README.md

metadata

title: NHS Clinical Assistant
emoji: 🩺
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8501
tags:
  - streamlit
  - healthcare
  - nhs
  - rag
  - llm
  - medical
pinned: false
short_description: RAG-powered NHS health information chatbot

NHS Clinical Assistant

A RAG-based chatbot for querying NHS health condition information. This application uses Retrieval-Augmented Generation to provide accurate, evidence-based responses from official NHS health documentation.

Available at: https://huggingface.co/spaces/matthewlewis06/NHS-CHAT

🌟 Features

• NHS Health Information Search: Search through NHS health conditions using semantic search powered by Voyage AI embeddings
• RAG-powered Chat: Ask questions and get contextually relevant answers from NHS health information with source citations
• Multiple LLM Support: Choose between Gemini models (2.5-flash, 2.5-flash-lite, 2.5-pro) for generating responses
• Source Attribution: All responses include links to original NHS web pages
• Streaming Responses: Real-time response generation for better user experience
• Interactive Interface: Clean Streamlit frontend optimized for healthcare information queries

📁 Project Structure

Core Application Files

src/streamlit_app.py

Main Streamlit application interface providing:

• User-friendly web interface for NHS health information queries
• Chat interface with conversation history
• Model selection (Gemini variants)
• Source attribution display with NHS links
• Suggested queries for common health topics

src/query_rag.py

RAG (Retrieval-Augmented Generation) system that handles:

• Query processing and validation
• Integration with search engine and LLM clients
• Context generation from NHS health documents
• Streaming response generation
• Source extraction and formatting
• Can be used as standalone CLI tool for testing

src/search_engine.py

Search functionality using Pinecone vector database:

• Similarity search using Voyage AI embeddings (voyage-context-3 model)
• Integration with Pinecone vector database
• NHS health information retrieval

Configuration

src/config.py

Centralized configuration management:

• NHS source configuration
• System prompts and error messages
• Default search parameters

Infrastructure

requirements.txt

Python dependencies:

• streamlit==1.40.1 - Web application framework
• openai - LLM client (used for Gemini API access)
• voyageai - Embedding generation
• pinecone - Vector database client
• pandas - Data manipulation
• altair - Visualization support

Dockerfile

Container configuration for deployment:

• Python 3.9 base image
• Production-ready setup
• Health check configuration
• Streamlit server configuration

🚀 Getting Started

Prerequisites

• Python 3.9+
• Gemini API key (for LLM responses)
• Voyage AI API key (for embeddings)
• Pinecone API key (for vector search)

Environment Variables

Set the following environment variables:

export GEMINI_API_KEY=your_gemini_api_key
export VOYAGE_API_KEY=your_voyage_api_key
export PINECONE_API_KEY=your_pinecone_api_key

Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

Run the application

streamlit run src/streamlit_app.py

The application will be available at http://localhost:8501

Docker Deployment

docker build -t nhs-clinical-assistant .
docker run -p 8501:8501 \
  -e GEMINI_API_KEY=your_gemini_api_key \
  -e VOYAGE_API_KEY=your_voyage_api_key \
  -e PINECONE_API_KEY=your_pinecone_api_key \
  nhs-clinical-assistant

🔧 Usage

Web Interface

1. Open the application in your browser
2. Select your preferred Gemini model from the sidebar
3. Type your NHS health-related question in the chat input
4. View the response with source attribution
5. Click "View Sources" to see NHS page references

CLI Usage

Test the RAG system directly:

python src/query_rag.py --query_text "What are the symptoms of ADHD in adults?" --llm_model "gemini-2.5-flash"

Example Queries

• "What are the symptoms of ADHD in adults?"
• "How is type 2 diabetes diagnosed?"
• "What are the treatment options for depression?"

🏗️ Architecture

The system uses a simple but effective RAG architecture:

1. Query Processing: User query is validated and processed
2. Vector Search: Query is embedded using Voyage AI and searched against Pinecone vector database containing NHS health information
3. Context Generation: Retrieved NHS documents are formatted into context
4. LLM Response: Gemini generates response based strictly on NHS context
5. Source Attribution: Original NHS page links are provided with responses

📊 Data Sources

The system is built on NHS health condition information, stored in a Pinecone vector database with the namespace nhs_guidelines_voyage_3_large. All responses include proper attribution to NHS sources with direct links to official NHS web pages.

⚠️ Important Notes

• Medical Disclaimer: This tool provides information from NHS sources but should not replace professional medical advice
• Data Accuracy: Always consult official NHS sources for the most current information
• Context Limitation: The system only responds based on information available in the indexed NHS documents

📄 License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).

Code License

The source code of this application is released under AGPL-3.0, which means:

• You can freely use, modify, and distribute this software
• Any modifications or derivative works must also be released under AGPL-3.0
• If you run this software as a network service, you must provide the source code to users
• See the LICENSE file for full terms

NHS Data Usage

This tool utilizes NHS health information under the Open Government Licence. All NHS content remains subject to their original terms and conditions and is used for informational purposes in compliance with UK public sector information licensing.

Note: While the application code is AGPL-3.0 licensed, the NHS health information content accessed through this application remains under Crown Copyright and the Open Government Licence.