# Logging Strategy Improvements

## Summary

Simplified verbose logging and ensured consistent error handling with stack traces across the Amazon Q JupyterLab extension.

## Changes Made

### 1. Log Level Optimization

**Before:**
```python
logger.info(f"Amazon Q client HTML path: {CLIENT_HTML_PATH}")
logger.info(f"Configuring handlers with base URL: {base_url}")
```

**After:**
```python
logger.debug(f"Client HTML: {CLIENT_HTML_PATH}")
logger.debug(f"Base URL: {base_url}")
```

**Rationale:** File paths and configuration details are implementation specifics better suited for DEBUG level.

### 2. Consistent Error Handling

**Before:**
```python
except Exception as e:
    logger.error(f"Error cleaning up credential manager: {e}")
```

**After:**
```python
except Exception as e:
    logger.error(f"Error cleaning up credential manager: {e}", exc_info=True)
```

**Rationale:** All errors should include stack traces for effective debugging, not just in debug mode.

### 3. Removed Verbose Comments

**Before:**
```python
# PRODUCTION LOGGING: Use structured logging for better observability
# Benefits: Enables proper monitoring, alerting, and troubleshooting
logger.error(error_msg)
logger.debug(f"Stack trace: {stack_trace}")
```

**After:**
```python
logger.error(f"Failed to load Amazon Q extension: {e}", exc_info=True)
```

**Rationale:** When comments duplicate log messages, remove comments to reduce verbosity.

### 4. Simplified Success Messages

**Before:**
```python
logger.info("Successfully registered Amazon Q extension handlers")
logger.debug(f"Handler patterns - Route: {route_pattern}, WebSocket: {websocket_pattern}")
```

**After:**
```python
logger.info("Amazon Q handlers registered")
```

**Rationale:** Concise messages reduce log noise while maintaining essential information.

## Log Level Strategy

| Level | Usage | Examples |
|-------|-------|----------|
| **ERROR** | Critical failures with stack traces | LSP server failures, credential errors |
| **WARNING** | Non-critical issues | Missing optional files, fallback scenarios |
| **INFO** | Important business events | Service initialization, handler registration |
| **DEBUG** | Implementation details | File paths, configuration values, progress updates |

## Benefits

1. **Reduced Log Noise**: INFO logs focus on meaningful business events
2. **Better Debugging**: All errors include full stack traces
3. **Production Ready**: Appropriate log levels for monitoring and alerting
4. **Maintainable**: Removed redundant comments and verbose messages

## Files Modified

- `__init__.py`: Simplified initialization logging, added stack traces to errors
- `handlers.py`: Moved file paths to DEBUG, simplified success messages
- `websocket_handler.py`: Added stack traces to LSP initialization errors
- `lsp_server_connection.py`: Converted print statements to proper logging
- `README.md`: Added logging strategy documentation

## Configuration

```bash
# Production: INFO level (default)
jupyter lab

# Development: DEBUG level for detailed diagnostics
JUPYTER_LOG_LEVEL=DEBUG jupyter lab
```