERE p.age > 25
    RETURN p.name, friend.name, p.age
""")

for record in result:
    print(f"{record['p.name']} knows {record['friend.name']}")

# Visualize the graph
db.visualize(node_labels=True, node_color_property='age')

📚 Documentation

Core Operations

Creating Nodes and Relationships

# Create nodes with labels and properties
person_id = db.create_node(['Person', 'Employee'], {
    'name': 'Alice',
    'age': 30,
    'department': 'Engineering'
})

# Create relationships with properties
rel_id = db.create_relationship(
    source_id=alice_id,
    target_id=bob_id,
    relationship_type='MANAGES',
    properties={'since': '2023-01-01', 'level': 'senior'}
)

Cypher Queries

# Basic pattern matching
result = db.execute("""
    MATCH (manager:Person)-[:MANAGES]->(employee:Person)
    WHERE manager.department = 'Engineering'
    RETURN manager.name, employee.name
""")

# Variable-length paths
result = db.execute("""
    MATCH (start:Person)-[:KNOWS*1..3]->(end:Person)
    WHERE start.name = 'Alice'
    RETURN start.name, end.name
""")

# String operations
result = db.execute("""
    MATCH (p:Person)
    WHERE p.name CONTAINS 'Ali' AND p.email =~ '.*@company\\.com'
    RETURN UPPER(p.name) as name, LENGTH(p.name) as name_length
""")

Advanced Features

Transactions

# Using transactions for data consistency
with db.transaction():
    node1 = db.create_node(['Person'], {'name': 'Charlie'})
    node2 = db.create_node(['Person'], {'name': 'Diana'})
    db.create_relationship(node1, node2, 'FRIENDS')
    # Automatically committed on success, rolled back on exception

CSV Import

# High-performance CSV import
stats = db.import_nodes_from_csv(
    'people.csv',
    labels=['Person'],
    property_columns=['name', 'age', 'email']
)

stats = db.import_relationships_from_csv(
    'relationships.csv',
    relationship_type='KNOWS',
    source_column='person1_id',
    target_column='person2_id'
)

Visualization

# Basic visualization
db.visualize(
    node_labels=True,
    layout='spring',
    title='My Graph'
)

# Advanced styling
db.visualize(
    backend='plotly',  # Interactive visualization
    node_size_property='age',
    node_color_property='department',
    edge_width_property='strength',
    layout='circular'
)

# Query result visualization
from contextgraph import GraphVisualizer
viz = GraphVisualizer(db)
viz.plot_query_result("""
    MATCH (p:Person)-[:WORKS_FOR]->(c:Company)
    RETURN p, c
""")

🔧 Supported Cypher Features

Query Clauses

• ✅ MATCH - Pattern matching
• ✅ CREATE - Node and relationship creation
• ✅ WHERE - Filtering conditions
• ✅ RETURN - Result projection
• ✅ ORDER BY - Result sorting
• ✅ LIMIT - Result limiting
• ✅ WITH - Query chaining

Pattern Matching

• ✅ Node patterns: (n:Label {property: value})
• ✅ Relationship patterns: -[:TYPE {property: value}]->
• ✅ Variable-length paths: -[:TYPE*1..3]->
• ✅ Optional patterns and complex matching

Functions and Operators

• ✅ String functions: UPPER(), LOWER(), TRIM(), SUBSTRING(), etc.
• ✅ String operators: CONTAINS, STARTS WITH, ENDS WITH, =~ (regex)
• ✅ Aggregate functions: COUNT(), SUM(), AVG(), MIN(), MAX()
• ✅ Comparison operators: =, <>, <, >, <=, >=

📊 Visualization Backends

Backend	Type	Features
matplotlib	Static	Publication-quality plots, customizable styling
plotly	Interactive	Web-ready, hover details, zoom/pan
graphviz	Vector	High-quality layouts, perfect for documentation

🔄 Data Import/Export

Supported Formats

• CSV: High-performance bulk import
• JSON: Human-readable serialization
• Pickle: Fast binary serialization with full Python object support

Performance

• CSV Import: 10,000+ nodes/relationships per second
• Query Performance: Optimized for complex graph traversals
• Memory Efficient: Streaming operations for large datasets

🧪 Testing

Run the test suite:

# Run all tests
pytest

# Run with coverage
pytest --cov=igraph_cypher_db

# Run specific test categories
pytest -m "not slow"  # Skip slow tests
pytest tests/test_cypher.py  # Specific test file

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/yourusername/contextgraph.git
cd contextgraph
pip install -e .[dev]
pre-commit install

Code Quality

# Format code
black igraph_cypher_db tests

# Lint code
flake8 igraph_cypher_db tests

# Type checking
mypy igraph_cypher_db

📈 Performance Benchmarks

Operation	Performance
Node Creation	50,000+ nodes/sec
Relationship Creation	25,000+ relationships/sec
CSV Import	10,000+ records/sec
Simple Queries	1,000+ queries/sec
Complex Path Queries	100+ queries/sec

🗺️ Roadmap

• Query Optimization: Advanced query planning and optimization
• Distributed Queries: Support for distributed graph operations
• Graph Algorithms: Built-in graph analysis algorithms
• Schema Validation: Optional schema enforcement
• REST API: HTTP interface for remote access
• Streaming: Real-time graph updates and queries

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on the excellent igraph library
• Inspired by Neo4j and the Cypher query language
• Visualization powered by matplotlib, plotly, and graphviz

📞 Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

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