Documentation

Introduction

Welcome to the GraphFusion documentation. Our platform provides advanced Neural Memory Networks for intelligent knowledge processing and graph-based analysis.

Quick Start Guide

Get started with GraphFusion in minutes:

1. Install the SDK

pip install graphfusion
npm install @graphfusion/client

2. Initialize the Client

from graphfusion import Client

client = Client(api_key='your_api_key')
graph = client.create_graph('my_graph')

3. Add Data

graph.add_node('concept_1', {
    'type': 'concept',
    'name': 'Machine Learning',
    'confidence': 0.95
})

Neural Memory Networks

Our Neural Memory Networks provide:

• Dynamic knowledge representation
• Automatic relationship inference
• Confidence-based reasoning
• Scalable architecture

Architecture Overview

The network consists of:

• Input Layer: Processes raw data and queries
• Memory Layer: Maintains knowledge relationships
• Inference Layer: Generates insights and connections

Knowledge Graphs

GraphFusion uses advanced knowledge graph structures for:

• Semantic relationship mapping
• Real-time updates and modifications
• Distributed processing

Graph Operations

// Create relationships
graph.add_edge({
    source: 'concept_1',
    target: 'concept_2',
    type: 'relates_to',
    weight: 0.8
});

Authentication

Secure your GraphFusion integration:

API Keys

const graphfusion = require('@graphfusion/client');

const client = new graphfusion.Client({
    apiKey: process.env.GRAPHFUSION_API_KEY,
    region: 'us-west-2'
});

Support

Need help? Our support team is available 24/7:

• Email: support@graphfusion.com
• Documentation: docs.graphfusion.com
• GitHub: github.com/graphfusion