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--- |
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license: cc0-1.0 |
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task_categories: |
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- text-generation |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- corpus |
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- leadership |
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- historical |
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- deku-corpus-builder |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Julius Caesar Agent |
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This corpus was automatically generated by the **Deku Corpus Builder** for use in RAG-based AI applications. |
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## Dataset Description |
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- **Subject**: JUlius Caesar |
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- **Subject Type**: personality |
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- **Total Items**: 729 |
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- **Has Embeddings**: Yes (all-MiniLM-L6-v2) |
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- **Created**: 2026-01-09 |
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## Dataset Structure |
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Each record contains: |
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- `text`: The content text |
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- `source_url`: Original source URL |
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- `source_title`: Title of the source document |
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- `source_domain`: Domain of the source |
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- `relevance_score`: Relevance to the subject (0-1) |
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- `quality_score`: Content quality score (0-1) |
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- `topics`: JSON array of detected topics |
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- `character_count`: Length of the text |
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- `subject_name`: The subject this content relates to |
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- `subject_type`: "personality" or "topic" |
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- `extraction_date`: When the content was extracted |
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- `embedding`: Pre-computed 384-dimensional embedding vector |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("PhillyMac/JUlius_Caesar_Corpus") |
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# Access the data |
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for item in dataset["train"]: |
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print(item["text"][:100]) |
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``` |
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## Integration with RAG |
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This dataset is designed to be integrated with existing embedded corpuses. The embeddings use the `sentence-transformers/all-MiniLM-L6-v2` model, compatible with FAISS indexing. |
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## License |
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Content is sourced from public domain and Creative Commons licensed materials. |
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## Generated By |
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[Deku Corpus Builder](https://github.com/PhillyMac/deku-corpus-builder) - An automated corpus building system for AI applications. |
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