mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
docs: Modern documentation redesign with enhanced navigation
- Reorganize navigation structure (left sidebar: Home, Quickstart, Installation, Cookbook Recipes, Learning More, Deep Dive, API References) - Add TOC sections on homepage (Features, How to Read this Documentation, Resources) - Create new pages: learning-more.md, deep-dive.md, community-projects.md, citation.md, license.md - Add Mermaid diagrams for architecture, workflows, and concepts - Enhance all documentation pages with better code examples and explanations - Add custom CSS for modern aesthetic (docs/css/custom.css) - Update mkdocs.yml with Material theme, Mermaid support, and enhanced features - Improve user-friendly navigation and clear visual hierarchy - Add diagrams and charts throughout documentation
This commit is contained in:
KaifAhmad1
+193
-24
@@ -1,8 +1,34 @@
|
||||
# Quick Start Guide
|
||||
# Quickstart
|
||||
|
||||
Get started with Semantica in 5 minutes!
|
||||
Get started with Semantica in 5 minutes. This guide will walk you through building your first knowledge graph.
|
||||
|
||||
## Basic Example
|
||||
## Overview
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A[Install] --> B[Initialize]
|
||||
B --> C[Load Data]
|
||||
C --> D[Extract]
|
||||
D --> E[Build Graph]
|
||||
E --> F[Visualize]
|
||||
|
||||
style A fill:#e3f2fd
|
||||
style F fill:#c8e6c9
|
||||
```
|
||||
|
||||
## Step 1: Installation
|
||||
|
||||
If you haven't installed Semantica yet:
|
||||
|
||||
```bash
|
||||
pip install semantica
|
||||
```
|
||||
|
||||
See the [Installation Guide](installation.md) for detailed instructions.
|
||||
|
||||
## Step 2: Your First Knowledge Graph
|
||||
|
||||
Let's build a knowledge graph from a document:
|
||||
|
||||
```python
|
||||
from semantica import Semantica
|
||||
@@ -24,60 +50,203 @@ statistics = result["statistics"]
|
||||
|
||||
print(f"Extracted {len(kg['entities'])} entities")
|
||||
print(f"Created {len(kg['relationships'])} relationships")
|
||||
print(f"Generated {len(embeddings)} embeddings")
|
||||
```
|
||||
|
||||
## Extract Entities and Relationships
|
||||
**Expected Output:**
|
||||
```
|
||||
Extracted 45 entities
|
||||
Created 32 relationships
|
||||
Generated 45 embeddings
|
||||
```
|
||||
|
||||
## Step 3: Extract Entities and Relationships
|
||||
|
||||
Extract structured information from text:
|
||||
|
||||
```python
|
||||
from semantica import Semantica
|
||||
|
||||
semantica = Semantica()
|
||||
|
||||
# Extract from text
|
||||
text = "Apple Inc. was founded by Steve Jobs in Cupertino, California."
|
||||
# Sample text
|
||||
text = """
|
||||
Apple Inc. was founded by Steve Jobs in Cupertino, California in 1976.
|
||||
The company designs and manufactures consumer electronics and software.
|
||||
Tim Cook is the current CEO of Apple.
|
||||
"""
|
||||
|
||||
result = semantica.semantic_extract.extract_entities(text)
|
||||
entities = result["entities"]
|
||||
# Extract entities
|
||||
entities_result = semantica.semantic_extract.extract_entities(text)
|
||||
entities = entities_result["entities"]
|
||||
|
||||
print("Extracted Entities:")
|
||||
for entity in entities:
|
||||
print(f"{entity['text']} - {entity['type']}")
|
||||
print(f" - {entity['text']} ({entity['type']})")
|
||||
|
||||
# Extract relationships
|
||||
relationships_result = semantica.semantic_extract.extract_relationships(text)
|
||||
relationships = relationships_result["relationships"]
|
||||
|
||||
print("\nExtracted Relationships:")
|
||||
for rel in relationships:
|
||||
print(f" - {rel['subject']} --[{rel['predicate']}]--> {rel['object']}")
|
||||
```
|
||||
|
||||
## Build Knowledge Graph
|
||||
**Expected Output:**
|
||||
```
|
||||
Extracted Entities:
|
||||
- Apple Inc. (ORGANIZATION)
|
||||
- Steve Jobs (PERSON)
|
||||
- Cupertino (LOCATION)
|
||||
- California (LOCATION)
|
||||
- Tim Cook (PERSON)
|
||||
|
||||
Extracted Relationships:
|
||||
- Apple Inc. --[founded_by]--> Steve Jobs
|
||||
- Apple Inc. --[located_in]--> Cupertino
|
||||
- Apple Inc. --[has_ceo]--> Tim Cook
|
||||
```
|
||||
|
||||
## Step 4: Build Knowledge Graph from Multiple Sources
|
||||
|
||||
Combine data from multiple sources:
|
||||
|
||||
```python
|
||||
from semantica import Semantica
|
||||
|
||||
semantica = Semantica()
|
||||
|
||||
# Build KG from multiple sources
|
||||
# Multiple data sources
|
||||
sources = [
|
||||
"document1.pdf",
|
||||
"document2.docx",
|
||||
"https://example.com/article"
|
||||
"documents/research_paper.pdf",
|
||||
"documents/company_report.docx",
|
||||
"https://example.com/news-article"
|
||||
]
|
||||
|
||||
kg = semantica.kg.build_graph(sources)
|
||||
semantica.kg.visualize(kg)
|
||||
# Build unified knowledge graph
|
||||
result = semantica.build_knowledge_base(
|
||||
sources=sources,
|
||||
embeddings=True,
|
||||
graph=True,
|
||||
normalize=True
|
||||
)
|
||||
|
||||
kg = result["knowledge_graph"]
|
||||
|
||||
# Analyze the graph
|
||||
print(f"Total entities: {len(kg['entities'])}")
|
||||
print(f"Total relationships: {len(kg['relationships'])}")
|
||||
print(f"Sources processed: {len(result['metadata']['sources'])}")
|
||||
```
|
||||
|
||||
## Export Knowledge Graph
|
||||
## Step 5: Visualize Your Knowledge Graph
|
||||
|
||||
Visualize the knowledge graph you created:
|
||||
|
||||
```python
|
||||
from semantica import Semantica
|
||||
|
||||
semantica = Semantica()
|
||||
kg = semantica.kg.build_graph(["data.pdf"])
|
||||
|
||||
# Build graph
|
||||
result = semantica.build_knowledge_base(["document.pdf"])
|
||||
kg = result["knowledge_graph"]
|
||||
|
||||
# Visualize
|
||||
semantica.kg.visualize(kg, output_path="graph.html")
|
||||
print("Graph visualization saved to graph.html")
|
||||
```
|
||||
|
||||
Open `graph.html` in your browser to see an interactive visualization.
|
||||
|
||||
## Step 6: Export Your Knowledge Graph
|
||||
|
||||
Export your knowledge graph in various formats:
|
||||
|
||||
```python
|
||||
from semantica import Semantica
|
||||
|
||||
semantica = Semantica()
|
||||
|
||||
# Build graph
|
||||
result = semantica.build_knowledge_base(["data.pdf"])
|
||||
kg = result["knowledge_graph"]
|
||||
|
||||
# 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_rdf(kg, "output.rdf") # RDF/XML format
|
||||
semantica.export.to_json(kg, "output.json") # JSON format
|
||||
semantica.export.to_csv(kg, "output.csv") # CSV format
|
||||
semantica.export.to_owl(kg, "output.owl") # OWL ontology format
|
||||
|
||||
print("Exported knowledge graph to multiple formats")
|
||||
```
|
||||
|
||||
## Common Patterns
|
||||
|
||||
### Pattern 1: Process Text Directly
|
||||
|
||||
```python
|
||||
from semantica import Semantica
|
||||
|
||||
semantica = Semantica()
|
||||
|
||||
text = "Your text content here..."
|
||||
result = semantica.process_document(text)
|
||||
```
|
||||
|
||||
### Pattern 2: Custom Configuration
|
||||
|
||||
```python
|
||||
from semantica import Semantica, Config
|
||||
|
||||
# Create custom configuration
|
||||
config = Config(
|
||||
embeddings=True,
|
||||
graph=True,
|
||||
normalize=True,
|
||||
conflict_resolution="voting"
|
||||
)
|
||||
|
||||
semantica = Semantica(config=config)
|
||||
result = semantica.build_knowledge_base(["document.pdf"])
|
||||
```
|
||||
|
||||
### Pattern 3: Incremental Building
|
||||
|
||||
```python
|
||||
from semantica import Semantica
|
||||
|
||||
semantica = Semantica()
|
||||
|
||||
# Build incrementally
|
||||
kg1 = semantica.kg.build_graph(["source1.pdf"])
|
||||
kg2 = semantica.kg.build_graph(["source2.pdf"])
|
||||
|
||||
# Merge knowledge graphs
|
||||
merged_kg = semantica.kg.merge([kg1, kg2])
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Full Documentation](../README.md)
|
||||
- [API Reference](api.md)
|
||||
- [More Examples](examples.md)
|
||||
Now that you've built your first knowledge graph:
|
||||
|
||||
1. **[Explore Examples](examples.md)** - See more advanced use cases
|
||||
2. **[API Reference](api.md)** - Learn about all available methods
|
||||
3. **[Cookbook](cookbook.md)** - Interactive Jupyter notebooks
|
||||
4. **[Full Documentation](../README.md)** - Comprehensive guide
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
**Issue**: No entities extracted
|
||||
- **Solution**: Check that your document contains text content. PDFs with images only won't work without OCR.
|
||||
|
||||
**Issue**: Slow processing
|
||||
- **Solution**: For large documents, consider processing in chunks or using GPU acceleration.
|
||||
|
||||
**Issue**: Memory errors
|
||||
- **Solution**: Process documents one at a time or reduce batch sizes.
|
||||
|
||||
Need help? Check the [Installation Troubleshooting](installation.md#troubleshooting) or [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues).
|
||||
|
||||
Reference in New Issue
Block a user