Medical knowledge assistant designed specifically for use in Obstetrics and Gynaecology (O&G).
Retrieval-Augmented Generation (RAG) System for Obstetrics & Gynaecology
This is an AI-powered knowledge retrieval tool designed to support medical students, resident doctors, and consultants with fast, accurate answers in Obstetrics and Gynaecology. The system combines retrieval-based search and generative AI to deliver contextual, real-time insights from clinical literature, guidelines, and EMRs.
- Context-aware Q&A using LLMs and a vector store backend
- Custom knowledge ingestion from O&G textbooks, lecture notes, and clinical protocols
- Hugging Face Embeddings for semantic similarity search
- ChromaDB as the vector database for fast retrieval
- Supports evidence-based decision-making in clinical settings
- Python 3.11+
- LangChain
- ChromaDB
- Hugging Face Transformers & Embeddings
- PyPDF
- Hugging Face (FLAN-T5)
git clone https://github.com/AhmadTigress/Rag_System.git
cd Rag_System
pip install -r requirements.txt