mirror of https://github.com/Mai-with-u/MaiBot.git
387 lines
16 KiB
Python
387 lines
16 KiB
Python
import argparse
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import sys
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from pathlib import Path
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from typing import List, Tuple, Dict, Any
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import json
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import os
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# 强制使用 utf-8，避免控制台编码报错
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try:
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if hasattr(sys.stdout, "reconfigure"):
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sys.stdout.reconfigure(encoding="utf-8")
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if hasattr(sys.stderr, "reconfigure"):
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sys.stderr.reconfigure(encoding="utf-8")
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except Exception:
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pass
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# 确保能找到 src 包
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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.embedding_store import EmbeddingManager
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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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logger = get_logger("delete_lpmm_items")
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def read_hashes(file_path: Path) -> List[str]:
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"""读取哈希列表，跳过空行"""
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hashes: List[str] = []
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for line in file_path.read_text(encoding="utf-8").splitlines():
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val = line.strip()
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if not val:
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continue
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hashes.append(val)
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return hashes
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def read_openie_hashes(file_path: Path) -> List[str]:
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"""从 OpenIE JSON 中提取 idx 作为段落哈希"""
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data: Dict[str, Any] = json.loads(file_path.read_text(encoding="utf-8"))
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docs = data.get("docs", []) if isinstance(data, dict) else []
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hashes: List[str] = []
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for doc in docs:
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idx = doc.get("idx") if isinstance(doc, dict) else None
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if isinstance(idx, str) and idx.strip():
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hashes.append(idx.strip())
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return hashes
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def normalize_paragraph_keys(raw_hashes: List[str]) -> Tuple[List[str], List[str]]:
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"""将输入规范为完整键和纯哈希两份列表"""
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keys: List[str] = []
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hashes: List[str] = []
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for h in raw_hashes:
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if h.startswith("paragraph-"):
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keys.append(h)
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hashes.append(h.replace("paragraph-", "", 1))
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else:
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keys.append(f"paragraph-{h}")
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hashes.append(h)
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return keys, hashes
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def main():
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parser = argparse.ArgumentParser(description="Delete paragraphs from LPMM knowledge base (vectors + graph).")
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parser.add_argument("--hash-file", help="文本文件路径，每行一个 paragraph 哈希或带前缀键")
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parser.add_argument("--openie-file", help="OpenIE 输出文件（JSON），将其 docs.idx 作为待删段落哈希")
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parser.add_argument("--raw-file", help="原始 txt 语料文件（按空行分段），可结合 --raw-index 使用")
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parser.add_argument(
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"--raw-index",
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help="在 --raw-file 中要删除的段落索引，1 基，支持逗号分隔，例如 1,3",
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)
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parser.add_argument("--search-text", help="在当前段落库中按子串搜索匹配段落并交互选择删除")
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parser.add_argument(
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"--search-limit",
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type=int,
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default=10,
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help="--search-text 模式下最多展示的候选段落数量",
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)
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parser.add_argument("--delete-entities", action="store_true", help="同时删除 OpenIE 文件中的实体节点/嵌入")
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parser.add_argument("--delete-relations", action="store_true", help="同时删除 OpenIE 文件中的关系嵌入")
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parser.add_argument("--remove-orphan-entities", action="store_true", help="删除删除后孤立的实体节点")
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parser.add_argument("--dry-run", action="store_true", help="仅预览将删除的项，不实际修改")
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parser.add_argument("--yes", action="store_true", help="跳过交互确认，直接执行删除（谨慎使用）")
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parser.add_argument(
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"--max-delete-nodes",
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type=int,
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default=2000,
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help="单次最大允许删除的节点数量（段落+实体），超过则需要显式确认或调整该参数",
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)
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parser.add_argument(
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"--non-interactive",
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action="store_true",
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help=(
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"非交互模式：不再通过 input() 询问任何信息；"
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"在该模式下，如果需要交互（例如 --search-text 未指定具体条目、未提供 --yes），"
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"会直接报错退出。"
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),
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)
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args = parser.parse_args()
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# 至少需要一种来源
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if not (args.hash_file or args.openie_file or args.raw_file or args.search_text):
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logger.error("必须指定 --hash-file / --openie-file / --raw-file / --search-text 之一")
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sys.exit(1)
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raw_hashes: List[str] = []
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raw_entities: List[str] = []
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raw_relations: List[str] = []
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if args.hash_file:
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hash_file = Path(args.hash_file)
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if not hash_file.exists():
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logger.error(f"哈希文件不存在: {hash_file}")
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sys.exit(1)
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raw_hashes.extend(read_hashes(hash_file))
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if args.openie_file:
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openie_path = Path(args.openie_file)
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if not openie_path.exists():
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logger.error(f"OpenIE 文件不存在: {openie_path}")
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sys.exit(1)
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# 段落
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raw_hashes.extend(read_openie_hashes(openie_path))
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# 实体/关系（实体同时包含 extracted_entities 与三元组主语/宾语，以匹配 KG 构图逻辑）
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try:
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data = json.loads(openie_path.read_text(encoding="utf-8"))
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docs = data.get("docs", []) if isinstance(data, dict) else []
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for doc in docs:
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if not isinstance(doc, dict):
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continue
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ents = doc.get("extracted_entities", [])
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if isinstance(ents, list):
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raw_entities.extend([e for e in ents if isinstance(e, str)])
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triples = doc.get("extracted_triples", [])
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if isinstance(triples, list):
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for t in triples:
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if isinstance(t, list) and len(t) == 3:
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subj, _, obj = t
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if isinstance(subj, str):
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raw_entities.append(subj)
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if isinstance(obj, str):
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raw_entities.append(obj)
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raw_relations.append(str(tuple(t)))
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except Exception as e:
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logger.error(f"读取 OpenIE 文件失败: {e}")
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sys.exit(1)
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# 从原始 txt 语料按段落索引选择删除
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if args.raw_file:
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raw_path = Path(args.raw_file)
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if not raw_path.exists():
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logger.error(f"原始语料文件不存在: {raw_path}")
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sys.exit(1)
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text = raw_path.read_text(encoding="utf-8")
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paragraphs: List[str] = []
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buf = []
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for line in text.splitlines():
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if line.strip() == "":
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if buf:
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paragraphs.append("\n".join(buf).strip())
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buf = []
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else:
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buf.append(line)
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if buf:
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paragraphs.append("\n".join(buf).strip())
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if not paragraphs:
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logger.error(f"原始语料文件 {raw_path} 中没有解析到任何段落")
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sys.exit(1)
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if not args.raw_index:
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logger.info(f"{raw_path} 共解析出 {len(paragraphs)} 个段落，请通过 --raw-index 指定要删除的段落，例如 --raw-index 1,3")
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sys.exit(1)
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# 解析索引列表（1-based）
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try:
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idx_list = [int(x.strip()) for x in str(args.raw_index).split(",") if x.strip()]
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except ValueError:
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logger.error(f"--raw-index 解析失败: {args.raw_index}")
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sys.exit(1)
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for idx in idx_list:
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if idx < 1 or idx > len(paragraphs):
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logger.error(f"--raw-index 包含无效索引 {idx}（有效范围 1~{len(paragraphs)}）")
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sys.exit(1)
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logger.info("根据原始语料选择段落：")
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for idx in idx_list:
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para = paragraphs[idx - 1]
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h = get_sha256(para)
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logger.info(f"- 第 {idx} 段，hash={h}，内容预览：{para[:80]}")
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raw_hashes.append(h)
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# 在现有库中按子串搜索候选段落并交互选择
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if args.search_text:
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search_text = args.search_text.strip()
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if not search_text:
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logger.error("--search-text 不能为空")
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sys.exit(1)
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logger.info(f"正在根据关键字在现有段落库中搜索：{search_text!r}")
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em_search = EmbeddingManager()
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try:
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em_search.load_from_file()
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except Exception as e:
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logger.error(f"加载嵌入库失败，无法使用 --search-text 功能: {e}")
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sys.exit(1)
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candidates = []
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for key, item in em_search.paragraphs_embedding_store.store.items():
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if search_text in item.str:
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candidates.append((key, item.str))
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if len(candidates) >= args.search_limit:
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break
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if not candidates:
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logger.info("未在现有段落库中找到包含该关键字的段落")
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else:
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logger.info("找到以下候选段落（输入序号选择要删除的条目，可用逗号分隔，多选）：")
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for i, (key, text) in enumerate(candidates, start=1):
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logger.info(f"{i}. {key} | {text[:80]}")
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if args.non_interactive:
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logger.error(
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"当前处于非交互模式，无法通过输入序号选择要删除的候选段落；"
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"如需脚本化删除，请改用 --hash-file / --openie-file / --raw-file 等方式。"
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)
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sys.exit(1)
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choice = input("请输入要删除的序号列表（如 1,3），或直接回车取消：").strip()
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if choice:
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try:
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idxs = [int(x.strip()) for x in choice.split(",") if x.strip()]
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except ValueError:
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logger.error("输入的序号列表无法解析，已取消 --search-text 删除")
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else:
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for i in idxs:
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if 1 <= i <= len(candidates):
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key, _ = candidates[i - 1]
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# key 已是完整的 paragraph-xxx
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if key.startswith("paragraph-"):
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raw_hashes.append(key.split("paragraph-", 1)[1])
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else:
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logger.warning(f"忽略无效序号: {i}")
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# 去重但保持顺序
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seen = set()
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raw_hashes = [h for h in raw_hashes if not (h in seen or seen.add(h))]
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if not raw_hashes:
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logger.error("未读取到任何待删哈希，无操作")
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sys.exit(1)
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keys, pg_hashes = normalize_paragraph_keys(raw_hashes)
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ent_hashes: List[str] = []
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rel_hashes: List[str] = []
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if args.delete_entities and raw_entities:
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ent_hashes = [get_sha256(e) for e in raw_entities]
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if args.delete_relations and raw_relations:
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rel_hashes = [get_sha256(r) for r in raw_relations]
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logger.info("=== 删除操作预备 ===")
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logger.info("请确保已备份 data/embedding 与 data/rag，必要时可使用 --dry-run 预览")
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logger.info(f"待删除段落数量: {len(keys)}")
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logger.info(f"示例: {keys[:5]}")
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if ent_hashes:
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logger.info(f"待删除实体数量: {len(ent_hashes)}")
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if rel_hashes:
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logger.info(f"待删除关系数量: {len(rel_hashes)}")
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total_nodes_to_delete = len(pg_hashes) + (len(ent_hashes) if args.delete_entities else 0)
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logger.info(f"本次预计删除节点总数（段落+实体）: {total_nodes_to_delete}")
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if args.dry_run:
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logger.info("dry-run 模式，未执行删除")
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return
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# 大批次删除保护
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if total_nodes_to_delete > args.max_delete_nodes and not args.yes:
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logger.error(
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f"本次预计删除节点 {total_nodes_to_delete} 个，超过阈值 {args.max_delete_nodes}。"
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" 为避免误删，请降低批次规模或使用 --max-delete-nodes 调整阈值，并加上 --yes 明确确认。"
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)
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sys.exit(1)
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# 交互确认
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if not args.yes:
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if args.non_interactive:
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logger.error(
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"当前处于非交互模式且未指定 --yes，出于安全考虑，删除操作已被拒绝。\n"
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"如确认需要在非交互模式下执行删除，请显式添加 --yes 参数。"
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)
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sys.exit(1)
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confirm = input("确认删除上述数据？输入大写 YES 以继续，其他任意键取消: ").strip()
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if confirm != "YES":
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logger.info("用户取消删除操作")
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return
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# 加载嵌入与图
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embed_manager = EmbeddingManager()
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kg_manager = KGManager()
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try:
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embed_manager.load_from_file()
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kg_manager.load_from_file()
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except Exception as e:
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logger.error(f"加载现有知识库失败: {e}")
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sys.exit(1)
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# 记录删除前全局统计，便于对比
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before_para_vec = len(embed_manager.paragraphs_embedding_store.store)
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before_ent_vec = len(embed_manager.entities_embedding_store.store)
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before_rel_vec = len(embed_manager.relation_embedding_store.store)
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before_nodes = len(kg_manager.graph.get_node_list())
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before_edges = len(kg_manager.graph.get_edge_list())
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logger.info(
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f"删除前统计: 段落向量={before_para_vec}, 实体向量={before_ent_vec}, 关系向量={before_rel_vec}, "
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f"KG节点={before_nodes}, KG边={before_edges}"
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)
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# 删除向量
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deleted, skipped = embed_manager.paragraphs_embedding_store.delete_items(keys)
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embed_manager.stored_pg_hashes = set(embed_manager.paragraphs_embedding_store.store.keys())
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logger.info(f"段落向量删除完成，删除: {deleted}, 跳过: {skipped}")
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ent_deleted = ent_skipped = rel_deleted = rel_skipped = 0
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if ent_hashes:
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ent_keys = [f"entity-{h}" for h in ent_hashes]
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ent_deleted, ent_skipped = embed_manager.entities_embedding_store.delete_items(ent_keys)
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logger.info(f"实体向量删除完成，删除: {ent_deleted}, 跳过: {ent_skipped}")
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if rel_hashes:
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rel_keys = [f"relation-{h}" for h in rel_hashes]
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rel_deleted, rel_skipped = embed_manager.relation_embedding_store.delete_items(rel_keys)
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logger.info(f"关系向量删除完成，删除: {rel_deleted}, 跳过: {rel_skipped}")
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# 删除图节点/边
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kg_result = kg_manager.delete_paragraphs(
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pg_hashes,
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ent_hashes=ent_hashes if args.delete_entities else None,
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remove_orphan_entities=args.remove_orphan_entities,
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)
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logger.info(
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f"KG 删除完成，删除: {kg_result.get('deleted', 0)}, 跳过: {kg_result.get('skipped', 0)}, "
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f"孤立实体清理: {kg_result.get('orphan_removed', 0)}"
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)
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# 重建索引并保存
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logger.info("重建 Faiss 索引并保存嵌入文件...")
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embed_manager.rebuild_faiss_index()
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embed_manager.save_to_file()
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logger.info("保存 KG 数据...")
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kg_manager.save_to_file()
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# 删除后统计
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after_para_vec = len(embed_manager.paragraphs_embedding_store.store)
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after_ent_vec = len(embed_manager.entities_embedding_store.store)
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after_rel_vec = len(embed_manager.relation_embedding_store.store)
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after_nodes = len(kg_manager.graph.get_node_list())
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after_edges = len(kg_manager.graph.get_edge_list())
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logger.info(
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"删除后统计: 段落向量=%d(%+d), 实体向量=%d(%+d), 关系向量=%d(%+d), KG节点=%d(%+d), KG边=%d(%+d)"
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% (
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after_para_vec,
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after_para_vec - before_para_vec,
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after_ent_vec,
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after_ent_vec - before_ent_vec,
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after_rel_vec,
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after_rel_vec - before_rel_vec,
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after_nodes,
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after_nodes - before_nodes,
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after_edges,
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after_edges - before_edges,
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)
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)
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logger.info("删除流程完成")
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print(
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"\n[NOTICE] 删除脚本执行完毕。如主程序（聊天 / WebUI）已在运行，"
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"请重启主程序，或在主程序内部调用一次 lpmm_start_up() 以应用最新 LPMM 知识库。"
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)
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print("[NOTICE] 如果不清楚 lpmm_start_up 是什么，直接重启主程序即可。")
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if __name__ == "__main__":
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main()
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