250 lines
11 KiB
Python
250 lines
11 KiB
Python
# try:
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# import src.plugins.knowledge.lib.quick_algo
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# except ImportError:
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# print("未找到quick_algo库，无法使用quick_algo算法")
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# print("请安装quick_algo库 - 在lib.quick_algo中，执行命令：python setup.py build_ext --inplace")
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import sys
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import os
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from time import sleep
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from src.chat.knowledge.lpmmconfig import PG_NAMESPACE, global_config
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from src.chat.knowledge.embedding_store import EmbeddingManager
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from src.chat.knowledge.llm_client import LLMClient
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from src.chat.knowledge.open_ie import OpenIE
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from src.chat.knowledge.kg_manager import KGManager
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from src.common.logger import get_logger
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from src.chat.knowledge.utils.hash import get_sha256
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# 添加项目根目录到 sys.path
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ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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OPENIE_DIR = global_config["persistence"]["openie_data_path"] or os.path.join(ROOT_PATH, "data", "openie")
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logger = get_logger("OpenIE导入")
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def ensure_openie_dir():
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"""确保OpenIE数据目录存在"""
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if not os.path.exists(OPENIE_DIR):
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os.makedirs(OPENIE_DIR)
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logger.info(f"创建OpenIE数据目录：{OPENIE_DIR}")
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else:
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logger.info(f"OpenIE数据目录已存在：{OPENIE_DIR}")
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def hash_deduplicate(
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raw_paragraphs: dict[str, str],
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triple_list_data: dict[str, list[list[str]]],
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stored_pg_hashes: set,
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stored_paragraph_hashes: set,
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):
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"""Hash去重
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Args:
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raw_paragraphs: 索引的段落原文
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triple_list_data: 索引的三元组列表
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stored_pg_hashes: 已存储的段落hash集合
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stored_paragraph_hashes: 已存储的段落hash集合
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Returns:
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new_raw_paragraphs: 去重后的段落
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new_triple_list_data: 去重后的三元组
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"""
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# 保存去重后的段落
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new_raw_paragraphs = {}
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# 保存去重后的三元组
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new_triple_list_data = {}
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for _, (raw_paragraph, triple_list) in enumerate(zip(raw_paragraphs.values(), triple_list_data.values())):
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# 段落hash
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paragraph_hash = get_sha256(raw_paragraph)
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if f"{PG_NAMESPACE}-{paragraph_hash}" in stored_pg_hashes and paragraph_hash in stored_paragraph_hashes:
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continue
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new_raw_paragraphs[paragraph_hash] = raw_paragraph
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new_triple_list_data[paragraph_hash] = triple_list
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return new_raw_paragraphs, new_triple_list_data
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def handle_import_openie(openie_data: OpenIE, embed_manager: EmbeddingManager, kg_manager: KGManager) -> bool:
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# sourcery skip: extract-method
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# 从OpenIE数据中提取段落原文与三元组列表
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# 索引的段落原文
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raw_paragraphs = openie_data.extract_raw_paragraph_dict()
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# 索引的实体列表
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entity_list_data = openie_data.extract_entity_dict()
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# 索引的三元组列表
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triple_list_data = openie_data.extract_triple_dict()
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# print(openie_data.docs)
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if len(raw_paragraphs) != len(entity_list_data) or len(raw_paragraphs) != len(triple_list_data):
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logger.error("OpenIE数据存在异常")
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logger.error(f"原始段落数量：{len(raw_paragraphs)}")
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logger.error(f"实体列表数量：{len(entity_list_data)}")
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logger.error(f"三元组列表数量：{len(triple_list_data)}")
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logger.error("OpenIE数据段落数量与实体列表数量或三元组列表数量不一致")
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logger.error("请保证你的原始数据分段良好，不要有类似于 “.....” 单独成一段的情况")
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logger.error("或者一段中只有符号的情况")
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# 新增：检查docs中每条数据的完整性
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logger.error("系统将于2秒后开始检查数据完整性")
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sleep(2)
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found_missing = False
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missing_idxs = []
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for doc in getattr(openie_data, "docs", []):
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idx = doc.get("idx", "<无idx>")
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passage = doc.get("passage", "<无passage>")
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missing = []
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# 检查字段是否存在且非空
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if "passage" not in doc or not doc.get("passage"):
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missing.append("passage")
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if "extracted_entities" not in doc or not isinstance(doc.get("extracted_entities"), list):
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missing.append("名词列表缺失")
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elif len(doc.get("extracted_entities", [])) == 0:
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missing.append("名词列表为空")
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if "extracted_triples" not in doc or not isinstance(doc.get("extracted_triples"), list):
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missing.append("主谓宾三元组缺失")
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elif len(doc.get("extracted_triples", [])) == 0:
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missing.append("主谓宾三元组为空")
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# 输出所有doc的idx
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# print(f"检查: idx={idx}")
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if missing:
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found_missing = True
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missing_idxs.append(idx)
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logger.error("\n")
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logger.error("数据缺失：")
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logger.error(f"对应哈希值：{idx}")
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logger.error(f"对应文段内容内容：{passage}")
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logger.error(f"非法原因：{', '.join(missing)}")
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# 确保提示在所有非法数据输出后再输出
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if not found_missing:
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logger.info("所有数据均完整，没有发现缺失字段。")
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return False
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# 新增：提示用户是否删除非法文段继续导入
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# 将print移到所有logger.error之后，确保不会被冲掉
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logger.info(f"\n检测到非法文段，共{len(missing_idxs)}条。")
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logger.info("\n是否删除所有非法文段后继续导入？(y/n): ", end="")
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user_choice = input().strip().lower()
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if user_choice != "y":
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logger.info("用户选择不删除非法文段，程序终止。")
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sys.exit(1)
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# 删除非法文段
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logger.info("正在删除非法文段并继续导入...")
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# 过滤掉非法文段
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openie_data.docs = [
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doc for doc in getattr(openie_data, "docs", []) if doc.get("idx", "<无idx>") not in missing_idxs
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]
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# 重新提取数据
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raw_paragraphs = openie_data.extract_raw_paragraph_dict()
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entity_list_data = openie_data.extract_entity_dict()
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triple_list_data = openie_data.extract_triple_dict()
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# 再次校验
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if len(raw_paragraphs) != len(entity_list_data) or len(raw_paragraphs) != len(triple_list_data):
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logger.error("删除非法文段后，数据仍不一致，程序终止。")
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sys.exit(1)
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# 将索引换为对应段落的hash值
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logger.info("正在进行段落去重与重索引")
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raw_paragraphs, triple_list_data = hash_deduplicate(
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raw_paragraphs,
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triple_list_data,
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embed_manager.stored_pg_hashes,
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kg_manager.stored_paragraph_hashes,
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)
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if len(raw_paragraphs) != 0:
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# 获取嵌入并保存
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logger.info(f"段落去重完成，剩余待处理的段落数量：{len(raw_paragraphs)}")
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logger.info("开始Embedding")
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embed_manager.store_new_data_set(raw_paragraphs, triple_list_data)
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# Embedding-Faiss重索引
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logger.info("正在重新构建向量索引")
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embed_manager.rebuild_faiss_index()
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logger.info("向量索引构建完成")
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embed_manager.save_to_file()
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logger.info("Embedding完成")
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# 构建新段落的RAG
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logger.info("开始构建RAG")
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kg_manager.build_kg(triple_list_data, embed_manager)
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kg_manager.save_to_file()
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logger.info("RAG构建完成")
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else:
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logger.info("无新段落需要处理")
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return True
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def main(): # sourcery skip: dict-comprehension
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# 新增确认提示
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print("=== 重要操作确认 ===")
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print("OpenIE导入时会大量发送请求，可能会撞到请求速度上限，请注意选用的模型")
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print("同之前样例：在本地模型下，在70分钟内我们发送了约8万条请求，在网络允许下，速度会更快")
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print("推荐使用硅基流动的Pro/BAAI/bge-m3")
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print("每百万Token费用为0.7元")
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print("知识导入时，会消耗大量系统资源，建议在较好配置电脑上运行")
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print("同上样例，导入时10700K几乎跑满，14900HX占用80%，峰值内存占用约3G")
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confirm = input("确认继续执行？(y/n): ").strip().lower()
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if confirm != "y":
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logger.info("用户取消操作")
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print("操作已取消")
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sys.exit(1)
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print("\n" + "=" * 40 + "\n")
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ensure_openie_dir() # 确保OpenIE目录存在
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logger.info("----开始导入openie数据----\n")
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logger.info("创建LLM客户端")
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llm_client_list = {}
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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(f"从文件加载Embedding库时发生错误：{e}")
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if "嵌入模型与本地存储不一致" in str(e):
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logger.error("检测到嵌入模型与本地存储不一致，已终止导入。请检查模型设置或清空嵌入库后重试。")
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logger.error("请保证你的嵌入模型从未更改,并且在导入时使用相同的模型")
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# print("检测到嵌入模型与本地存储不一致，已终止导入。请检查模型设置或清空嵌入库后重试。")
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sys.exit(1)
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if "不存在" in str(e):
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logger.error("如果你是第一次导入知识，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(f"从文件加载KG时发生错误：{e}")
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logger.error("如果你是第一次导入知识，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 = f"{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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logger.info("正在导入OpenIE数据文件")
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try:
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openie_data = OpenIE.load()
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except Exception as e:
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logger.error(f"导入OpenIE数据文件时发生错误：{e}")
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return False
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if handle_import_openie(openie_data, embed_manager, kg_manager) is False:
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logger.error("处理OpenIE数据时发生错误")
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return False
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return None
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if __name__ == "__main__":
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# logger.info(f"111111111111111111111111{ROOT_PATH}")
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main()
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