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README.md

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+---
+license: mit
+library_name: colbert
+tags:
+- rag
+- visual-rag
+- colqwen
+- sceletium-tortuosum
+- thesis
+model-index:
+- name: LAYRA v2
+  results:
+  - task:
+      type: retrieval-augmented-generation
+      name: Retrieval Augmented Generation
+    dataset:
+      name: Kanna RAG Gold Standard
+      type: SAINTHALF/kanna-rag-gold-standard
+    metrics:
+    - name: MRR@20
+      type: mrr
+      value: 0.7403174603174603
+    - name: Recall@20
+      type: recall
+      value: 1.0
+---
+
+# LAYRA v2 (Large Academic Visual RAG Agent)
+
+**LAYRA v2** is a specialized Visual RAG system designed for the ethnopharmacology of *Sceletium tortuosum*. It processes full PDF pages as images, preserving layout and visual information, and uses a hybrid retrieval stack.
+
+## Architecture
+
+- **Visual Encoder:** ColQwen 2.5 (ColBERT + Qwen-VL)
+- **Retrieval:** Hybrid (Sparse BM25 + Dense ColBERT)
+- **Reranking:** LLM-based Reranking (Generative Listwise)
+- **Vector DB:** Milvus 2.5.5
+- **Infrastructure:** Docker Compose (Lean Stack)
+
+## Performance
+
+Evaluated on [SAINTHALF/kanna-rag-gold-standard](https://huggingface.co/datasets/SAINTHALF/kanna-rag-gold-standard):
+
+| Metric | Score |
+| :--- | :--- |
+| **MRR@20** | **0.7403** |
+| **Recall@20** | **1.0000** |
+| **Latency (P50)** | 1.2s |
+
+## Usage
+
+This model represents the deployed system configuration described in the thesis.
+
+*Supersedes `SAINTHALF/layra-v1-hybrid`.*