Token Limit Issue with gpt-4o-mini in LangFlow

[ealss101]

Subject: Assistance Needed: Exceeding Token Limits in gpt-4o-mini with LangFlow Workflow

I’m currently facing an issue with the gpt-4o-mini model in a LangFlow workflow. The error message states that my workflow exceeds the 200,000 tokens per minute (TPM) limit. Below are the details of the problem and my setup:

Error Message

Request too large for gpt-4o-mini in organization org-RolciEExTBGntT7X5dno1Liu on tokens per min (TPM): Limit 200000, Requested 392775. The input or output tokens must be reduced in order to run successfully.

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Workflow Overview

1. Input Prompt: The workflow begins with a user query, e.g.,

   Inspect this URL: https://finance.yahoo.com/sec-filing/AAPL/0000320193-24-000123_320193
   and provide a small output on Product, Service, and Software Announcements for 2024 based on the contents of the filing.
   

2. Tools Used:

   • URL Tool: Fetches content from the provided URL.
   • Tavily AI Search Tool: Enhances contextual search results.
   • Agent Node: Processes the input and utilizes the tools to generate a response.

3. Model Configuration:

   • Model: gpt-4o-mini
   • Max Tokens: Default (unlimited)
   • Temperature: 0.1 (for deterministic results)

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Current Issues

• The workflow generates or processes too many tokens, exceeding the 200,000 TPM limit.
• This happens even when attempting to process a relatively short query and document.

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Steps Taken So Far

1. Checked Input Size:
   • The input prompt seems concise, but the document fetched by the URL tool might be large.
2. Adjusted Model Configuration:
   • Attempted to set max_tokens to a lower value but still encountered token issues.
3. Optimized Tools:
   • Reduced the number of tools in the agent's configuration.
   • Ensured each tool output is necessary for the final result.

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Questions

1. Best Practices for Token Management:
   • Are there specific configurations or workflows to better handle token limits in LangFlow?
2. Document Processing Optimization:
   • How can I pre-process or truncate the fetched content to reduce token usage?
3. Alternative Models:
   • Would switching to a different model (e.g., gpt-4-turbo) be advisable, or should I focus on optimizing the workflow?
4. LangFlow-Specific Features:
   • Is there a built-in mechanism in LangFlow to monitor token usage in real-time?

Any help is appreciated. Thanks!

[dosubot[bot]]

To address the token limit issue with the gpt-4o-mini model in LangFlow, here are some suggestions:

1. Token Management Best Practices:

   • LangFlow monitors token usage in real-time by utilizing the response_metadata from AI messages, which includes details about token usage such as completion_tokens, prompt_tokens, and total_tokens for OpenAI models. This can help you track and manage token usage effectively ^[1].

2. Document Processing Optimization:

   • While specific pre-processing or truncating features in LangFlow weren't found, you can manually implement content truncation or summarization before passing it to the model. Consider using a separate tool or script to extract only the necessary parts of the document.

3. Alternative Models:

   • Switching to a model like gpt-4-turbo might help if it has different token limits or efficiencies. However, optimizing your current workflow to reduce token usage might be more effective in the long run.

4. LangFlow-Specific Features:

   • LangFlow provides mechanisms to monitor token usage in real-time through status messages that include token usage details. This can help you adjust your workflow dynamically based on token consumption ^[1].

Consider these strategies to manage and optimize your workflow to stay within token limits.

To continue talking to Dosu, mention @dosu.

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