KaifAhmad1
0c10d5c876
- Enhanced Graph Store notebook with comprehensive examples and clean formatting - Fixed GraphStore API usage across all documentation files - Updated examples to use keyword arguments (labels, properties, start_node_id, end_node_id, rel_type) - Removed emojis and links from notebook for cleaner markdown - Made summary section more concise - Ensured consistency across cookbook notebooks, docs, and module code
6.1 KiB
Examples
Real-world examples and use cases for Semantica.
!!! tip "Interactive Learning" For hands-on interactive tutorials, check out our Cookbook with Jupyter notebooks covering everything from basics to advanced use cases.
Example Gallery
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:material-school: Getting Started
Quick examples to get you up and running in 5 minutes.
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:material-cogs: Core Workflows
Common workflows for building production-ready graphs.
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:material-rocket: Advanced Patterns
Complex use cases and production deployments.
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:material-factory: Production Patterns
Scalable deployment patterns for enterprise use.
Getting Started (5 min examples)
Example 1: Basic Knowledge Graph
Difficulty: Beginner
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
Difficulty: Beginner
Extract entities from text using Named Entity Recognition.
from semantica import Semantica
semantica = Semantica()
text = "Apple Inc. is a technology company founded by Steve Jobs."
entities = semantica.semantic_extract.extract_entities(text)
for entity in entities["entities"]:
print(f"{entity['text']}: {entity['type']}")
Example 3: Multi-Source Integration
Difficulty: Beginner
Combine data from multiple sources into a unified knowledge graph.
from semantica import Semantica
semantica = Semantica()
sources = [
"documents/finance_report.pdf",
"https://example.com/news-article"
]
result = semantica.build_knowledge_base(sources)
print(f"Unified graph: {len(result['knowledge_graph']['entities'])} entities")
Core Workflows (15 min examples)
Example 4: Conflict Resolution
Difficulty: Intermediate
Resolve conflicts in data from multiple sources.
from semantica import Semantica
from semantica.conflicts import ConflictResolver
semantica = Semantica()
result = semantica.build_knowledge_base(["source1.pdf", "source2.pdf"])
# Detect and resolve conflicts
conflicts = semantica.kg.detect_conflicts(result["knowledge_graph"])
resolver = ConflictResolver(default_strategy="voting")
resolved = resolver.resolve_conflicts(conflicts)
Example 5: Custom Configuration
Difficulty: Intermediate
Use custom configuration for specific use cases.
from semantica import Semantica, Config
config = Config(
embeddings=True,
graph=True,
normalize=True,
conflict_resolution="highest_confidence"
)
semantica = Semantica(config=config)
result = semantica.build_knowledge_base(["document.pdf"])
Example 6: Incremental Graph Building
Difficulty: Intermediate
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"])
# Merge into unified graph
merged_kg = semantica.kg.merge([kg1, kg2])
Advanced Patterns (30+ min examples)
Example 7: Graph Store (Persistent Storage)
Difficulty: Intermediate
Store and query knowledge graphs in a persistent graph database like Neo4j.
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 8: 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()
Production Patterns
Example 9: Streaming Data Processing
Difficulty: Advanced
Process data streams in real-time.
from semantica.ingest import StreamIngestor
from semantica import Semantica
semantica = Semantica()
stream_ingestor = StreamIngestor(stream_uri="kafka://localhost:9092/topic")
for batch in stream_ingestor.stream(batch_size=100):
result = semantica.build_knowledge_base(
sources=batch,
embeddings=True,
graph=True
)
# Process results
Example 10: Batch Processing Large Datasets
Difficulty: Intermediate
Process large datasets efficiently with batching.
from semantica import Semantica
semantica = Semantica()
sources = [f"data/doc_{i}.pdf" for i in range(1000)]
batch_size = 50
for i in range(0, len(sources), batch_size):
batch = sources[i:i+batch_size]
result = semantica.build_knowledge_base(batch)
# Save intermediate results
More Resources
- Quick Start Guide - Step-by-step tutorial
- API Reference - Complete API documentation
- Cookbook - Interactive Jupyter notebooks
- Use Cases - Real-world applications
!!! info "Contribute" Have an example to share? Contribute on GitHub
Last Updated: 2024