Calculated available context size -353 was not non-negative.

[aiwalter]

Getting ValueError: Calculated available context size -353 was not non-negative. using GPT-4o and an input token size of 1313 according to tiktoken using o200k_base as encoding. What am I doing wrong here? I have set the max_tokens to 1300.

index.as_query_engine(
    prompt_helper=prompt_helper,
    chunk_size=int(env("CHUNK_SIZE")),
    similarity_top_k=int(env("SIMILARITY_TOP_K")),
    ResponseMode=ResponseMode.TREE_SUMMARIZE,
)
.query(sys_promt)
.response

I have also:

SIMILARITY_TOP_K=13
CHUNK_SIZE=150

[dosubot[bot]]

The error you're encountering, "Calculated available context size -353 was not non-negative," suggests that the total number of tokens required for the prompt and related components exceeds the maximum context size allowed by the model. Here are a few things to consider:

1. Token Calculation: The prompt_helper calculates the number of tokens used in the prompt, system prompt, and other components. If the sum of these tokens exceeds the max_tokens limit, it results in a negative available context size ^[1].

2. Encoding and Context Size: The o200k_base encoding might have a specific maximum context size that you need to adhere to. Ensure that the total tokens, including the input token size and any additional tokens from the system prompt and other settings, do not exceed this limit.

3. Adjust Parameters: Consider reducing the similarity_top_k value, as a higher value retrieves more nodes, increasing token usage ^[2]. Additionally, check if the chunk_size is appropriate for your data; a smaller chunk size can lead to more granular nodes, potentially increasing token usage ^[3].

4. System Prompt: Ensure that the sys_prompt does not add excessive tokens to the total count. The token size of sys_prompt contributes to the overall context size ^[1].

By adjusting these parameters, you should be able to resolve the issue with the negative context size.

To continue talking to Dosu, mention @dosu.

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[aiwalter]

@dosubot how can I best use PromptHelper to dynamically reduce input tokens e.g. by reducing dynamically similarity_top_k?

[dosubot[bot]]

To dynamically reduce input tokens using PromptHelper, you can adjust parameters like similarity_top_k based on the current token count. While the PromptHelper class doesn't provide built-in methods for dynamic adjustment, you can implement a custom logic to modify similarity_top_k before making a query. Here's a general approach:

1. Calculate Current Token Usage: Use the PromptHelper methods to estimate the current token usage, such as get_tokens_from_response or get_llm_token_counts.

2. Adjust similarity_top_k: Based on the token usage, adjust similarity_top_k to ensure the total token count stays within the desired limit. For example, if the token count is too high, reduce similarity_top_k to fetch fewer nodes.

3. Implement Logic: Create a function that checks the token count and adjusts similarity_top_k accordingly before executing the query.

Here's a simple example of how you might implement this:

def adjust_similarity_top_k(prompt_helper, current_token_count, max_tokens):
    # Calculate available tokens
    available_tokens = max_tokens - current_token_count
    
    # Adjust similarity_top_k based on available tokens
    if available_tokens < threshold:  # Define a threshold for adjustment
        new_similarity_top_k = max(1, prompt_helper.similarity_top_k - 1)
        prompt_helper.similarity_top_k = new_similarity_top_k

# Example usage
current_token_count = prompt_helper.get_llm_token_counts(payload)
adjust_similarity_top_k(prompt_helper, current_token_count, max_tokens=1300)

This approach allows you to dynamically adjust similarity_top_k based on the token count, helping to manage the total token usage effectively.

To continue talking to Dosu, mention @dosu.

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