AI provider normalization

When integrating multiple AI model providers, implement proper abstraction layers to normalize differences in APIs, data formats, and behaviors. Different providers often have inconsistent interfaces that require careful handling to maintain application consistency.

Key practices:

• Create format conversion methods for provider-specific data structures
• Handle provider configuration differences explicitly
• Implement fallback logic for provider-specific error messages
• Document provider limitations and required workarounds

Example from LiteLLM provider handling:

def _format_request_message_contents(self, role: str, content: ContentBlock) -> list[dict[str, Any]]:
    """Format LiteLLM compatible message contents.
    
    LiteLLM expects content to be a string for simple text messages, not a list of content blocks.
    This method flattens the content structure to be compatible with LiteLLM providers like Cerebras and Groq.
    """
    # Provider-specific format conversion logic here

# Handle provider-specific error messages
LITELLM_CONTEXT_WINDOW_OVERFLOW_MESSAGES = [
    "Context Window Error",
    "Context Window Exceeded", 
    "Input is too long",
    # Common error substrings from different providers
]

This approach prevents provider inconsistencies from breaking application logic and ensures consistent behavior across different AI model backends.