Error Handling

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What is Error Handling?

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Common Errors and Solutions

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Authentication Errors

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Error: “Invalid API Key” or 401 Unauthorized

1. Check that your API key starts with sk-
2. Verify you copied the entire API key correctly
3. Make sure you’re including it in the Authorization header: Bearer sk-your-key
4. If you lost your key, create a new one in the console

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Error: “API key not found”

1. Verify the API key exists in the console
2. Create a new API key if needed
3. Make sure you’re using the correct API key

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Model Errors

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Error: “Model not found” or “Model does not exist”

1. Check List Available Models to see which models you can use
2. Verify the model name is spelled correctly (model names are case-sensitive)
3. Use the exact model id from the model list
4. Contact your administrator if you need access to a specific model

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Error: “Model is not available”

1. Check which models are available to your API key
2. Contact your administrator to enable the model for your group
3. See Limits and Prerequisites for more information

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Quota Errors

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Error: “Insufficient quota” or “Quota exceeded”

1. Check your balance in the console under Billing → Overview
2. View your usage to see how much you’ve consumed
3. Top up credits, or ask your team admin to raise your member quota
4. Confirm you’re in the intended space — personal and team contexts draw on different quota pools

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Rate Limiting Errors

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Error: 429 “Too Many Requests” or “Rate limit exceeded”

1. Wait a moment and try again
2. Reduce the frequency of your requests
3. Implement exponential backoff in your code
4. Batch requests if possible
5. See Reliability and Failover for retry strategies

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Request Format Errors

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Error: “Invalid request format” or 400 Bad Request

1. Check that your request body is valid JSON
2. Verify all required fields are present
3. Make sure field names and types match the API specification
4. Review the Direct API Requests guide for correct format

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Error: “Missing required field”

1. Check the API documentation for required fields
2. Verify all required parameters are included
3. Check that field names are spelled correctly

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Network Errors

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Error: “Connection timeout” or “Network error”

1. Check your internet connection
2. Verify the API endpoint URL is correct: https://app.memorylake.ai
3. Try again after a moment
4. Check if there are any network issues
5. See Reliability and Failover for automatic retry mechanisms

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Streaming Errors

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Error: “Stream connection closed unexpectedly”

1. This is normal for long streams - implement reconnection logic
2. Check your network stability
3. Handle stream interruptions gracefully in your code
4. See Reliability and Failover for streaming best practices

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Error Response Format

{
  "error": {
    "message": "Model not found",
    "type": "invalid_request_error",
    "code": "model_not_found"
  }
}

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Best Practices for Error Handling

1. Always check the response status: Handle both success and error cases
2. Read error messages: The error message usually tells you what went wrong
3. Implement retry logic: For transient errors like rate limits or network issues
4. Log errors: Keep track of errors for debugging
5. Handle gracefully: Don’t crash your application on API errors

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Example: Error Handling in Code

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Python Example

import requests

def make_request(api_key, model, messages):
    try:
        response = requests.post(
            "https://app.memorylake.ai/v1/chat/completions",
            headers={
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json"
            },
            json={
                "model": model,
                "messages": messages
            }
        )
        response.raise_for_status()
        return response.json()
    except requests.exceptions.HTTPError as e:
        if response.status_code == 401:
            print("Error: Invalid API key")
        elif response.status_code == 404:
            print("Error: Model not found")
        elif response.status_code == 429:
            print("Error: Rate limit exceeded. Please wait and try again.")
        else:
            print(f"Error: {response.status_code} - {response.text}")
        raise
    except requests.exceptions.RequestException as e:
        print(f"Network error: {e}")
        raise

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Getting Help

1. Check the Limits and Prerequisites guide
2. Review the Reliability and Failover documentation
3. Check your API key and model availability
4. Contact support@memorylake.ai with:
   • The exact error message
   • Your API key (masked)
   • The request you were making
   • Any relevant logs

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Related Documentation

• Limits and Prerequisites
• Reliability and Failover
• List Available Models
• View Usage and Billing
• Direct API Requests