# 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
```python
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.