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Getting Started

Welcome to Semantica

Semantica is a comprehensive knowledge graph and semantic processing framework designed for building production-ready semantic AI applications.

🎯 What You'll Learn

What Semantica is and why it's useful
How to install and configure the framework
Understanding the framework architecture
Key concepts and terminology
Next steps for getting started

🚀 What is Semantica?

Semantica is a powerful, production-ready framework for:

Building Knowledge Graphs: Transform unstructured data into structured knowledge graphs.
Semantic Processing: Extract entities, relationships, and meaning from text, images, and audio.
GraphRAG: Next-generation retrieval augmented generation using knowledge graphs.
Temporal Analysis: Time-aware knowledge graphs for tracking changes over time.
Multi-Modal Processing: Handle text, images, audio, and structured data.
Enterprise Features: Quality assurance, conflict resolution, ontology generation, and more.

💡 Use Cases

Domain	Application
Cybersecurity	Threat intelligence and analysis
Healthcare	Medical research and patient data analysis
Finance	Fraud detection and financial analysis
Supply Chain	Optimization and risk management
Research	Knowledge management and literature review
AI Systems	Multi-agent memory and reasoning

📦 Installation & Setup

Prerequisites

Before installing Semantica, ensure you have:

Python 3.8 or higher
pip package manager
(Optional) Virtual environment for isolation

Installation Methods

=== "PyPI (Stable)" bash pip install semantica

=== "Source (Dev)" bash git clone https://github.com/your-org/semantica.git cd semantica pip install -e .

=== "Extras" bash pip install semantica[all] # Install all optional dependencies pip install semantica[gpu] # Install GPU support pip install semantica[visualization] # Install visualization tools

Verify Installation

import semantica
print(semantica.version)

⚙️ Configuration

Semantica can be configured using environment variables or a configuration file.

Environment Variables

export SEMANTICA_API_KEY=your_openai_key
export SEMANTICA_EMBEDDING_PROVIDER=openai
export SEMANTICA_MODEL_NAME=gpt-4

Config File (`config.yaml`)

api_keys:
  openai: your_key_here
  anthropic: your_key_here

embedding:
  provider: openai
  model: text-embedding-3-large
  dimensions: 3072

knowledge_graph:
  backend: networkx # or neo4j, arangodb
  temporal: true

⏭️ Next Steps

Now that you understand the basics, here are recommended next steps:

Your First Knowledge Graph: Build your first knowledge graph from a document.
Configuration Basics: Set up configuration files and API keys.
Core Workflows: Learn common patterns and workflows.
Use Cases: Explore domain-specific applications.

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