gh-jeremylongshore-claude-code-plugins-plus-plugins-ai-ml-nlp-text-analyzer/skills/nlp-text-analyzer/assets/error_handling_examples.md

Error Handling Examples for NLP Text Analyzer Plugin

This document provides examples of how to handle common errors that may arise when using the NLP Text Analyzer plugin. Implementing robust error handling is crucial for building reliable and user-friendly applications.

1. API Rate Limits

API rate limits are a common occurrence, especially when using external NLP services. When you exceed the rate limit, the API will typically return an error code (e.g., 429 Too Many Requests).

Handling Strategy:

• Retry Mechanism: Implement an exponential backoff retry mechanism. This involves waiting for an increasing amount of time before retrying the request.

Example:

# Placeholder:  Replace with your specific NLP API call
def analyze_text_with_retry(text, max_retries=5, initial_delay=1):
    retries = 0
    delay = initial_delay
    while retries < max_retries:
        try:
            # Placeholder: Replace with your API call function
            result = analyze_text(text)
            return result
        except Exception as e:
            # Placeholder:  Check for specific rate limit error code (e.g., 429)
            if "rate limit exceeded" in str(e).lower():
                retries += 1
                print(f"Rate limit exceeded. Retrying in {delay} seconds...")
                time.sleep(delay)
                delay *= 2  # Exponential backoff
            else:
                raise  # Re-raise other exceptions
    raise Exception("Max retries reached. Unable to analyze text due to rate limits.")

# Example Usage:
# text = "Placeholder: Your text to analyze"
# try:
#     analysis_result = analyze_text_with_retry(text)
#     print(analysis_result)
# except Exception as e:
#     print(f"Error during analysis: {e}")

Instructions:

1. Replace the placeholder comments with your actual code.
2. Adjust max_retries and initial_delay based on the API's recommendations.
3. Ensure the error check specifically targets rate-limiting errors. Different APIs have different error messages and codes.

2. Invalid Input

Invalid input can range from malformed text to unsupported languages.

Handling Strategy:

• Input Validation: Validate the input text before sending it to the NLP API.
• Error Messages: Provide informative error messages to the user.

Example:

# Placeholder: Replace with your specific NLP API call and language detection method
def analyze_text(text, language="en"):
    # Placeholder: Add language detection here, if needed.  Example:
    # detected_language = detect_language(text)
    # if detected_language != language:
    #    raise ValueError(f"Input language is not {language}. Detected: {detected_language}")

    if not isinstance(text, str):
        raise TypeError("Input must be a string.")

    if not text:
        raise ValueError("Input text cannot be empty.")

    try:
        # Placeholder: Replace with your API call function
        result = perform_nlp_analysis(text, language)
        return result
    except Exception as e:
        # Placeholder: Catch specific API errors related to invalid input
        if "invalid input" in str(e).lower() or "bad request" in str(e).lower():
            raise ValueError(f"Invalid input: {e}")
        else:
            raise  # Re-raise other exceptions

# Example Usage:
# text = "Placeholder: Your text to analyze"
# try:
#     analysis_result = analyze_text(text)
#     print(analysis_result)
# except ValueError as e:
#     print(f"Input error: {e}")
# except Exception as e:
#     print(f"Other error: {e}")

Instructions:

1. Replace the placeholder comments with your actual code.
2. Implement input validation checks relevant to your specific NLP task (e.g., maximum text length, allowed characters).
3. Catch specific API errors related to invalid input and provide helpful error messages.
4. Consider adding language detection to automatically determine the input language and handle unsupported languages gracefully.

3. Network Errors

Network errors can occur due to connectivity issues or server problems.

Handling Strategy:

• Retry Mechanism: Similar to API rate limits, implement a retry mechanism with exponential backoff.
• Timeout: Set a timeout for API requests to prevent the application from hanging indefinitely.

Example:

import requests
import time

# Placeholder: Replace with your specific NLP API endpoint
API_ENDPOINT = "Placeholder: Your API endpoint URL"

def analyze_text_with_retry(text, max_retries=3, initial_delay=1, timeout=10):
    retries = 0
    delay = initial_delay
    while retries < max_retries:
        try:
            # Placeholder: Replace with your API key or authentication method
            headers = {"Authorization": "Bearer Placeholder: Your API Key"}
            data = {"text": text}
            response = requests.post(API_ENDPOINT, headers=headers, json=data, timeout=timeout)
            response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
            return response.json()
        except requests.exceptions.RequestException as e:
            retries += 1
            print(f"Network error: {e}. Retrying in {delay} seconds...")
            time.sleep(delay)
            delay *= 2
    raise Exception("Max retries reached. Unable to analyze text due to network errors.")

# Example Usage:
# text = "Placeholder: Your text to analyze"
# try:
#     analysis_result = analyze_text_with_retry(text)
#     print(analysis_result)
# except Exception as e:
#     print(f"Error during analysis: {e}")

Instructions:

1. Replace the placeholder comments with your actual code, including the API endpoint, authentication details, and request parameters.
2. Adjust max_retries, initial_delay, and timeout based on your network conditions and API requirements.
3. Use the requests library (or your preferred HTTP client) to make API requests.
4. Handle requests.exceptions.RequestException to catch various network-related errors.

4. Server-Side Errors

Server-side errors (e.g., 500 Internal Server Error) indicate problems on the NLP API's server.

Handling Strategy:

• Retry Mechanism: Implement a retry mechanism with exponential backoff, as these errors can be temporary.
• Logging: Log the error details for debugging purposes.
• User Notification: Inform the user that there is a problem with the service and that they should try again later.

Example:

import requests
import time
import logging

# Configure logging (optional)
logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s')

# Placeholder: Replace with your specific NLP API endpoint
API_ENDPOINT = "Placeholder: Your API endpoint URL"

def analyze_text_with_retry(text, max_retries=3, initial_delay=1, timeout=10):
    retries = 0
    delay = initial_delay
    while retries < max_retries:
        try:
            # Placeholder: Replace with your API key or authentication method
            headers = {"Authorization": "Bearer Placeholder: Your API Key"}
            data = {"text": text}
            response = requests.post(API_ENDPOINT, headers=headers, json=data, timeout=timeout)
            response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
            return response.json()
        except requests.exceptions.RequestException as e:
            logging.error(f"API request failed: {e}") # Log the error
            retries += 1
            print(f"Server error. Retrying in {delay} seconds...")
            time.sleep(delay)
            delay *= 2
    raise Exception("Max retries reached. Unable to analyze text due to server errors.")

# Example Usage:
# text = "Placeholder: Your text to analyze"
# try:
#     analysis_result = analyze_text_with_retry(text)
#     print(analysis_result)
# except Exception as e:
#     print(f"Error during analysis: {e}")
#     print("There was a problem with the service. Please try again later.") # User notification

Instructions:

1. Replace the placeholder comments with your actual code, including the API endpoint, authentication details, and request parameters.
2. Adjust max_retries, initial_delay, and timeout based on your network conditions and API requirements.
3. Implement logging to record error details for debugging.
4. Provide a user-friendly message to inform the user about the server-side error.

By implementing these error handling strategies, you can create more robust and reliable NLP applications. Remember to tailor these examples to your specific use case and the requirements of the NLP APIs you are using.