Dermatology RAG Assistant

This project implements a Retrieval-Augmented Generation (RAG) assistant designed to answer dermatology-related questions using the Dermatology Handbook, 3rd Edition as its knowledge source. It was created as part of the Agentic AI Developer Certification (AAIDC) program.

Overview

The assistant works by retrieving sections of the handbook that relate to a user's question and then generating an answer based strictly on those sections. This approach ensures that responses come from the handbook and not from outside assumptions.

Key Features

• PDF Processing: Loads and processes the dermatology handbook.
• Text Chunking: Splits the text into manageable, overlapping sections.
• Vector Embeddings: Converts text into numerical vectors with Sentence-Transformers.
• Persistent Storage: Stores embeddings using ChromaDB.
• Semantic Search: Finds relevant text chunks based on the meaning of the user’s question.
• Context-Based Answering: Uses an LLM to generate answers based only on the retrieved context.
• Support for Multiple LLM Providers: Works with OpenAI, Groq, or Google Gemini depending on the available API key.

Tech Stack

• Language: Python 3.9+
• Framework: LangChain
• Vector Database: ChromaDB
• Embedding Model: sentence-transformers/all-MiniLM-L6-v2
• LLM Providers: OpenAI, Groq, Google Gemini
• Document Loader: PyPDFLoader
• Environment Management: python-dotenv

System Architecture

Ingestion Pipeline

1. Load the Dermatology Handbook PDF from the data/ directory.
2. Split the text into overlapping chunks.
3. Create embeddings for each chunk and store them in ChromaDB.
4. This step runs once and creates the persistent vector store.

Query Pipeline

1. The user asks a question.
2. The question is converted into a vector using the same embedding model.
3. The system performs a similarity search to find the most relevant chunks.
4. These chunks and the original question are placed into a prompt template.
5. The LLM generates an answer based only on the retrieved material.

Setup and Installation

Prerequisites

• Python 3.8 or higher
• An API key for OpenAI, Groq, or Google Gemini

1. Clone the Repository

git clone <https://github.com/10486-JosephMutua/dermatology-rag-assistant.git>
cd <dermatology-rag-assistant>

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file:

Linux/macOS:
cp .env.example .env

Windows:
copy .env.example .env

Open .env and add one provider key:

# OPENAI_API_KEY="..."
# GROQ_API_KEY="..."
# GOOGLE_API_KEY="..."

4. Add the PDF Document

Place the handbook file into the data/ directory:

data/
└── Derm_Handbook_3rd-Edition-_Nov_2020-FINAL.pdf

Running the Application

python src/app.py

On the first run, the system will process and embed the entire PDF. This may take a few minutes. Once done, the vector store is saved in chroma_db/, and later runs will start immediately.

Example Questions

• "Describe the symptoms of psoriasis."
• "How is acne diagnosed?"
• "What is the difference between a papule and a pustule?"

Project Structure

.
├── src/
│   ├── app.py           # Main application logic
│   └── vectordb.py      # ChromaDB interface
├── data/
│   └── Dermatology Handbook PDF
├── chroma_db/           # Persistent vector store
├── requirements.txt
├── .env.example
├── .gitignore
└── README.md

License

This project is released under the MIT License.

Acknowledgments

This project was developed following the guidelines and template provided by the ReadyTensor Agentic AI Developer Certification program.