IMPROVEMENTS.md

Medical Chatbot - Recent Improvements

Issues Fixed

1. Model Initialization Error

Problem: "404 models/gemini-1.5-flash is not found" Solution:

• Added automatic model fallback mechanism
• Tries multiple model names until one works:
  • models/gemini-pro
  • gemini-pro
  • models/gemini-1.5-pro
  • gemini-1.5-pro

2. Wrong/Inaccurate Answers

Problem: The model was giving incorrect or irrelevant answers

Solutions Applied:

A. Improved Prompt Engineering

• Before: Complex multi-step instructions
• After: Direct, clear instructions to use ONLY context information
• Added "DO NOT make up or guess information"
• Structured prompt with clear sections

B. Lower Temperature Setting

• Set temperature=0.3 (default is 0.7)
• This makes responses more factual and less creative
• Better for medical information accuracy

C. Better Context Formatting

• Clear source citations in context
• Better structured context presentation
• Easier for model to parse and use information

D. Enhanced Generation Config

generation_config={
    "temperature": 0.3,  # Lower for factual responses
    "top_p": 0.8,        # Nucleus sampling
    "top_k": 40,         # Token selection limit
    "max_output_tokens": 500,  # Concise responses
}

E. Improved Retrieval

• Filters results by similarity threshold (0.5)
• Only returns highly relevant medical content
• Better context quality = better answers

Current Configuration

• Embedding Model: sentence-transformers/all-MiniLM-L6-v2
• LLM Model: Auto-detected Gemini model
• Database: 3,012 medical documents from MultiMedQA
• Top K Retrieval: 5 most relevant chunks
• Similarity Threshold: 0.5 (minimum relevance score)

How It Works Now

1. User asks a medical question
2. Query is embedded using Sentence Transformers
3. Pinecone searches for similar medical content (top 5 results)
4. Results are filtered by similarity score (≥0.5)
5. Context is formatted with clear citations
6. Gemini generates answer using ONLY the retrieved context
7. Response includes:
   • Factual answer from medical database
   • Citations with sources
   • Confidence score
   • Medical disclaimer

Testing the Improvements

Try these questions to verify accuracy:

• "What are the symptoms of diabetes?"
• "How is hypertension treated?"
• "Explain cardiac arrhythmia"
• "What causes chest pain?"

Key Improvements Summary

✅ Model auto-detection (tries multiple models) ✅ Lower temperature for factual responses ✅ Clearer prompt instructions ✅ Better context formatting ✅ Improved error handling ✅ Debug logging for troubleshooting

The chatbot should now provide accurate, factual medical information based solely on the retrieved context from the medical database.