| Name | mgraph-ai JSON |
| Version |
0.11.0
JSON |
| download |
| home_page | None |
| Summary | MGraph-AI |
| upload_time | 2025-01-29 02:09:34 |
| 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-AI - A Memory-based GraphDB for GenAI, Semantic Web and Serverless
MGraph-AI is a lightweight, memory-first graph database implementation in Python, designed specifically for AI, semantic web,
and serverless applications. What sets it apart is its focus on in-memory performance while maintaining persistence through
serialized JSON data structures. This makes it particularly well-suited for:
- AI/ML applications requiring fast graph traversals
- Serverless environments where quick startup and low memory overhead are crucial
- Semantic web applications needing flexible graph structures
- Applications requiring both graph capabilities and JSON compatibility
The library provides a robust, type-safe implementation with a clean, layered architecture that prioritizes maintainability
and scalability. By keeping the graph in memory and serializing to JSON, MGraph-AI achieves excellent performance from small to read-heavy
workloads while maintaining data persistence.
## Features
- Memory-first architecture with JSON serialization for persistence
- High-performance graph operations optimized for in-memory access
- Type-safe implementation with comprehensive validation
- Clean, layered architecture with focused action interfaces
- Rich attribute support for nodes and edges
- Extensible storage backends with JSON as the primary format
- Minimal dependencies making it ideal for serverless deployments
- Built-in support for semantic web concepts
- Optimized for AI/ML workloads
- Small memory footprint with efficient serialization
## Installation
```bash
pip install mgraph-ai
```
## Quick Start
```python
from mgraph_ai.mgraph import MGraph
from osbot_utils.helpers.Safe_Id import Safe_Id
# Create a new graph
mgraph = MGraph()
# Add nodes and edges
with mgraph.edit() as edit:
# Create nodes
node1 = edit.new_node(value="Node 1")
node2 = edit.new_node(value="Node 2")
# Add attributes to nodes
node1.add_attribute(name=Safe_Id("color"), value="blue")
node2.add_attribute(name=Safe_Id("size"), value=5)
# Create an edge between nodes
edge = edit.new_edge(from_node_id=node1.node_id(),
to_node_id=node2.node_id())
# Add attribute to edge
edge.add_attribute(name=Safe_Id("weight"), value=1.5)
# Query the graph
with mgraph.data() as data:
# Get all nodes
nodes = data.nodes()
# Get all edges
edges = data.edges()
# Get specific node
node = data.node(node1.node_id())
```
## Architecture
MGraph-AI implements a layered architecture:
```
Actions Layer (Data, Edit, Filter, Storage)
↓
Model Layer (Business Logic)
↓
Schema Layer (Type Definitions)
```
### Action Classes
Operations are organized into focused interfaces:
- `MGraph__Data`: Read operations and queries
- `MGraph__Edit`: Modification operations
- `MGraph__Filter`: Search and filtering capabilities
- `MGraph__Storage`: Persistence operations
### Type Safety
MGraph-AI enforces type safety at all layers:
```python
# Type-safe node creation
node = edit.new_node(value="test", value_type=str) # OK
node = edit.new_node(value=123, value_type=str) # Raises TypeError
# Type-safe attribute addition
node.add_attribute(name=Safe_Id("count"),
value=42,
attr_type=int) # OK
```
## Advanced Usage
### Custom Node Types
```python
from mgraph_ai.mgraph.schemas.Schema__MGraph__Node import Schema__MGraph__Node
class Custom_Node(Schema__MGraph__Node):
def custom_method(self):
return f"Node value: {self.value}"
# Use custom node type
mgraph = MGraph(node_type=Custom_Node)
```
### Graph Persistence
```python
# Save graph state
with mgraph.storage() as storage:
storage.save()
# Create new graph
with mgraph.storage() as storage:
storage.create()
# Delete graph
with mgraph.storage() as storage:
storage.delete()
```
### Attribute Operations
```python
# Add typed attributes
node.add_attribute(name=Safe_Id("count"), value=42, attr_type=int)
node.add_attribute(name=Safe_Id("name"), value="test", attr_type=str)
# Get attribute value
attr = node.attribute(attribute_id)
value = attr.value()
# Get all attributes
attributes = node.attributes()
```
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## Development Setup
```bash
# Clone the repository
git clone https://github.com/owasp-sbot/MGraph-AI.git
cd MGraph-AI
# Install dependencies with Poetry
poetry install
# Enter the poetry shell
poetry shell
# Run tests
poetry run pytest tests/
```
## License
This project is licensed under the Apache 2.0 License 2.0 - see the [LICENSE](LICENSE) file for details.
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"description": "# MGraph-AI - A Memory-based GraphDB for GenAI, Semantic Web and Serverless\n\n\n\nMGraph-AI is a lightweight, memory-first graph database implementation in Python, designed specifically for AI, semantic web, \nand serverless applications. What sets it apart is its focus on in-memory performance while maintaining persistence through \nserialized JSON data structures. This makes it particularly well-suited for:\n\n- AI/ML applications requiring fast graph traversals\n- Serverless environments where quick startup and low memory overhead are crucial\n- Semantic web applications needing flexible graph structures\n- Applications requiring both graph capabilities and JSON compatibility\n\nThe library provides a robust, type-safe implementation with a clean, layered architecture that prioritizes maintainability \nand scalability. By keeping the graph in memory and serializing to JSON, MGraph-AI achieves excellent performance from small to read-heavy \nworkloads while maintaining data persistence.\n\n## Features\n\n- Memory-first architecture with JSON serialization for persistence\n- High-performance graph operations optimized for in-memory access\n- Type-safe implementation with comprehensive validation\n- Clean, layered architecture with focused action interfaces\n- Rich attribute support for nodes and edges\n- Extensible storage backends with JSON as the primary format\n- Minimal dependencies making it ideal for serverless deployments\n- Built-in support for semantic web concepts\n- Optimized for AI/ML workloads\n- Small memory footprint with efficient serialization\n\n## Installation\n\n```bash\npip install mgraph-ai\n```\n\n## Quick Start\n\n```python\nfrom mgraph_ai.mgraph import MGraph\nfrom osbot_utils.helpers.Safe_Id import Safe_Id\n\n# Create a new graph\nmgraph = MGraph()\n\n# Add nodes and edges\nwith mgraph.edit() as edit:\n # Create nodes\n node1 = edit.new_node(value=\"Node 1\")\n node2 = edit.new_node(value=\"Node 2\")\n \n # Add attributes to nodes\n node1.add_attribute(name=Safe_Id(\"color\"), value=\"blue\")\n node2.add_attribute(name=Safe_Id(\"size\"), value=5)\n \n # Create an edge between nodes\n edge = edit.new_edge(from_node_id=node1.node_id(), \n to_node_id=node2.node_id())\n \n # Add attribute to edge\n edge.add_attribute(name=Safe_Id(\"weight\"), value=1.5)\n\n# Query the graph\nwith mgraph.data() as data:\n # Get all nodes\n nodes = data.nodes()\n \n # Get all edges\n edges = data.edges()\n \n # Get specific node\n node = data.node(node1.node_id())\n```\n\n## Architecture\n\nMGraph-AI implements a layered architecture:\n\n```\nActions Layer (Data, Edit, Filter, Storage)\n \u2193\nModel Layer (Business Logic)\n \u2193\nSchema Layer (Type Definitions)\n```\n\n### Action Classes\n\nOperations are organized into focused interfaces:\n\n- `MGraph__Data`: Read operations and queries\n- `MGraph__Edit`: Modification operations\n- `MGraph__Filter`: Search and filtering capabilities\n- `MGraph__Storage`: Persistence operations\n\n### Type Safety\n\nMGraph-AI enforces type safety at all layers:\n\n```python\n# Type-safe node creation\nnode = edit.new_node(value=\"test\", value_type=str) # OK\nnode = edit.new_node(value=123, value_type=str) # Raises TypeError\n\n# Type-safe attribute addition\nnode.add_attribute(name=Safe_Id(\"count\"), \n value=42, \n attr_type=int) # OK\n```\n\n## Advanced Usage\n\n### Custom Node Types\n\n```python\nfrom mgraph_ai.mgraph.schemas.Schema__MGraph__Node import Schema__MGraph__Node\n\nclass Custom_Node(Schema__MGraph__Node):\n def custom_method(self):\n return f\"Node value: {self.value}\"\n\n# Use custom node type\nmgraph = MGraph(node_type=Custom_Node)\n```\n\n### Graph Persistence\n\n```python\n# Save graph state\nwith mgraph.storage() as storage:\n storage.save()\n\n# Create new graph\nwith mgraph.storage() as storage:\n storage.create()\n\n# Delete graph\nwith mgraph.storage() as storage:\n storage.delete()\n```\n\n### Attribute Operations\n\n```python\n# Add typed attributes\nnode.add_attribute(name=Safe_Id(\"count\"), value=42, attr_type=int)\nnode.add_attribute(name=Safe_Id(\"name\"), value=\"test\", attr_type=str)\n\n# Get attribute value\nattr = node.attribute(attribute_id)\nvalue = attr.value()\n\n# Get all attributes\nattributes = node.attributes()\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## Development Setup\n\n```bash\n# Clone the repository\ngit clone https://github.com/owasp-sbot/MGraph-AI.git\ncd MGraph-AI\n\n# Install dependencies with Poetry\npoetry install\n\n# Enter the poetry shell\npoetry shell\n\n# Run tests\npoetry run pytest tests/\n```\n\n## License\n\nThis project is licensed under the Apache 2.0 License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n",
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