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Add comprehensive rate limit handling across API providers

This PR implements robust rate limit handling across all API providers used in the AI-Scientist framework, addressing the continuous retry issue (#155).

Changes

  • Add RateLimitHandler class for centralized rate limit management
  • Implement provider-specific request queues and locks
  • Add proper error handling and logging for rate limit events
  • Extend backoff patterns to all API providers (OpenAI, Anthropic, Google, xAI)
  • Add user feedback during rate limiting
  • Add configurable minimum request intervals per provider

Implementation Details

  • Created new rate_limit.py module for rate limit handling
  • Added provider-specific rate limit detection
  • Implemented request queuing mechanism
  • Added comprehensive logging for debugging
  • Extended backoff patterns with proper error type detection

Testing

The changes have been tested by:

  • Verifying rate limit detection for different providers
  • Testing backoff behavior with simulated rate limits
  • Checking proper queue management
  • Validating logging output

Impact

These changes make the system more robust by:

  • Preventing continuous retries on rate limits
  • Providing better error messages and logging
  • Managing request rates across different providers
  • Improving overall stability of API interactions

Fixes #155

Link to Devin run: https://app.devin.ai/sessions/2ec43d6fe7a84849a348753167e5a895

devin-ai-integration bot and others added 9 commits December 16, 2024 13:15
- Add new model identifiers to AVAILABLE_LLMS
- Extend create_client for new model support
- Add environment variable validation
- Implement model-specific response handling
- Update requirements.txt with google-cloud-aiplatform

Co-Authored-By: Erkin Alp Güney <[email protected]>
It is quite difficult for weak models to do so based on the default template, as one error in replaced text can lead to termination of editing in the *SEARCH/REPLACE mode*. Splitting the text into segments and enabling "whole" mode editing helps solve this problem.

The approach was tested on the "2d_diffusion" task using the "groq/llama3-8b-8192".
…n with segmented templates

Co-Authored-By: Erkin Alp Güney <[email protected]>
…rs, and template compatibility

Co-Authored-By: Erkin Alp Güney <[email protected]>
- Add retry limit and better error handling in extract_json_between_markers
- Replace assert statements with try-catch blocks across all files
- Add proper error messages and recovery mechanisms
- Prevent infinite loops when JSON extraction fails

Fixes SakanaAI#154

Co-Authored-By: Erkin Alp Güney <[email protected]>
- Add RateLimitHandler class for managing rate limits
- Implement provider-specific request queues and locks
- Add proper error handling and logging
- Extend backoff patterns to all API providers
- Add user feedback during rate limiting

Fixes SakanaAI#155

Co-Authored-By: Erkin Alp Güney <[email protected]>
@erkinalp erkinalp closed this Dec 18, 2024
@Krakaur
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Krakaur commented Dec 30, 2024

Thanx

@erkinalp erkinalp reopened this Dec 30, 2024
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rate_limit

3 participants