Create Notebook Example to Showcase Library Usage

### Issue Description:
To help users understand how to use **graphrag-tagger**, we need a well-documented Jupyter Notebook that demonstrates its core functionalities step by step. This will serve as a hands-on guide for users who want to integrate topic extraction and graph-based retrieval into their workflows.

### Expected Content:
1. **Installation & Setup**
- Install `graphrag-tagger` and required dependencies.
- Load necessary libraries.

2. **Basic Usage**
- Load a sample PDF document.
- Extract text from the document.
- Segment the text into meaningful chunks.

3. **Topic Extraction & Refinement**
- Perform topic modeling using LDA or ktrain.
- Use LLM-based refinement to clean and enhance topics.

4. **Graph Construction & Visualization**
- Build a topic similarity graph.
- Visualize the connections between extracted topics.

5. **Real-World Application Example**
- Showcase how the extracted topics and graph can improve retrieval in a RAG/GraphRAG setup.

### Deliverable:
A Jupyter Notebook (`examples/graphrag_tagger_demo.ipynb`) that provides a practical, easy-to-follow guide for new users.

Would love any feedback or additional suggestions! 🚀