graphfusion 0.1.4
GraphFusion Neural Memory Network SDK for adaptive knowledge management.
Requires Python: >=3.6

# GraphFusion

GraphFusion is a Neural Memory Network (NMN) SDK designed for efficient knowledge management, adaptive memory retrieval, and recommendation systems. It enables businesses and researchers to develop intelligent, context-aware applications, especially in domains like healthcare, finance, and education, where knowledge is complex and needs to be constantly updated. With GraphFusion, users can store, retrieve, and update data with confidence, leveraging NMN’s memory-driven approach for enhanced decision support and recommendations.

## Features

- **Flexible Memory Storage**: Use embedding-based memory storage to capture and retrieve knowledge.
- **Context-Aware Recommendations**: Generate recommendations based on semantic similarity and relevance feedback.
- **Graph-Structured Knowledge Representation**: Organize knowledge with directed links and confidence scores for efficient data navigation.
- **Adaptive Learning**: Self-healing and feedback-aware updates enable the model to learn from new data and improve over time.

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## Installation

To install GraphFusion, you can use pip:

```bash
pip install graphfusion
```

### Requirements

GraphFusion requires Python 3.7 or later. It depends on several libraries, which will be installed automatically with pip, including:

- `numpy`
- `scikit-learn`
- `torch`
- `transformers`
- `pandas`
- `networkx`

## Usage

Below is a quick start guide on how to use GraphFusion for a clinical recommendation system. We’ll demonstrate initialization, memory storage, and retrieving recommendations based on user queries.

### Initializing GraphFusion

Import and initialize the core components:

```python
from graphfusion.nmn_core import NeuralMemoryNetwork
from graphfusion.memory_manager import MemoryManager
from graphfusion.knowledge_graph import KnowledgeGraph
from graphfusion.recommendation_engine import RecommendationEngine

# Initialize components
nmn = NeuralMemoryNetwork()
memory_manager = MemoryManager()
knowledge_graph = KnowledgeGraph()
recommendation_engine = RecommendationEngine(memory_manager, knowledge_graph)
```

### Storing Information in Memory

You can store embeddings and associated clinical data within the memory for future retrieval:

```python
import numpy as np

# Example embedding and data
embedding = np.random.rand(128)  # Replace with actual embedding from model
data = {
    "patient_id": "12345",
    "symptoms": ["fever", "cough"],
    "diagnosis": "flu",
    "treatment": "rest and hydration"
}

# Store data with embedding in memory
memory_manager.store_in_memory(embedding, data, label="clinical_case")
```

### Retrieving Similar Cases

Given a new query, retrieve the top-k most similar cases to support decision-making:

```python
# Example query embedding
query_embedding = np.random.rand(128)  # Replace with actual query embedding

# Retrieve top-3 similar entries
similar_cases = memory_manager.retrieve_similar(query_embedding, top_k=3)

for case in similar_cases:
    print("Similar Case:", case["data"])
```

### Knowledge Graph Linking

Link similar cases with directed edges in the knowledge graph, building a relationship network that aids in further recommendations.

```python
knowledge_graph.add_node("case_123")
knowledge_graph.add_node("case_124")
knowledge_graph.add_edge("case_123", "case_124", confidence=0.9, relationship_type="related")
```

### Generating Recommendations

Use the `RecommendationEngine` to produce recommendations from the knowledge graph based on recent similar cases:

```python
recommendations = recommendation_engine.generate_recommendations(query_embedding)
for rec in recommendations:
    print("Recommendation:", rec)
```

## Project Structure

Here’s an overview of the project files and directories:

```plaintext
graphfusion/
├── src/
│   ├── graphfusion/
│   │   ├── __init__.py
│   │   ├── nmn_core.py            # Core Neural Memory Network logic
│   │   ├── memory_manager.py      # Embedding-based memory management
│   │   ├── knowledge_graph.py     # Knowledge graph for data relationships
│   │   └── recommendation_engine.py # Recommendation engine for similar cases
│   ├── tests/
│   │   ├── test_nmn_core.py
│   │   ├── test_memory_manager.py
│   │   └── test_knowledge_graph.py
├── setup.py
├── README.md
└── requirements.txt
```

## Contributing

Contributions to GraphFusion are welcome! To contribute, please follow these steps:

1. Fork the repository.
2. Create a new feature branch.
3. Commit your changes.
4. Submit a pull request.

Please ensure your code adheres to the existing style and passes all tests.

## License

This is an internal project, hence it should be developed according to our internal guidelines and policies.

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## Support

For any issues or support requests, please contact us at `support@graphfusion.onmicrosoft.com`.