A sophisticated health recommendation system that combines user health metrics with Retrieval-Augmented Generation (RAG) to provide personalized health advice and recommendations.

• Health Metrics Monitoring

  • Heart rate tracking
  • Blood pressure monitoring
  • Daily steps counter
  • Temperature tracking

• Interactive Query System

  • Natural language health-related questions
  • Personalized responses based on user metrics
  • Structured health recommendations

• Advanced RAG Implementation

  • Multiple LLM integrations:
    • Local LLM using Ollama (llama3)
    • Cloud LLM using Groq (llama-3.1-70b-versatile)
  • Document processing with LangChain
  • Multiple vector store options:
    • ChromaDB for web content
    • Qdrant for medical data
  • Advanced text processing:
    • Efficient text splitting with RecursiveCharacterTextSplitter
    • PDF document parsing using PyMuPDF
    • Contextual compression with FlashrankRerank
  • High-quality embeddings using BAAI/bge-base-en-v1.5

• Frontend & UI

  • Streamlit
  • Text wrapping for better readability
  • Interactive input forms
  • JSON-formatted responses

• Backend & Processing

  • LangChain for document processing and chains
  • Multiple LLM providers:
    • Ollama for local processing
    • Groq for cloud processing
  • Vector Stores:
    • ChromaDB for web content
    • Qdrant for medical data
  • FastEmbed embeddings with BAAI/bge-base-en-v1.5
  • PyMuPDF for PDF processing
  • Weather API integration for environmental context

• Data Processing

  • RecursiveCharacterTextSplitter for optimal chunking
  • FlashrankRerank for context compression
  • Structured JSON output format

• Python 3.x
• Ollama with llama3 model installed
• Required Python packages:

  streamlit
  langchain
  langchain_community
  ollama
  chromadb
  qdrant-client
  PyMuPDF
  python-dotenv
  requests
  fastembed
  langchain-groq
  

• API Keys (store in .env file):

  GROQ_API_KEY=your_groq_api_key
  WEATHER_API=your_weather_api_key
  

1. Clone the repository
2. Install the required packages:

   pip install -r requirements.txt

3. Ensure Ollama is installed and the llama3 model is available

1. Start the Streamlit application:

   streamlit run app.py

2. Enter your health metrics:

   • Heart rate (bpm)
   • Blood pressure
   • Steps taken today
   • Body temperature

3. Ask health-related questions and receive personalized recommendations

• Real-time input validation
• Comprehensive health parameter tracking
• User-friendly interface for data entry

• Multiple document sources support (PDF, web content)
• Efficient document retrieval with dual vector store approach
• Context-aware responses with FlashrankRerank
• Integration with both local and cloud LLMs
• Customizable chunk sizes for optimal performance
• Environmental context integration (weather data)

• Structured health advice
• Context-based suggestions
• Multiple recommendation categories:
  • Exercise recommendations
  • Health advisories
  • General wellness tips

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.

This system is designed to provide general health recommendations and should not be used as a substitute for professional medical advice. Always consult with healthcare professionals for medical decisions.