semantica/DEVELOPMENT_GUIDE.md

# 🚀 SemantiCore Development Guide

🎯 Development Philosophy

Modular Architecture

• Independent Components: Each processor, extractor, and module works independently
• Plugin System: Easy to add new data formats and domain-specific processors
• Clear Interfaces: Well-defined APIs between components
• Minimal Dependencies: Core components have minimal external dependencies

Community-First Design

• Easy Contribution: Clear contribution guidelines and development setup
• Comprehensive Testing: Unit, integration, and performance tests
• Documentation: Extensive documentation and examples
• Code Quality: Automated linting, formatting, and quality checks

🏗️ Component Development Guidelines

1. Core Engine Development

# semanticore/core/engine.py
class SemantiCore:
    """
    Main SemantiCore engine that orchestrates all processing.
    
    Responsibilities:
    - Initialize and configure all components
    - Coordinate data processing workflows
    - Manage knowledge base construction
    - Provide unified API for all operations
    """
    
    def __init__(self, config: Config):
        self.config = config
        self.processors = self._initialize_processors()
        self.extractors = self._initialize_extractors()
        self.knowledge_graph = self._initialize_knowledge_graph()
    
    def build_knowledge_base(self, sources: List[str]) -> KnowledgeBase:
        """Main entry point for building knowledge base from any sources."""
        pass
    
    def _initialize_processors(self) -> Dict[str, BaseProcessor]:
        """Initialize all available processors."""
        pass

2. Processor Development Pattern

# semanticore/processors/base.py
from abc import ABC, abstractmethod
from typing import Any, Dict, List

class BaseProcessor(ABC):
    """Base class for all data processors."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.supported_formats = self._get_supported_formats()
    
    @abstractmethod
    def can_process(self, source: str) -> bool:
        """Check if this processor can handle the given source."""
        pass
    
    @abstractmethod
    def process(self, source: str) -> ProcessedContent:
        """Process the source and return structured content."""
        pass
    
    @abstractmethod
    def extract_metadata(self, content: Any) -> Dict[str, Any]:
        """Extract metadata from processed content."""
        pass

# semanticore/processors/document/pdf_processor.py
class PDFProcessor(BaseProcessor):
    """Process PDF documents with semantic understanding."""
    
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.extract_tables = config.get('extract_tables', True)
        self.extract_images = config.get('extract_images', True)
    
    def can_process(self, source: str) -> bool:
        return source.lower().endswith('.pdf')
    
    def process(self, source: str) -> ProcessedContent:
        # Implementation for PDF processing
        pass

3. Extraction Module Pattern

# semanticore/extraction/entities.py
class EntityExtractor:
    """Extract named entities from processed content."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.models = self._load_models()
    
    def extract_entities(self, text: str) -> List[Entity]:
        """Extract entities from text."""
        pass
    
    def extract_entities_batch(self, texts: List[str]) -> List[List[Entity]]:
        """Extract entities from multiple texts efficiently."""
        pass

# semanticore/extraction/triples.py
class TripleExtractor:
    """Generate RDF triples from extracted entities and relationships."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.confidence_threshold = config.get('confidence_threshold', 0.8)
    
    def extract_triples(self, content: ProcessedContent) -> List[Triple]:
        """Extract triples from processed content."""
        pass
    
    def to_rdf(self, triples: List[Triple]) -> str:
        """Convert triples to RDF format."""
        pass

4. Knowledge Graph Integration

# semanticore/knowledge_graph/builder.py
class KnowledgeGraphBuilder:
    """Build and manage knowledge graphs from extracted triples."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.storage = self._initialize_storage()
    
    def add_triples(self, triples: List[Triple]) -> None:
        """Add triples to the knowledge graph."""
        pass
    
    def query(self, sparql_query: str) -> QueryResult:
        """Execute SPARQL query on the knowledge graph."""
        pass
    
    def export(self, format: str) -> str:
        """Export knowledge graph in various formats."""
        pass

🧪 Testing Strategy

1. Unit Tests

# tests/unit/test_processors/test_pdf_processor.py
import pytest
from semanticore.processors.document.pdf_processor import PDFProcessor

class TestPDFProcessor:
    def setup_method(self):
        self.processor = PDFProcessor({
            'extract_tables': True,
            'extract_images': False
        })
    
    def test_can_process_pdf(self):
        assert self.processor.can_process("document.pdf")
        assert not self.processor.can_process("document.txt")
    
    def test_process_pdf(self, sample_pdf_path):
        result = self.processor.process(sample_pdf_path)
        assert result.content is not None
        assert len(result.metadata) > 0

2. Integration Tests

# tests/integration/test_end_to_end/test_basic_workflow.py
import pytest
from semanticore import SemantiCore

class TestBasicWorkflow:
    def test_pdf_to_knowledge_graph(self, sample_pdf_path):
        core = SemantiCore({
            'llm_provider': 'openai',
            'embedding_model': 'text-embedding-3-large'
        })
        
        knowledge_base = core.build_knowledge_base([sample_pdf_path])
        
        assert len(knowledge_base.documents) > 0
        assert len(knowledge_base.entities) > 0
        assert len(knowledge_base.triples) > 0

📦 Package Configuration

1. pyproject.toml

[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "semanticore"
version = "0.1.0"
description = "Open Source Semantic Layer Toolkit"
readme = "README.md"
license = {text = "MIT"}
authors = [
    {name = "SemantiCore Team", email = "team@semanticore.io"}
]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.8",
    "Programming Language :: Python :: 3.9",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
]

dependencies = [
    "requests>=2.28.0",
    "beautifulsoup4>=4.11.0",
    "lxml>=4.9.0",
    "pandas>=1.5.0",
    "numpy>=1.21.0",
    "scikit-learn>=1.1.0",
    "transformers>=4.20.0",
    "torch>=1.12.0",
    "rdflib>=6.2.0",
    "pydantic>=1.10.0",
]

[project.optional-dependencies]
pdf = ["PyPDF2>=3.0.0", "pdfplumber>=0.7.0"]
office = ["python-docx>=0.8.11", "openpyxl>=3.0.10"]
web = ["selenium>=4.0.0", "feedparser>=6.0.0"]
feeds = ["feedparser>=6.0.0", "requests-html>=0.10.0"]
database = ["neo4j>=5.0.0", "pymongo>=4.0.0"]
ml = ["sentence-transformers>=2.2.0", "faiss-cpu>=1.7.0"]
dev = [
    "pytest>=7.0.0",
    "pytest-cov>=4.0.0",
    "black>=22.0.0",
    "isort>=5.10.0",
    "flake8>=5.0.0",
    "mypy>=0.991",
    "pre-commit>=2.20.0",
]

2. Requirements Management

# requirements/base.txt - Core dependencies
requests>=2.28.0
beautifulsoup4>=4.11.0
lxml>=4.9.0
pandas>=1.5.0
numpy>=1.21.0

# requirements/optional/pdf.txt - PDF processing
PyPDF2>=3.0.0
pdfplumber>=0.7.0
pdf2image>=1.16.0

# requirements/optional/web.txt - Web processing
selenium>=4.0.0
feedparser>=6.0.0
requests-html>=0.10.0

🚀 Development Workflow

1. Setup Development Environment

# Clone repository
git clone https://github.com/semanticore/semanticore.git
cd semanticore

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Setup pre-commit hooks
pre-commit install

2. Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=semanticore

# Run specific test file
pytest tests/unit/test_core/test_engine.py

# Run integration tests
pytest tests/integration/

3. Code Quality

# Format code
black semanticore/
isort semanticore/

# Lint code
flake8 semanticore/
mypy semanticore/

# Run all quality checks
pre-commit run --all-files

4. Documentation

# Build documentation
cd docs
make html

# Serve documentation locally
python -m http.server 8000

🎯 Next Steps

1. Implement Core Components: Start with the core engine and basic processors
2. Add Tests: Write comprehensive tests for each component
3. Create Examples: Build practical examples for different use cases
4. Documentation: Write API documentation and tutorials
5. Community: Set up contribution guidelines and issue templates

This development guide provides a solid foundation for building SemantiCore in a modular, maintainable way that encourages community contributions while ensuring code quality and scalability.