Error Handling

This guide covers comprehensive error handling strategies when using the ViewAI Python SDK.

Overview

The ViewAI SDK provides a structured exception hierarchy to help you handle errors gracefully. Understanding these exceptions and implementing proper error handling ensures robust, production-ready applications.

Exception Hierarchy

The ViewAI SDK uses a hierarchical exception structure:

ViewAIError (base)
├── AuthenticationError
├── ValidationError
├── APIError
├── NetworkError
├── ConfigurationError
├── DeploymentError
├── SchemaError
├── ExportError
├── TimeoutError
└── ModelError

Base Exception: ViewAIError

All SDK exceptions inherit from ViewAIError:

from viewai_client.exceptions import ViewAIError

try:
    # ViewAI operations
    result = client.execute_prediction(data, model_id="model-123")
except ViewAIError as e:
    # Catch all ViewAI SDK errors
    print(f"ViewAI error: {e.message}")
    print(f"Details: {e.details}")

ViewAIError Attributes

try:
    result = client.execute_prediction(data, model_id="invalid")
except ViewAIError as e:
    # Error message
    print(f"Message: {e.message}")

    # Additional details dictionary
    print(f"Details: {e.details}")

    # String representation
    print(f"Error: {e}")

Common Errors

AuthenticationError

Raised when API authentication fails:

from viewai_client.exceptions import AuthenticationError

try:
    client = ViewAIClient(api_key="invalid-key")
    workspace = client.retrieve_default_workspace()

except AuthenticationError as e:
    print(f"Authentication failed: {e.message}")
    # Solution: Check your API key
    print("Solution: Verify your API key is correct")

Common causes:

Invalid API key
Expired API key
Missing API key
Incorrect API key format

Solutions:

# Check API key format
def validate_api_key(api_key):
    """Validate API key format."""
    if not api_key:
        raise ValueError("API key is required")

    if len(api_key) < 32:
        raise ValueError("API key appears to be invalid (too short)")

    return True

# Use validation
try:
    validate_api_key(api_key)
    client = ViewAIClient(api_key=api_key)
except ValueError as e:
    print(f"API key validation error: {e}")

ValidationError

Raised when data validation fails:

from viewai_client.exceptions import ValidationError

try:
    # Missing required features
    result = client.execute_single_point_prediction(
        data={"age": 35},  # Missing other required features
        model_id="model-123"
    )

except ValidationError as e:
    print(f"Validation error: {e.message}")
    print(f"Details: {e.details}")
    # Solution: Include all required features

Common causes:

Missing required features
Invalid data types
Values out of range
Invalid category values
Empty or null values

Solutions:

def validate_prediction_data(data, required_features, feature_types):
    """Validate prediction data before submission."""
    errors = []

    # Check required features
    missing = [f for f in required_features if f not in data]
    if missing:
        errors.append(f"Missing features: {missing}")

    # Check data types
    for feature, expected_type in feature_types.items():
        if feature in data:
            value = data[feature]
            if not isinstance(value, expected_type):
                errors.append(
                    f"Feature {feature}: expected {expected_type}, "
                    f"got {type(value)}"
                )

    if errors:
        raise ValidationError(
            "Data validation failed",
            details={"errors": errors}
        )

    return True

# Use validation
required_features = ["age", "income", "credit_score"]
feature_types = {"age": int, "income": float, "credit_score": int}

try:
    validate_prediction_data(data, required_features, feature_types)
    result = client.execute_single_point_prediction(data, "model-123")
except ValidationError as e:
    print(f"Validation failed: {e.message}")
    for error in e.details.get("errors", []):
        print(f"  - {error}")

APIError

Raised when API requests fail:

from viewai_client.exceptions import APIError

try:
    result = client.execute_prediction(data, model_id="invalid-id")

except APIError as e:
    print(f"API error: {e.message}")
    print(f"Status code: {e.status_code}")
    print(f"Response: {e.response}")

    # Handle based on status code
    if e.status_code == 404:
        print("Model not found")
    elif e.status_code == 429:
        print("Rate limit exceeded")
    elif e.status_code >= 500:
        print("Server error - retry later")

Common status codes:

400: Bad request (invalid data)
401: Unauthorized (authentication failed)
403: Forbidden (insufficient permissions)
404: Not found (model/workspace doesn't exist)
429: Too many requests (rate limited)
500: Internal server error
503: Service unavailable

Solutions:

def handle_api_error(error):
    """Handle API errors based on status code."""
    status_code = error.status_code

    if status_code == 400:
        return "Check your input data format"
    elif status_code == 401:
        return "Verify your API key is correct"
    elif status_code == 404:
        return "Check that model/workspace exists"
    elif status_code == 429:
        return "Wait before retrying (rate limited)"
    elif status_code >= 500:
        return "Server error - retry with exponential backoff"
    else:
        return f"Unknown error (status {status_code})"

# Use error handler
try:
    result = client.execute_prediction(data, "model-123")
except APIError as e:
    solution = handle_api_error(e)
    print(f"Error: {e.message}")
    print(f"Solution: {solution}")

NetworkError

Raised when network operations fail:

from viewai_client.exceptions import NetworkError

try:
    job = client.execute_batch_prediction(
        data=large_df,
        model_id="model-123"
    )

except NetworkError as e:
    print(f"Network error: {e.message}")
    # Solution: Check internet connection and retry

Common causes:

Connection timeout
DNS resolution failure
Network unreachable
Connection refused
SSL/TLS errors

Solutions:

import time
from requests.exceptions import RequestException

def retry_on_network_error(func, max_retries=3, backoff_factor=2):
    """Retry function on network errors."""
    for attempt in range(max_retries):
        try:
            return func()
        except NetworkError as e:
            if attempt < max_retries - 1:
                wait_time = backoff_factor ** attempt
                print(f"Network error, retrying in {wait_time}s...")
                time.sleep(wait_time)
            else:
                raise

# Use retry wrapper
def make_prediction():
    return client.execute_prediction(data, "model-123")

try:
    result = retry_on_network_error(make_prediction)
except NetworkError as e:
    print(f"Network error after retries: {e.message}")

ConfigurationError

Raised when configuration is invalid:

from viewai_client.exceptions import ConfigurationError
from viewai_client.config import ClientConfiguration

try:
    config = ClientConfiguration(
        api_key="",  # Empty API key
        timeout=-1   # Invalid timeout
    )
    client = ViewAIClient(config=config)

except ConfigurationError as e:
    print(f"Configuration error: {e.message}")

Common causes:

Missing required configuration
Invalid configuration values
Incompatible configuration options

Solutions:

def validate_configuration(config_dict):
    """Validate client configuration."""
    errors = []

    # Check required fields
    if not config_dict.get("api_key"):
        errors.append("API key is required")

    # Check numeric fields
    timeout = config_dict.get("timeout", 30)
    if timeout <= 0:
        errors.append("Timeout must be positive")

    max_retries = config_dict.get("max_retries", 3)
    if max_retries < 0:
        errors.append("Max retries cannot be negative")

    if errors:
        raise ConfigurationError(
            "Invalid configuration",
            details={"errors": errors}
        )

    return True

# Use validation
try:
    validate_configuration({"api_key": api_key, "timeout": 30})
    client = ViewAIClient(api_key=api_key, timeout=30)
except ConfigurationError as e:
    print(f"Configuration error: {e.message}")

DeploymentError

Raised when model deployment fails:

from viewai_client.exceptions import DeploymentError

try:
    deployable = client.create_deployable_model(
        model=model,
        dataset=df,
        target_column="target",
        model_name="My Model",
        workspace=workspace
    )
    dashboard_id = deployable.deploy()

except DeploymentError as e:
    print(f"Deployment failed: {e.message}")
    print(f"Details: {e.details}")

Common causes:

Upload failure (network issues)
Invalid model format
Schema errors
Insufficient permissions
Storage quota exceeded

TimeoutError

Raised when operations timeout:

from viewai_client.exceptions import TimeoutError

try:
    # Operation with timeout
    result = client.execute_prediction(data, "model-123")

except TimeoutError as e:
    print(f"Operation timed out: {e.message}")
    # Solution: Increase timeout or use async operations

Solutions:

from viewai_client.config import ClientConfiguration

# Increase timeouts
config = ClientConfiguration(
    api_key="your-api-key",
    connection_timeout=30,  # Connection timeout
    read_timeout=60         # Read timeout
)

client = ViewAIClient(config=config)

Error Handling Patterns

Basic Try-Except

Basic error handling:

from viewai_client.exceptions import ViewAIError

try:
    result = client.execute_prediction(data, "model-123")

    if result:
        print(f"Prediction: {result.get_probabilities()}")
    else:
        print("Prediction returned None")

except ViewAIError as e:
    print(f"Error: {e.message}")

Specific Exception Handling

Handle specific exceptions differently:

from viewai_client.exceptions import (
    AuthenticationError,
    ValidationError,
    APIError,
    NetworkError
)

try:
    result = client.execute_prediction(data, "model-123")

except AuthenticationError as e:
    print(f"Authentication failed: {e.message}")
    # Re-authenticate or request new API key
    handle_authentication_failure()

except ValidationError as e:
    print(f"Data validation failed: {e.message}")
    # Fix data and retry
    fixed_data = fix_validation_errors(data, e.details)
    result = client.execute_prediction(fixed_data, "model-123")

except APIError as e:
    print(f"API error: {e.message} (status: {e.status_code})")
    # Handle based on status code
    if e.status_code >= 500:
        # Server error - retry
        time.sleep(5)
        result = client.execute_prediction(data, "model-123")

except NetworkError as e:
    print(f"Network error: {e.message}")
    # Retry with exponential backoff
    result = retry_with_backoff(
        lambda: client.execute_prediction(data, "model-123")
    )

except Exception as e:
    print(f"Unexpected error: {e}")
    # Log and notify
    log_unexpected_error(e)

Context Manager Pattern

Use context managers for resource cleanup:

class ErrorHandlingClient:
    """Client wrapper with error handling."""

    def __init__(self, api_key):
        self.client = ViewAIClient(api_key=api_key)

    def __enter__(self):
        return self.client

    def __exit__(self, exc_type, exc_val, exc_tb):
        # Handle exceptions and cleanup
        if exc_type is None:
            return True  # No exception

        if issubclass(exc_type, ViewAIError):
            print(f"ViewAI error occurred: {exc_val.message}")
            # Log error
            log_error(exc_val)
            return True  # Suppress exception

        return False  # Don't suppress other exceptions

# Usage
with ErrorHandlingClient("your-api-key") as client:
    result = client.execute_prediction(data, "model-123")
    # Errors are logged but don't crash the program

Decorator Pattern

Create error handling decorators:

from functools import wraps

def handle_viewai_errors(func):
    """Decorator to handle ViewAI errors."""

    @wraps(func)
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except ViewAIError as e:
            print(f"ViewAI error in {func.__name__}: {e.message}")
            return None
        except Exception as e:
            print(f"Unexpected error in {func.__name__}: {e}")
            raise

    return wrapper

# Use decorator
@handle_viewai_errors
def make_prediction(client, data, model_id):
    """Make prediction with error handling."""
    return client.execute_prediction(data, model_id)

# Errors are handled automatically
result = make_prediction(client, data, "model-123")

Retry Strategies

Exponential Backoff

Implement exponential backoff for retries:

import time
from typing import Callable, Any

def exponential_backoff_retry(
    func: Callable,
    max_retries: int = 3,
    base_delay: float = 1.0,
    max_delay: float = 60.0,
    exceptions: tuple = (NetworkError, APIError)
) -> Any:
    """Retry function with exponential backoff."""

    for attempt in range(max_retries):
        try:
            return func()

        except exceptions as e:
            if attempt >= max_retries - 1:
                # Last attempt failed
                raise

            # Calculate delay with exponential backoff
            delay = min(base_delay * (2 ** attempt), max_delay)

            print(f"Attempt {attempt + 1} failed: {e}")
            print(f"Retrying in {delay:.1f}s...")

            time.sleep(delay)

# Use exponential backoff
def make_prediction():
    return client.execute_prediction(data, "model-123")

try:
    result = exponential_backoff_retry(
        make_prediction,
        max_retries=5,
        base_delay=1.0
    )
except Exception as e:
    print(f"Failed after retries: {e}")

Retry with Jitter

Add randomness to prevent thundering herd:

import random

def retry_with_jitter(
    func: Callable,
    max_retries: int = 3,
    base_delay: float = 1.0
) -> Any:
    """Retry with jittered exponential backoff."""

    for attempt in range(max_retries):
        try:
            return func()

        except (NetworkError, APIError) as e:
            if attempt >= max_retries - 1:
                raise

            # Exponential backoff with jitter
            delay = base_delay * (2 ** attempt)
            jittered_delay = delay * (0.5 + random.random())

            print(f"Retrying in {jittered_delay:.1f}s...")
            time.sleep(jittered_delay)

# Use jittered retry
result = retry_with_jitter(
    lambda: client.execute_prediction(data, "model-123")
)

Configurable Retry Strategy

Create configurable retry strategies:

from viewai_client.config import RetryStrategy

class RetryConfig:
    """Retry configuration."""

    def __init__(
        self,
        max_attempts: int = 3,
        base_delay: float = 1.0,
        max_delay: float = 60.0,
        exponential_base: float = 2.0,
        jitter: bool = True
    ):
        self.max_attempts = max_attempts
        self.base_delay = base_delay
        self.max_delay = max_delay
        self.exponential_base = exponential_base
        self.jitter = jitter

    def calculate_delay(self, attempt: int) -> float:
        """Calculate delay for given attempt."""
        delay = self.base_delay * (self.exponential_base ** attempt)
        delay = min(delay, self.max_delay)

        if self.jitter:
            delay *= (0.5 + random.random())

        return delay

def retry_with_config(func: Callable, config: RetryConfig) -> Any:
    """Retry with custom configuration."""

    for attempt in range(config.max_attempts):
        try:
            return func()

        except (NetworkError, APIError) as e:
            if attempt >= config.max_attempts - 1:
                raise

            delay = config.calculate_delay(attempt)
            print(f"Retrying in {delay:.1f}s...")
            time.sleep(delay)

# Use custom retry config
retry_config = RetryConfig(
    max_attempts=5,
    base_delay=2.0,
    max_delay=30.0,
    jitter=True
)

result = retry_with_config(
    lambda: client.execute_prediction(data, "model-123"),
    retry_config
)

Logging and Debugging

Configure SDK Logging

Enable SDK logging for debugging:

import logging

# Configure logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('viewai.log'),
        logging.StreamHandler()
    ]
)

# Set ViewAI client log level
client = ViewAIClient(api_key="your-api-key")
client.configure_logging_level(logging.DEBUG)

# Now all SDK operations are logged
result = client.execute_prediction(data, "model-123")

Custom Error Logging

Implement custom error logging:

import logging
from datetime import datetime

class ErrorLogger:
    """Custom error logger for ViewAI operations."""

    def __init__(self, log_file="errors.log"):
        self.logger = logging.getLogger("ViewAIErrors")
        self.logger.setLevel(logging.ERROR)

        # File handler
        fh = logging.FileHandler(log_file)
        fh.setLevel(logging.ERROR)

        # Formatter
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        fh.setFormatter(formatter)

        self.logger.addHandler(fh)

    def log_error(self, error, context=None):
        """Log error with context."""
        error_info = {
            "timestamp": datetime.now().isoformat(),
            "error_type": type(error).__name__,
            "message": str(error),
            "context": context or {}
        }

        if isinstance(error, ViewAIError):
            error_info["details"] = error.details

        if isinstance(error, APIError):
            error_info["status_code"] = error.status_code
            error_info["response"] = error.response

        self.logger.error(json.dumps(error_info))

# Use error logger
error_logger = ErrorLogger("viewai_errors.log")

try:
    result = client.execute_prediction(data, "model-123")
except ViewAIError as e:
    error_logger.log_error(
        e,
        context={"operation": "prediction", "model_id": "model-123"}
    )

Debug Mode

Implement debug mode for development:

class DebugClient:
    """ViewAI client with debug mode."""

    def __init__(self, api_key, debug=False):
        self.client = ViewAIClient(api_key=api_key)
        self.debug = debug

        if debug:
            self.client.configure_logging_level(logging.DEBUG)

    def execute_prediction(self, data, model_id):
        """Execute prediction with debug logging."""
        if self.debug:
            print(f"[DEBUG] Executing prediction")
            print(f"[DEBUG] Model ID: {model_id}")
            print(f"[DEBUG] Data: {data}")

        try:
            result = self.client.execute_prediction(data, model_id)

            if self.debug:
                print(f"[DEBUG] Prediction successful")
                print(f"[DEBUG] Result: {result}")

            return result

        except Exception as e:
            if self.debug:
                print(f"[DEBUG] Prediction failed: {e}")
                import traceback
                traceback.print_exc()
            raise

# Use debug client
debug_client = DebugClient("your-api-key", debug=True)
result = debug_client.execute_prediction(data, "model-123")

Production Error Handling

Comprehensive Error Handler

Production-ready error handling:

import logging
from typing import Optional, Dict, Any

class ProductionErrorHandler:
    """Production-grade error handler."""

    def __init__(self, client: ViewAIClient, alert_webhook: Optional[str] = None):
        self.client = client
        self.alert_webhook = alert_webhook
        self.logger = logging.getLogger("ViewAI.Production")

    def execute_with_handling(
        self,
        func: Callable,
        operation_name: str,
        context: Dict[str, Any] = None
    ) -> Optional[Any]:
        """Execute function with comprehensive error handling."""

        context = context or {}

        try:
            return func()

        except AuthenticationError as e:
            self._handle_auth_error(e, operation_name, context)
            return None

        except ValidationError as e:
            self._handle_validation_error(e, operation_name, context)
            return None

        except APIError as e:
            return self._handle_api_error(e, operation_name, context, func)

        except NetworkError as e:
            return self._handle_network_error(e, operation_name, context, func)

        except Exception as e:
            self._handle_unexpected_error(e, operation_name, context)
            return None

    def _handle_auth_error(self, error, operation, context):
        """Handle authentication errors."""
        self.logger.critical(
            f"Authentication failed in {operation}: {error.message}"
        )
        # Alert ops team
        self._send_alert("CRITICAL: Authentication failed", error, context)

    def _handle_validation_error(self, error, operation, context):
        """Handle validation errors."""
        self.logger.error(
            f"Validation failed in {operation}: {error.message}"
        )
        # Log for data team to review
        self._log_validation_failure(error, context)

    def _handle_api_error(self, error, operation, context, retry_func):
        """Handle API errors with retry logic."""
        self.logger.error(
            f"API error in {operation}: {error.message} "
            f"(status: {error.status_code})"
        )

        # Retry for server errors
        if error.status_code >= 500:
            self.logger.info(f"Retrying {operation} due to server error")
            return exponential_backoff_retry(retry_func, max_retries=3)

        return None

    def _handle_network_error(self, error, operation, context, retry_func):
        """Handle network errors with retry."""
        self.logger.warning(
            f"Network error in {operation}: {error.message}"
        )

        # Retry network errors
        return exponential_backoff_retry(retry_func, max_retries=5)

    def _handle_unexpected_error(self, error, operation, context):
        """Handle unexpected errors."""
        self.logger.exception(
            f"Unexpected error in {operation}: {error}"
        )
        # Alert ops team
        self._send_alert("CRITICAL: Unexpected error", error, context)

    def _send_alert(self, title, error, context):
        """Send alert to monitoring system."""
        if self.alert_webhook:
            # Send to webhook (Slack, PagerDuty, etc.)
            pass

    def _log_validation_failure(self, error, context):
        """Log validation failure for analysis."""
        # Save to database for data team review
        pass

# Use production error handler
handler = ProductionErrorHandler(
    client=client,
    alert_webhook="https://hooks.slack.com/..."
)

result = handler.execute_with_handling(
    func=lambda: client.execute_prediction(data, "model-123"),
    operation_name="customer_churn_prediction",
    context={"customer_id": "12345"}
)

Best Practices

Always Handle Exceptions

Never leave exceptions unhandled.

Bad:

result = client.execute_prediction(data, "model-123")

Good:

try:
    result = client.execute_prediction(data, "model-123")
except ViewAIError as e:
    print(f"Error: {e.message}")
    result = None

Use Specific Exceptions

Catch specific exceptions when possible.

Less specific:

try:
    result = client.execute_prediction(data, "model-123")
except Exception as e:
    print(f"Error: {e}")

More specific:

try:
    result = client.execute_prediction(data, "model-123")
except ValidationError as e:
    print(f"Validation error: {e.message}")
except APIError as e:
    print(f"API error (status {e.status_code}): {e.message}")
except ViewAIError as e:
    print(f"ViewAI error: {e.message}")

Implement Retry Logic

Add retry logic for transient failures.

def make_prediction_with_retry(client, data, model_id):
    """Make prediction with retry logic."""
    return exponential_backoff_retry(
        lambda: client.execute_prediction(data, model_id),
        max_retries=3,
        exceptions=(NetworkError, APIError)
    )

Log All Errors

Always log errors for debugging.

import logging

logger = logging.getLogger(__name__)

try:
    result = client.execute_prediction(data, "model-123")
except ViewAIError as e:
    logger.error(f"Prediction failed: {e.message}", exc_info=True)
    # Handle error

Provide User-Friendly Messages

Convert technical errors to user-friendly messages.

def get_user_friendly_message(error):
    """Convert error to user-friendly message."""
    if isinstance(error, AuthenticationError):
        return "Unable to authenticate. Please check your credentials."

    if isinstance(error, ValidationError):
        return "Invalid input data. Please check your data and try again."

    if isinstance(error, NetworkError):
        return "Connection error. Please check your internet connection."

    if isinstance(error, APIError):
        if error.status_code == 404:
            return "Model not found. Please verify the model ID."
        elif error.status_code == 429:
            return "Too many requests. Please try again in a few moments."
        elif error.status_code >= 500:
            return "Service temporarily unavailable. Please try again later."

    return "An unexpected error occurred. Please contact support."

# Use user-friendly messages
try:
    result = client.execute_prediction(data, "model-123")
except ViewAIError as e:
    user_message = get_user_friendly_message(e)
    print(user_message)

Fail Gracefully

Provide fallback behavior.

def predict_with_fallback(client, data, model_id, fallback_value=None):
    """Make prediction with fallback value."""
    try:
        result = client.execute_prediction(data, model_id)
        return result

    except ViewAIError as e:
        logger.error(f"Prediction failed: {e.message}")

        # Return fallback value
        return fallback_value

# Use with fallback
result = predict_with_fallback(
    client,
    data,
    "model-123",
    fallback_value={"prediction": 0, "confidence": 0.0}
)

Monitor Error Rates

Track and monitor error rates.

from collections import defaultdict
from datetime import datetime

class ErrorMetrics:
    """Track error metrics."""

    def __init__(self):
        self.error_counts = defaultdict(int)
        self.total_operations = 0

    def record_error(self, error_type):
        """Record an error occurrence."""
        self.error_counts[error_type] += 1

    def record_success(self):
        """Record a successful operation."""
        self.total_operations += 1

    def get_error_rate(self, error_type):
        """Get error rate for specific error type."""
        if self.total_operations == 0:
            return 0.0

        return self.error_counts[error_type] / self.total_operations

    def get_summary(self):
        """Get error summary."""
        return {
            "total_operations": self.total_operations,
            "error_counts": dict(self.error_counts),
            "error_rates": {
                error_type: self.get_error_rate(error_type)
                for error_type in self.error_counts
            }
        }

# Use error metrics
metrics = ErrorMetrics()

try:
    result = client.execute_prediction(data, "model-123")
    metrics.record_success()
except ViewAIError as e:
    metrics.record_error(type(e).__name__)

# Check metrics
summary = metrics.get_summary()
print(f"Error summary: {summary}")

hashtagOverview

hashtagException Hierarchy

hashtagBase Exception: ViewAIError

hashtagViewAIError Attributes

hashtagCommon Errors

hashtagAuthenticationError

hashtagValidationError

hashtagAPIError

hashtagNetworkError

hashtagConfigurationError

hashtagDeploymentError

hashtagTimeoutError

hashtagError Handling Patterns

hashtagBasic Try-Except

hashtagSpecific Exception Handling

hashtagContext Manager Pattern

hashtagDecorator Pattern

hashtagRetry Strategies

hashtagExponential Backoff

hashtagRetry with Jitter

hashtagConfigurable Retry Strategy

hashtagLogging and Debugging

hashtagConfigure SDK Logging

hashtagCustom Error Logging

hashtagDebug Mode

hashtagProduction Error Handling

hashtagComprehensive Error Handler

hashtagBest Practices

hashtagAlways Handle Exceptions

hashtagUse Specific Exceptions

hashtagImplement Retry Logic

hashtagLog All Errors

hashtagProvide User-Friendly Messages

hashtagFail Gracefully

hashtagMonitor Error Rates

hashtagSee Also

Overview

Exception Hierarchy

Base Exception: ViewAIError

ViewAIError Attributes

Common Errors

AuthenticationError

ValidationError

APIError

NetworkError

ConfigurationError

DeploymentError

TimeoutError

Error Handling Patterns

Basic Try-Except

Specific Exception Handling

Context Manager Pattern

Decorator Pattern

Retry Strategies

Exponential Backoff

Retry with Jitter

Configurable Retry Strategy

Logging and Debugging

Configure SDK Logging

Custom Error Logging

Debug Mode

Production Error Handling

Comprehensive Error Handler

Best Practices

Always Handle Exceptions

Use Specific Exceptions

Implement Retry Logic

Log All Errors

Provide User-Friendly Messages

Fail Gracefully

Monitor Error Rates

See Also