An advanced Retrieval-Augmented Generation (RAG) system designed to enhance diagnostic reasoning from clinical notes. This project integrates LangChain, Bio_ClinicalBERT, and FAISS for semantic retrieval, while leveraging LLaMA 3 (via Groq API) for context-aware medical text generation.

• 🩺 Clinical Reasoning Support — Generates diagnostic insights from unstructured medical notes.
• 🔍 Semantic Retrieval — Uses Bio_ClinicalBERT embeddings and FAISS for efficient context retrieval.
• 🤖 LLM Integration — Employs LLaMA 3 (Groq API) for fast and accurate clinical reasoning.
• 💬 Interactive Interface — Built with Streamlit for smooth and intuitive user interaction.
• ⚡ Real-Time Inference — Utilizes Groq’s ultra-fast inference engine for near-instant responses.

User Query → Text Embedding (Bio_ClinicalBERT) → 
Vector Search (FAISS) → Context Retrieval → 
Prompt Construction → LLaMA 3 (Groq API) → Diagnostic Response

• Language Model: LLaMA 3 (Groq API)
• Framework: LangChain
• Embeddings: Bio_ClinicalBERT
• Vector Database: FAISS
• Frontend: Streamlit
• Language: Python

1. Clone the repository

   git clone https://github.com/Muavia1/RAG-for-Diagnostic-Reasoning-of-Clinical-Notes.git
   cd RAG-for-Diagnostic-Reasoning-of-Clinical-Notes

2. Create and activate a virtual environment

   python -m venv venv
   source venv/bin/activate   # for Linux/Mac
   venv\Scripts\activate      # for Windows

3. Install dependencies

   pip install -r requirements.txt

4. Add your API keys Create a .env file and include your keys:

   GROQ_API_KEY=your_groq_api_key

5. Run the Streamlit app

   streamlit run app.py

Upload or input clinical notes describing a patient’s symptoms. The system retrieves relevant literature and generates evidence-based diagnostic reasoning, assisting clinicians in decision-making.

LangChain · LLaMA 3 · FAISS · Bio_ClinicalBERT · Groq API · Streamlit · Retrieval-Augmented Generation · Natural Language Processing · Machine Learning · Clinical AI

• Integrate PubMed and MIMIC-III datasets for richer medical context.
• Add evaluation metrics for clinical reasoning accuracy.
• Support multilingual clinical note processing.

Muavia Ijaz

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