81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
from src.chat.knowledge.embedding_store import EmbeddingManager
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from src.chat.knowledge.qa_manager import QAManager
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from src.chat.knowledge.kg_manager import KGManager
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from src.chat.knowledge.global_logger import logger
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from src.config.config import global_config
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import os
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INVALID_ENTITY = [
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"",
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"你",
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"他",
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"她",
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"它",
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"我们",
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"你们",
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"他们",
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"她们",
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"它们",
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]
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RAG_GRAPH_NAMESPACE = "rag-graph"
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RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
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RAG_PG_HASH_NAMESPACE = "rag-pg-hash"
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ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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DATA_PATH = os.path.join(ROOT_PATH, "data")
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qa_manager = None
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inspire_manager = None
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# 检查LPMM知识库是否启用
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if global_config.lpmm_knowledge.enable:
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logger.info("正在初始化Mai-LPMM")
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logger.info("创建LLM客户端")
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# 初始化Embedding库
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embed_manager = EmbeddingManager()
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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.warning(f"此消息不会影响正常使用：从文件加载Embedding库时，{e}")
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# logger.warning("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.warning(f"此消息不会影响正常使用：从文件加载KG时，{e}")
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# logger.warning("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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# 使用与EmbeddingStore中一致的命名空间格式
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key = f"paragraph-{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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)
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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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else:
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logger.info("LPMM知识库已禁用，跳过初始化")
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# 创建空的占位符对象，避免导入错误
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