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Message Transforms

Transform prompt messages when using A4F API Services.

When working with Large Language Models (LLMs), you often need to manipulate or transform messages before sending them to the model or after receiving a response. This can be for various reasons, such as managing context window limits, formatting data, or implementing specific conversational patterns.

A4F API Services primarily acts as a pass-through for your API requests to the underlying model providers. This means that A4F does not offer built-in, server-side message transformation features like automatic "middle-out" compression or other complex prompt modifications that some specialized platforms might provide via API parameters.

Instead, any transformations to your message list (e.g., to fit within a model's context window, or to enforce a certain number of messages) should be implemented in your client-side application code before you send the request to A4F.

Client-Side Strategies for Message Transformation

Here are some common scenarios and how you might handle message transformations in your application:

Managing Context Window Size

Prompts that exceed a model's maximum context size will result in an error. Your application needs to manage the total token count of your messages.

This can be useful for situations where perfect recall of the entire conversation history is not strictly required. A common strategy is "middle-out" truncation, where you remove or truncate messages from the middle of the prompt until it fits within the model's context window. This approach aims to preserve the initial system prompt and the most recent exchanges, which are often crucial for context.

def truncate_messages_middle_out(messages, max_tokens_allowed, estimate_token_count_func):
    """
    Conceptually truncates messages from the middle if total tokens exceed max_tokens_allowed.
    'messages' should be a list of message objects (e.g., {"role": "user", "content": "..."}).
    'estimate_token_count_func' is a placeholder for your token counting logic.
    """
    system_prompt = None
    if messages and messages[0]["role"] == "system":
        system_prompt = messages.pop(0)


    current_tokens = estimate_token_count_func(messages) + (estimate_token_count_func([system_prompt]) if system_prompt else 0)


    while current_tokens > max_tokens_allowed and len(messages) > 1:
        if len(messages) > 2:
            messages.pop(len(messages) // 2) 
        elif messages:
            messages.pop()
        else:
            break
        
        current_tokens = estimate_token_count_func(messages) + (estimate_token_count_func([system_prompt]) if system_prompt else 0)
        if not messages: break




    if system_prompt:
        messages.insert(0, system_prompt)
        
    if current_tokens > max_tokens_allowed:
        print(f"Warning: Messages still exceed token limit ({current_tokens}/{max_tokens_allowed}) after truncation.")


    return messages


# def my_simple_token_estimator(msg_list):
#     return sum(len(str(msg.get("content", ""))) // 4 for msg in msg_list if msg)


# example_messages = [
#     {"role": "system", "content": "You are helpful."},
#     {"role": "user", "content": "Long user message part 1... " * 100},
#     {"role": "assistant", "content": "Long assistant response part 1... " * 100},
#     {"role": "user", "content": "Long user message part 2... " * 100},
#     {"role": "assistant", "content": "Long assistant response part 2... " * 100},
#     {"role": "user", "content": "My final question?"}
# ]
# MAX_MODEL_TOKENS = 500
# truncated = truncate_messages_middle_out(list(example_messages), MAX_MODEL_TOKENS, my_simple_token_estimator)


# response = a4f_client.chat.completions.create(model="provider-X/model-Y", messages=truncated)

Managing Number of Messages

In some cases, the issue is not the total token length, but the actual number of messages. For instance, some models might enforce a maximum number of messages in a conversation (e.g., older Claude models had a 1000 message limit example in the reference image).

If you encounter such limits with a specific model via A4F, your client application would need to implement logic to, for example, keep half of the messages from the start and half from the end of the conversation if the limit is exceeded.

How A4F Handles Long Prompts (Without Client-Side Truncation)

If your prompt (total tokens of all messages) exceeds the chosen model's context length, A4F will pass this request to the underlying provider. The provider will then typically return an error indicating that the context limit has been exceeded. Your application should be prepared to handle such errors.

The middle of the prompt is often where truncation occurs (if done automatically by some systems or manually by users) because LLMs may pay less attention to the middle of sequences. Preserving the beginning (system prompts, initial instructions) and the end (most recent interactions) is generally preferred.

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