import os
import toml
import sys

# import argparse
from .global_logger import logger

PG_NAMESPACE = "paragraph"
ENT_NAMESPACE = "entity"
REL_NAMESPACE = "relation"

RAG_GRAPH_NAMESPACE = "rag-graph"
RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
RAG_PG_HASH_NAMESPACE = "rag-pg-hash"

# 无效实体
INVALID_ENTITY = [
    "",
    "你",
    "他",
    "她",
    "它",
    "我们",
    "你们",
    "他们",
    "她们",
    "它们",
]


def _load_config(config, config_file_path):
    """读取TOML格式的配置文件"""
    if not os.path.exists(config_file_path):
        return
    with open(config_file_path, "r", encoding="utf-8") as f:
        file_config = toml.load(f)

    # Check if all top-level keys from default config exist in the file config
    for key in config.keys():
        if key not in file_config:
            logger.critical(f"警告: 配置文件 '{config_file_path}' 缺少必需的顶级键: '{key}'。请检查配置文件。")
            logger.critical("请通过template/lpmm_config_template.toml文件进行更新")
            sys.exit(1)

    if "llm_providers" in file_config:
        for provider in file_config["llm_providers"]:
            if provider["name"] not in config["llm_providers"]:
                config["llm_providers"][provider["name"]] = dict()
            config["llm_providers"][provider["name"]]["base_url"] = provider["base_url"]
            config["llm_providers"][provider["name"]]["api_key"] = provider["api_key"]

    if "entity_extract" in file_config:
        config["entity_extract"] = file_config["entity_extract"]

    if "rdf_build" in file_config:
        config["rdf_build"] = file_config["rdf_build"]

    if "embedding" in file_config:
        config["embedding"] = file_config["embedding"]

    if "rag" in file_config:
        config["rag"] = file_config["rag"]

    if "qa" in file_config:
        config["qa"] = file_config["qa"]

    if "persistence" in file_config:
        config["persistence"] = file_config["persistence"]
    # print(config)
    logger.info(f"从文件中读取配置: {config_file_path}")


global_config = dict(
    {
        "lpmm": {
            "version": "0.1.0",
        },
        "llm_providers": {
            "localhost": {
                "base_url": "https://api.siliconflow.cn/v1",
                "api_key": "sk-ospynxadyorf",
            }
        },
        "entity_extract": {
            "llm": {
                "provider": "localhost",
                "model": "Pro/deepseek-ai/DeepSeek-V3",
            }
        },
        "rdf_build": {
            "llm": {
                "provider": "localhost",
                "model": "Pro/deepseek-ai/DeepSeek-V3",
            }
        },
        "embedding": {
            "provider": "localhost",
            "model": "Pro/BAAI/bge-m3",
            "dimension": 1024,
        },
        "rag": {
            "params": {
                "synonym_search_top_k": 10,
                "synonym_threshold": 0.75,
            }
        },
        "qa": {
            "params": {
                "relation_search_top_k": 10,
                "relation_threshold": 0.75,
                "paragraph_search_top_k": 10,
                "paragraph_node_weight": 0.05,
                "ent_filter_top_k": 10,
                "ppr_damping": 0.8,
                "res_top_k": 10,
            },
            "llm": {
                "provider": "localhost",
                "model": "qa",
            },
        },
        "persistence": {
            "data_root_path": "data",
            "raw_data_path": "data/raw.json",
            "openie_data_path": "data/openie.json",
            "embedding_data_dir": "data/embedding",
            "rag_data_dir": "data/rag",
        },
        "info_extraction": {
            "workers": 10,
        },
    }
)

# _load_config(global_config, parser.parse_args().config_path)
# file_path = os.path.abspath(__file__)
# dir_path = os.path.dirname(file_path)
ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
config_path = os.path.join(ROOT_PATH, "config", "lpmm_config.toml")
_load_config(global_config, config_path)