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Code Issues Packages Projects Releases Wiki Activity

Rebrand from SemantiCore to Semantica

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- Updated all package references from semanticore to semantica
- Updated all class names from SemantiCore to Semantica
- Renamed logo files: SemantiCore Logo.png -> Semantica Logo.png
- Renamed logo files: SemantiCore Logo Dark.png -> Semantica Logo Dark.png
- Renamed roadmap file: SemantiCore_Development_Roadmap.md -> Semantica_Development_Roadmap.md
- Updated all URLs, links, and documentation references
- Updated all code examples and installation instructions
- Maintained all functionality while updating branding
This commit is contained in:
KaifAhmad1
2025-10-21 15:14:34 +05:30
parent fa2f346c54
commit c3ba6541a8
9 changed files with 215 additions and 215 deletions
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CodeExamples.md
+43 -43
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@@ -4,27 +4,27 @@
```bash
# Complete installation with all format support
pip install "semanticore[all]"
pip install "semantica[all]"
# Lightweight installation
pip install semanticore
pip install semantica
# Specific format support
pip install "semanticore[pdf,web,feeds,office]"
pip install "semantica[pdf,web,feeds,office]"
# Development installation
git clone https://github.com/semanticore/semanticore.git
cd semanticore
git clone https://github.com/semantica/semantica.git
cd semantica
pip install -e ".[dev]"
```
### ⚡ 30-Second Demo: From Any Format to Knowledge
```python
from semanticore import SemantiCore
from semantica import Semantica
# Initialize with preferred providers
core = SemantiCore(
core = Semantica(
llm_provider="openai",
embedding_model="text-embedding-3-large",
vector_store="pinecone",
@@ -61,7 +61,7 @@ results = knowledge_base.query("What are the key financial trends?")
Process complex document formats with semantic understanding:
```python
from semanticore.processors import DocumentProcessor
from semantica.processors import DocumentProcessor
# Initialize document processor
doc_processor = DocumentProcessor(
@@ -89,7 +89,7 @@ for content in [pdf_content, docx_content, pptx_content]:
Real-time web content and feed processing:
```python
from semanticore.processors import WebProcessor, FeedProcessor
from semantica.processors import WebProcessor, FeedProcessor
# Web content processor
web_processor = WebProcessor(
@@ -131,7 +131,7 @@ async for item in feed_processor.stream_items():
Handle structured and semi-structured data formats:
```python
from semanticore.processors import StructuredDataProcessor
from semantica.processors import StructuredDataProcessor
# Initialize structured data processor
structured_processor = StructuredDataProcessor(
@@ -158,7 +158,7 @@ for data in [json_data, csv_data, yaml_data, xml_data]:
Process email archives and compressed files:
```python
from semanticore.processors import EmailProcessor, ArchiveProcessor
from semantica.processors import EmailProcessor, ArchiveProcessor
# Email processing
email_processor = EmailProcessor(
@@ -192,7 +192,7 @@ for content in archive_contents:
Specialized processing for academic and scientific content:
```python
from semanticore.processors import AcademicProcessor
from semantica.processors import AcademicProcessor
# Academic content processor
academic_processor = AcademicProcessor(
@@ -223,7 +223,7 @@ for content in [latex_content, bibtex_content, jats_content]:
Generate RDF triples from any content automatically:
```python
from semanticore.extraction import TripleExtractor
from semantica.extraction import TripleExtractor
# Initialize triple extractor
triple_extractor = TripleExtractor(
@@ -255,7 +255,7 @@ jsonld_format = triple_extractor.to_jsonld(triples)
Automatically generate ontologies from extracted semantic patterns:
```python
from semanticore.ontology import OntologyGenerator
from semantica.ontology import OntologyGenerator
# Initialize ontology generator
ontology_gen = OntologyGenerator(
@@ -283,7 +283,7 @@ ontology.save_to_triple_store("http://localhost:9999/blazegraph/sparql")
Create context-aware embeddings optimized for semantic search:
```python
from semanticore.embeddings import SemanticEmbedder
from semantica.embeddings import SemanticEmbedder
# Initialize semantic embedder
embedder = SemanticEmbedder(
@@ -316,7 +316,7 @@ results = vector_store.semantic_search(query, top_k=10)
Monitor and process live data feeds with semantic understanding:
```python
from semanticore.streaming import LiveFeedProcessor
from semantica.streaming import LiveFeedProcessor
# Initialize live feed processor
feed_processor = LiveFeedProcessor(
@@ -358,7 +358,7 @@ async for feed_item in feed_processor.stream():
Integrate with popular streaming platforms:
```python
from semanticore.streaming import StreamProcessor
from semantica.streaming import StreamProcessor
# Kafka integration
kafka_processor = StreamProcessor(
@@ -400,7 +400,7 @@ async for message in kafka_processor.consume():
### 🔐 Multi-Format Cybersecurity Intelligence
```python
from semanticore.domains.cyber import CyberIntelProcessor
from semantica.domains.cyber import CyberIntelProcessor
# Initialize cybersecurity processor
cyber_processor = CyberIntelProcessor(
@@ -435,7 +435,7 @@ cyber_knowledge.export_to_opencti()
### 🧬 Biomedical Literature Processing
```python
from semanticore.domains.biomedical import BiomedicalProcessor
from semantica.domains.biomedical import BiomedicalProcessor
# Initialize biomedical processor
bio_processor = BiomedicalProcessor(
@@ -466,7 +466,7 @@ biomedical_knowledge.export_to_bioportal()
### 📊 Financial Data Aggregation & Analysis
```python
from semanticore.domains.finance import FinancialProcessor
from semantica.domains.finance import FinancialProcessor
# Initialize financial processor
finance_processor = FinancialProcessor(
@@ -502,7 +502,7 @@ financial_knowledge.export_to_refinitiv()
### 🚀 Scalable Deployment Options
```python
from semanticore.deployment import ScaleManager
from semantica.deployment import ScaleManager
# Kubernetes deployment configuration
k8s_config = {
@@ -532,7 +532,7 @@ print(f"Memory usage: {metrics.memory_usage_percent}%")
### 🔧 Custom Pipeline Configuration
```python
from semanticore.pipeline import PipelineBuilder
from semantica.pipeline import PipelineBuilder
# Build custom processing pipeline
pipeline = PipelineBuilder() \
@@ -572,7 +572,7 @@ results = pipeline.process(input_sources)
### 📊 Real-Time Analytics Dashboard
```python
from semanticore.monitoring import AnalyticsDashboard
from semantica.monitoring import AnalyticsDashboard
# Initialize analytics dashboard
dashboard = AnalyticsDashboard(
@@ -604,7 +604,7 @@ dashboard.add_alert(
### 🔍 Quality Assurance & Validation
```python
from semanticore.quality import QualityAssurance
from semantica.quality import QualityAssurance
# Initialize quality assurance
qa = QualityAssurance(
@@ -635,10 +635,10 @@ qa.enable_continuous_monitoring()
### 📋 Schema-First Knowledge Graph Construction
Unlike other libraries that infer schemas, SemantiCore enforces predefined business schemas:
Unlike other libraries that infer schemas, Semantica enforces predefined business schemas:
```python
from semanticore.schema import SchemaManager, BusinessEntity
from semantica.schema import SchemaManager, BusinessEntity
from pydantic import BaseModel
from typing import List, Optional
@@ -671,7 +671,7 @@ schema_manager = SchemaManager()
schema_manager.register_entities([Employee, Department, Product])
# Process documents with schema enforcement
core = SemantiCore(schema_manager=schema_manager)
core = Semantica(schema_manager=schema_manager)
results = core.process_with_schema("hr_documents/", strict_mode=True)
# Only entities matching your schema are extracted and validated
@@ -685,7 +685,7 @@ print(f"Schema violations: {len(results.violations)}")
Start with known entities and enhance with automated extraction:
```python
from semanticore.knowledge import SeedManager
from semantica.knowledge import SeedManager
# Initialize with known business entities
seed_manager = SeedManager()
@@ -715,7 +715,7 @@ print(f"Linked to existing: {len(results.linked_entities)}")
Automatic deduplication with configurable business rules:
```python
from semanticore.deduplication import EntityDeduplicator
from semantica.deduplication import EntityDeduplicator
# Configure deduplication rules for each entity type
dedup_config = {
@@ -765,7 +765,7 @@ for merge in results.auto_merged:
Flag contradictions with complete source tracking:
```python
from semanticore.conflicts import ConflictDetector
from semantica.conflicts import ConflictDetector
# Configure conflict detection rules
conflict_detector = ConflictDetector(
@@ -819,7 +819,7 @@ resolved_conflicts = conflict_detector.resolve_conflicts(
Implement custom business logic and constraints:
```python
from semanticore.validation import BusinessRuleEngine
from semantica.validation import BusinessRuleEngine
# Define business rules
rules = BusinessRuleEngine()
@@ -878,7 +878,7 @@ for violation in validation_report.violations:
Built-in UI for reviewing and resolving conflicts:
```python
from semanticore.ui import ConflictResolutionDashboard
from semantica.ui import ConflictResolutionDashboard
# Start interactive dashboard
dashboard = ConflictResolutionDashboard(
@@ -912,18 +912,18 @@ knowledge_graph.apply_resolutions(resolved_data)
### 🎓 Learning Resources
- **📚 [Documentation](https://semanticore.readthedocs.io/)** - Comprehensive guides and API reference
- **🎯 [Tutorials](https://semanticore.readthedocs.io/tutorials/)** - Step-by-step tutorials for common use cases
- **💡 [Examples Repository](https://github.com/semanticore/examples)** - Real-world implementation examples
- **🎥 [Video Tutorials](https://youtube.com/semanticore)** - Visual learning content
- **📖 [Blog](https://blog.semanticore.io/)** - Latest updates and best practices
- **📚 [Documentation](https://semantica.readthedocs.io/)** - Comprehensive guides and API reference
- **🎯 [Tutorials](https://semantica.readthedocs.io/tutorials/)** - Step-by-step tutorials for common use cases
- **💡 [Examples Repository](https://github.com/semantica/examples)** - Real-world implementation examples
- **🎥 [Video Tutorials](https://youtube.com/semantica)** - Visual learning content
- **📖 [Blog](https://blog.semantica.io/)** - Latest updates and best practices
### 💬 Community Support
- **💬 [Discord Community](https://discord.gg/semanticore)** - Real-time chat and support
- **🐙 [GitHub Discussions](https://github.com/semanticore/semanticore/discussions)** - Community Q&A
- **📧 [Mailing List](https://groups.google.com/g/semanticore)** - Announcements and updates
- **🐦 [Twitter](https://twitter.com/semanticore)** - Latest news and tips
- **💬 [Discord Community](https://discord.gg/semantica)** - Real-time chat and support
- **🐙 [GitHub Discussions](https://github.com/semantica/semantica/discussions)** - Community Q&A
- **📧 [Mailing List](https://groups.google.com/g/semantica)** - Announcements and updates
- **🐦 [Twitter](https://twitter.com/semantica)** - Latest news and tips
### 🏢 Enterprise Support
@@ -943,7 +943,7 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
## 🙏 Acknowledgments
- **🧠 Research Community** - Built upon cutting-edge research in NLP and semantic web
- **🤝 Open Source Contributors** - Hundreds of contributors making SemantiCore better
- **🤝 Open Source Contributors** - Hundreds of contributors making Semantica better
- **🏢 Enterprise Partners** - Real-world feedback and requirements shaping development
- **🎓 Academic Institutions** - Research collaborations and validation
@@ -953,6 +953,6 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
**🚀 Ready to transform your data into intelligent knowledge?**
[Get Started Now](https://semanticore.readthedocs.io/quickstart/) • [View Examples](https://github.com/semanticore/examples) • [Join Community](https://discord.gg/semanticore)
[Get Started Now](https://semantica.readthedocs.io/quickstart/) • [View Examples](https://github.com/semantica/examples) • [Join Community](https://discord.gg/semantica)
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