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README.md

@@ -1,3 +1,14 @@
+---
+title: DashVector Experiment Matrix
+emoji: ⚡
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: false
+---
+
 # dashVectorspace (xVector)
 
 **Production-Grade Learned Hybrid Retrieval Engine**
@@ -54,17 +65,3 @@ dashVectorspace/
     -   **Layer 1**: Always searches the Freshness Shard.
     -   **Layer 2**: Falls back to **Global Search** if Router confidence is low (< 0.5).
 -   **Active Learning**: Logs "Hard Negatives" (low confidence or zero results) to `logs/active_learning_queue.jsonl` for future model retraining.
-
-## Hugging Face Space Deployment
-
-This project is ready for deployment on Hugging Face Spaces.
-
-1.  **Create a New Space**: Select "Gradio" as the SDK.
-2.  **Upload Files**: Upload the entire `dashVectorspace` folder content to the Space.
-3.  **Set Secrets**: Go to "Settings" -> "Repository secrets" and add:
-    -   `QDRANT_URL`: Your Qdrant Cloud Cluster URL.
-    -   `QDRANT_API_KEY`: Your Qdrant Cloud API Key.
-4.  **Ingest Data**:
-    -   Run `python scripts/ingest_ms_marco.py` locally (with env vars set) to populate your Qdrant Cloud instance and generate `models/router_v1.pkl`.
-    -   **Upload `models/router_v1.pkl`** to the Space (inside a `models/` folder).
-5.  **Run**: The Space will automatically launch `app.py`.