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README.md

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-# Medical RAG-using-Meditron-7B-LLM
-Medical RAG QA App using Meditron 7B LLM, Qdrant Vector Database, and PubMedBERT Embedding Model.
+---
+title: Medical RAG with Meditron-7B-LLM
+emoji: 📚
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+sdk_version: 20.10.17
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: A specialized AI assistant for medical information retrieval
+---
+
+# Medical RAG QA System
+
+A Retrieval-Augmented Generation (RAG) system for medical question answering using:
+- Meditron-7B LLM
+- Qdrant Vector Database
+- PubMedBERT Embeddings
+
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Model Card](https://img.shields.io/badge/🤗%20Model-MedITRON--7B--GGUF-blue)](https://huggingface.co/joshnader/meditron-7b-Q4_K_M-GGUF)
+
+## Features
+- PDF document ingestion
+- Semantic search with medical embeddings
+- LLM-powered question answering
+- Source document citation
+
+## 🚀 Usage
+
+1. **Query Interface**:
+```python
+import requests
+
+response = requests.post("https://d221/Qdrant_Backend.hf.space/get_response", 
+                         data={"query": "What are the symptoms of diabetes?"})
+print(response.json())