Optimizing Image Processing with LLM Classify and Async Techniques

WARNI [phoenix.evals.executors] 🐌!! If running llm_classify inside a notebook, patching the event loop with nest_asyncio will allow asynchronous eval submission, and is significantly faster. To patch the event loop, run `nest_asyncio.apply()`.
run_evals |          | 0/8 (0.0%) | ⏳ 00:02<? | ?it/s WARNI [phoenix.evals.executors] 🐌!! If running llm_classify inside a notebook, patching the event loop with nest_asyncio will allow asynchronous eval submission, and is significantly faster. To patch the event loop, run `nest_asyncio.apply()`.
Retries exhausted after 1 attempts: Error code: 400 - {'error': {'message': "This model's maximum context length is 128000 tokens. However, your messages resulted in 561425 tokens (561359 in the messages, 66 in the functions). Please reduce the length of the messages or functions.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}
                                                      
run_evals |          | 0/8 (0.0%) | ⏳ 00:04<? | ?it/s  
run_evals |          | 0/16 (0.0%) | ⏳ 00:01<? | ?it/s
Retries exhausted after 1 attempts: Error code: 400 - {'error': {'message': "This model's maximum context length is 128000 tokens. However, your messages resulted in 261442 tokens (261378 in the messages, 64 in the functions). Please reduce the length of the messages or functions.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}