d221/Qdrant_Backend

Medical RAG QA System

A Retrieval-Augmented Generation (RAG) system for medical question answering using:

• —Meditron-7B LLM
• —Qdrant Vector Database
• —PubMedBERT Embeddings

 

Features

• —PDF document ingestion
• —Semantic search with medical embeddings
• —LLM-powered question answering
• —Source document citation

🚀 Usage

1. 1.Query Interface:

import requests

response = requests.post("https://d221/Qdrant_Backend.hf.space/get_response", 
                         data={"query": "What are the symptoms of diabetes?"})
print(response.json())