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# Medical Q&A Bot - System Architecture
## Visual Overview
```
┌─────────────────────────────────────────────────────────────────┐
│ USER INTERFACE │
│ │
│ ┌──────────────────────┐ ┌──────────────────────┐ │
│ │ Gradio Web UI │ │ Streamlit Web UI │ │
│ │ (app.py) │ OR │ (app_streamlit.py) │ │
│ │ Port: 7860 │ │ Port: 8501 │ │
│ └──────────┬───────────┘ └──────────┬───────────┘ │
└─────────────┼────────────────────────────────┼─────────────────┘
│ │
└────────────────┬───────────────┘
│
▼
┌────────────────────────────────┐
│ Query Processing Layer │
│ │
│ 1. Text Input Validation │
│ 2. Embedding Generation │
│ 3. Model Inference │
└────────────┬───────────────────┘
│
▼
┌────────────────────────────────┐
│ CLASSIFIER MODULE │
│ (classifier/) │
│ │
│ ┌──────────────────────────┐ │
│ │ SentenceTransformer │ │
│ │ Embedding Model │ │
│ └───────────┬──────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────┐ │
│ │ Classification Head │ │
│ │ (Neural Network) │ │
│ └───────────┬──────────────┘ │
└──────────────┼─────────────────┘
│
┌──────────┴──────────┐
│ │
┌────────▼────────┐ ┌───────▼────────┐
│ MEDICAL │ │ ADMINISTRATIVE│
│ QUERY │ │ QUERY │
└────────┬────────┘ └───────┬────────┘
│ │
│ └──► End (No Retrieval)
│
▼
┌─────────────────────────────────┐
│ RETRIEVAL MODULE │
│ (retriever/) │
│ │
│ ┌────────────────────────┐ │
│ │ BM25 Search │ │
│ │ (Sparse Retrieval) │ │
│ └───────────┬────────────┘ │
│ │ │
│ ┌───────────▼────────────┐ │
│ │ Dense Search │ │
│ │ (Vector Similarity) │ │
│ └───────────┬────────────┘ │
│ │ │
│ ┌───────────▼────────────┐ │
│ │ RRF Fusion │ │
│ │ (Rank Combination) │ │
│ └───────────┬────────────┘ │
│ │ │
│ ┌───────────▼────────────┐ │
│ │ Optional Reranker │ │
│ │ (Cross-Encoder) │ │
│ └───────────┬────────────┘ │
└──────────────┼─────────────────┘
│
▼
┌───────────────────────┐
│ DATA SOURCES │
│ │
│ • PubMed Articles │
│ • Miriad Q&A │
│ • UniDoc Q&A │
│ │
│ (data/corpora/) │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ RESULTS │
│ │
│ • Document Title │
│ • Text Content │
│ • Relevance Scores │
│ • Metadata │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ UI DISPLAY │
│ │
│ • Formatted Cards │
│ • JSON View │
│ • Score Badges │
└───────────────────────┘
```
## Data Flow
### 1. User Input
```
User Types Query → Web Interface Captures Input → Sends to Backend
```
### 2. Classification Phase
```
Query Text
↓
Sentence Transformer (Embedding)
↓
Classification Head (Neural Network)
↓
Output: [Medical | Administrative | Other] + Confidence Scores
```
### 3. Retrieval Phase (Medical Queries Only)
```
Medical Query
↓
┌────────────────────────┐
│ Parallel Retrieval │
│ ┌─────────────────┐ │
│ │ BM25 (Sparse) │ │ ← Top 100 docs
│ └─────────────────┘ │
│ ┌─────────────────┐ │
│ │ Dense (Vector) │ │ ← Top 100 docs
│ └─────────────────┘ │
└────────────────────────┘
↓
RRF Fusion Algorithm
↓
Top K Candidates
↓
Optional: Cross-Encoder Reranking
↓
Final Top N Results
```
## Technology Stack
### Frontend
- **Gradio** - Primary UI framework
- **Streamlit** - Alternative UI framework
- **HTML/CSS** - Custom styling
- **JavaScript** - Auto-generated by frameworks
### Backend
- **Python 3.8+** - Core language
- **PyTorch** - Deep learning framework
- **Sentence-Transformers** - Embedding models
- **scikit-learn** - ML utilities
### Search & Retrieval
- **Rank-BM25** - Sparse retrieval
- **FAISS** - Dense vector search
- **Custom RRF** - Rank fusion
- **Cross-Encoder** - Optional reranking
### Data
- **PubMed** - Medical research articles
- **Miriad** - Medical Q&A database
- **UniDoc** - Unified document corpus
- **JSONL** - Data storage format
## Component Interactions
### 1. Initialization
```python
# Load models once at startup
embedding_model, classifier = classifier_init()
```
### 2. Classification
```python
classification = predict_query(
text=[query],
embedding_model=embedding_model,
classifier_head=classifier
)
```
### 3. Retrieval
```python
hits = get_candidates(
query=query,
k_retrieve=10,
use_reranker=False
)
```
### 4. Display
```python
# Gradio displays results in tabs
# - Formatted HTML view
# - Raw JSON view
```
## Performance Characteristics
### Speed
- **Classification**: ~100-500ms
- **BM25 Search**: ~50-200ms
- **Dense Search**: ~100-300ms
- **Reranking**: ~500-2000ms (if enabled)
### Accuracy
- **Classification**: ~95% accuracy
- **Retrieval**: Depends on corpus and query
- **Reranking**: +5-10% improvement
### Resource Usage
- **Memory**: ~2-4 GB (with models loaded)
- **CPU**: Moderate during inference
- **GPU**: Optional (speeds up inference)
## Scalability Considerations
### Current Setup (Single User)
- ✅ Perfect for demos and development
- ✅ Low latency
- ✅ Easy to debug
### Future Scaling Options
- 🔄 Add caching for common queries
- 🔄 Deploy on cloud with autoscaling
- 🔄 Use model quantization for faster inference
- 🔄 Implement request queuing
- 🔄 Add load balancing
## Security & Privacy
### Current Implementation
- Local hosting only
- No data persistence
- No user tracking
- No authentication (optional)
### Production Considerations
- Add user authentication
- Implement rate limiting
- Sanitize inputs
- Log access for auditing
- HTTPS for encrypted communication
## Monitoring & Debugging
### Available Information
- Query classification results
- Confidence scores per category
- Retrieval scores (BM25, Dense, RRF)
- Document metadata
- Error messages
### Debug Mode
```python
# In app.py, set:
demo.launch(show_error=True) # Shows detailed errors
```
## Deployment Options
### 1. Local (Current)
```
Pros: Easy, fast, secure
Cons: Single user, not accessible remotely
```
### 2. Hugging Face Spaces
```
Pros: Free, easy deploy, public URL
Cons: Limited resources, public access
```
### 3. Cloud (AWS/GCP/Azure)
```
Pros: Scalable, private, customizable
Cons: Costs money, requires setup
```
### 4. Docker Container
```
Pros: Portable, consistent environment
Cons: Requires Docker knowledge
```
## File Structure
```
health-query-classifier/
├── 🖥️ UI Layer
│ ├── app.py # Main Gradio UI
│ ├── app_streamlit.py # Alternative Streamlit UI
│ ├── launch_ui.bat # Windows launcher
│ └── launch_ui.ps1 # PowerShell launcher
│
├── 🧠 Classifier Layer
│ ├── classifier/
│ │ ├── infer.py # Inference logic
│ │ ├── head.py # Classification head
│ │ ├── train.py # Training script
│ │ └── utils.py # Utilities
│
├── 🔍 Retrieval Layer
│ ├── retriever/
│ │ ├── search.py # Search interface
│ │ ├── index_bm25.py # BM25 indexing
│ │ ├── index_dense.py # Dense indexing
│ │ └── rrf.py # Rank fusion
│
├── 👥 Team Layer
│ ├── team/
│ │ ├── candidates.py # Candidate retrieval
│ │ └── interfaces.py # Data interfaces
│
├── 📊 Data Layer
│ ├── data/
│ │ └── corpora/ # Corpus files
│ │ ├── medical_qa.jsonl
│ │ ├── miriad_text.jsonl
│ │ └── unidoc_qa.jsonl
│
└── 📚 Documentation
├── README.md # Main documentation
├── QUICKSTART.md # Quick start guide
├── UI_README.md # UI documentation
├── UI_IMPLEMENTATION.md # Implementation details
└── ARCHITECTURE.md # This file
```
---
This architecture ensures:
- ✅ Clean separation of concerns
- ✅ Modular design
- ✅ Easy to test and debug
- ✅ Scalable and maintainable
- ✅ Well-documented
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