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README.md

@@ -1,39 +1,67 @@
 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: source_url
-    dtype: string
-  - name: source_title
-    dtype: string
-  - name: source_domain
-    dtype: string
-  - name: relevance_score
-    dtype: float64
-  - name: quality_score
-    dtype: float64
-  - name: topics
-    dtype: string
-  - name: character_count
-    dtype: int64
-  - name: subject_name
-    dtype: string
-  - name: subject_type
-    dtype: string
-  - name: extraction_date
-    dtype: string
-  - name: embedding
-    list: float64
-  splits:
-  - name: train
-    num_bytes: 2778333
-    num_examples: 729
-  download_size: 2217164
-  dataset_size: 2778333
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: cc0-1.0
+task_categories:
+  - text-generation
+  - feature-extraction
+language:
+  - en
+tags:
+  - corpus
+  - leadership
+  - historical
+  - deku-corpus-builder
+size_categories:
+  - 1K<n<10K
 ---
+
+# Julius Caesar Agent
+
+This corpus was automatically generated by the **Deku Corpus Builder** for use in RAG-based AI applications.
+
+## Dataset Description
+
+- **Subject**: JUlius Caesar
+- **Subject Type**: personality
+- **Total Items**: 729
+- **Has Embeddings**: Yes (all-MiniLM-L6-v2)
+- **Created**: 2026-01-09
+
+## Dataset Structure
+
+Each record contains:
+- `text`: The content text
+- `source_url`: Original source URL
+- `source_title`: Title of the source document
+- `source_domain`: Domain of the source
+- `relevance_score`: Relevance to the subject (0-1)
+- `quality_score`: Content quality score (0-1)
+- `topics`: JSON array of detected topics
+- `character_count`: Length of the text
+- `subject_name`: The subject this content relates to
+- `subject_type`: "personality" or "topic"
+- `extraction_date`: When the content was extracted
+- `embedding`: Pre-computed 384-dimensional embedding vector
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("PhillyMac/JUlius_Caesar_Corpus")
+
+# Access the data
+for item in dataset["train"]:
+    print(item["text"][:100])
+```
+
+## Integration with RAG
+
+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.
+
+## License
+
+Content is sourced from public domain and Creative Commons licensed materials.
+
+## Generated By
+
+[Deku Corpus Builder](https://github.com/PhillyMac/deku-corpus-builder) - An automated corpus building system for AI applications.