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# Developer Documentation
## Development Setup
### Prerequisites
- Python 3.9 or higher
- Git
- Azure OpenAI account
- Azure Document Intelligence account
### Local Development Environment
1. **Clone the repository**
```bash
git clone <repository-url>
cd doctorecord
```
2. **Create virtual environment**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Set up environment variables**
```bash
cp .env.example .env
# Edit .env with your Azure credentials
```
5. **Run the application**
```bash
streamlit run src/app.py
```
## Project Structure
```
doctorecord/
βββ src/
β βββ agents/ # Agent implementations
β β βββ base_agent.py # Base agent class
β β βββ pdf_agent.py # PDF text extraction
β β βββ table_agent.py # Table processing
β β βββ field_mapper_agent.py # Field extraction
β β βββ unique_indices_combinator.py # Unique combinations
β β βββ unique_indices_loop_agent.py # Loop processing
β βββ services/ # Service layer
β β βββ llm_client.py # Azure OpenAI client
β β βββ azure_di_service.py # Document Intelligence
β β βββ cost_tracker.py # Cost tracking
β β βββ embedding_client.py # Semantic search
β βββ orchestrator/ # Orchestration layer
β β βββ planner.py # Plan generation
β β βββ executor.py # Plan execution
β βββ config/ # Configuration
β β βββ settings.py # Settings management
β βββ app.py # Streamlit application
βββ tests/ # Test files
βββ logs/ # Log files
βββ requirements.txt # Python dependencies
βββ README.md # Project documentation
```
## Coding Standards
### Python Style Guide
- Follow PEP 8 style guidelines
- Use type hints for function parameters and return values
- Maximum line length: 88 characters (Black formatter)
- Use descriptive variable and function names
### Code Organization
```python
# Standard imports
import logging
from typing import Dict, Any, Optional, List
# Third-party imports
import pandas as pd
from azure.ai.documentintelligence import DocumentIntelligenceClient
# Local imports
from .base_agent import BaseAgent
from services.llm_client import LLMClient
```
### Logging Standards
```python
class MyAgent(BaseAgent):
def __init__(self):
self.logger = logging.getLogger(__name__)
def execute(self, ctx: Dict[str, Any]) -> Optional[str]:
self.logger.info("Starting execution")
self.logger.debug(f"Context keys: {list(ctx.keys())}")
try:
# Implementation
self.logger.info("Execution completed successfully")
return result
except Exception as e:
self.logger.error(f"Execution failed: {str(e)}", exc_info=True)
return None
```
### Error Handling
```python
def safe_execution(self, operation):
try:
return operation()
except Exception as e:
self.logger.error(f"Operation failed: {str(e)}", exc_info=True)
# Return appropriate fallback or re-raise
raise
```
## Agent Development
### Creating a New Agent
1. **Inherit from BaseAgent**
```python
from .base_agent import BaseAgent
class MyNewAgent(BaseAgent):
def __init__(self):
super().__init__()
self.logger = logging.getLogger(__name__)
```
2. **Implement the execute method**
```python
def execute(self, ctx: Dict[str, Any]) -> Optional[str]:
"""
Execute the agent's main functionality.
Args:
ctx: Context dictionary containing input data
Returns:
Result string or None if failed
"""
self.logger.info("Starting MyNewAgent execution")
# Store context for use in helper methods
self.ctx = ctx
# Implementation here
result = self._process_data(ctx)
return result
```
3. **Add to executor**
```python
# In src/orchestrator/executor.py
from agents.my_new_agent import MyNewAgent
class Executor:
def __init__(self, settings, cost_tracker=None):
self.tools = {
# ... existing tools
"MyNewAgent": MyNewAgent(),
}
```
### Agent Best Practices
1. **Context Management**
```python
def execute(self, ctx: Dict[str, Any]) -> Optional[str]:
# Store context for helper methods
self.ctx = ctx
# Access context data
text = ctx.get("text", "")
fields = ctx.get("fields", [])
```
2. **Cost Tracking Integration**
```python
def _call_llm(self, prompt: str, description: str) -> str:
# Get cost tracker from context
cost_tracker = self.ctx.get("cost_tracker") if hasattr(self, 'ctx') else None
result = self.llm.responses(
prompt, temperature=0.0,
ctx={"cost_tracker": cost_tracker} if cost_tracker else None,
description=description
)
return result
```
3. **Error Handling**
```python
def execute(self, ctx: Dict[str, Any]) -> Optional[str]:
try:
# Implementation
return result
except Exception as e:
self.logger.error(f"Agent execution failed: {str(e)}", exc_info=True)
return None
```
## Service Development
### LLM Client Usage
```python
from services.llm_client import LLMClient
from config.settings import settings
class MyAgent(BaseAgent):
def __init__(self):
self.llm = LLMClient(settings)
def _extract_data(self, text: str) -> str:
prompt = f"Extract data from: {text}"
# Get cost tracker from context
cost_tracker = self.ctx.get("cost_tracker") if hasattr(self, 'ctx') else None
result = self.llm.responses(
prompt, temperature=0.0,
ctx={"cost_tracker": cost_tracker} if cost_tracker else None,
description="Data Extraction"
)
return result
```
### Cost Tracking Integration
```python
from services.cost_tracker import CostTracker
# In executor or main application
cost_tracker = CostTracker()
# Pass to agents via context
ctx = {
"cost_tracker": cost_tracker,
# ... other context data
}
# Track costs
costs = cost_tracker.calculate_current_file_costs()
print(f"Total cost: ${costs['openai']['total_cost']:.4f}")
```
## Testing
### Running Tests
```bash
# Run all tests
python -m pytest tests/
# Run specific test file
python -m pytest tests/test_cost_tracking.py
# Run with coverage
python -m pytest --cov=src tests/
```
### Writing Tests
```python
import pytest
from unittest.mock import Mock, patch
from src.agents.my_agent import MyAgent
def test_my_agent_execution():
"""Test MyAgent execution with mock data."""
agent = MyAgent()
# Mock context
ctx = {
"text": "Test document content",
"fields": ["field1", "field2"],
"cost_tracker": Mock()
}
# Mock LLM response
with patch.object(agent.llm, 'responses') as mock_llm:
mock_llm.return_value = '{"field1": "value1", "field2": "value2"}'
result = agent.execute(ctx)
assert result is not None
assert "field1" in result
assert "field2" in result
```
### Test Structure
```
tests/
βββ test_agents/ # Agent tests
β βββ test_field_mapper_agent.py
β βββ test_unique_indices_combinator.py
βββ test_services/ # Service tests
β βββ test_llm_client.py
β βββ test_cost_tracker.py
βββ test_orchestrator/ # Orchestrator tests
β βββ test_planner.py
β βββ test_executor.py
βββ integration/ # Integration tests
βββ test_end_to_end.py
```
## Configuration Management
### Settings Structure
```python
# src/config/settings.py
from pydantic_settings import BaseSettings
class Settings(BaseSettings):
# Azure OpenAI
AZURE_OPENAI_ENDPOINT: str
AZURE_OPENAI_API_KEY: str
AZURE_OPENAI_DEPLOYMENT: str
AZURE_OPENAI_API_VERSION: str = "2025-03-01-preview"
# Azure Document Intelligence
AZURE_DI_ENDPOINT: str
AZURE_DI_KEY: str
# Retry Configuration
LLM_MAX_RETRIES: int = 5
LLM_BASE_DELAY: float = 1.0
LLM_MAX_DELAY: float = 60.0
class Config:
env_file = ".env"
```
### Environment Variables
```bash
# .env file
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_API_KEY=your-api-key
AZURE_OPENAI_DEPLOYMENT=your-deployment-name
AZURE_DI_ENDPOINT=https://your-resource.cognitiveservices.azure.com/
AZURE_DI_KEY=your-di-key
```
## Debugging
### Logging Configuration
```python
import logging
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
# Set specific logger levels
logging.getLogger('azure').setLevel(logging.WARNING)
logging.getLogger('openai').setLevel(logging.WARNING)
```
### Debug Mode
```python
# Enable debug logging
logging.getLogger().setLevel(logging.DEBUG)
# In agents
self.logger.debug(f"Processing data: {data[:200]}...")
```
### Cost Tracking Debug
```python
# Check cost tracker state
print(f"LLM calls: {len(cost_tracker.llm_calls)}")
print(f"Input tokens: {cost_tracker.llm_input_tokens}")
print(f"Output tokens: {cost_tracker.llm_output_tokens}")
# Get detailed costs
costs_df = cost_tracker.get_detailed_costs_table()
print(costs_df)
```
## Performance Optimization
### Memory Management
```python
# Process large documents in chunks
def process_large_document(self, text: str, chunk_size: int = 10000):
chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
results = []
for chunk in chunks:
result = self._process_chunk(chunk)
results.append(result)
return self._combine_results(results)
```
### Caching
```python
# Use session state for caching
if 'processed_data' not in st.session_state:
st.session_state.processed_data = {}
# Check cache before processing
if key in st.session_state.processed_data:
return st.session_state.processed_data[key]
```
### Batch Processing
```python
# Process multiple items efficiently
def process_batch(self, items: List[str]) -> List[str]:
results = []
for item in items:
try:
result = self._process_item(item)
results.append(result)
except Exception as e:
self.logger.error(f"Failed to process item: {str(e)}")
results.append(None)
return results
```
## Deployment
### Production Setup
1. **Environment Configuration**
```bash
# Set production environment variables
export AZURE_OPENAI_ENDPOINT=...
export AZURE_OPENAI_API_KEY=...
```
2. **Dependencies**
```bash
pip install -r requirements.txt
```
3. **Run Application**
```bash
streamlit run src/app.py --server.port 8501
```
### Docker Deployment
```dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY src/ ./src/
COPY .env .
EXPOSE 8501
CMD ["streamlit", "run", "src/app.py", "--server.port=8501"]
```
## Contributing
### Development Workflow
1. Create feature branch: `git checkout -b feature/new-feature`
2. Make changes following coding standards
3. Add tests for new functionality
4. Run tests: `python -m pytest tests/`
5. Update documentation
6. Submit pull request
### Code Review Checklist
- [ ] Code follows style guidelines
- [ ] Tests are included and passing
- [ ] Documentation is updated
- [ ] Error handling is implemented
- [ ] Cost tracking is integrated
- [ ] Logging is appropriate
### Release Process
1. Update version in `__init__.py`
2. Update CHANGELOG.md
3. Create release tag
4. Deploy to production
5. Update documentation
## Troubleshooting
### Common Issues
**Azure OpenAI Connection Errors**
```python
# Check configuration
print(f"Endpoint: {settings.AZURE_OPENAI_ENDPOINT}")
print(f"Deployment: {settings.AZURE_OPENAI_DEPLOYMENT}")
print(f"API Version: {settings.AZURE_OPENAI_API_VERSION}")
```
**Cost Tracking Issues**
```python
# Verify cost tracker is passed correctly
if 'cost_tracker' not in ctx:
self.logger.warning("No cost tracker in context")
# Check if agents store context
if not hasattr(self, 'ctx'):
self.logger.warning("Agent doesn't store context")
```
**Memory Issues**
```python
# Monitor memory usage
import psutil
process = psutil.Process()
print(f"Memory usage: {process.memory_info().rss / 1024 / 1024:.2f} MB")
```
### Debug Tools
- **Log Analysis**: Check logs for error patterns
- **Cost Monitoring**: Track API usage and costs
- **Performance Profiling**: Monitor execution times
- **Memory Profiling**: Track memory usage
## API Reference
### Agent Base Class
```python
class BaseAgent:
def execute(self, ctx: Dict[str, Any]) -> Optional[str]:
"""Execute the agent's main functionality."""
raise NotImplementedError
```
### LLM Client
```python
class LLMClient:
def responses(self, prompt: str, **kwargs) -> str:
"""Send prompt to Azure OpenAI and return response."""
```
### Cost Tracker
```python
class CostTracker:
def add_llm_tokens(self, input_tokens: int, output_tokens: int, description: str):
"""Track LLM token usage and costs."""
def calculate_current_file_costs(self) -> Dict[str, Any]:
"""Calculate costs for current file processing."""
```
For more detailed information, refer to the inline documentation in the source code. |