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| input_args_list = ['model_state', 'my_db_state', 'selection_docs_state', 'requests_state'] | |
| no_default_param_names = [ | |
| 'instruction', | |
| 'iinput', | |
| 'context', | |
| 'instruction_nochat', | |
| 'iinput_nochat', | |
| ] | |
| gen_hyper0 = ['num_beams', | |
| 'max_new_tokens', | |
| 'min_new_tokens', | |
| 'early_stopping', | |
| 'max_time', | |
| 'repetition_penalty', | |
| 'num_return_sequences', | |
| 'do_sample', | |
| ] | |
| gen_hyper = ['temperature', | |
| 'top_p', | |
| 'top_k'] + gen_hyper0 | |
| reader_names = ['image_loaders', 'pdf_loaders', 'url_loaders', 'jq_schema'] | |
| eval_func_param_names = ['instruction', | |
| 'iinput', | |
| 'context', | |
| 'stream_output', | |
| 'prompt_type', | |
| 'prompt_dict'] + \ | |
| gen_hyper + \ | |
| ['chat', | |
| 'instruction_nochat', | |
| 'iinput_nochat', | |
| 'langchain_mode', | |
| 'add_chat_history_to_context', | |
| 'langchain_action', | |
| 'langchain_agents', | |
| 'top_k_docs', | |
| 'chunk', | |
| 'chunk_size', | |
| 'document_subset', | |
| 'document_choice', | |
| 'pre_prompt_query', | |
| 'prompt_query', | |
| 'pre_prompt_summary', | |
| 'prompt_summary', | |
| 'system_prompt', | |
| ] + \ | |
| reader_names + \ | |
| ['visible_models', | |
| 'h2ogpt_key', | |
| 'add_search_to_context', | |
| 'chat_conversation', | |
| 'text_context_list', | |
| 'docs_ordering_type', | |
| 'min_max_new_tokens', | |
| ] | |
| # form evaluate defaults for submit_nochat_api | |
| eval_func_param_names_defaults = eval_func_param_names.copy() | |
| for k in no_default_param_names: | |
| if k in eval_func_param_names_defaults: | |
| eval_func_param_names_defaults.remove(k) | |
| eval_extra_columns = ['prompt', 'response', 'score'] | |
| # override default_kwargs if user_kwargs None for args evaluate() uses that are not just in model_state | |
| # ensure prompt_type consistent with prep_bot(), so nochat API works same way | |
| # see how default_kwargs is set in gradio_runner.py | |
| key_overrides = ['prompt_type', 'prompt_dict'] | |