Frequently Asked Questions¶

Common questions and answers about Semantica.

General Questions¶

What is Semantica?¶

Semantica is an open-source framework for building semantic layers and knowledge graphs from unstructured data. It transforms raw data into structured, queryable knowledge that powers AI applications.

What can I use Semantica for?¶

• Building knowledge graphs from documents
• Creating semantic layers for AI applications
• Extracting entities and relationships
• Powering GraphRAG systems
• Integrating multi-source data
• Building AI agent memory

Is Semantica free?¶

Yes! Semantica is 100% open source and free to use under the MIT License.

What makes Semantica different?¶

• Modular: Use only what you need
• Extensible: Plug in custom models
• Production-ready: Built for scale
• Open source: Fully transparent

Installation & Setup¶

How do I install Semantica?¶

pip install semantica

See the Installation Guide for details.

What Python version do I need?¶

Python 3.8 or higher. Python 3.11+ is recommended for best performance.

Do I need a GPU?¶

No, GPU is optional. Semantica works on CPU, but GPU acceleration is available for faster processing.

How do I get started?¶

1. Install: pip install semantica
2. Follow the Quick Start Guide
3. Try the Examples

Knowledge Graphs¶

What is a knowledge graph?¶

A structured representation where entities (nodes) are connected by relationships (edges). It captures semantic meaning and relationships in data.

How do I build a knowledge graph?¶

from semantica.ingest import FileIngestor
from semantica.parse import DocumentParser
from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder

# Use individual modules
ingestor = FileIngestor()
parser = DocumentParser()
ner = NERExtractor()
rel_extractor = RelationExtractor()

doc = ingestor.ingest_file("document.pdf")
parsed = parser.parse_document("document.pdf")
text = parsed.get("full_text", "")

entities = ner.extract_entities(text)
relationships = rel_extractor.extract_relations(text, entities=entities)

builder = GraphBuilder()
kg = builder.build_graph(entities=entities, relationships=relationships)

Can I merge multiple knowledge graphs?¶

Yes! Use the merge method:

merged = semantica.kg.merge([kg1, kg2, kg3])

How do I visualize a knowledge graph?¶

semantica.kg.visualize(kg, output_path="graph.html")

Usage & Features¶

Can I process PDF files?¶

Yes! Semantica supports PDF, DOCX, HTML, JSON, CSV, and many other formats.

How do I extract entities from text?¶

from semantica.semantic_extract import NERExtractor

# Use NER extractor directly
ner = NERExtractor()
entities = ner.extract_entities("Your text")

Can I use my own models?¶

Yes, Semantica is extensible. You can plug in custom models for entity extraction, embeddings, and more.

What export formats are supported?¶

• RDF/XML
• OWL (Ontology)
• JSON
• CSV
• YAML
• And more

Conflict Resolution¶

What is conflict resolution?¶

When the same entity appears in multiple sources with different information, conflict resolution determines which information to use.

What strategies are available?¶

• Voting: Majority wins
• Credibility Weighted: Weight by source credibility
• Most Recent: Use latest information
• Highest Confidence: Use highest confidence score

How do I set a resolution strategy?¶

from semantica.conflicts import ConflictResolver

resolver = ConflictResolver(default_strategy="voting")

Integration¶

Can I use Semantica with other tools?¶

Yes! Semantica exports to standard formats that work with:

• Neo4j
• Graph databases
• RDF stores
• Vector databases
• Any tool that accepts RDF/JSON/CSV

Does it work with LangChain?¶

Yes, Semantica can be integrated with LangChain for RAG applications.

Can I connect to databases?¶

Yes, Semantica supports connections to Neo4j, FalkorDB, and other graph databases.

Performance¶

How fast is Semantica?¶

Performance depends on:

• Document size
• Number of documents
• Hardware (CPU/GPU)
• Configuration options

For typical documents, processing takes seconds to minutes.

Can I process large datasets?¶

Yes, but consider:

• Processing in batches
• Using GPU acceleration
• Incremental building
• Optimizing configuration

How can I improve performance?¶

• Enable GPU if available
• Process in smaller batches
• Use faster models
• Optimize configuration
• Cache embeddings

Troubleshooting¶

Installation fails¶

• Upgrade pip: pip install --upgrade pip
• Use virtual environment
• Check Python version: python --version

No entities extracted¶

• Verify document contains text (not just images)
• Check document format is supported
• Review extraction configuration

Memory errors¶

• Process documents one at a time
• Reduce batch sizes
• Use smaller models
• Increase available RAM

Slow processing¶

• Enable GPU if available
• Process in smaller batches
• Optimize configuration
• Use faster models

Getting Help¶

Where can I get help?¶

• Documentation: This site
• GitHub Issues: Report bugs or ask questions

How do I report a bug?¶

Open an issue on GitHub with:

• Description of the problem
• Steps to reproduce
• Expected vs actual behavior
• Environment details

Can I contribute?¶

Yes! We welcome contributions. See our Contributing Guide.

How do I request a feature?¶

Open a feature request on GitHub Issues with:

• Use case description
• Proposed solution
• Benefits to the community