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README.md

@@ -1,47 +1,80 @@
 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: source_url
-    dtype: string
-  - name: source_title
-    dtype: string
-  - name: source_domain
-    dtype: string
-  - name: license_type
-    dtype: string
-  - name: attribution_required
-    dtype: bool
-  - name: attribution_text
-    dtype: string
-  - name: license_url
-    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: 594264
-    num_examples: 152
-  download_size: 519440
-  dataset_size: 594264
-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
 ---
+
+# Communication-Topic-1
+
+This corpus was automatically generated by the **Deku Corpus Builder** for use in RAG-based AI applications.
+
+## Dataset Description
+
+- **Subject**: Communication
+- **Subject Type**: topic
+- **Total Items**: 152
+- **Items Requiring Attribution**: 0
+- **Has Embeddings**: Yes (all-MiniLM-L6-v2)
+- **Created**: 2026-03-17
+
+## 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
+- `license_type`: License classification (e.g. `public_domain`, `cc_by`, `cc_by_sa`)
+- `attribution_required`: Boolean — True for CC BY / CC BY-SA and other attribution-required licenses
+- `attribution_text`: Formatted Creative Commons attribution string (empty if not required)
+- `license_url`: URL to the CC license deed (empty if not required)
+- `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
+
+## Attribution
+
+0 of 152 chunks in this corpus require attribution under their source license.
+When building lessons from these chunks, the `attribution_text` field must be surfaced
+in the lesson output per the Legend Leadership Attribution Tracking Spec.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("PhillyMac/Communication_Topic_1")
+
+# Access attribution-required chunks
+for item in dataset["train"]:
+    if item["attribution_required"]:
+        print(item["attribution_text"])
+```
+
+## 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.
+See individual `license_type` fields for per-chunk licensing details.
+
+## Generated By
+
+[Deku Corpus Builder](https://github.com/PhillyMac/deku-corpus-builder) - An automated corpus building system for AI applications.