Modules & Architecture¶

Semantica is built with a modular architecture, designed to be flexible, extensible, and scalable. This guide provides a comprehensive overview of all modules, their responsibilities, key features, and components.

Module Overview¶

Semantica's modules are organized into six logical layers:

Input Layer¶

These modules handle data ingestion, parsing, chunking, and preparation.

Ingest Module¶

Key Features:

• Multiple file format support (PDF, DOCX, HTML, JSON, CSV, etc.)
• Web scraping with JavaScript rendering
• Database integration (SQL, NoSQL)
• Real-time streaming support
• MCP (Model Context Protocol) server integration
• Batch processing capabilities
• Metadata extraction and preservation

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 MCP servers for resource and tool-based ingestion

Try It:

• Data Ingestion Cookbook: Learn to ingest from multiple sources
• Topics: File, web, feed, stream, database ingestion
• Difficulty: Beginner
• Time: 15-20 minutes
• Use Cases: Loading data from various sources

API Reference: Ingest Module

Parse Module¶

Key Features:

• Multiple 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)
• Code file parsing with syntax awareness

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

Try It:

• Document Parsing Cookbook: Learn to parse various document formats
• Topics: PDF, DOCX, HTML, JSON parsing, OCR, table extraction
• Difficulty: Beginner
• Time: 15-20 minutes
• Use Cases: Extracting text from different file formats

API Reference: Parse Module

Split Module¶

Key Features:

• Multiple standard splitting methods (recursive, token, sentence, paragraph)
• Semantic-based chunking using NLP and embeddings
• Entity-aware chunking for GraphRAG workflows
• Relation-aware chunking for KG preservation
• Graph-based and ontology-aware chunking
• Hierarchical multi-level chunking
• Community detection-based splitting
• Sliding window chunking with overlap
• Table-specific chunking
• Chunk validation and quality assessment
• Provenance tracking for data lineage

Components:

• TextSplitter — Unified text splitter with method parameter
• SemanticChunker — Semantic-based chunking coordinator
• StructuralChunker — Structure-aware chunking (headings, lists)
• SlidingWindowChunker — Fixed-size sliding window chunking
• TableChunker — Table-specific chunking
• EntityAwareChunker — Entity boundary-preserving chunker
• RelationAwareChunker — Triplet-preserving chunker
• GraphBasedChunker — Graph structure-based chunker
• OntologyAwareChunker — Ontology concept-based chunker
• HierarchicalChunker — Multi-level hierarchical chunker
• ChunkValidator — Chunk quality validation
• ProvenanceTracker — Chunk provenance tracking

Supported Methods:

Try It:

• Text Splitting Cookbook: Learn different splitting methods
• Topics: Recursive, token, sentence splitting, entity-aware chunking
• Difficulty: Beginner
• Time: 15-20 minutes
• Use Cases: Document chunking for processing

Normalize Module¶

Key Features:

• Text cleaning and noise removal
• Encoding normalization (Unicode handling)
• Entity name standardization
• Date and number formatting
• Language detection
• Whitespace normalization
• Special character handling

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

Try It:

• Data Normalization Cookbook: Learn text normalization
• Topics: Text cleaning, encoding normalization, entity standardization
• Difficulty: Beginner
• Time: 15-20 minutes
• Use Cases: Preparing text for processing

API Reference: Normalize Module

Core Processing Layer¶

These modules form the intelligence core—extracting meaning, building relationships, and inferring knowledge.

Semantic Extract Module¶

Key Features:

• Multiple NER methods (rule-based, ML, LLM)
• Relationship extraction with confidence scoring
• Event extraction
• Custom entity type support
• Multi-language support
• Semantic network extraction
• Coreference resolution

Components:

• NERExtractor — Named Entity Recognition
• RelationExtractor — Extract relationships between entities
• SemanticAnalyzer — Deep semantic analysis
• SemanticNetworkExtractor — Extract semantic networks
• EventExtractor — Extract events from text
• CoreferenceResolver — Resolve entity coreferences

Try It:

• Entity Extraction Cookbook: Learn entity extraction
• Topics: Named entity recognition, entity types, extraction methods
• Difficulty: Beginner
• Time: 15-20 minutes

• Use Cases: Understanding entity extraction

• Relation Extraction Cookbook: Learn relationship extraction

• Topics: Relationship extraction, dependency parsing, semantic role labeling
• Difficulty: Beginner
• Time: 15-20 minutes
• Use Cases: Building rich knowledge graphs

Quick Example:

from semantica.semantic_extract import NERExtractor, RelationExtractor

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

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

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

API Reference: Semantic Extract Module

Knowledge Graph (KG) Module¶

Key Features:

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

Components:

• GraphBuilder — Construct knowledge graphs
• GraphAnalyzer — Analyze graph structure and properties
• EntityResolver — Resolve entity conflicts and duplicates
• ConflictDetector — Detect conflicting information
• CentralityCalculator — Calculate node importance metrics
• CommunityDetector — Detect community structure
• ConnectivityAnalyzer — Analyze graph connectivity
• SeedManager — Manage seed data for KG initialization
• TemporalQuery — Query temporal knowledge graphs
• Deduplicator — Remove duplicate entities/relationships

Quick Example:

from semantica.kg import GraphBuilder, GraphAnalyzer

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

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

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

API Reference: Knowledge Graph Module

Ontology Module¶

Key Features:

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

Components:

• OntologyGenerator — Generate ontologies from knowledge graphs
• OntologyValidator — Validate ontologies
• OWLGenerator — Generate OWL format ontologies
• PropertyGenerator — Generate ontology properties
• ClassInferrer — Infer ontology classes
• OntologyMerger — Merge multiple ontologies
• ReasonerInterface — Interface with symbolic reasoners

Quick Example:

from semantica.ontology import OntologyEngine

# Initialize engine
engine = OntologyEngine(base_uri="https://example.org/ontology/")

# Generate ontology from data
ontology = engine.from_data({
    "entities": [...],
    "relationships": [...]
})

# Validate ontology
result = engine.validate(ontology)
if result.valid:
    print("Ontology is valid!")

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

API Reference: Ontology Module

Reasoning Module¶

Key Features:

• Forward-chaining inference engine
• IF-THEN rule support with variable substitution
• High-performance Rete algorithm for large-scale rule matching
• Natural language explanation generation for inferred facts
• SPARQL query expansion for RDF graphs
• Conflict detection in inferences
• Priority-based rule execution

Components:

• Reasoner — High-level facade for all reasoning tasks
• ReteEngine — High-performance pattern matching (Rete algorithm)
• ExplanationGenerator — Generate justifications for inferred facts
• SPARQLReasoner — Query expansion for triplet stores

Quick Example:

from semantica.reasoning import Reasoner

reasoner = Reasoner()

# Add rules and facts
reasoner.add_rule("IF Person(?x) AND Parent(?x, ?y) THEN ParentOfPerson(?x, ?y)")
reasoner.add_fact("Person(Alice)")
reasoner.add_fact("Parent(Alice, Bob)")

# Perform inference
inferred = reasoner.infer_facts(["Person(Alice)", "Parent(Alice, Bob)"])
# Inferred: ["ParentOfPerson(Alice, Bob)"]

API Reference: Reasoning Module

Storage Layer¶

These modules handle persistence and retrieval of vectors, graphs, and triplets.

Embeddings Module¶

Key Features:

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

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
• ProviderStores — Support for OpenAI, Cohere, etc.

Quick 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}")

API Reference: Embeddings Module

Vector Store Module¶

Key Features:

• Multiple backend support (FAISS, Weaviate, Qdrant, Milvus)
• Hybrid search (vector + keyword)
• Metadata filtering
• Batch operations
• Similarity search with scoring
• Index management
• Namespace support

Components:

• VectorStore — Main vector store interface
• FAISSStore — FAISS integration
• WeaviateStore — Weaviate integration
• HybridSearch — Combine vector and keyword search
• VectorRetriever — Retrieve relevant vectors

Quick 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"}
)

API Reference: Vector Store Module

Graph Store Module¶

Key Features:

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

Components:

• GraphStore — Main graph store interface
• Neo4jStore — Neo4j database integration
• FalkorDBStore — FalkorDB (Redis-based) integration
• NodeManager — Node CRUD operations
• RelationshipManager — Relationship CRUD operations
• QueryEngine — Cypher query execution
• GraphAnalytics — Graph algorithms and analytics

Quick Example:

from semantica.graph_store import GraphStore

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

# Create nodes and relationships
alice = store.create_node(
    labels=["Person"],
    properties={"name": "Alice", "age": 30}
)
bob = store.create_node(
    labels=["Person"],
    properties={"name": "Bob", "age": 25}
)
store.create_relationship(
    start_node_id=alice["id"],
    end_node_id=bob["id"],
    rel_type="KNOWS",
    properties={"since": 2020}
)

# Query with Cypher
results = store.execute_query("MATCH (p:Person) RETURN p.name")

API Reference: Graph Store Module

Triplet Store Module¶

Key Features:

• Multi-backend support (Blazegraph, Jena, RDF4J)
• CRUD operations for RDF triplets
• SPARQL query execution and optimization
• Bulk data loading with progress tracking
• Query caching and optimization
• Transaction support
• Store backend pattern

Components:

• TripletStore — Main triplet store interface
• QueryEngine — SPARQL query execution and optimization
• BulkLoader — High-volume data loading with progress tracking
• BlazegraphStore — Blazegraph integration
• JenaStore — Apache Jena integration
• RDF4JStore — Eclipse RDF4J integration
• QueryPlan — Query execution plan dataclass
• LoadProgress — Bulk loading progress tracking

Algorithms:

Quick Example:

from semantica.triplet_store import TripletStore

store = TripletStore(backend="blazegraph", endpoint="http://localhost:9999/blazegraph")

# Add triplet
result = store.add_triplet({
    "subject": "http://example.org/Alice",
    "predicate": "http://example.org/knows",
    "object": "http://example.org/Bob"
})

# Execute SPARQL
query_result = store.execute_query("SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10")

API Reference: Triplet Store Module

Quality Assurance Layer¶

These modules ensure data quality, handle duplicates, and resolve conflicts.

Deduplication Module¶

Key Features:

• Multiple similarity methods (exact, Levenshtein, Jaro-Winkler, cosine, embedding)
• Advanced String Matching: Jaro-Winkler by default for better company/person name resolution
• Smart Property Handling: Neutral scoring for disjoint properties to prevent false negatives
• Duplicate detection with confidence scoring
• Entity merging with configurable strategies
• Cluster-based batch deduplication
• Provenance preservation during merges
• Relationship preservation
• Incremental processing support

Components:

• DuplicateDetector — Detects duplicate entities using similarity metrics
• EntityMerger — Merges duplicate entities using configurable strategies
• SimilarityCalculator — Multi-factor similarity between entities
• MergeStrategyManager — Manages merge strategies and conflict resolution
• ClusterBuilder — Builds clusters for batch deduplication

Merge Strategies:

Quick Example:

from semantica.deduplication import DuplicateDetector, EntityMerger

# Detect duplicates
detector = DuplicateDetector(similarity_threshold=0.8)
duplicate_groups = detector.detect_duplicate_groups(entities)

# Merge duplicates
merger = EntityMerger(preserve_provenance=True)
merge_operations = merger.merge_duplicates(
    entities,
    strategy="keep_most_complete"
)

merged_entities = [op.merged_entity for op in merge_operations]
print(f"Reduced from {len(entities)} to {len(merged_entities)} entities")

Conflicts Module¶

Key Features:

• Multi-source conflict detection (value, type, relationship, temporal, logical)
• Source tracking and provenance management
• Conflict analysis and pattern identification
• Multiple resolution strategies (voting, credibility-weighted, recency)
• Investigation guide generation
• Source credibility scoring
• Conflict reporting and statistics

Components:

• ConflictDetector — Detects conflicts from multiple sources
• ConflictResolver — Resolves conflicts using various strategies
• ConflictAnalyzer — Analyzes conflict patterns and trends
• SourceTracker — Tracks source information and provenance
• InvestigationGuideGenerator — Generates investigation guides

Resolution Strategies:

Quick Example:

from semantica.conflicts import ConflictDetector, ConflictResolver

detector = ConflictDetector()
conflicts = detector.detect_value_conflicts(entities, "name")

resolver = ConflictResolver()
results = resolver.resolve_conflicts(conflicts, strategy="voting")

KG Quality Assurance Module¶

Key Features:

• Quality metrics calculation (overall, completeness, consistency)
• Consistency checking (logical, temporal, hierarchical)
• Completeness validation (entity, relationship, property)
• Automated fixes (duplicates, inconsistencies, missing properties)
• Quality reporting with issue tracking
• Validation engine with rules and constraints
• Improvement suggestions

Components:

• KGQualityAssessor — Overall quality assessment coordinator
• ConsistencyChecker — Consistency validation engine
• CompletenessValidator — Completeness validation engine
• QualityMetrics — Quality metrics calculator
• ValidationEngine — Rule and constraint validation
• RuleValidator — Rule-based validation
• ConstraintValidator — Constraint-based validation
• QualityReporter — Quality report generation
• IssueTracker — Issue tracking and management
• ImprovementSuggestions — Improvement suggestions generator
• AutomatedFixer — Automated issue fixing
• AutoMerger — Automatic merging of duplicates
• AutoResolver — Automatic conflict resolution

Note: The KG quality assessment module has been temporarily removed and will be reintroduced in a future release.

Context & Memory Layer¶

These modules provide context engineering for agents and foundation data management.

Context Module¶

Key Features:

• Context graph construction from entities, relationships, and conversations
• Agent memory management with RAG integration
• Entity linking across sources with URI assignment
• Hybrid context retrieval (vector + graph + memory)
• Multi-hop reasoning through knowledge graphs
• LLM-generated responses grounded in graph context
• Reasoning trace showing entity relationship paths
• Conversation history management
• Context accumulation and synthesis
• Graph-based context traversal

Components:

• ContextGraph — In-memory context graph store and builder methods
• ContextNode — Context graph node data structure
• ContextEdge — Context graph edge data structure
• AgentMemory — Manages persistent agent memory with RAG
• AgentContext — High-level context interface with GraphRAG capabilities
• ContextRetriever — Retrieves relevant context with multi-hop reasoning
• MemoryItem — Memory item data structure
• EntityLinker — Links entities across sources with URI assignment

Algorithms:

Quick Example:

from semantica.context import AgentContext, ContextGraph, AgentMemory
from semantica.llms import Groq
from semantica.vector_store import VectorStore
import os

# Using AgentContext with GraphRAG reasoning
context = AgentContext(
    vector_store=VectorStore(backend="faiss"),
    knowledge_graph=kg
)

# Configure LLM provider
llm_provider = Groq(
    model="llama-3.1-8b-instant",
    api_key=os.getenv("GROQ_API_KEY")
)

# Query with multi-hop reasoning and LLM-generated response
result = context.query_with_reasoning(
    query="What IPs are associated with security alerts?",
    llm_provider=llm_provider,
    max_results=10,
    max_hops=2
)

print(f"Response: {result['response']}")
print(f"Reasoning Path: {result['reasoning_path']}")
print(f"Confidence: {result['confidence']:.3f}")

# Traditional context graph and memory
graph = ContextGraph()
graph_data = graph.build_from_entities_and_relationships(entities, relationships)

memory = AgentMemory(vector_store=vs, knowledge_graph=kg)
memory_id = memory.store("User asked about Python", metadata={"type": "conversation"})
results = memory.retrieve("Python", max_results=5)

API Reference: Context Module

LLM Providers Module¶

Key Features:

• Unified Interface: Same generate() and generate_structured() methods across all providers
• Multiple Providers: Groq, OpenAI, HuggingFace, and LiteLLM (100+ LLMs)
• Clean Imports: Simple from semantica.llms import Groq, OpenAI, HuggingFaceLLM, LiteLLM
• Structured Output: JSON generation support
• API Key Management: Environment variable and direct key support
• Error Handling: Graceful fallback when providers unavailable

Components:

• Groq — Groq API provider for fast inference
• OpenAI — OpenAI API provider (GPT-3.5, GPT-4, etc.)
• HuggingFaceLLM — HuggingFace Transformers for local LLM inference
• LiteLLM — Unified interface to 100+ LLM providers (OpenAI, Anthropic, Azure, Bedrock, Vertex AI, etc.)

Supported Providers via LiteLLM:

• OpenAI, Anthropic, Groq, Azure, Bedrock, Vertex AI, Cohere, Mistral, and 90+ more

Quick Example:

from semantica.llms import Groq, OpenAI, HuggingFaceLLM, LiteLLM
import os

# Groq - Fast inference
groq = Groq(
    model="llama-3.1-8b-instant",
    api_key=os.getenv("GROQ_API_KEY")
)
response = groq.generate("What is AI?")

# OpenAI
openai = OpenAI(
    model="gpt-4",
    api_key=os.getenv("OPENAI_API_KEY")
)
response = openai.generate("What is AI?")

# HuggingFace - Local models
hf = HuggingFaceLLM(model_name="gpt2")  # or model="gpt2" for consistency
response = hf.generate("What is AI?")

# LiteLLM - Unified interface to 100+ LLMs
litellm = LiteLLM(
    model="openai/gpt-4o",  # or "anthropic/claude-sonnet-4-20250514", etc.
    api_key=os.getenv("OPENAI_API_KEY")
)
response = litellm.generate("What is AI?")

# Structured output
structured = groq.generate_structured("Extract entities from: Apple Inc. was founded by Steve Jobs.")

API Reference: LLM Providers Module

Seed Module¶

Key Features:

• Multi-source seed data loading (CSV, JSON, Database, API)
• Foundation graph creation from seed data
• Seed data quality validation
• Integration with extracted data using configurable merge strategies
• Version management for seed sources
• Export capabilities (JSON, CSV)
• Schema template validation

Components:

• SeedDataManager — Main coordinator for seed data operations
• SeedDataSource — Seed data source definition
• SeedData — Seed data container

Merge Strategies:

Quick Example:

from semantica.seed import SeedDataManager

manager = SeedDataManager()
manager.register_source("entities", "json", "data/entities.json")
foundation = manager.create_foundation_graph()
validation = manager.validate_quality(foundation)

Output & Orchestration Layer¶

These modules handle export, visualization, and workflow management.

Export Module¶

Key Features:

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

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

Quick Example:

from semantica.export import JSONExporter, RDFExporter, CSVExporter

# Export to multiple formats
JSONExporter().export(kg, "output.json")
RDFExporter().export(kg, "output.rdf")
CSVExporter().export(kg, "output.csv")

API Reference: Export Module

Visualization Module¶

Key Features:

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

Components:

• KGVisualizer — Visualize knowledge graphs
• EmbeddingVisualizer — Visualize embeddings (t-SNE, PCA, UMAP)
• AnalyticsVisualizer — Visualize graph analytics
• TemporalVisualizer — Visualize temporal data
• OntologyVisualizer — Visualize ontology structure
• SemanticNetworkVisualizer — Visualize semantic networks

Quick Example:

from semantica.visualization import KGVisualizer, EmbeddingVisualizer

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

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

API Reference: Visualization Module

Pipeline Module¶

Key Features:

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

Components:

• PipelineBuilder — Build complex pipelines
• ExecutionEngine — Execute pipelines
• FailureHandler — Handle pipeline failures
• ParallelismManager — Enable parallel processing
• ResourceScheduler — Schedule resources
• PipelineValidator — Validate pipeline configuration

Quick 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()

result = pipeline.execute(sources=["data/"], parallel=True)

API Reference: Pipeline Module

Integration Patterns¶

Pattern 1: Complete Knowledge Graph Pipeline¶

Build a complete knowledge graph from documents using the full pipeline.

For complete examples, see: - Your First Knowledge Graph Cookbook: Complete pipeline walkthrough - Topics: Ingestion, parsing, extraction, graph building, embeddings - Difficulty: Beginner - Time: 20-30 minutes - Use Cases: Learning the complete workflow

Pattern 2: Custom Pipeline with Module Selection¶

Build custom pipelines with specific module selections and quality assurance.

For examples, see: - Building Knowledge Graphs Cookbook: Advanced graph construction - Topics: Custom pipelines, entity merging, conflict resolution - Difficulty: Intermediate - Time: 30-45 minutes - Use Cases: Production graph construction

Pattern 3: GraphRAG with Hybrid Search¶

Build GraphRAG systems with hybrid search combining vector and graph retrieval.

For complete examples, see: - GraphRAG Complete Cookbook: Production GraphRAG system - Topics: GraphRAG, hybrid retrieval, graph traversal, LLM integration - Difficulty: Advanced - Time: 1-2 hours - Use Cases: Production RAG applications

Pattern 4: Temporal Graph with Reasoning¶

Build temporal graphs with logical reasoning capabilities.

For examples, see: - Temporal Graphs Cookbook: Temporal graph construction - Topics: Time-stamped entities, temporal relationships, historical queries - Difficulty: Intermediate - Time: 30-45 minutes - Use Cases: Time-aware knowledge graphs

Quick Reference: All Modules¶

Next Steps¶

• Core Concepts — Understand the fundamental concepts
• Use Cases — See real-world applications
• Examples — Practical code examples
• Cookbook — Interactive Jupyter notebook tutorials
• API Reference — Detailed API documentation

🍳 Recommended Cookbooks¶

• Welcome to Semantica: Comprehensive introduction to all modules
• Topics: Framework overview, all modules, architecture
• Difficulty: Beginner
• Time: 30-45 minutes

• Use Cases: Understanding the complete framework

• Your First Knowledge Graph: Build your first knowledge graph

• Topics: Complete pipeline from ingestion to graph construction
• Difficulty: Beginner
• Time: 20-30 minutes
• Use Cases: Hands-on practice with all modules