from .src.lpmmconfig import PG_NAMESPACE, global_config
from .src.embedding_store import EmbeddingManager
from .src.llm_client import LLMClient
from .src.mem_active_manager import MemoryActiveManager
from .src.qa_manager import QAManager
from .src.kg_manager import KGManager
from .src.global_logger import logger
# try:
#     import quick_algo
# except ImportError:
#     print("quick_algo not found, please install it first")

logger.info("正在初始化Mai-LPMM\n")
logger.info("创建LLM客户端")
llm_client_list = dict()
for key in global_config["llm_providers"]:
    llm_client_list[key] = LLMClient(
        global_config["llm_providers"][key]["base_url"],
        global_config["llm_providers"][key]["api_key"],
    )

# 初始化Embedding库
embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
logger.info("正在从文件加载Embedding库")
try:
    embed_manager.load_from_file()
except Exception as e:
    logger.error("从文件加载Embedding库时发生错误：{}".format(e))
    logger.error("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
logger.info("Embedding库加载完成")
# 初始化KG
kg_manager = KGManager()
logger.info("正在从文件加载KG")
try:
    kg_manager.load_from_file()
except Exception as e:
    logger.error("从文件加载KG时发生错误：{}".format(e))
    logger.error("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
logger.info("KG加载完成")

logger.info(f"KG节点数量：{len(kg_manager.graph.get_node_list())}")
logger.info(f"KG边数量：{len(kg_manager.graph.get_edge_list())}")


# 数据比对：Embedding库与KG的段落hash集合
for pg_hash in kg_manager.stored_paragraph_hashes:
    key = PG_NAMESPACE + "-" + pg_hash
    if key not in embed_manager.stored_pg_hashes:
        logger.warning(f"KG中存在Embedding库中不存在的段落：{key}")

# 问答系统（用于知识库）
qa_manager = QAManager(
    embed_manager,
    kg_manager,
    llm_client_list[global_config["embedding"]["provider"]],
    llm_client_list[global_config["qa"]["llm"]["provider"]],
    llm_client_list[global_config["qa"]["llm"]["provider"]],
)

# 记忆激活（用于记忆库）
inspire_manager = MemoryActiveManager(
    embed_manager,
    llm_client_list[global_config["embedding"]["provider"]],
)