# Learning More

Additional resources, tutorials, and advanced learning materials for Semantica.

## Additional Tutorials

### Video Tutorials

Coming soon! We're working on video tutorials covering:

- Getting started with Semantica
- Building your first knowledge graph
- Advanced techniques and patterns
- Real-world use cases

### Blog Posts & Articles

Stay tuned for blog posts covering:

- Best practices for knowledge graph construction
- Performance optimization tips
- Integration guides
- Case studies and success stories

## Best Practices

### Knowledge Graph Design

1. **Start with Clear Objectives**
   - Define what you want to extract
   - Identify key entities and relationships
   - Plan your schema before processing

2. **Iterate and Refine**
   - Start with a small dataset
   - Validate extracted entities
   - Refine extraction patterns
   - Scale up gradually

3. **Quality Over Quantity**
   - Focus on accuracy
   - Validate relationships
   - Resolve conflicts early
   - Maintain data quality

### Performance Tips

```python
# Process in batches for large datasets
sources = ["doc1.pdf", "doc2.pdf", "doc3.pdf"]
batch_size = 10

for i in range(0, len(sources), batch_size):
    batch = sources[i:i+batch_size]
    result = semantica.build_knowledge_base(batch)
    # Process and save results
```

### Integration Patterns

#### Pattern 1: Incremental Building

```python
# Build knowledge graph incrementally
kg = None
for source in sources:
    result = semantica.build_knowledge_base([source])
    if kg is None:
        kg = result["knowledge_graph"]
    else:
        kg = semantica.kg.merge([kg, result["knowledge_graph"]])
```

#### Pattern 2: Pipeline Processing

```python
# Create a processing pipeline
pipeline = [
    ("ingest", semantica.ingest.from_file),
    ("parse", semantica.parse.document),
    ("extract", semantica.semantic_extract.entities),
    ("build", semantica.kg.build_graph)
]

for step_name, step_func in pipeline:
    data = step_func(data)
```

## Advanced Topics

### Custom Extractors

Create custom entity extractors:

```python
from semantica.semantic_extract import BaseExtractor

class CustomExtractor(BaseExtractor):
    def extract(self, text):
        # Your custom extraction logic
        return entities
```

### Custom Export Formats

Add custom export formats:

```python
from semantica.export import BaseExporter

class CustomExporter(BaseExporter):
    def export(self, kg, path):
        # Your custom export logic
        pass
```

### Performance Optimization

- Use GPU acceleration when available
- Process documents in parallel
- Cache embeddings
- Optimize graph queries

## Community Resources

### GitHub Discussions

Join discussions on:
- [General Discussion](https://github.com/Hawksight-AI/semantica/discussions)
- [Q&A](https://github.com/Hawksight-AI/semantica/discussions/categories/q-a)
- [Show and Tell](https://github.com/Hawksight-AI/semantica/discussions/categories/show-and-tell)

### Contributing

Want to contribute? See our [Contributing Guide](https://github.com/Hawksight-AI/semantica/blob/main/CONTRIBUTING.md).

### Examples Repository

Check out the [examples repository](https://github.com/Hawksight-AI/semantica/tree/main/examples) for more code samples.

## Related Projects

### GraphRAG

Semantica works great with GraphRAG implementations. See our [GraphRAG examples](cookbook.md#advanced-rag).

### Vector Databases

Integrate with vector databases:
- Pinecone
- Weaviate
- Qdrant
- Milvus

### Knowledge Graph Databases

Export to and work with:
- Neo4j
- Amazon Neptune
- ArangoDB
- Blazegraph

## Next Steps

- **[Deep Dive](deep-dive.md)** - Advanced architecture and internals
- **[API Reference](api.md)** - Complete API documentation
- **[Cookbook](cookbook.md)** - Interactive tutorials
- **[Examples](examples.md)** - More code examples

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Have questions or suggestions? [Open an issue](https://github.com/Hawksight-AI/semantica/issues) or [start a discussion](https://github.com/Hawksight-AI/semantica/discussions)!