65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
from .src.lpmmconfig import PG_NAMESPACE, global_config
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from .src.embedding_store import EmbeddingManager
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from .src.llm_client import LLMClient
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from .src.mem_active_manager import MemoryActiveManager
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from .src.qa_manager import QAManager
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from .src.kg_manager import KGManager
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from .src.global_logger import logger
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# try:
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# import quick_algo
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# except ImportError:
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# print("quick_algo not found, please install it first")
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logger.info("正在初始化Mai-LPMM\n")
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logger.info("创建LLM客户端")
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llm_client_list = dict()
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for key in global_config["llm_providers"]:
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llm_client_list[key] = LLMClient(
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global_config["llm_providers"][key]["base_url"],
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global_config["llm_providers"][key]["api_key"],
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)
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# 初始化Embedding库
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embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
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logger.info("正在从文件加载Embedding库")
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try:
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embed_manager.load_from_file()
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except Exception as e:
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logger.error("从文件加载Embedding库时发生错误：{}".format(e))
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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")
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logger.info("Embedding库加载完成")
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# 初始化KG
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kg_manager = KGManager()
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logger.info("正在从文件加载KG")
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try:
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kg_manager.load_from_file()
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except Exception as e:
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logger.error("从文件加载KG时发生错误：{}".format(e))
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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")
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logger.info("KG加载完成")
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logger.info(f"KG节点数量：{len(kg_manager.graph.get_node_list())}")
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logger.info(f"KG边数量：{len(kg_manager.graph.get_edge_list())}")
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# 数据比对：Embedding库与KG的段落hash集合
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for pg_hash in kg_manager.stored_paragraph_hashes:
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key = PG_NAMESPACE + "-" + pg_hash
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if key not in embed_manager.stored_pg_hashes:
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logger.warning(f"KG中存在Embedding库中不存在的段落：{key}")
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# 问答系统（用于知识库）
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qa_manager = QAManager(
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embed_manager,
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kg_manager,
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llm_client_list[global_config["embedding"]["provider"]],
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llm_client_list[global_config["qa"]["llm"]["provider"]],
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llm_client_list[global_config["qa"]["llm"]["provider"]],
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)
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# 记忆激活（用于记忆库）
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inspire_manager = MemoryActiveManager(
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embed_manager,
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llm_client_list[global_config["embedding"]["provider"]],
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)
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