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Getting Started

Overview

Semantica is a semantic intelligence layer that bridges the gap between raw data and trustworthy AI. It transforms unstructured data into explainable, auditable knowledge graphs perfect for high-stakes domains.

What You Can Build

  • GraphRAG Systems - Enhanced retrieval with semantic reasoning
  • AI Agents - Trustworthy agents with explainable memory
  • Knowledge Graphs - Production-ready semantic databases
  • Compliance-Ready AI - Auditable systems with full provenance

Installation

pip install semantica

Or with all features:

pip install semantica[all]

Verify installation:

import semantica
print(f"Semantica {semantica.__version__} installed!")

Quick Start

from semantica.semantic_extract import NERExtractor
from semantica.kg import GraphBuilder

# Extract entities
ner = NERExtractor(method="ml", model="en_core_web_sm")
entities = ner.extract("Apple Inc. was founded by Steve Jobs in 1976.")

# Build knowledge graph
kg = GraphBuilder().build({"entities": entities, "relationships": []})
print(f"Built KG with {len(kg.get('entities', []))} entities")

What this does: - Extracts entities (people, organizations, dates) from text - Builds a knowledge graph from extracted entities - Outputs the number of entities found

Core Architecture

Semantica uses a modular architecture - use only what you need:

1️⃣ Input Layer - Data Ingestion

from semantica.ingest import FileIngestor
documents = FileIngestor().ingest_directory("docs/")

2️⃣ Semantic Layer - Intelligence Engine

from semantica.semantic_extract import NERExtractor, RelationExtractor
entities = NERExtractor().extract(text)
relationships = RelationExtractor().extract(text, entities)

3️⃣ Output Layer - Knowledge Assets

from semantica.kg import GraphBuilder
kg = GraphBuilder().build_graph(entities, relationships)

Next Steps

🍳 Interactive Tutorials

  1. Welcome to Semantica - Complete framework overview
  2. Your First Knowledge Graph - Hands-on graph building
  3. GraphRAG Complete - Production-ready RAG

📚 Learn More

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