yourbench_space/config.py

import yaml
from loguru import logger
from yourbench_space.utils import CONFIG_PATH


def generate_base_config(hf_org: str, hf_dataset_name: str, session_uid: str):
    """Creates the base config dictionary"""
    return {
        "hf_configuration": {
            "token": "$HF_TOKEN",
            "private": True,
            "hf_organization": hf_org,
            "hf_dataset_name": hf_dataset_name,
        },
        "model_list": [
            {
                "model_name": "meta-llama/Llama-3.3-70B-Instruct",
                "provider": "novita",
                "max_concurrent_requests": 32,
            },
            {
                "model_name": "Qwen/Qwen2.5-72B-Instruct",
                "provider": "novita",
                "max_concurrent_requests": 32,
            }
        ],
        "model_roles": {
            "ingestion": ["meta-llama/Llama-3.3-70B-Instruct"],
            "summarization": ["Qwen/Qwen2.5-72B-Instruct"],
            "single_shot_question_generation": ["meta-llama/Llama-3.3-70B-Instruct"],
            "multi_hop_question_generation": ["meta-llama/Llama-3.3-70B-Instruct"],
            "answer_generation": ["Qwen/Qwen2.5-72B-Instruct"],
            "judge_answers": ["meta-llama/Llama-3.3-70B-Instruct"],
        },
        "pipeline": {
            "ingestion": {
                "source_documents_dir": f"/app/{session_uid}/uploaded_files/",
                "output_dir": f"/app/{session_uid}/ingested",
                "run": True,
            },
            "upload_ingest_to_hub": {
                "source_documents_dir": f"/app/{session_uid}/ingested",
                "run": True,
            },
            "summarization": {"run": True},
            "chunking": {
                "chunking_configuration": {
                    "l_min_tokens": 64,
                    "l_max_tokens": 128,
                    "tau_threshold": 0.3,
                    "h_min": 2,
                    "h_max": 4,
                },
                "run": True,
            },
            "single_shot_question_generation": {
                "diversification_seed": "24 year old adult",
                "run": True,
            },
            "multi_hop_question_generation": {"run": False},
            "answer_generation": {
                "question_type": "single_shot",
                "run": True,
                "strategies": [
                    {
                        "name": "zeroshot",
                        "prompt": "ZEROSHOT_QA_USER_PROMPT",
                        "model_name": "meta-llama/Llama-3.3-70B-Instruct",
                    },
                    {
                        "name": "gold",
                        "prompt": "GOLD_QA_USER_PROMPT",
                        "model_name": "meta-llama/Llama-3.3-70B-Instruct",
                    },
                ],
            },
            "judge_answers": {
                "run": False, # to change when fixed
                "comparing_strategies": [["zeroshot", "gold"]],
                "chunk_column_index": 0,
                "random_seed": 42,
            },
        },
    }


def save_yaml_file(config: str, path: str):
    """Saves the given config dictionary to a YAML file"""
    with open(path, "w") as file:
        yaml.dump(config, file, default_flow_style=False, sort_keys=False)
    return path


def generate_and_save_config(hf_org: str, hf_name: str, session_uid: str, config_path: str):
    """Generates and saves the YAML configuration file"""
    logger.debug(f"Generating config with org: {hf_org}, dataset name: {hf_name}")
    config = generate_base_config(hf_org, hf_name, session_uid)
    file_path = save_yaml_file(config, config_path)
    logger.success(f"Config saved at: {file_path}")
    return file_path