Ahmed-Selem/Shifaa_Medical_RAG_VectorDB

Shifaa Medical RAG System

Advanced Retrieval-Augmented Generation (RAG) system for Arabic medical consultations.

🎯 Overview

The Shifaa Medical RAG system provides intelligent medical information retrieval through a four-stage pipeline:

Query → Specialty Detection → Topic Paths → Consultation Retrieval → Insight Extraction

Key Features

• Automatic Specialty Detection: Identifies relevant medical specialties from 23 categories
• Hierarchical Topic Navigation: Pinpoints specific medical topics from 585 diagnoses
• Semantic Search: Retrieves similar consultations from 84K+ medical cases
• Insight Extraction: Distills actionable medical information from retrieved consultations
• Multi-lingual Support: Primary support for Arabic with multilingual capabilities
• Auto-Download: Automatically manages vector database downloads

🚀 Quick Start

Basic Usage

from shifaa.rag import MedicalRAGSystem

# Initialize the system (auto-downloads vector DB if needed)
rag = MedicalRAGSystem()

# Process a medical query
query = "ما هي أعراض ارتجاع المريء؟"
results = rag.process_query(query)

# Access results
print("Specialties:", [s.specialty for s in results.specialties])
print("Topics:", [t.path for t in results.topic_paths])
print("Insights:", [i.information for i in results.insights])

With Google API Key

from shifaa.rag import MedicalRAGSystem

rag = MedicalRAGSystem(
    google_api_key="your-api-key-here"
)

results = rag.process_query("ما علاج الصداع المزمن؟")

📦 Installation & Setup

Prerequisites

pip install shifaa