semantica/docs/modules.md

Modules & Architecture

Semantica is built with a modular architecture, designed to be flexible, extensible, and scalable. This guide provides a comprehensive overview of the key modules, their responsibilities, and how they interact to build powerful semantic applications.

!!! info "About This Guide" This guide covers all 13 core modules in Semantica, organized by their functional layer. It details their purpose, key features, usage patterns, and configuration options.

---

Module Overview

Semantica's modules are organized into five logical layers. You can use these modules independently or combine them into a complete pipeline.

Layer	Modules	Description
Input Layer	Ingest, Parse, Normalize	Handles data ingestion, parsing, and cleaning from various sources.
Core Processing	Semantic Extract, Knowledge Graph, Ontology, Reasoning	The "brain" of the system. Extracts meaning, builds graphs, and infers new knowledge.
Storage & Embeddings	Embeddings, Vector Store, Graph Store	Manages persistent storage and retrieval of vectors and graphs.
Output Layer	Export, Visualization	Tools for visualizing data and exporting it to external systems.
Orchestration	Pipeline	Manages workflows, execution, and resource scheduling.

---

Input Layer

These modules are responsible for getting data into the system and preparing it for processing.

1. Ingest Module

!!! abstract "Purpose" The ingest module is the entry point for data ingestion. It handles the complexity of connecting to different data sources, from local files to web streams, and supports connecting to your own MCP (Model Context Protocol) servers.

Key Features:

• Support for 50+ file formats (PDF, DOCX, HTML, JSON, CSV, etc.)
• Web scraping with JavaScript rendering
• Database integration (SQL, NoSQL)
• Real-time streaming support
• MCP server integration
• Batch processing capabilities
• Metadata extraction

Input/Output Specification:

Input	Type	Description
source	str, Path, List	File path, URL, directory, or list of sources
recursive	bool	Whether to recursively scan directories
filters	Dict	Filters for file types, sizes, etc.

Output	Type	Description
documents	List[Document]	List of Document objects with content and metadata
metadata	Dict	Source metadata (paths, timestamps, formats)

Components:

• FileIngestor: Read files (PDF, DOCX, HTML, JSON, CSV, etc.)
• WebIngestor: Scrape and ingest web pages
• FeedIngestor: Process RSS/Atom feeds
• StreamIngestor: Real-time data streaming
• DBIngestor: Database queries and ingestion
• EmailIngestor: Process email messages
• RepoIngestor: Git repository analysis
• MCPIngestor: Connect to your own Python/FastMCP MCP servers via URL for resource and tool-based data ingestion

Complete Code Example:

from semantica.ingest import FileIngestor, WebIngestor, DBIngestor, MCPIngestor

# Example 1: Ingest local files
file_ingestor = FileIngestor()
documents = file_ingestor.ingest("data/", recursive=True)  # (1)
print(f"Ingested {len(documents)} documents")

# Example 2: Ingest web content
web_ingestor = WebIngestor()
web_docs = web_ingestor.ingest("https://example.com")  # (2)
print(f"Web content: {web_docs[0].content[:100]}...")

# Example 3: Ingest from database
db_ingestor = DBIngestor(connection_string="postgresql://user:pass@localhost/db")
db_docs = db_ingestor.ingest("SELECT * FROM documents")

# Example 4: Ingest from MCP server
mcp_ingestor = MCPIngestor()
mcp_docs = mcp_ingestor.ingest("https://your-mcp-server.com")

1. Recursively scans the directory for supported file types (PDF, DOCX, etc.) and converts them to standard Document objects.
2. Fetches the URL, renders JavaScript if necessary, and extracts the main content while stripping boilerplate.

Configuration Options:

Option	Type	Default	Description
recursive	bool	False	Recursively scan directories
file_filters	List[str]	None	Filter by file extensions
max_file_size	int	None	Maximum file size in bytes
timeout	int	30	Request timeout in seconds
user_agent	str	None	Custom user agent for web requests

API Reference: Ingest Module

2. Parse Module

!!! abstract "Purpose" Once data is ingested, the parse module extracts the raw text and metadata. It supports a wide range of formats and includes OCR capabilities.

Key Features:

• 50+ file format support
• OCR for images and scanned documents
• Table extraction from PDFs and spreadsheets
• Metadata preservation
• Automatic format detection
• Structured data parsing (JSON, CSV, XML)

Input/Output Specification:

Input	Type	Description
documents	List[Document]	Documents from ingest module
ocr_enabled	bool	Enable OCR for images

Output	Type	Description
parsed_docs	List[ParsedDocument]	Documents with extracted text and metadata
tables	List[Table]	Extracted tables (if any)

Components:

• DocumentParser: Main parser orchestrator
• PDFParser: Extract text, tables, images from PDFs
• DOCXParser: Parse Word documents
• HTMLParser: Extract content from HTML
• JSONParser: Parse structured JSON data
• ExcelParser: Process spreadsheets
• ImageParser: OCR and image analysis
• CodeParser: Parse source code files

Complete Code Example:

from semantica.parse import DocumentParser

parser = DocumentParser(ocr_enabled=True)
parsed_docs = parser.parse(documents)  # (1)

# Access parsed content
for doc in parsed_docs:
    print(f"Content: {doc.content[:100]}...")
    print(f"Metadata: {doc.metadata}")
    if doc.tables:
        print(f"Found {len(doc.tables)} tables")

1. Automatically detects the file type of each document and routes it to the appropriate specialized parser (e.g., PDFParser for .pdf).

Configuration Options:

Option	Type	Default	Description
ocr_enabled	bool	False	Enable OCR for images
extract_tables	bool	True	Extract tables from documents
extract_images	bool	False	Extract and process images

API Reference: Parse Module

3. Normalize Module

!!! abstract "Purpose" Raw text is often noisy. The normalize module cleans, standardizes, and prepares text for semantic extraction.

Key Features:

• Text cleaning and noise removal
• Encoding normalization
• Entity name standardization
• Date and number formatting
• Language detection
• Unicode normalization

Input/Output Specification:

Input	Type	Description
documents	List[ParsedDocument]	Parsed documents
normalize_entities	bool	Normalize entity names

Output	Type	Description
normalized_docs	List[NormalizedDocument]	Cleaned and normalized documents

Components:

• TextNormalizer: Main normalization orchestrator
• TextCleaner: Remove noise, fix encoding
• DataCleaner: Clean structured data
• EntityNormalizer: Normalize entity names
• DateNormalizer: Standardize date formats
• NumberNormalizer: Normalize numeric values
• LanguageDetector: Detect document language
• EncodingHandler: Handle character encoding

Complete Code Example:

from semantica.normalize import TextNormalizer

normalizer = TextNormalizer(
    normalize_entities=True,
    normalize_dates=True,
    detect_language=True
)
normalized = normalizer.normalize(parsed_docs)

# Check normalization results
for doc in normalized:
    print(f"Language: {doc.language}")
    print(f"Normalized text: {doc.content[:100]}...")

Configuration Options:

Option	Type	Default	Description
normalize_entities	bool	True	Normalize entity names
normalize_dates	bool	True	Standardize date formats
normalize_numbers	bool	True	Normalize numeric values
detect_language	bool	False	Detect document language

API Reference: Normalize Module

---

Core Processing Layer

These modules form the intelligence of Semantica, extracting meaning, building relationships, and inferring new knowledge.

4. Semantic Extract Module

!!! abstract "Purpose" This is the brain of the operation. It uses LLMs and NLP techniques to understand the text and extract structured knowledge.

Key Features:

• Multiple NER methods (rule-based, ML, LLM)
• Relationship extraction
• Semantic analysis
• Custom entity types
• Confidence scoring
• Multi-language support

Input/Output Specification:

Input	Type	Description
documents	List[NormalizedDocument]	Normalized documents
entity_types	List[str]	Custom entity types to extract
method	str	Extraction method (rule/ml/llm)

Output	Type	Description
entities	List[Entity]	Extracted entities with types and confidence
relationships	List[Relationship]	Extracted relationships

Components:

• NERExtractor: Named Entity Recognition
• RelationExtractor: Extract relationships between entities
• SemanticAnalyzer: Deep semantic analysis
• SemanticNetworkExtractor: Extract semantic networks

Complete Code Example:

from semantica.semantic_extract import NERExtractor, RelationExtractor

# Extract entities
extractor = NERExtractor(method="llm", model="gpt-4")
entities = extractor.extract(normalized_docs)

print(f"Extracted {len(entities)} entities")
for entity in entities[:5]:
    print(f"  - {entity.text} ({entity.label}) - Confidence: {entity.confidence:.2f}")

# Extract relationships
relation_extractor = RelationExtractor()
relationships = relation_extractor.extract(normalized_docs, entities=entities)

print(f"\nExtracted {len(relationships)} relationships")
for rel in relationships[:5]:
    print(f"  {rel.subject.text} --[{rel.predicate}]--> {rel.object.text}")

Configuration Options:

Option	Type	Default	Description
method	str	"ml"	Extraction method (rule/ml/llm)
model	str	None	Model name for ML/LLM methods
confidence_threshold	float	0.5	Minimum confidence score
custom_entity_types	List[str]	[]	Custom entity types

API Reference: Semantic Extract Module

5. Knowledge Graph (KG) Module

!!! abstract "Purpose" The kg module constructs the graph from extracted entities and relationships, handling complex tasks like resolution and analysis.

Key Features:

• Graph construction from entities/relationships
• Multiple backend support (NetworkX, Neo4j, KuzuDB)
• Temporal graph support
• Graph analytics and metrics
• Conflict detection and resolution
• Entity deduplication
• Community detection
• Centrality calculations

Input/Output Specification:

Input	Type	Description
entities	List[Entity]	Extracted entities
relationships	List[Relationship]	Extracted relationships
backend	str	Graph backend (networkx/neo4j/kuzu)

Output	Type	Description
knowledge_graph	KnowledgeGraph	Constructed knowledge graph
metrics	Dict	Graph metrics (density, centrality, etc.)

Components:

• GraphBuilder: Construct knowledge graphs from entities/relationships
• GraphAnalyzer: Analyze graph structure and properties
• GraphValidator: Validate graph quality and consistency
• EntityResolver: Resolve entity conflicts and duplicates
• ConflictDetector: Detect conflicting information
• CentralityCalculator: Calculate node importance metrics
• CommunityDetector: Detect communities in graphs
• ConnectivityAnalyzer: Analyze graph connectivity
• TemporalQuery: Query temporal knowledge graphs
• Deduplicator: Remove duplicate entities/relationships

Complete Code Example:

from semantica.kg import GraphBuilder, GraphAnalyzer

# Build graph
builder = GraphBuilder(backend="networkx", temporal=True)
kg = builder.build(entities, relationships)  # (1)

# Analyze graph
analyzer = GraphAnalyzer()
metrics = analyzer.analyze(kg)  # (2)

print(f"Nodes: {metrics['nodes']}")
print(f"Edges: {metrics['edges']}")
print(f"Density: {metrics['density']:.3f}")
print(f"Communities: {metrics['communities']}")

1. Constructs a NetworkX or Neo4j graph from the extracted entities and relationships, handling node merging and edge attributes.
2. Computes graph-theoretic metrics like density, diameter, and centrality to assess the quality and structure of the knowledge graph.

Configuration Options:

Option	Type	Default	Description
backend	str	"networkx"	Graph backend (networkx/neo4j/kuzu)
temporal	bool	False	Enable temporal graph support
merge_duplicates	bool	True	Automatically merge duplicate entities

API Reference: Knowledge Graph Module

9. Reasoning Module

!!! abstract "Purpose" Goes beyond simple retrieval to infer new facts and validate existing knowledge using logical rules.

Key Features:

• Forward and backward chaining
• Rule-based inference
• Deductive and abductive reasoning
• Explanation generation
• RETE algorithm support
• Custom rule definition

Input/Output Specification:

Input	Type	Description
knowledge_graph	KnowledgeGraph	Input knowledge graph
rules	List[Rule]	Inference rules
method	str	Reasoning method

Output	Type	Description
new_facts	List[Fact]	Inferred facts
explanations	List[Explanation]	Reasoning explanations

Components:

• InferenceEngine: Main inference orchestrator
• RuleManager: Manage inference rules
• DeductiveReasoner: Deductive reasoning
• AbductiveReasoner: Abductive reasoning
• ExplanationGenerator: Generate explanations for inferences
• RETEEngine: RETE algorithm for rule matching

Complete Code Example:

from semantica.reasoning import InferenceEngine, RuleManager

inference_engine = InferenceEngine()
rule_manager = RuleManager()

# Define rules
rules = [
    "IF Person worksFor Company AND Company locatedIn City THEN Person livesIn City",
    "IF Person hasFriend Person2 AND Person2 hasFriend Person3 THEN Person knows Person3"
]

rule_manager.add_rules(rules)

# Perform inference
new_facts = inference_engine.forward_chain(kg, rule_manager)
print(f"Inferred {len(new_facts)} new facts")

# Get explanations
for fact in new_facts:
    explanation = inference_engine.explain(fact)
    print(f"{fact}: {explanation}")

Configuration Options:

Option	Type	Default	Description
method	str	"forward_chain"	Reasoning method
max_iterations	int	100	Maximum inference iterations

API Reference: Reasoning Module

10. Ontology Module

!!! abstract "Purpose" Defines the schema and structure of your knowledge domain, ensuring consistency and enabling interoperability.

Key Features:

• Automatic ontology generation (6-stage pipeline)
• OWL/RDF export
• Class and property inference
• Ontology validation
• Symbolic reasoning (HermiT, Pellet)
• Version management

Input/Output Specification:

Input	Type	Description
knowledge_graph	KnowledgeGraph	Input knowledge graph
base_uri	str	Base URI for ontology

Output	Type	Description
ontology	Ontology	Generated ontology
owl_content	str	OWL/Turtle format

Components:

• OntologyGenerator: Generate ontologies from knowledge graphs
• OntologyValidator: Validate ontology structure
• OWLGenerator: Generate OWL format ontologies
• PropertyGenerator: Generate ontology properties
• ClassInferrer: Infer ontology classes

Complete Code Example:

from semantica.ontology import OntologyGenerator

generator = OntologyGenerator(base_uri="https://example.org/ontology/")
ontology = generator.generate_from_graph(kg)

# Validate ontology
validator = generator.validate(ontology)
print(f"Valid: {validator.is_valid}")

# Export to OWL
owl_content = generator.export_owl(ontology, format="turtle")
print(f"Generated {len(owl_content)} lines of OWL")

Configuration Options:

Option	Type	Default	Description
base_uri	str	None	Base URI for ontology
reasoner	str	"hermit"	Reasoner (hermit/pellet)

API Reference: Ontology Module

---

Storage & Embeddings Layer

These modules handle the persistence and retrieval of data, both as vectors and as graphs.

6. Embeddings Module

!!! abstract "Purpose" Embeddings are crucial for semantic search. This module generates vectors for text, images, and graph nodes.

Key Features:

• Multiple provider support (OpenAI, Cohere, HuggingFace)
• Text, image, and audio embeddings
• Multimodal embeddings
• Batch processing
• Caching support
• Custom models

Input/Output Specification:

Input	Type	Description
texts	List[str]	Text to embed
provider	str	Embedding provider (openai/cohere/hf)
model	str	Model name

Output	Type	Description
embeddings	np.ndarray	Array of embedding vectors
metadata	Dict	Embedding metadata

Components:

• EmbeddingGenerator: Main embedding orchestrator
• TextEmbedder: Generate text embeddings
• ImageEmbedder: Generate image embeddings
• AudioEmbedder: Generate audio embeddings
• MultimodalEmbedder: Combine multiple modalities
• EmbeddingOptimizer: Optimize embedding quality
• ProviderAdapters: Support for OpenAI, Cohere, etc.

Complete Code Example:

from semantica.embeddings import EmbeddingGenerator

generator = EmbeddingGenerator(
    provider="openai",
    model="text-embedding-3-small"
)
embeddings = generator.generate(documents)

# Calculate similarity
similarity = generator.similarity(embeddings[0], embeddings[1])
print(f"Similarity: {similarity:.3f}")

Configuration Options:

Option	Type	Default	Description
provider	str	"openai"	Embedding provider
model	str	None	Model name
batch_size	int	100	Batch size for processing
cache	bool	True	Enable caching

API Reference: Embeddings Module

7. Vector Store Module

!!! abstract "Purpose" Manages the storage and retrieval of high-dimensional vectors, supporting hybrid search strategies.

Key Features:

• Multiple backend support (FAISS, Pinecone, Weaviate)
• Hybrid search (vector + keyword)
• Metadata filtering
• Batch operations
• Similarity search
• Index management

Input/Output Specification:

Input	Type	Description
embeddings	np.ndarray	Vector embeddings
documents	List[Document]	Associated documents
metadata	Dict	Metadata for filtering

Output	Type	Description
results	List[SearchResult]	Search results with scores

Components:

• VectorStore: Main vector store interface
• FAISSAdapter: FAISS integration
• HybridSearch: Combine vector and keyword search
• VectorRetriever: Retrieve relevant vectors

Complete Code Example:

from semantica.vector_store import VectorStore, HybridSearch

vector_store = VectorStore(backend="faiss")
vector_store.store(embeddings, documents, metadata)

# Hybrid search
hybrid_search = HybridSearch(vector_store)
results = hybrid_search.search(
    query="machine learning",
    top_k=10,
    filters={"category": "AI"}
)

for result in results:
    print(f"Score: {result.score:.3f} - {result.document.content[:50]}...")

Configuration Options:

Option	Type	Default	Description
backend	str	"faiss"	Vector store backend
index_type	str	"flat"	FAISS index type
hybrid_search	bool	True	Enable hybrid search

API Reference: Vector Store Module

8. Graph Store Module

!!! abstract "Purpose" The graph_store module provides integration with property graph databases like Neo4j, KuzuDB, and FalkorDB for storing and querying knowledge graphs.

Key Features:

• Multiple backend support (Neo4j, KuzuDB, FalkorDB)
• Cypher query language
• Graph algorithms and analytics
• Transaction support
• Index management
• High-performance queries

Input/Output Specification:

Input	Type	Description
backend	str	Backend (neo4j/kuzu/falkordb)
connection_uri	str	Database connection URI
cypher_query	str	Cypher query string

Output	Type	Description
result	Dict	Query results
node_id	str	Created node ID

Components:

• GraphStore: Main graph store interface
• Neo4jAdapter: Neo4j database integration
• KuzuAdapter: KuzuDB embedded database integration
• FalkorDBAdapter: FalkorDB (Redis-based) integration
• NodeManager: Node CRUD operations
• RelationshipManager: Relationship CRUD operations
• QueryEngine: Cypher query execution
• GraphAnalytics: Graph algorithms and analytics

Complete Code Example:

from semantica.graph_store import GraphStore, create_node, create_relationship

# Using GraphStore class
store = GraphStore(backend="neo4j", uri="bolt://localhost:7687")
store.connect()

# Create nodes
alice = store.create_node(["Person"], {"name": "Alice", "age": 30})
bob = store.create_node(["Person"], {"name": "Bob", "age": 25})

# Create relationship
store.create_relationship(alice["id"], bob["id"], "KNOWS", {"since": 2020})

# Query with Cypher
results = store.execute_query("MATCH (p:Person) RETURN p.name")
print(f"Found {len(results)} people")

# Graph analytics
path = store.shortest_path(alice["id"], bob["id"])
print(f"Shortest path: {path}")

# Or use convenience functions
node = create_node(labels=["Entity"], properties={"name": "Test"})

Configuration Options:

Option	Type	Default	Description
backend	str	"neo4j"	Graph database backend
uri	str	None	Database connection URI
database	str	"neo4j"	Database name

API Reference: Graph Store Module

---

Output Layer

These modules handle the export and visualization of your data.

11. Export Module

!!! abstract "Purpose" Allows you to take your knowledge graph and data out of Semantica for use in other tools.

Key Features:

• Multiple export formats (JSON, RDF, CSV, OWL, GraphML)
• Custom export formats
• Batch export
• Metadata preservation
• Streaming export for large graphs

Input/Output Specification:

Input	Type	Description
knowledge_graph	KnowledgeGraph	Graph to export
format	str	Export format
output_path	str	Output file path

Output	Type	Description
exported_file	str	Path to exported file
metadata	Dict	Export metadata

Components:

• JSONExporter: Export to JSON
• RDFExporter: Export to RDF/XML
• CSVExporter: Export to CSV
• GraphExporter: Export to graph formats (GraphML, GEXF)
• OWLExporter: Export to OWL
• VectorExporter: Export vectors

Complete Code Example:

from semantica.export import JSONExporter, RDFExporter, CSVExporter

# Export to JSON
json_exporter = JSONExporter()
json_exporter.export(kg, "output.json")

# Export to RDF
rdf_exporter = RDFExporter()
rdf_exporter.export(kg, "output.rdf")

# Export to CSV
csv_exporter = CSVExporter()
csv_exporter.export(kg, "output.csv")

Configuration Options:

Option	Type	Default	Description
format	str	"json"	Export format
include_metadata	bool	True	Include metadata
pretty_print	bool	False	Pretty print JSON

API Reference: Export Module

12. Visualization Module

!!! abstract "Purpose" Provides tools to visually explore your data, making it easier to understand complex relationships.

Key Features:

• Interactive graph visualization
• Embedding visualization (t-SNE, PCA, UMAP)
• Quality metrics visualization
• Temporal data visualization
• Multiple output formats (HTML, PNG, SVG)
• Custom styling

Input/Output Specification:

Input	Type	Description
knowledge_graph	KnowledgeGraph	Graph to visualize
output_format	str	Output format (html/png/svg)
output_path	str	Output file path

Output	Type	Description
visualization_file	str	Path to visualization file

Components:

• KGVisualizer: Visualize knowledge graphs
• EmbeddingVisualizer: Visualize embeddings (t-SNE, PCA, UMAP)
• QualityVisualizer: Visualize quality metrics
• AnalyticsVisualizer: Visualize graph analytics
• TemporalVisualizer: Visualize temporal data

Complete Code Example:

from semantica.visualization import KGVisualizer, EmbeddingVisualizer

# Visualize knowledge graph
kg_visualizer = KGVisualizer()
kg_visualizer.visualize(
    kg,
    output_format="html",
    output_path="graph.html"
)

# Visualize embeddings
embed_visualizer = EmbeddingVisualizer()
embed_visualizer.visualize(
    embeddings,
    method="tsne",
    output_path="embeddings.png"
)

Configuration Options:

Option	Type	Default	Description
output_format	str	"html"	Output format
layout	str	"force"	Graph layout algorithm
max_nodes	int	1000	Maximum nodes to visualize

API Reference: Visualization Module

---

Orchestration

This layer connects all other modules into a cohesive workflow.

13. Pipeline Module

!!! abstract "Purpose" Orchestrates the entire flow, connecting modules together into robust, executable workflows.

Key Features:

• Pipeline construction DSL
• Parallel execution
• Error handling and recovery
• Resource scheduling
• Pipeline validation
• Monitoring and logging

Input/Output Specification:

Input	Type	Description
steps	List[PipelineStep]	Pipeline steps
parallel	bool	Enable parallel execution

Output	Type	Description
result	PipelineResult	Execution result
metrics	Dict	Performance metrics

Components:

• PipelineBuilder: Build complex pipelines
• ExecutionEngine: Execute pipelines
• FailureHandler: Handle pipeline failures
• ParallelismManager: Enable parallel processing
• ResourceScheduler: Schedule resources

Complete Code Example:

from semantica.pipeline import PipelineBuilder
from semantica.ingest import FileIngestor
from semantica.parse import DocumentParser
from semantica.semantic_extract import NERExtractor

builder = PipelineBuilder()
pipeline = builder \
    .add_step("ingest", FileIngestor()) \
    .add_step("parse", DocumentParser()) \
    .add_step("extract", NERExtractor()) \
    .build()

# Execute pipeline
result = pipeline.execute(sources=["data/"], parallel=True)
print(f"Processed {len(result.documents)} documents")

Configuration Options:

Option	Type	Default	Description
parallel	bool	False	Enable parallel execution
max_workers	int	4	Maximum parallel workers
retry_on_failure	bool	True	Retry failed steps

API Reference: Pipeline Module

---

Integration Patterns

This section shows common patterns for integrating multiple modules together.

Pattern 1: Complete Knowledge Graph Pipeline

from semantica import Semantica

# High-level API - all modules integrated
semantica = Semantica()
result = semantica.build_knowledge_base(
    sources=["documents/"],
    embeddings=True,
    graph=True,
    normalize=True
)

Pattern 2: Custom Pipeline with Module Selection

from semantica.ingest import FileIngestor
from semantica.parse import DocumentParser
from semantica.normalize import TextNormalizer
from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder
from semantica.embeddings import EmbeddingGenerator
from semantica.vector_store import VectorStore

# Step-by-step pipeline
ingestor = FileIngestor()
documents = ingestor.ingest("data/")

parser = DocumentParser()
parsed = parser.parse(documents)

normalizer = TextNormalizer()
normalized = normalizer.normalize(parsed)

extractor = NERExtractor()
entities = extractor.extract(normalized)

rel_extractor = RelationExtractor()
relationships = rel_extractor.extract(normalized, entities)

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

generator = EmbeddingGenerator()
embeddings = generator.generate(normalized)

vector_store = VectorStore()
vector_store.store(embeddings, normalized)

Pattern 3: GraphRAG Integration

from semantica import Semantica
from semantica.kg import GraphBuilder
from semantica.vector_store import VectorStore, HybridSearch

# Build knowledge base
semantica = Semantica()
result = semantica.build_knowledge_base(["documents/"])

# Set up GraphRAG
kg = result["knowledge_graph"]
embeddings = result["embeddings"]
vector_store = VectorStore()
vector_store.store(embeddings, result["documents"])

# Query with GraphRAG
hybrid_search = HybridSearch(vector_store)
results = hybrid_search.search(
    query="What is the relationship between X and Y?",
    graph=kg,
    top_k=10
)

Pattern 4: Temporal Graph with Reasoning

from semantica.kg import GraphBuilder
from semantica.reasoning import InferenceEngine, RuleManager

# Build temporal graph
builder = GraphBuilder(temporal=True)
kg = builder.build(entities, relationships)

# Add reasoning
inference_engine = InferenceEngine()
rule_manager = RuleManager()
rules = ["IF A THEN B"]
rule_manager.add_rules(rules)

new_facts = inference_engine.forward_chain(kg, rule_manager)

---

Performance Considerations

Module Performance Characteristics

Module	Typical Speed	Memory Usage	Scalability	Optimization Tips
Ingest	Fast	Low	High	Batch processing, parallel I/O
Parse	Medium	Medium	Medium	OCR only when needed, cache results
Normalize	Fast	Low	High	Batch processing
Semantic Extract	Slow (LLM)	Medium	Medium	Use faster models, batch processing
KG	Fast	High (large graphs)	Medium	Use graph stores for large graphs
Embeddings	Slow (API)	Low	High	Batch processing, caching
Vector Store	Fast	Medium	High	Use FAISS for large datasets
Graph Store	Fast	Low	High	Index optimization, query tuning
Reasoning	Slow	Medium	Low	Limit iterations, optimize rules
Ontology	Medium	Medium	Medium	Cache validation results
Export	Fast	Low	High	Streaming for large graphs
Visualization	Slow	High	Low	Limit nodes, use sampling
Pipeline	Varies	Varies	High	Parallel execution, resource limits

Optimization Strategies

1. Batch Processing: Process multiple documents together

   # Good: Batch processing
   documents = ingestor.ingest("data/", batch_size=100)
   

2. Caching: Cache expensive operations

   # Cache embeddings
   generator = EmbeddingGenerator(cache=True)
   

3. Parallel Execution: Use pipeline parallelism

   pipeline = builder.build()
   result = pipeline.execute(sources=sources, parallel=True, max_workers=8)
   

4. Backend Selection: Choose appropriate backends

   # For large graphs, use Neo4j instead of NetworkX
   builder = GraphBuilder(backend="neo4j")
   

5. Resource Limits: Set appropriate limits

   # Limit memory usage
   parser = DocumentParser(max_file_size=10_000_000)  # 10MB
   

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Next Steps

• Core Concepts - Understand the fundamental concepts
• Use Cases - See real-world applications
• Examples - Practical code examples
• API Reference - Detailed API documentation

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Last Updated: 2024