Fix InstructorRetryException

✅ Solution

InstructorRetryException in ragas usually stems from rate limits or temporary unavailability of the LLM service used by Instructor. Implement retry logic with exponential backoff within your LLM service calls, and also ensure you're adhering to the specific API rate limits outlined in your LLM provider's (e.g., OpenAI) documentation. Consider increasing timeout values or reducing batch sizes if rate limits are still consistently hit after implementing retries.