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README.md

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+---
+license: mit
+task_categories:
+  - text-retrieval
+language:
+  - en
+size_categories:
+  - 10K<n<100K
+---
+
+# CAG-Lab AWS Docs Vectors
+
+89,221 document chunk embeddings from AWS public documentation.
+
+- **Embedding model**: OpenAI `text-embedding-3-small` (512 dimensions)
+- **Distance metric**: Cosine
+- **Source**: AWS public documentation chunks
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| id | string | Deterministic UUID |
+| embedding | list[float32] | 512-dim vector |
+| content | string | Document chunk text |
+| filePath | string | Original file path |
+| chunkIndex | string | Chunk position |
+| _pinecone_id | string | Original Pinecone vector ID |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("mouadja/aws-docs")
+```
+
+Or use with CAG-Lab:
+
+```bash
+python scripts/setup_vectordb.py
+```