Spaces:
Sleeping
Sleeping
File size: 2,851 Bytes
0a5c991 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
# 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.
|