semantica/docs/examples.md

Examples

Real-world examples and use cases for Semantica.

Basic Examples

Example 1: Basic Knowledge Graph

Build a knowledge graph from a single document:

from semantica import Semantica

semantica = Semantica()

# Build KG from PDF
result = semantica.build_knowledge_base(
    sources=["research_paper.pdf"],
    embeddings=True,
    graph=True
)

kg = result["knowledge_graph"]
print(f"Entities: {len(kg['entities'])}")
print(f"Relationships: {len(kg['relationships'])}")

Example 2: Entity Extraction

Extract entities from text:

from semantica import Semantica

semantica = Semantica()

text = """
Apple Inc. is a technology company founded by Steve Jobs.
The company is headquartered in Cupertino, California.
Tim Cook is the current CEO of Apple.
"""

entities = semantica.semantic_extract.extract_entities(text)
for entity in entities["entities"]:
    print(f"{entity['text']}: {entity['type']}")

Output:

Apple Inc.: ORGANIZATION
Steve Jobs: PERSON
Cupertino: LOCATION
California: LOCATION
Tim Cook: PERSON

Example 3: Multi-Source Integration

Combine data from multiple sources:

from semantica import Semantica

semantica = Semantica()

sources = [
    "documents/finance_report.pdf",
    "documents/market_analysis.docx",
    "https://example.com/news-article"
]

result = semantica.build_knowledge_base(sources)
kg = result["knowledge_graph"]

print(f"Unified knowledge graph with {len(kg['entities'])} entities")

Example 4: Export Formats

Export knowledge graph to multiple formats:

from semantica import Semantica

semantica = Semantica()
kg = semantica.kg.build_graph(["data.pdf"])

# Export to different formats
semantica.export.to_rdf(kg, "output.rdf")
semantica.export.to_json(kg, "output.json")
semantica.export.to_csv(kg, "output.csv")
semantica.export.to_owl(kg, "output.owl")

Advanced Examples

Example 5: Conflict Resolution

Resolve conflicts in data from multiple sources:

from semantica import Semantica
from semantica.conflicts import ConflictResolver

semantica = Semantica()

# Build graph from multiple sources
result = semantica.build_knowledge_base([
    "source1.pdf",
    "source2.pdf",
    "source3.pdf"
])

# Detect conflicts
conflicts = semantica.kg.detect_conflicts(result["knowledge_graph"])

# Resolve conflicts
resolver = ConflictResolver(default_strategy="voting")
resolved = resolver.resolve_conflicts(conflicts)

print(f"Resolved {len(resolved)} conflicts")

Example 6: Custom Configuration

Use custom configuration for specific use cases:

from semantica import Semantica, Config

# Custom configuration
config = Config(
    embeddings=True,
    graph=True,
    normalize=True,
    conflict_resolution="highest_confidence"
)

semantica = Semantica(config=config)
result = semantica.build_knowledge_base(["document.pdf"])

Example 7: Incremental Graph Building

Build knowledge graph incrementally:

from semantica import Semantica

semantica = Semantica()

# Build graphs separately
kg1 = semantica.kg.build_graph(["source1.pdf"])
kg2 = semantica.kg.build_graph(["source2.pdf"])
kg3 = semantica.kg.build_graph(["source3.pdf"])

# Merge into unified graph
merged_kg = semantica.kg.merge([kg1, kg2, kg3])

print(f"Merged graph: {len(merged_kg['entities'])} entities")

Example 8: Visualization

Create interactive visualizations:

from semantica import Semantica

semantica = Semantica()

# Build graph
result = semantica.build_knowledge_base(["document.pdf"])
kg = result["knowledge_graph"]

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

# Also analyze
analysis = semantica.kg.analyze(kg)
print(f"Graph density: {analysis['density']}")
print(f"Connected components: {analysis['components']}")

Example 9: Persistent Storage (Neo4j)

Difficulty: Intermediate

Store and query knowledge graphs in a persistent graph database.

from semantica.graph_store import GraphStore

# Initialize with Neo4j
store = GraphStore(
    backend="neo4j",
    uri="bolt://localhost:7687",
    user="neo4j",
    password="password"
)
store.connect()

# Create nodes and relationships
apple = store.create_node(
    labels=["Company"],
    properties={"name": "Apple Inc."}
)
tim = store.create_node(
    labels=["Person"],
    properties={"name": "Tim Cook"}
)
store.create_relationship(
    start_node_id=tim["id"],
    end_node_id=apple["id"],
    rel_type="CEO_OF"
)

store.close()

Example 10: FalkorDB for Real-Time Applications

Difficulty: Intermediate

Ultra-fast graph queries for LLM applications using FalkorDB.

from semantica.graph_store import GraphStore

store = GraphStore(
    backend="falkordb",
    host="localhost",
    port=6379,
    graph_name="knowledge_graph"
)
store.connect()

# Fast queries
results = store.execute_query("MATCH (n)-[r]->(m) WHERE n.name CONTAINS 'AI' RETURN n")
store.close()

Example 11: GraphRAG (Knowledge-Powered Retrieval)

Difficulty: Advanced

Build a production-ready GraphRAG system with logical inference and hybrid retrieval.

from semantica.context import AgentContext
from semantica.reasoning import InferenceEngine

# Initialize context with Hybrid Retrieval
context = AgentContext(
    vector_store=vs, 
    knowledge_graph=kg,
    use_graph_expansion=True,
    hybrid_alpha=0.7
)

# Add logical reasoning rules
engine = InferenceEngine(strategy="forward")
engine.add_rule("IF ?x :type 'Library' AND ?y :type 'Language' THEN ?x :builtWith ?y")

# Retrieve context for a query
results = context.retrieve("What technologies are used in this project?")

View Complete GraphRAG Tutorial

Example 12: RAG vs. GraphRAG Comparison

Difficulty: Intermediate

Benchmark standard Vector RAG against Graph-enhanced retrieval.

View RAG vs. GraphRAG Comparison

---

Production Patterns

Example 13: Streaming Data Processing

Difficulty: Advanced

Process data streams in real-time.

from semantica.ingest import StreamIngestor
from semantica.core import Semantica

Use Case Examples

Research Paper Analysis

Extract knowledge from research papers:

from semantica import Semantica

semantica = Semantica()

# Process research paper
result = semantica.build_knowledge_base([
    "papers/ai_research.pdf",
    "papers/ml_survey.pdf"
])

kg = result["knowledge_graph"]

# Find key concepts
concepts = [e for e in kg["entities"] if e["type"] == "CONCEPT"]
print(f"Found {len(concepts)} key concepts")

Company Intelligence

Build knowledge graph from company documents:

from semantica import Semantica

semantica = Semantica()

# Company documents
sources = [
    "company/annual_report.pdf",
    "company/press_releases/",
    "company/website_content.html"
]

result = semantica.build_knowledge_base(sources)
kg = result["knowledge_graph"]

# Export for analysis
semantica.export.to_json(kg, "company_intelligence.json")

News Article Processing

Process and analyze news articles:

from semantica import Semantica

semantica = Semantica()

# News articles
articles = [
    "https://example.com/article1",
    "https://example.com/article2",
    "https://example.com/article3"
]

result = semantica.build_knowledge_base(articles)
kg = result["knowledge_graph"]

# Extract key entities
people = [e for e in kg["entities"] if e["type"] == "PERSON"]
organizations = [e for e in kg["entities"] if e["type"] == "ORGANIZATION"]

print(f"People mentioned: {len(people)}")
print(f"Organizations: {len(organizations)}")

Interactive Examples

For more interactive examples and tutorials, check out our Cookbook with Jupyter notebooks covering:

• Introduction: Getting started tutorials
• Advanced: Advanced techniques and patterns
• Use Cases: Real-world applications in various domains

More Resources

• Quick Start Guide - Step-by-step tutorial
• API Reference - Complete API documentation
• Cookbook - Interactive Jupyter notebooks
• Code Examples - Additional code samples