| Name | mgraph-db JSON |
| Version |
1.1.9
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| home_page | None |
| Summary | MGraph-DB |
| upload_time | 2025-02-25 02:06:11 |
| maintainer | None |
| docs_url | None |
| author | Dinis Cruz |
| requires_python | <4.0,>=3.11 |
| license | Apache 2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# MGraph-DB: A Memory-based GraphDB for Python, GenAI, Semantic Web and Serverless
> **QUICKSTART GUIDE**: For working code examples, check out our test suite at:
> ```
> tests/quickstart/test_README_examples.py
> ```
> This file contains executable versions of all examples shown below, providing a hands-on way to learn the library.
MGraph-DB is a high-performance, type-safe graph database implementation in Python, optimized for in-memory operations with JSON persistence. Its architecture makes it particularly well-suited for:
- **GenAI Applications**
- Knowledge graph construction and querying
- Semantic relationship modeling
- Context management for LLMs
- Graph-based reasoning systems
- **Semantic Web**
- RDF data processing
- Ontology management
- Linked data applications
- SPARQL-compatible queries
- **Serverless Deployments**
- Quick cold starts with memory-first design
- Efficient JSON serialization
- Low memory footprint
- Built-in serverless support via [MGraph_DB_Serverless](https://github.com/owasp-sbot/MGraph-DB-Serverless)
- **Python Ecosystem**
- Native Python implementation
- Full type safety and validation
- Clean, Pythonic API
- Rich integration capabilities
## Major Features
### Production-Ready Type System
- Complete implementation of the three-layer architecture (Domain, Model, Schema)
- Comprehensive runtime type checking across all layers
- Type-safe property accessors and method decorators
- Robust validation for nested data structures
- Clean class hierarchies with explicit interfaces
### Advanced Graph Operations
- High-performance in-memory graph operations
- Sophisticated query system with chainable operations
- Rich traversal capabilities with type filtering
- Flexible node and edge attribute management
- Comprehensive CRUD operations for graph elements
### Optimized Indexing System
- O(1) lookups for all core operations
- Multi-dimensional indexing (type, attribute, relationship)
- Efficient graph traversal support
- Advanced query optimization
- Index persistence and restoration
### Query System Enhancements
- View-based query results with navigation
- Rich filtering and traversal operations
- Chainable query interface
- Query result caching
- Query operation history tracking
### Export Capabilities
- Support for multiple export formats:
- GraphML
- DOT
- Mermaid
- RDF/Turtle
- N-Triples
- GEXF
- TGF
- Cypher
- CSV
- JSON
### Visualization Support
- Integration with common visualization libraries
- Custom layout algorithms
- Interactive graph exploration
- Support for large graph visualization
- Multiple visualization format exports
## Quick Start
Here's a simple example showing basic graph operations:
```python
def test_basic_graph_operations():
from mgraph_db.mgraph.MGraph import MGraph
# Create a new graph
mgraph = MGraph()
with mgraph.edit() as edit:
# Create two nodes
node1 = edit.new_node(node_data={"value": "First Node"})
node2 = edit.new_node(node_data={"value": "Second Node"})
# Connect nodes with an edge
edge = edit.new_edge(from_node_id=node1.node_id,
to_node_id=node2.node_id)
# Verify nodes and edge were created
assert node1.node_id is not None
assert node2.node_id is not None
assert edge.edge_id is not None
# Query the graph
with mgraph.data() as data:
nodes = data.nodes()
edges = data.edges()
assert len(nodes) == 2
assert len(edges) == 1
```
## Use Cases
### GenAI Integration
```python
def test_genai_integration():
from mgraph_db.mgraph.MGraph import MGraph
mgraph = MGraph()
with mgraph.edit() as edit:
# Create a knowledge graph for LLM context
context = edit.new_node(node_data={"type": "context",
"value": "user query"})
entity = edit.new_node(node_data={"type": "entity",
"value": "named entity"})
relation = edit.new_edge(from_node_id=context.node_id,
to_node_id=entity.node_id,
edge_data={"type": "contains"})
# Verify the knowledge graph
assert context.node_data.value == "user query"
assert entity.node_data.value == "named entity"
assert relation.edge_data.type == "contains"
```
### Semantic Web Applications
```python
def test_semantic_web():
from mgraph_db.mgraph.MGraph import MGraph
mgraph = MGraph()
with mgraph.edit() as edit:
# Create RDF-style triples
subject = edit.new_node(node_data={"uri": "http://example.org/subject"})
object = edit.new_node(node_data={"uri": "http://example.org/object"})
predicate = edit.new_edge(from_node_id=subject.node_id,
to_node_id=object.node_id,
edge_data={"predicate": "relates_to"})
# Verify triple structure
assert subject.node_data.uri == "http://example.org/subject"
assert object.node_data.uri == "http://example.org/object"
assert predicate.edge_data.predicate == "relates_to"
```
## Advanced Usage
### Type-Safe Operations
```python
def test_type_safe_operations():
from mgraph_db.mgraph.MGraph import MGraph
from mgraph_db.mgraph.schemas.Schema__MGraph__Node import Schema__MGraph__Node
from mgraph_db.mgraph.schemas.Schema__MGraph__Node__Data import Schema__MGraph__Node__Data
# Custom node data with runtime type checking
class Custom_Node_Data(Schema__MGraph__Node__Data):
name: str
value: int
priority: float
# Type-safe node definition
class Custom_Node(Schema__MGraph__Node):
node_data: Custom_Node_Data
mgraph = MGraph()
with mgraph.edit() as edit:
# Create typed node
node = edit.new_node(node_type=Custom_Node,
name="test",
value=42,
priority=1.5)
# Verify type safety
assert node.node_data.name == "test"
assert node.node_data.value == 42
assert node.node_data.priority == 1.5
# Demonstrate type-safe processing
result = node.node_data.priority * 2.0
assert result == 3.0
```
### Using the Index System
```python
def test_index_system():
from mgraph_db.mgraph.MGraph import MGraph
mgraph = MGraph()
with mgraph.edit() as edit:
# Create nodes
node1 = edit.new_node(node_type=Custom_Node,
name="test",
value=42,
priority=1.5)
# Access index
with edit.index() as index:
# Get nodes by type
nodes = index.get_nodes_by_type(Custom_Node)
assert len(nodes) == 1
# Get by relationship
nodes_by_type = index.get_nodes_by_type(Custom_Node)
assert node1.node_id in nodes_by_type
```
### Graph Export and Visualization
```python
def test_export_and_visualization():
from mgraph_db.mgraph.MGraph import MGraph
import os
mgraph = MGraph()
with mgraph.edit() as edit:
# Create sample graph
node1 = edit.new_node()
node2 = edit.new_node()
edge = edit.new_edge(from_node_id=node1.node_id,
to_node_id=node2.node_id)
# Test various export formats
with mgraph.export() as export:
# DOT format
dot = export.to__dot()
assert 'digraph' in dot
# Mermaid format
mermaid = export.to__mermaid()
assert 'graph TD' in mermaid
# GraphML format
graphml = export.to__graphml()
assert 'graphml' in graphml
# RDF/Turtle format
turtle = export.to__turtle()
assert '@prefix' in turtle
# Test visualization
output_file = 'test_graph.png'
with mgraph.screenshot() as screenshot:
screenshot.save_to(output_file)
assert os.path.exists(output_file)
if os.path.exists(output_file):
os.remove(output_file)
```
## Installation
```bash
pip install mgraph-db
```
## Development Setup
1. Clone the repository:
```bash
git clone https://github.com/your-org/mgraph.git
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run tests:
```bash
python -m pytest tests/
```
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run tests (`python -m pytest tests/`)
5. Submit a Pull Request
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
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"description": "# MGraph-DB: A Memory-based GraphDB for Python, GenAI, Semantic Web and Serverless\n\n\n\n> **QUICKSTART GUIDE**: For working code examples, check out our test suite at:\n> ```\n> tests/quickstart/test_README_examples.py\n> ```\n> This file contains executable versions of all examples shown below, providing a hands-on way to learn the library.\n\nMGraph-DB is a high-performance, type-safe graph database implementation in Python, optimized for in-memory operations with JSON persistence. Its architecture makes it particularly well-suited for:\n\n- **GenAI Applications**\n - Knowledge graph construction and querying\n - Semantic relationship modeling\n - Context management for LLMs\n - Graph-based reasoning systems\n\n- **Semantic Web**\n - RDF data processing\n - Ontology management\n - Linked data applications\n - SPARQL-compatible queries\n\n- **Serverless Deployments**\n - Quick cold starts with memory-first design\n - Efficient JSON serialization\n - Low memory footprint\n - Built-in serverless support via [MGraph_DB_Serverless](https://github.com/owasp-sbot/MGraph-DB-Serverless)\n\n- **Python Ecosystem**\n - Native Python implementation\n - Full type safety and validation\n - Clean, Pythonic API\n - Rich integration capabilities\n\n## Major Features\n\n### Production-Ready Type System\n- Complete implementation of the three-layer architecture (Domain, Model, Schema)\n- Comprehensive runtime type checking across all layers\n- Type-safe property accessors and method decorators\n- Robust validation for nested data structures\n- Clean class hierarchies with explicit interfaces\n\n### Advanced Graph Operations\n- High-performance in-memory graph operations\n- Sophisticated query system with chainable operations\n- Rich traversal capabilities with type filtering\n- Flexible node and edge attribute management\n- Comprehensive CRUD operations for graph elements\n\n### Optimized Indexing System\n- O(1) lookups for all core operations\n- Multi-dimensional indexing (type, attribute, relationship)\n- Efficient graph traversal support\n- Advanced query optimization\n- Index persistence and restoration\n\n### Query System Enhancements\n- View-based query results with navigation\n- Rich filtering and traversal operations\n- Chainable query interface\n- Query result caching\n- Query operation history tracking\n\n### Export Capabilities\n- Support for multiple export formats:\n - GraphML\n - DOT\n - Mermaid\n - RDF/Turtle\n - N-Triples\n - GEXF\n - TGF\n - Cypher\n - CSV\n - JSON\n\n### Visualization Support\n- Integration with common visualization libraries\n- Custom layout algorithms\n- Interactive graph exploration\n- Support for large graph visualization\n- Multiple visualization format exports\n\n## Quick Start\n\nHere's a simple example showing basic graph operations:\n\n```python\ndef test_basic_graph_operations():\n from mgraph_db.mgraph.MGraph import MGraph\n \n # Create a new graph\n mgraph = MGraph()\n \n with mgraph.edit() as edit:\n # Create two nodes\n node1 = edit.new_node(node_data={\"value\": \"First Node\"})\n node2 = edit.new_node(node_data={\"value\": \"Second Node\"})\n \n # Connect nodes with an edge\n edge = edit.new_edge(from_node_id=node1.node_id, \n to_node_id=node2.node_id)\n \n # Verify nodes and edge were created\n assert node1.node_id is not None\n assert node2.node_id is not None\n assert edge.edge_id is not None\n \n # Query the graph\n with mgraph.data() as data:\n nodes = data.nodes()\n edges = data.edges()\n \n assert len(nodes) == 2\n assert len(edges) == 1\n```\n\n## Use Cases\n\n### GenAI Integration\n\n```python\ndef test_genai_integration():\n from mgraph_db.mgraph.MGraph import MGraph\n \n mgraph = MGraph()\n with mgraph.edit() as edit:\n # Create a knowledge graph for LLM context\n context = edit.new_node(node_data={\"type\": \"context\", \n \"value\": \"user query\"})\n entity = edit.new_node(node_data={\"type\": \"entity\", \n \"value\": \"named entity\"})\n relation = edit.new_edge(from_node_id=context.node_id,\n to_node_id=entity.node_id,\n edge_data={\"type\": \"contains\"})\n \n # Verify the knowledge graph\n assert context.node_data.value == \"user query\"\n assert entity.node_data.value == \"named entity\"\n assert relation.edge_data.type == \"contains\"\n```\n\n### Semantic Web Applications\n\n```python\ndef test_semantic_web():\n from mgraph_db.mgraph.MGraph import MGraph\n \n mgraph = MGraph()\n with mgraph.edit() as edit:\n # Create RDF-style triples\n subject = edit.new_node(node_data={\"uri\": \"http://example.org/subject\"})\n object = edit.new_node(node_data={\"uri\": \"http://example.org/object\"})\n predicate = edit.new_edge(from_node_id=subject.node_id,\n to_node_id=object.node_id,\n edge_data={\"predicate\": \"relates_to\"})\n \n # Verify triple structure\n assert subject.node_data.uri == \"http://example.org/subject\"\n assert object.node_data.uri == \"http://example.org/object\"\n assert predicate.edge_data.predicate == \"relates_to\"\n```\n\n## Advanced Usage\n\n### Type-Safe Operations\n\n```python\ndef test_type_safe_operations():\n from mgraph_db.mgraph.MGraph import MGraph\n from mgraph_db.mgraph.schemas.Schema__MGraph__Node import Schema__MGraph__Node\n from mgraph_db.mgraph.schemas.Schema__MGraph__Node__Data import Schema__MGraph__Node__Data\n \n # Custom node data with runtime type checking\n class Custom_Node_Data(Schema__MGraph__Node__Data):\n name: str\n value: int\n priority: float\n \n # Type-safe node definition\n class Custom_Node(Schema__MGraph__Node):\n node_data: Custom_Node_Data\n \n mgraph = MGraph()\n with mgraph.edit() as edit:\n # Create typed node\n node = edit.new_node(node_type=Custom_Node,\n name=\"test\",\n value=42,\n priority=1.5)\n \n # Verify type safety\n assert node.node_data.name == \"test\"\n assert node.node_data.value == 42\n assert node.node_data.priority == 1.5\n \n # Demonstrate type-safe processing\n result = node.node_data.priority * 2.0\n assert result == 3.0\n```\n\n### Using the Index System\n\n```python\ndef test_index_system():\n from mgraph_db.mgraph.MGraph import MGraph\n \n mgraph = MGraph()\n with mgraph.edit() as edit:\n # Create nodes\n node1 = edit.new_node(node_type=Custom_Node,\n name=\"test\",\n value=42,\n priority=1.5)\n \n # Access index\n with edit.index() as index:\n # Get nodes by type\n nodes = index.get_nodes_by_type(Custom_Node)\n assert len(nodes) == 1\n \n # Get by relationship\n nodes_by_type = index.get_nodes_by_type(Custom_Node)\n assert node1.node_id in nodes_by_type\n```\n\n### Graph Export and Visualization\n\n```python\ndef test_export_and_visualization():\n from mgraph_db.mgraph.MGraph import MGraph\n import os\n \n mgraph = MGraph()\n with mgraph.edit() as edit:\n # Create sample graph\n node1 = edit.new_node()\n node2 = edit.new_node()\n edge = edit.new_edge(from_node_id=node1.node_id,\n to_node_id=node2.node_id)\n \n # Test various export formats\n with mgraph.export() as export:\n # DOT format\n dot = export.to__dot()\n assert 'digraph' in dot\n \n # Mermaid format\n mermaid = export.to__mermaid()\n assert 'graph TD' in mermaid\n \n # GraphML format\n graphml = export.to__graphml()\n assert 'graphml' in graphml\n \n # RDF/Turtle format\n turtle = export.to__turtle()\n assert '@prefix' in turtle\n \n # Test visualization\n output_file = 'test_graph.png'\n with mgraph.screenshot() as screenshot:\n screenshot.save_to(output_file)\n assert os.path.exists(output_file)\n if os.path.exists(output_file):\n os.remove(output_file)\n```\n\n## Installation\n\n```bash\npip install mgraph-db\n```\n\n## Development Setup\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/your-org/mgraph.git\n```\n\n2. Install dependencies:\n```bash\npip install -r requirements.txt\n```\n\n3. Run tests:\n```bash\npython -m pytest tests/\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Make your changes\n4. Run tests (`python -m pytest tests/`)\n5. Submit a Pull Request\n\n## License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n",
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