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feat: 新增LPMM知识库模块及工具支持

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- 新增LPMM知识库模块，包括实体提取、RDF构建、Embedding存储、KG管理等功能
- 新增`lpmm_get_knowledge`工具，支持从LPMM知识库中检索相关信息
- 新增OpenIE数据处理模块，支持信息提取、数据导入等功能
- 新增知识库初始化脚本，支持从原始数据到知识库的完整处理流程
- 新增配置文件`lpmm_config.toml`，支持自定义知识库相关参数
- 新增日志模块`LPMM_STYLE_CONFIG`，支持知识库相关日志输出
- 新增`raw_data_preprocessor.py`、`info_extraction.py`、`import_openie.py`等脚本，支持知识库数据预处理
This commit is contained in:
墨梓柒
2025-04-23 10:28:05 +08:00
parent 6265fd6c14
commit 2b07c9e81b
32 changed files with 2940 additions and 60 deletions
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46
![新版麦麦开始学习.bat Normal file
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@echo off
CHCP 65001 > nul
setlocal enabledelayedexpansion
REM 查找venv虚拟环境
set "venv_path=%~dp0venv\Scripts\activate.bat"
if not exist "%venv_path%" (
echo 错误: 未找到虚拟环境，请确保venv目录存在
pause
exit /b 1
)
REM 激活虚拟环境
call "%venv_path%"
if %ERRORLEVEL% neq 0 (
echo 错误: 虚拟环境激活失败
pause
exit /b 1
)
REM 运行预处理脚本
python "%~dp0raw_data_preprocessor.py"
if %ERRORLEVEL% neq 0 (
echo 错误: raw_data_preprocessor.py 执行失败
pause
exit /b 1
)
REM 运行信息提取脚本
python "%~dp0info_extraction.py"
if %ERRORLEVEL% neq 0 (
echo 错误: info_extraction.py 执行失败
pause
exit /b 1
)
REM 运行OpenIE导入脚本
python "%~dp0import_openie.py"
if %ERRORLEVEL% neq 0 (
echo 错误: import_openie.py 执行失败
pause
exit /b 1
)
echo 所有处理步骤完成!
pause

3
.gitignore vendored
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@@ -28,6 +28,8 @@ memory_graph.gml
config/bot_config_dev.toml
config/bot_config.toml
config/bot_config.toml.bak
config/lpmm_config.toml
config/lpmm_config.toml.bak
src/plugins/remote/client_uuid.json
（测试版）麦麦生成人格.bat
（临时版）麦麦开始学习.bat
@@ -240,4 +242,5 @@ logs
/config/*
config/old/bot_config_20250405_212257.toml
temp/

10
bot.py
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@@ -52,6 +52,16 @@ def init_config():
shutil.copy("template/bot_config_template.toml", "config/bot_config.toml")
logger.info("复制完成，请修改config/bot_config.toml和.env中的配置后重新启动")
if not os.path.exists("config/lpmm_config.toml"):
logger.warning("检测到lpmm_config.toml不存在，正在从模板复制")
# 检查config目录是否存在
if not os.path.exists("config"):
os.makedirs("config")
logger.info("创建config目录")
shutil.copy("template/lpmm_config_template.toml", "config/lpmm_config.toml")
logger.info("复制完成，请修改config/lpmm_config.toml和.env中的配置后重新启动")
def init_env():

162
import_openie.py Normal file
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# try:
# import src.plugins.knowledge.lib.quick_algo
# except ImportError:
# print("未找到quick_algo库，无法使用quick_algo算法")
# print("请安装quick_algo库 - 在lib.quick_algo中，执行命令：python setup.py build_ext --inplace")
from typing import Dict, List
from src.plugins.knowledge.src.lpmmconfig import PG_NAMESPACE, global_config
from src.plugins.knowledge.src.embedding_store import EmbeddingManager
from src.plugins.knowledge.src.llm_client import LLMClient
from src.plugins.knowledge.src.open_ie import OpenIE
from src.plugins.knowledge.src.kg_manager import KGManager
from src.common.logger import get_module_logger
from src.plugins.knowledge.src.utils.hash import get_sha256
# 添加在现有导入之后
import sys
logger = get_module_logger("LPMM知识库-OpenIE导入")
def hash_deduplicate(
raw_paragraphs: Dict[str, str],
triple_list_data: Dict[str, List[List[str]]],
stored_pg_hashes: set,
stored_paragraph_hashes: set,
):
"""Hash去重
Args:
raw_paragraphs: 索引的段落原文
triple_list_data: 索引的三元组列表
stored_pg_hashes: 已存储的段落hash集合
stored_paragraph_hashes: 已存储的段落hash集合
Returns:
new_raw_paragraphs: 去重后的段落
new_triple_list_data: 去重后的三元组
"""
# 保存去重后的段落
new_raw_paragraphs = dict()
# 保存去重后的三元组
new_triple_list_data = dict()
for _, (raw_paragraph, triple_list) in enumerate(zip(raw_paragraphs.values(), triple_list_data.values())):
# 段落hash
paragraph_hash = get_sha256(raw_paragraph)
if ((PG_NAMESPACE + "-" + paragraph_hash) in stored_pg_hashes) and (paragraph_hash in stored_paragraph_hashes):
continue
new_raw_paragraphs[paragraph_hash] = raw_paragraph
new_triple_list_data[paragraph_hash] = triple_list
return new_raw_paragraphs, new_triple_list_data
def handle_import_openie(openie_data: OpenIE, embed_manager: EmbeddingManager, kg_manager: KGManager) -> bool:
# 从OpenIE数据中提取段落原文与三元组列表
# 索引的段落原文
raw_paragraphs = openie_data.extract_raw_paragraph_dict()
# 索引的实体列表
entity_list_data = openie_data.extract_entity_dict()
# 索引的三元组列表
triple_list_data = openie_data.extract_triple_dict()
if len(raw_paragraphs) != len(entity_list_data) or len(raw_paragraphs) != len(triple_list_data):
logger.error("OpenIE数据存在异常")
return False
# 将索引换为对应段落的hash值
logger.info("正在进行段落去重与重索引")
raw_paragraphs, triple_list_data = hash_deduplicate(
raw_paragraphs,
triple_list_data,
embed_manager.stored_pg_hashes,
kg_manager.stored_paragraph_hashes,
)
if len(raw_paragraphs) != 0:
# 获取嵌入并保存
logger.info(f"段落去重完成，剩余待处理的段落数量：{len(raw_paragraphs)}")
logger.info("开始Embedding")
embed_manager.store_new_data_set(raw_paragraphs, triple_list_data)
# Embedding-Faiss重索引
logger.info("正在重新构建向量索引")
embed_manager.rebuild_faiss_index()
logger.info("向量索引构建完成")
embed_manager.save_to_file()
logger.info("Embedding完成")
# 构建新段落的RAG
logger.info("开始构建RAG")
kg_manager.build_kg(triple_list_data, embed_manager)
kg_manager.save_to_file()
logger.info("RAG构建完成")
else:
logger.info("无新段落需要处理")
return True
def main():
# 新增确认提示
print("=== 重要操作确认 ===")
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")
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")
print("推荐使用硅基流动的Pro/BAAI/bge-m3")
print("每百万Token费用为0.7元")
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")
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")
confirm = input("确认继续执行？(y/n): ").strip().lower()
if confirm != "y":
logger.info("用户取消操作")
print("操作已取消")
sys.exit(1)
print("\n" + "=" * 40 + "\n")
logger.info("----开始导入openie数据----\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 = 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.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.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}")
logger.info("正在导入OpenIE数据文件")
try:
openie_data = OpenIE.load()
except Exception as e:
logger.error("导入OpenIE数据文件时发生错误：{}".format(e))
return False
if handle_import_openie(openie_data, embed_manager, kg_manager) is False:
logger.error("处理OpenIE数据时发生错误")
return False
if __name__ == "__main__":
main()

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info_extraction.py Normal file
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@@ -0,0 +1,175 @@
import json
import os
import signal
from concurrent.futures import ThreadPoolExecutor, as_completed
from threading import Lock, Event
import sys
import tqdm
from src.common.logger import get_module_logger
from src.plugins.knowledge.src.lpmmconfig import global_config
from src.plugins.knowledge.src.ie_process import info_extract_from_str
from src.plugins.knowledge.src.llm_client import LLMClient
from src.plugins.knowledge.src.open_ie import OpenIE
from src.plugins.knowledge.src.raw_processing import load_raw_data
logger = get_module_logger("LPMM知识库-信息提取")
TEMP_DIR = "./temp"
# 创建一个线程安全的锁，用于保护文件操作和共享数据
file_lock = Lock()
open_ie_doc_lock = Lock()
# 创建一个事件标志，用于控制程序终止
shutdown_event = Event()
def process_single_text(pg_hash, raw_data, llm_client_list):
"""处理单个文本的函数，用于线程池"""
temp_file_path = f"{TEMP_DIR}/{pg_hash}.json"
# 使用文件锁检查和读取缓存文件
with file_lock:
if os.path.exists(temp_file_path):
try:
# 存在对应的提取结果
logger.info(f"找到缓存的提取结果：{pg_hash}")
with open(temp_file_path, "r", encoding="utf-8") as f:
return json.load(f), None
except json.JSONDecodeError:
# 如果JSON文件损坏，删除它并重新处理
logger.warning(f"缓存文件损坏，重新处理：{pg_hash}")
os.remove(temp_file_path)
entity_list, rdf_triple_list = info_extract_from_str(
llm_client_list[global_config["entity_extract"]["llm"]["provider"]],
llm_client_list[global_config["rdf_build"]["llm"]["provider"]],
raw_data,
)
if entity_list is None or rdf_triple_list is None:
return None, pg_hash
else:
doc_item = {
"idx": pg_hash,
"passage": raw_data,
"extracted_entities": entity_list,
"extracted_triples": rdf_triple_list,
}
# 保存临时提取结果
with file_lock:
try:
with open(temp_file_path, "w", encoding="utf-8") as f:
json.dump(doc_item, f, ensure_ascii=False, indent=4)
except Exception as e:
logger.error(f"保存缓存文件失败：{pg_hash}, 错误：{e}")
# 如果保存失败，确保不会留下损坏的文件
if os.path.exists(temp_file_path):
os.remove(temp_file_path)
# 设置shutdown_event以终止程序
shutdown_event.set()
return None, pg_hash
return doc_item, None
def signal_handler(signum, frame):
"""处理Ctrl+C信号"""
logger.info("\n接收到中断信号，正在优雅地关闭程序...")
shutdown_event.set()
def main():
# 设置信号处理器
signal.signal(signal.SIGINT, signal_handler)
# 新增用户确认提示
print("=== 重要操作确认 ===")
print("实体提取操作将会花费较多资金和时间，建议在空闲时段执行。")
print("举例：600万字全剧情，提取选用deepseek v3 0324，消耗约40元，约3小时。")
print("建议使用硅基流动的非Pro模型")
print("或者使用可以用赠金抵扣的Pro模型")
print("请确保账户余额充足，并且在执行前确认无误。")
confirm = input("确认继续执行？(y/n): ").strip().lower()
if confirm != "y":
logger.info("用户取消操作")
print("操作已取消")
sys.exit(1)
print("\n" + "=" * 40 + "\n")
logger.info("--------进行信息提取--------\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"],
)
logger.info("正在加载原始数据")
sha256_list, raw_datas = load_raw_data()
logger.info("原始数据加载完成\n")
# 创建临时目录
if not os.path.exists(f"{TEMP_DIR}"):
os.makedirs(f"{TEMP_DIR}")
failed_sha256 = []
open_ie_doc = []
# 创建线程池，最大线程数为50
workers = global_config["info_extraction"]["workers"]
with ThreadPoolExecutor(max_workers=workers) as executor:
# 提交所有任务到线程池
future_to_hash = {
executor.submit(process_single_text, pg_hash, raw_data, llm_client_list): pg_hash
for pg_hash, raw_data in zip(sha256_list, raw_datas)
}
# 使用tqdm显示进度
with tqdm.tqdm(total=len(future_to_hash), postfix="正在进行提取：") as pbar:
# 处理完成的任务
try:
for future in as_completed(future_to_hash):
if shutdown_event.is_set():
# 取消所有未完成的任务
for f in future_to_hash:
if not f.done():
f.cancel()
break
doc_item, failed_hash = future.result()
if failed_hash:
failed_sha256.append(failed_hash)
logger.error(f"提取失败：{failed_hash}")
elif doc_item:
with open_ie_doc_lock:
open_ie_doc.append(doc_item)
pbar.update(1)
except KeyboardInterrupt:
# 如果在这里捕获到KeyboardInterrupt，说明signal_handler可能没有正常工作
logger.info("\n接收到中断信号，正在优雅地关闭程序...")
shutdown_event.set()
# 取消所有未完成的任务
for f in future_to_hash:
if not f.done():
f.cancel()
# 保存信息提取结果
sum_phrase_chars = sum([len(e) for chunk in open_ie_doc for e in chunk["extracted_entities"]])
sum_phrase_words = sum([len(e.split()) for chunk in open_ie_doc for e in chunk["extracted_entities"]])
num_phrases = sum([len(chunk["extracted_entities"]) for chunk in open_ie_doc])
openie_obj = OpenIE(
open_ie_doc,
round(sum_phrase_chars / num_phrases, 4),
round(sum_phrase_words / num_phrases, 4),
)
OpenIE.save(openie_obj)
logger.info("--------信息提取完成--------")
logger.info(f"提取失败的文段SHA256：{failed_sha256}")
if __name__ == "__main__":
main()

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raw_data_preprocessor.py Normal file
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import json
import os
from pathlib import Path
import sys # 新增系统模块导入
from src.common.logger import get_module_logger
logger = get_module_logger("LPMM数据库-原始数据处理")
def check_and_create_dirs():
"""检查并创建必要的目录"""
required_dirs = ["data/lpmm_raw_data", "data/imported_lpmm_data"]
for dir_path in required_dirs:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
logger.info(f"已创建目录: {dir_path}")
def process_text_file(file_path):
"""处理单个文本文件，返回段落列表"""
with open(file_path, "r", encoding="utf-8") as f:
raw = f.read()
paragraphs = []
paragraph = ""
for line in raw.split("\n"):
if line.strip() == "":
if paragraph != "":
paragraphs.append(paragraph.strip())
paragraph = ""
else:
paragraph += line + "\n"
if paragraph != "":
paragraphs.append(paragraph.strip())
return paragraphs
def main():
# 新增用户确认提示
print("=== 重要操作确认 ===")
print("如果你并非第一次导入知识")
print("请先删除data/import.json文件，备份data/openie.json文件")
print("在进行知识库导入之前")
print("请修改config/lpmm_config.toml中的配置项")
confirm = input("确认继续执行？(y/n): ").strip().lower()
if confirm != "y":
logger.error("操作已取消")
sys.exit(1)
print("\n" + "=" * 40 + "\n")
# 检查并创建必要的目录
check_and_create_dirs()
# 检查输出文件是否存在
if os.path.exists("data/import.json"):
logger.error("错误: data/import.json 已存在，请先处理或删除该文件")
sys.exit(1)
if os.path.exists("data/openie.json"):
logger.error("错误: data/openie.json 已存在，请先处理或删除该文件")
sys.exit(1)
# 获取所有原始文本文件
raw_files = list(Path("data/lpmm_raw_data").glob("*.txt"))
if not raw_files:
logger.warning("警告: data/lpmm_raw_data 中没有找到任何 .txt 文件")
sys.exit(1)
# 处理所有文件
all_paragraphs = []
for file in raw_files:
logger.info(f"正在处理文件: {file.name}")
paragraphs = process_text_file(file)
all_paragraphs.extend(paragraphs)
# 保存合并后的结果
output_path = "data/import.json"
with open(output_path, "w", encoding="utf-8") as f:
json.dump(all_paragraphs, f, ensure_ascii=False, indent=4)
logger.info(f"处理完成，结果已保存到: {output_path}")
if __name__ == "__main__":
main()

21
src/common/logger.py
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@@ -325,6 +325,26 @@ WILLING_STYLE_CONFIG = {
},
}
# LPMM配置
LPMM_STYLE_CONFIG = {
"advanced": {
"console_format": (
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
"<level>{level: <8}</level> | "
"<cyan>{extra[module]: <12}</cyan> | "
"<light-yellow>LPMM</light-yellow> | "
"<level>{message}</level>"
),
"file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | LPMM | {message}",
},
"simple": {
"console_format": (
"<green>{time:MM-DD HH:mm}</green> | <light-green>LPMM</light-green> | <light-green>{message}</light-green>"
),
"file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | LPMM | {message}",
},
}
CONFIRM_STYLE_CONFIG = {
"console_format": "<RED>{message}</RED>", # noqa: E501
"file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | EULA与PRIVACY确认 | {message}",
@@ -347,6 +367,7 @@ WILLING_STYLE_CONFIG = WILLING_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else WILL
CONFIG_STYLE_CONFIG = CONFIG_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else CONFIG_STYLE_CONFIG["advanced"]
TOOL_USE_STYLE_CONFIG = TOOL_USE_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOOL_USE_STYLE_CONFIG["advanced"]
PFC_STYLE_CONFIG = PFC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else PFC_STYLE_CONFIG["advanced"]
LPMM_STYLE_CONFIG = LPMM_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else LPMM_STYLE_CONFIG["advanced"]
def is_registered_module(record: dict) -> bool:

139
src/do_tool/tool_can_use/lpmm_get_knowledge.py Normal file
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@@ -0,0 +1,139 @@
from src.do_tool.tool_can_use.base_tool import BaseTool
from src.plugins.chat.utils import get_embedding
# from src.common.database import db
from src.common.logger import get_module_logger
from typing import Dict, Any
from src.plugins.knowledge.knowledge_lib import qa_manager
logger = get_module_logger("lpmm_get_knowledge_tool")
class SearchKnowledgeFromLPMMTool(BaseTool):
"""从LPMM知识库中搜索相关信息的工具"""
name = "lpmm_search_knowledge"
description = "从知识库中搜索相关信息"
parameters = {
"type": "object",
"properties": {
"query": {"type": "string", "description": "搜索查询关键词"},
"threshold": {"type": "number", "description": "相似度阈值，0.0到1.0之间"},
},
"required": ["query"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行知识库搜索
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
query = function_args.get("query", message_txt)
# threshold = function_args.get("threshold", 0.4)
# 调用知识库搜索
embedding = await get_embedding(query, request_type="info_retrieval")
if embedding:
knowledge_info = qa_manager.get_knowledge(query)
logger.debug(f"知识库查询结果: {knowledge_info}")
if knowledge_info:
content = f"你知道这些知识: {knowledge_info}"
else:
content = f"你不太了解有关{query}的知识"
return {"name": "search_knowledge", "content": content}
return {"name": "search_knowledge", "content": f"无法获取关于'{query}'的嵌入向量"}
except Exception as e:
logger.error(f"知识库搜索工具执行失败: {str(e)}")
return {"name": "search_knowledge", "content": f"知识库搜索失败: {str(e)}"}
# def get_info_from_db(
# self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
# ) -> Union[str, list]:
# """从数据库中获取相关信息
# Args:
# query_embedding: 查询的嵌入向量
# limit: 最大返回结果数
# threshold: 相似度阈值
# return_raw: 是否返回原始结果
# Returns:
# Union[str, list]: 格式化的信息字符串或原始结果列表
# """
# if not query_embedding:
# return "" if not return_raw else []
# # 使用余弦相似度计算
# pipeline = [
# {
# "$addFields": {
# "dotProduct": {
# "$reduce": {
# "input": {"$range": [0, {"$size": "$embedding"}]},
# "initialValue": 0,
# "in": {
# "$add": [
# "$$value",
# {
# "$multiply": [
# {"$arrayElemAt": ["$embedding", "$$this"]},
# {"$arrayElemAt": [query_embedding, "$$this"]},
# ]
# },
# ]
# },
# }
# },
# "magnitude1": {
# "$sqrt": {
# "$reduce": {
# "input": "$embedding",
# "initialValue": 0,
# "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
# }
# }
# },
# "magnitude2": {
# "$sqrt": {
# "$reduce": {
# "input": query_embedding,
# "initialValue": 0,
# "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
# }
# }
# },
# }
# },
# {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
# {
# "$match": {
# "similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
# }
# },
# {"$sort": {"similarity": -1}},
# {"$limit": limit},
# {"$project": {"content": 1, "similarity": 1}},
# ]
# results = list(db.knowledges.aggregate(pipeline))
# logger.debug(f"知识库查询结果数量: {len(results)}")
# if not results:
# return "" if not return_raw else []
# if return_raw:
# return results
# else:
# # 返回所有找到的内容，用换行分隔
# return "\n".join(str(result["content"]) for result in results)
# 注册工具
# register_tool(SearchKnowledgeTool)

1
src/do_tool/tool_use.py
View File

@@ -50,6 +50,7 @@ class ToolUser:
prompt += message_txt
# prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
prompt += f"注意你就是{bot_name}，{bot_name}是你的名字。根据之前的聊天记录补充问题信息，搜索时避开你的名字。\n"
prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
prompt += "你现在需要对群里的聊天内容进行回复，现在选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。"
prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt)

33
src/plugins/heartFC_chat/heartflow_prompt_builder.py
View File

@@ -5,14 +5,15 @@ from ...individuality.individuality import Individuality
from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
from src.plugins.person_info.relationship_manager import relationship_manager
from src.plugins.chat.utils import parse_text_timestamps
from src.plugins.chat.utils import get_embedding, parse_text_timestamps
import time
from typing import Union
from ...common.database import db
from ..chat.utils import get_embedding, get_recent_group_speaker
from ..chat.utils import get_recent_group_speaker
from ..moods.moods import MoodManager
from ..memory_system.Hippocampus import HippocampusManager
from ..schedule.schedule_generator import bot_schedule
from ..knowledge.knowledge_lib import qa_manager
logger = get_module_logger("prompt")
@@ -325,11 +326,10 @@ class PromptBuilder:
return prompt
async def get_prompt_info(self, message: str, threshold: float):
async def get_prompt_info_old(self, message: str, threshold: float):
start_time = time.time()
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
@@ -475,6 +475,31 @@ class PromptBuilder:
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info
async def get_prompt_info(self, message: str, threshold: float):
related_info = ""
start_time = time.time()
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 从LPMM知识库获取知识
found_knowledge_from_lpmm = qa_manager.get_knowledge(message)
end_time = time.time()
if found_knowledge_from_lpmm is not None:
logger.debug(f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}")
related_info += found_knowledge_from_lpmm
logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
return related_info
else:
logger.debug("从LPMM知识库获取知识失败，使用旧版数据库进行检索")
knowledge_from_old = await self.get_prompt_info_old(message, threshold=0.38)
related_info += knowledge_from_old
logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
return related_info
@staticmethod
def get_info_from_db(
query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False

674
src/plugins/knowledge/LICENSE Normal file
View File

@@ -0,0 +1,674 @@
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
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then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
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A patent license is "discriminatory" if it does not include within
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conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
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not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
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14. Revised Versions of this License.
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Also add information on how to contact you by electronic and paper mail.
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The hypothetical commands `show w' and `show c' should show the appropriate
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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
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<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
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0
src/plugins/knowledge/__init__.py Normal file
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src/plugins/knowledge/knowledge_lib.py Normal file
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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.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.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"]],
)

0
src/plugins/knowledge/src/__init__.py Normal file
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src/plugins/knowledge/src/embedding_store.py Normal file
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from dataclasses import dataclass
import json
import os
from typing import Dict, List, Tuple
import numpy as np
import pandas as pd
import tqdm
import faiss
from .llm_client import LLMClient
from .lpmmconfig import ENT_NAMESPACE, PG_NAMESPACE, REL_NAMESPACE, global_config
from .utils.hash import get_sha256
from .global_logger import logger
@dataclass
class EmbeddingStoreItem:
"""嵌入库中的项"""
def __init__(self, item_hash: str, embedding: List[float], content: str):
self.hash = item_hash
self.embedding = embedding
self.str = content
def to_dict(self) -> dict:
"""转为dict"""
return {
"hash": self.hash,
"embedding": self.embedding,
"str": self.str,
}
class EmbeddingStore:
def __init__(self, llm_client: LLMClient, namespace: str, dir_path: str):
self.namespace = namespace
self.llm_client = llm_client
self.dir = dir_path
self.embedding_file_path = dir_path + "/" + namespace + ".parquet"
self.index_file_path = dir_path + "/" + namespace + ".index"
self.idx2hash_file_path = dir_path + "/" + namespace + "_i2h.json"
self.store = dict()
self.faiss_index = None
self.idx2hash = None
def _get_embedding(self, s: str) -> List[float]:
return self.llm_client.send_embedding_request(global_config["embedding"]["model"], s)
def batch_insert_strs(self, strs: List[str]) -> None:
"""向库中存入字符串"""
# 逐项处理
for s in tqdm.tqdm(strs, desc="存入嵌入库", unit="items"):
# 计算hash去重
item_hash = self.namespace + "-" + get_sha256(s)
if item_hash in self.store:
continue
# 获取embedding
embedding = self._get_embedding(s)
# 存入
self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
def save_to_file(self) -> None:
"""保存到文件"""
data = []
logger.info(f"正在保存{self.namespace}嵌入库到文件{self.embedding_file_path}")
for item in self.store.values():
data.append(item.to_dict())
data_frame = pd.DataFrame(data)
if not os.path.exists(self.dir):
os.makedirs(self.dir, exist_ok=True)
if not os.path.exists(self.embedding_file_path):
open(self.embedding_file_path, "w").close()
data_frame.to_parquet(self.embedding_file_path, engine="pyarrow", index=False)
logger.info(f"{self.namespace}嵌入库保存成功")
if self.faiss_index is not None and self.idx2hash is not None:
logger.info(f"正在保存{self.namespace}嵌入库的FaissIndex到文件{self.index_file_path}")
faiss.write_index(self.faiss_index, self.index_file_path)
logger.info(f"{self.namespace}嵌入库的FaissIndex保存成功")
logger.info(f"正在保存{self.namespace}嵌入库的idx2hash映射到文件{self.idx2hash_file_path}")
with open(self.idx2hash_file_path, "w", encoding="utf-8") as f:
f.write(json.dumps(self.idx2hash, ensure_ascii=False, indent=4))
logger.info(f"{self.namespace}嵌入库的idx2hash映射保存成功")
def load_from_file(self) -> None:
"""从文件中加载"""
if not os.path.exists(self.embedding_file_path):
raise Exception(f"文件{self.embedding_file_path}不存在")
logger.info(f"正在从文件{self.embedding_file_path}中加载{self.namespace}嵌入库")
data_frame = pd.read_parquet(self.embedding_file_path, engine="pyarrow")
for _, row in tqdm.tqdm(data_frame.iterrows(), total=len(data_frame)):
self.store[row["hash"]] = EmbeddingStoreItem(row["hash"], row["embedding"], row["str"])
logger.info(f"{self.namespace}嵌入库加载成功")
try:
if os.path.exists(self.index_file_path):
logger.info(f"正在从文件{self.index_file_path}中加载{self.namespace}嵌入库的FaissIndex")
self.faiss_index = faiss.read_index(self.index_file_path)
logger.info(f"{self.namespace}嵌入库的FaissIndex加载成功")
else:
raise Exception(f"文件{self.index_file_path}不存在")
if os.path.exists(self.idx2hash_file_path):
logger.info(f"正在从文件{self.idx2hash_file_path}中加载{self.namespace}嵌入库的idx2hash映射")
with open(self.idx2hash_file_path, "r") as f:
self.idx2hash = json.load(f)
logger.info(f"{self.namespace}嵌入库的idx2hash映射加载成功")
else:
raise Exception(f"文件{self.idx2hash_file_path}不存在")
except Exception as e:
logger.error(f"加载{self.namespace}嵌入库的FaissIndex时发生错误：{e}")
logger.warning("正在重建Faiss索引")
self.build_faiss_index()
logger.info(f"{self.namespace}嵌入库的FaissIndex重建成功")
self.save_to_file()
def build_faiss_index(self) -> None:
"""重新构建Faiss索引，以余弦相似度为度量"""
# 获取所有的embedding
array = []
self.idx2hash = dict()
for key in self.store:
array.append(self.store[key].embedding)
self.idx2hash[str(len(array) - 1)] = key
embeddings = np.array(array, dtype=np.float32)
# L2归一化
faiss.normalize_L2(embeddings)
# 构建索引
self.faiss_index = faiss.IndexFlatIP(global_config["embedding"]["dimension"])
self.faiss_index.add(embeddings)
def search_top_k(self, query: List[float], k: int) -> List[Tuple[str, float]]:
"""搜索最相似的k个项，以余弦相似度为度量
Args:
query: 查询的embedding
k: 返回的最相似的k个项
Returns:
result: 最相似的k个项的(hash, 余弦相似度)列表
"""
if self.faiss_index is None:
logger.warning("FaissIndex尚未构建,返回None")
return None
if self.idx2hash is None:
logger.warning("idx2hash尚未构建,返回None")
return None
# L2归一化
faiss.normalize_L2(np.array([query], dtype=np.float32))
# 搜索
distances, indices = self.faiss_index.search(np.array([query]), k)
# 整理结果
indices = list(indices.flatten())
distances = list(distances.flatten())
result = [
(self.idx2hash[str(int(idx))], float(sim))
for (idx, sim) in zip(indices, distances)
if idx in range(len(self.idx2hash))
]
return result
class EmbeddingManager:
def __init__(self, llm_client: LLMClient):
self.paragraphs_embedding_store = EmbeddingStore(
llm_client,
PG_NAMESPACE,
global_config["persistence"]["embedding_data_dir"],
)
self.entities_embedding_store = EmbeddingStore(
llm_client,
ENT_NAMESPACE,
global_config["persistence"]["embedding_data_dir"],
)
self.relation_embedding_store = EmbeddingStore(
llm_client,
REL_NAMESPACE,
global_config["persistence"]["embedding_data_dir"],
)
self.stored_pg_hashes = set()
def _store_pg_into_embedding(self, raw_paragraphs: Dict[str, str]):
"""将段落编码存入Embedding库"""
self.paragraphs_embedding_store.batch_insert_strs(list(raw_paragraphs.values()))
def _store_ent_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]):
"""将实体编码存入Embedding库"""
entities = set()
for triple_list in triple_list_data.values():
for triple in triple_list:
entities.add(triple[0])
entities.add(triple[2])
self.entities_embedding_store.batch_insert_strs(list(entities))
def _store_rel_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]):
"""将关系编码存入Embedding库"""
graph_triples = [] # a list of unique relation triple (in tuple) from all chunks
for triples in triple_list_data.values():
graph_triples.extend([tuple(t) for t in triples])
graph_triples = list(set(graph_triples))
self.relation_embedding_store.batch_insert_strs([str(triple) for triple in graph_triples])
def load_from_file(self):
"""从文件加载"""
self.paragraphs_embedding_store.load_from_file()
self.entities_embedding_store.load_from_file()
self.relation_embedding_store.load_from_file()
# 从段落库中获取已存储的hash
self.stored_pg_hashes = set(self.paragraphs_embedding_store.store.keys())
def store_new_data_set(
self,
raw_paragraphs: Dict[str, str],
triple_list_data: Dict[str, List[List[str]]],
):
"""存储新的数据集"""
self._store_pg_into_embedding(raw_paragraphs)
self._store_ent_into_embedding(triple_list_data)
self._store_rel_into_embedding(triple_list_data)
self.stored_pg_hashes.update(raw_paragraphs.keys())
def save_to_file(self):
"""保存到文件"""
self.paragraphs_embedding_store.save_to_file()
self.entities_embedding_store.save_to_file()
self.relation_embedding_store.save_to_file()
def rebuild_faiss_index(self):
"""重建Faiss索引（请在添加新数据后调用）"""
self.paragraphs_embedding_store.build_faiss_index()
self.entities_embedding_store.build_faiss_index()
self.relation_embedding_store.build_faiss_index()

10
src/plugins/knowledge/src/global_logger.py Normal file
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# Configure logger
from src.common.logger import get_module_logger, LogConfig, LPMM_STYLE_CONFIG
lpmm_log_config = LogConfig(
console_format=LPMM_STYLE_CONFIG["console_format"],
file_format=LPMM_STYLE_CONFIG["file_format"],
)
logger = get_module_logger("LPMM", config=lpmm_log_config)

98
src/plugins/knowledge/src/ie_process.py Normal file
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import json
import time
from typing import List
from .global_logger import logger
from . import prompt_template
from .lpmmconfig import global_config, INVALID_ENTITY
from .llm_client import LLMClient
from .utils.json_fix import fix_broken_generated_json
def _entity_extract(llm_client: LLMClient, paragraph: str) -> List[str]:
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
entity_extract_context = prompt_template.build_entity_extract_context(paragraph)
_, request_result = llm_client.send_chat_request(
global_config["entity_extract"]["llm"]["model"], entity_extract_context
)
# 去除‘{’前的内容（结果中可能有多个‘{’）
if "[" in request_result:
request_result = request_result[request_result.index("[") :]
# 去除最后一个‘}’后的内容（结果中可能有多个‘}’）
if "]" in request_result:
request_result = request_result[: request_result.rindex("]") + 1]
entity_extract_result = json.loads(fix_broken_generated_json(request_result))
entity_extract_result = [
entity
for entity in entity_extract_result
if (entity is not None) and (entity != "") and (entity not in INVALID_ENTITY)
]
if len(entity_extract_result) == 0:
raise Exception("实体提取结果为空")
return entity_extract_result
def _rdf_triple_extract(llm_client: LLMClient, paragraph: str, entities: list) -> List[List[str]]:
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
entity_extract_context = prompt_template.build_rdf_triple_extract_context(
paragraph, entities=json.dumps(entities, ensure_ascii=False)
)
_, request_result = llm_client.send_chat_request(global_config["rdf_build"]["llm"]["model"], entity_extract_context)
# 去除‘{’前的内容（结果中可能有多个‘{’）
if "[" in request_result:
request_result = request_result[request_result.index("[") :]
# 去除最后一个‘}’后的内容（结果中可能有多个‘}’）
if "]" in request_result:
request_result = request_result[: request_result.rindex("]") + 1]
entity_extract_result = json.loads(fix_broken_generated_json(request_result))
for triple in entity_extract_result:
if len(triple) != 3 or (triple[0] is None or triple[1] is None or triple[2] is None) or "" in triple:
raise Exception("RDF提取结果格式错误")
return entity_extract_result
def info_extract_from_str(
llm_client_for_ner: LLMClient, llm_client_for_rdf: LLMClient, paragraph: str
) -> tuple[None, None] | tuple[list[str], list[list[str]]]:
try_count = 0
while True:
try:
entity_extract_result = _entity_extract(llm_client_for_ner, paragraph)
break
except Exception as e:
logger.warning(f"实体提取失败，错误信息：{e}")
try_count += 1
if try_count < 3:
logger.warning("将于5秒后重试")
time.sleep(5)
else:
logger.error("实体提取失败，已达最大重试次数")
return None, None
try_count = 0
while True:
try:
rdf_triple_extract_result = _rdf_triple_extract(llm_client_for_rdf, paragraph, entity_extract_result)
break
except Exception as e:
logger.warning(f"实体提取失败，错误信息：{e}")
try_count += 1
if try_count < 3:
logger.warning("将于5秒后重试")
time.sleep(5)
else:
logger.error("实体提取失败，已达最大重试次数")
return None, None
return entity_extract_result, rdf_triple_extract_result

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src/plugins/knowledge/src/kg_manager.py Normal file
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import json
import os
import time
from typing import Dict, List, Tuple
import numpy as np
import pandas as pd
import tqdm
from quick_algo import di_graph, pagerank
from .utils.hash import get_sha256
from .embedding_store import EmbeddingManager, EmbeddingStoreItem
from .lpmmconfig import (
ENT_NAMESPACE,
PG_NAMESPACE,
RAG_ENT_CNT_NAMESPACE,
RAG_GRAPH_NAMESPACE,
RAG_PG_HASH_NAMESPACE,
global_config,
)
from .global_logger import logger
class KGManager:
def __init__(self):
# 会被保存的字段
# 存储段落的hash值，用于去重
self.stored_paragraph_hashes = set()
# 实体出现次数
self.ent_appear_cnt = dict()
# KG
self.graph = di_graph.DiGraph()
# 持久化相关
self.dir_path = global_config["persistence"]["rag_data_dir"]
self.graph_data_path = self.dir_path + "/" + RAG_GRAPH_NAMESPACE + ".graphml"
self.ent_cnt_data_path = self.dir_path + "/" + RAG_ENT_CNT_NAMESPACE + ".parquet"
self.pg_hash_file_path = self.dir_path + "/" + RAG_PG_HASH_NAMESPACE + ".json"
def save_to_file(self):
"""将KG数据保存到文件"""
# 确保目录存在
if not os.path.exists(self.dir_path):
os.makedirs(self.dir_path, exist_ok=True)
# 保存KG
di_graph.save_to_file(self.graph, self.graph_data_path)
# 保存实体计数到文件
ent_cnt_df = pd.DataFrame([{"hash_key": k, "appear_cnt": v} for k, v in self.ent_appear_cnt.items()])
ent_cnt_df.to_parquet(self.ent_cnt_data_path, engine="pyarrow", index=False)
# 保存段落hash到文件
with open(self.pg_hash_file_path, "w", encoding="utf-8") as f:
data = {"stored_paragraph_hashes": list(self.stored_paragraph_hashes)}
f.write(json.dumps(data, ensure_ascii=False, indent=4))
def load_from_file(self):
"""从文件加载KG数据"""
# 确保文件存在
if not os.path.exists(self.pg_hash_file_path):
raise Exception(f"KG段落hash文件{self.pg_hash_file_path}不存在")
if not os.path.exists(self.ent_cnt_data_path):
raise Exception(f"KG实体计数文件{self.ent_cnt_data_path}不存在")
if not os.path.exists(self.graph_data_path):
raise Exception(f"KG图文件{self.graph_data_path}不存在")
# 加载段落hash
with open(self.pg_hash_file_path, "r", encoding="utf-8") as f:
data = json.load(f)
self.stored_paragraph_hashes = set(data["stored_paragraph_hashes"])
# 加载实体计数
ent_cnt_df = pd.read_parquet(self.ent_cnt_data_path, engine="pyarrow")
self.ent_appear_cnt = dict({row["hash_key"]: row["appear_cnt"] for _, row in ent_cnt_df.iterrows()})
# 加载KG
self.graph = di_graph.load_from_file(self.graph_data_path)
def _build_edges_between_ent(
self,
node_to_node: Dict[Tuple[str, str], float],
triple_list_data: Dict[str, List[List[str]]],
):
"""构建实体节点之间的关系，同时统计实体出现次数"""
for triple_list in triple_list_data.values():
entity_set = set()
for triple in triple_list:
if triple[0] == triple[2]:
# 避免自连接
continue
# 一个triple就是一条边（同时构建双向联系）
hash_key1 = ENT_NAMESPACE + "-" + get_sha256(triple[0])
hash_key2 = ENT_NAMESPACE + "-" + get_sha256(triple[2])
node_to_node[(hash_key1, hash_key2)] = node_to_node.get((hash_key1, hash_key2), 0) + 1.0
node_to_node[(hash_key2, hash_key1)] = node_to_node.get((hash_key2, hash_key1), 0) + 1.0
entity_set.add(hash_key1)
entity_set.add(hash_key2)
# 实体出现次数统计
for hash_key in entity_set:
self.ent_appear_cnt[hash_key] = self.ent_appear_cnt.get(hash_key, 0) + 1.0
@staticmethod
def _build_edges_between_ent_pg(
node_to_node: Dict[Tuple[str, str], float],
triple_list_data: Dict[str, List[List[str]]],
):
"""构建实体节点与文段节点之间的关系"""
for idx in triple_list_data:
for triple in triple_list_data[idx]:
ent_hash_key = ENT_NAMESPACE + "-" + get_sha256(triple[0])
pg_hash_key = PG_NAMESPACE + "-" + str(idx)
node_to_node[(ent_hash_key, pg_hash_key)] = node_to_node.get((ent_hash_key, pg_hash_key), 0) + 1.0
@staticmethod
def _synonym_connect(
node_to_node: Dict[Tuple[str, str], float],
triple_list_data: Dict[str, List[List[str]]],
embedding_manager: EmbeddingManager,
) -> int:
"""同义词连接"""
new_edge_cnt = 0
# 获取所有实体节点的hash值
ent_hash_list = set()
for triple_list in triple_list_data.values():
for triple in triple_list:
ent_hash_list.add(ENT_NAMESPACE + "-" + get_sha256(triple[0]))
ent_hash_list.add(ENT_NAMESPACE + "-" + get_sha256(triple[2]))
ent_hash_list = list(ent_hash_list)
synonym_hash_set = set()
synonym_result = dict()
# 对每个实体节点，查找其相似的实体节点，建立扩展连接
for ent_hash in tqdm.tqdm(ent_hash_list):
if ent_hash in synonym_hash_set:
# 避免同一批次内重复添加
continue
ent = embedding_manager.entities_embedding_store.store.get(ent_hash)
assert isinstance(ent, EmbeddingStoreItem)
if ent is None:
continue
# 查询相似实体
similar_ents = embedding_manager.entities_embedding_store.search_top_k(
ent.embedding, global_config["rag"]["params"]["synonym_search_top_k"]
)
res_ent = [] # Debug
for res_ent_hash, similarity in similar_ents:
if res_ent_hash == ent_hash:
# 避免自连接
continue
if similarity < global_config["rag"]["params"]["synonym_threshold"]:
# 相似度阈值
continue
node_to_node[(res_ent_hash, ent_hash)] = similarity
node_to_node[(ent_hash, res_ent_hash)] = similarity
synonym_hash_set.add(res_ent_hash)
new_edge_cnt += 1
res_ent.append(
(
embedding_manager.entities_embedding_store.store[res_ent_hash].str,
similarity,
)
) # Debug
synonym_result[ent.str] = res_ent
for k, v in synonym_result.items():
print(f'"{k}"的相似实体为：{v}')
return new_edge_cnt
def _update_graph(
self,
node_to_node: Dict[Tuple[str, str], float],
embedding_manager: EmbeddingManager,
):
"""更新KG图结构
流程：
1. 更新图结构：遍历所有待添加的新边
- 若是新边，则添加到图中
- 若是已存在的边，则更新边的权重
2. 更新新节点的属性
"""
existed_nodes = self.graph.get_node_list()
existed_edges = [str((edge[0], edge[1])) for edge in self.graph.get_edge_list()]
now_time = time.time()
# 更新图结构
for src_tgt, weight in node_to_node.items():
key = str(src_tgt)
# 检查边是否已存在
if key not in existed_edges:
# 新边
self.graph.add_edge(
di_graph.DiEdge(
src_tgt[0],
src_tgt[1],
{
"weight": weight,
"create_time": now_time,
"update_time": now_time,
},
)
)
else:
# 已存在的边
edge_item = self.graph[src_tgt[0], src_tgt[1]]
edge_item["weight"] += weight
edge_item["update_time"] = now_time
self.graph.update_edge(edge_item)
# 更新新节点属性
for src_tgt in node_to_node.keys():
for node_hash in src_tgt:
if node_hash not in existed_nodes:
if node_hash.startswith(ENT_NAMESPACE):
# 新增实体节点
node = embedding_manager.entities_embedding_store.store[node_hash]
assert isinstance(node, EmbeddingStoreItem)
node_item = self.graph[node_hash]
node_item["content"] = node.str
node_item["type"] = "ent"
node_item["create_time"] = now_time
self.graph.update_node(node_item)
elif node_hash.startswith(PG_NAMESPACE):
# 新增文段节点
node = embedding_manager.paragraphs_embedding_store.store[node_hash]
assert isinstance(node, EmbeddingStoreItem)
content = node.str.replace("\n", " ")
node_item = self.graph[node_hash]
node_item["content"] = content if len(content) < 8 else content[:8] + "..."
node_item["type"] = "pg"
node_item["create_time"] = now_time
self.graph.update_node(node_item)
def build_kg(
self,
triple_list_data: Dict[str, List[List[str]]],
embedding_manager: EmbeddingManager,
):
"""增量式构建KG
注意：应当在调用该方法后保存KG
Args:
triple_list_data: 三元组数据
embedding_manager: EmbeddingManager对象
"""
# 实体之间的联系
node_to_node = dict()
# 构建实体节点之间的关系，同时统计实体出现次数
logger.info("正在构建KG实体节点之间的关系，同时统计实体出现次数")
# 从三元组提取实体对
self._build_edges_between_ent(node_to_node, triple_list_data)
# 构建实体节点与文段节点之间的关系
logger.info("正在构建KG实体节点与文段节点之间的关系")
self._build_edges_between_ent_pg(node_to_node, triple_list_data)
# 近义词扩展链接
# 对每个实体节点，找到最相似的实体节点，建立扩展连接
logger.info("正在进行近义词扩展链接")
self._synonym_connect(node_to_node, triple_list_data, embedding_manager)
# 构建图
self._update_graph(node_to_node, embedding_manager)
# 记录已处理（存储）的段落hash
for idx in triple_list_data:
self.stored_paragraph_hashes.add(str(idx))
def kg_search(
self,
relation_search_result: List[Tuple[Tuple[str, str, str], float]],
paragraph_search_result: List[Tuple[str, float]],
embed_manager: EmbeddingManager,
):
"""RAG搜索与PageRank
Args:
relation_search_result: RelationEmbedding的搜索结果（relation_tripple, similarity）
paragraph_search_result: ParagraphEmbedding的搜索结果（paragraph_hash, similarity）
embed_manager: EmbeddingManager对象
"""
# 图中存在的节点总集
existed_nodes = self.graph.get_node_list()
# 准备PPR使用的数据
# 节点权重：实体
ent_weights = {}
# 节点权重：文段
pg_weights = {}
# 以下部分处理实体权重ent_weights
# 针对每个关系，提取出其中的主宾短语作为两个实体，并记录对应的三元组的相似度作为权重依据
ent_sim_scores = {}
for relation_hash, similarity, _ in relation_search_result:
# 提取主宾短语
relation = embed_manager.relation_embedding_store.store.get(relation_hash).str
assert relation is not None # 断言：relation不为空
# 关系三元组
triple = relation[2:-2].split("', '")
for ent in [(triple[0]), (triple[2])]:
ent_hash = ENT_NAMESPACE + "-" + get_sha256(ent)
if ent_hash in existed_nodes: # 该实体需在KG中存在
if ent_hash not in ent_sim_scores: # 尚未记录的实体
ent_sim_scores[ent_hash] = []
ent_sim_scores[ent_hash].append(similarity)
ent_mean_scores = {} # 记录实体的平均相似度
for ent_hash, scores in ent_sim_scores.items():
# 先对相似度进行累加，然后与实体计数相除获取最终权重
ent_weights[ent_hash] = float(np.sum(scores)) / self.ent_appear_cnt[ent_hash]
# 记录实体的平均相似度，用于后续的top_k筛选
ent_mean_scores[ent_hash] = float(np.mean(scores))
del ent_sim_scores
ent_weights_max = max(ent_weights.values())
ent_weights_min = min(ent_weights.values())
if ent_weights_max == ent_weights_min:
# 只有一个相似度，则全赋值为1
for ent_hash in ent_weights.keys():
ent_weights[ent_hash] = 1.0
else:
down_edge = global_config["qa"]["params"]["paragraph_node_weight"]
# 缩放取值区间至[down_edge, 1]
for ent_hash, score in ent_weights.items():
# 缩放相似度
ent_weights[ent_hash] = (
(score - ent_weights_min) * (1 - down_edge) / (ent_weights_max - ent_weights_min)
) + down_edge
# 取平均相似度的top_k实体
top_k = global_config["qa"]["params"]["ent_filter_top_k"]
if len(ent_mean_scores) > top_k:
# 从大到小排序，取后len - k个
ent_mean_scores = {k: v for k, v in sorted(ent_mean_scores.items(), key=lambda item: item[1], reverse=True)}
for ent_hash, _ in ent_mean_scores.items():
# 删除被淘汰的实体节点权重设置
del ent_weights[ent_hash]
del top_k, ent_mean_scores
# 以下部分处理文段权重pg_weights
# 将搜索结果中文段的相似度归一化作为权重
pg_sim_scores = {}
pg_sim_score_max = 0.0
pg_sim_score_min = 1.0
for pg_hash, similarity in paragraph_search_result:
# 查找最大和最小值
pg_sim_score_max = max(pg_sim_score_max, similarity)
pg_sim_score_min = min(pg_sim_score_min, similarity)
pg_sim_scores[pg_hash] = similarity
# 归一化
for pg_hash, similarity in pg_sim_scores.items():
# 归一化相似度
pg_sim_scores[pg_hash] = (similarity - pg_sim_score_min) / (pg_sim_score_max - pg_sim_score_min)
del pg_sim_score_max, pg_sim_score_min
for pg_hash, score in pg_sim_scores.items():
pg_weights[pg_hash] = (
score * global_config["qa"]["params"]["paragraph_node_weight"]
) # 文段权重 = 归一化相似度 * 文段节点权重参数
del pg_sim_scores
# 最终权重数据 = 实体权重 + 文段权重
ppr_node_weights = {k: v for d in [ent_weights, pg_weights] for k, v in d.items()}
del ent_weights, pg_weights
# PersonalizedPageRank
ppr_res = pagerank.run_pagerank(
self.graph,
personalization=ppr_node_weights,
max_iter=100,
alpha=global_config["qa"]["params"]["ppr_damping"],
)
# 获取最终结果
# 从搜索结果中提取文段节点的结果
passage_node_res = [
(node_key, score) for node_key, score in ppr_res.items() if node_key.startswith(PG_NAMESPACE)
]
del ppr_res
# 排序：按照分数从大到小
passage_node_res = sorted(passage_node_res, key=lambda item: item[1], reverse=True)
return passage_node_res, ppr_node_weights

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src/plugins/knowledge/src/llm_client.py Normal file
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from openai import OpenAI
class LLMMessage:
def __init__(self, role, content):
self.role = role
self.content = content
def to_dict(self):
return {"role": self.role, "content": self.content}
class LLMClient:
"""LLM客户端，对应一个API服务商"""
def __init__(self, url, api_key):
self.client = OpenAI(
base_url=url,
api_key=api_key,
)
def send_chat_request(self, model, messages):
"""发送对话请求，等待返回结果"""
response = self.client.chat.completions.create(model=model, messages=messages, stream=False)
if hasattr(response.choices[0].message, "reasoning_content"):
# 有单独的推理内容块
reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
else:
# 无单独的推理内容块
response = response.choices[0].message.content.split("<think>")[-1].split("</think>")
# 如果有推理内容，则分割推理内容和内容
if len(response) == 2:
reasoning_content = response[0]
content = response[1]
else:
reasoning_content = None
content = response[0]
return reasoning_content, content
def send_embedding_request(self, model, text):
"""发送嵌入请求，等待返回结果"""
text = text.replace("\n", " ")
return self.client.embeddings.create(input=[text], model=model).data[0].embedding

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src/plugins/knowledge/src/lpmmconfig.py Normal file
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import os
import toml
import sys
import argparse
PG_NAMESPACE = "paragraph"
ENT_NAMESPACE = "entity"
REL_NAMESPACE = "relation"
RAG_GRAPH_NAMESPACE = "rag-graph"
RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
RAG_PG_HASH_NAMESPACE = "rag-pg-hash"
# 无效实体
INVALID_ENTITY = [
"",
"你",
"他",
"她",
"它",
"我们",
"你们",
"他们",
"她们",
"它们",
]
def _load_config(config, config_file_path):
"""读取TOML格式的配置文件"""
if not os.path.exists(config_file_path):
return
with open(config_file_path, "r", encoding="utf-8") as f:
file_config = toml.load(f)
# Check if all top-level keys from default config exist in the file config
for key in config.keys():
if key not in file_config:
print(f"警告: 配置文件 '{config_file_path}' 缺少必需的顶级键: '{key}'。请检查配置文件。")
sys.exit(1)
if "llm_providers" in file_config:
for provider in file_config["llm_providers"]:
if provider["name"] not in config["llm_providers"]:
config["llm_providers"][provider["name"]] = dict()
config["llm_providers"][provider["name"]]["base_url"] = provider["base_url"]
config["llm_providers"][provider["name"]]["api_key"] = provider["api_key"]
if "entity_extract" in file_config:
config["entity_extract"] = file_config["entity_extract"]
if "rdf_build" in file_config:
config["rdf_build"] = file_config["rdf_build"]
if "embedding" in file_config:
config["embedding"] = file_config["embedding"]
if "rag" in file_config:
config["rag"] = file_config["rag"]
if "qa" in file_config:
config["qa"] = file_config["qa"]
if "persistence" in file_config:
config["persistence"] = file_config["persistence"]
print(config)
print("Configurations loaded from file: ", config_file_path)
parser = argparse.ArgumentParser(description="Configurations for the pipeline")
parser.add_argument(
"--config_path",
type=str,
default="lpmm_config.toml",
help="Path to the configuration file",
)
global_config = dict(
{
"llm_providers": {
"localhost": {
"base_url": "https://api.siliconflow.cn/v1",
"api_key": "sk-ospynxadyorf",
}
},
"entity_extract": {
"llm": {
"provider": "localhost",
"model": "Pro/deepseek-ai/DeepSeek-V3",
}
},
"rdf_build": {
"llm": {
"provider": "localhost",
"model": "Pro/deepseek-ai/DeepSeek-V3",
}
},
"embedding": {
"provider": "localhost",
"model": "Pro/BAAI/bge-m3",
"dimension": 1024,
},
"rag": {
"params": {
"synonym_search_top_k": 10,
"synonym_threshold": 0.75,
}
},
"qa": {
"params": {
"relation_search_top_k": 10,
"relation_threshold": 0.75,
"paragraph_search_top_k": 10,
"paragraph_node_weight": 0.05,
"ent_filter_top_k": 10,
"ppr_damping": 0.8,
"res_top_k": 10,
},
"llm": {
"provider": "localhost",
"model": "qa",
},
},
"persistence": {
"data_root_path": "data",
"raw_data_path": "data/raw.json",
"openie_data_path": "data/openie.json",
"embedding_data_dir": "data/embedding",
"rag_data_dir": "data/rag",
},
"info_extraction": {
"workers": 10,
},
}
)
# _load_config(global_config, parser.parse_args().config_path)
file_path = os.path.abspath(__file__)
dir_path = os.path.dirname(file_path)
root_path = os.path.join(dir_path, os.pardir, os.pardir, os.pardir, os.pardir)
config_path = os.path.join(root_path, "config", "lpmm_config.toml")
_load_config(global_config, config_path)

32
src/plugins/knowledge/src/mem_active_manager.py Normal file
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from .lpmmconfig import global_config
from .embedding_store import EmbeddingManager
from .llm_client import LLMClient
from .utils.dyn_topk import dyn_select_top_k
class MemoryActiveManager:
def __init__(
self,
embed_manager: EmbeddingManager,
llm_client_embedding: LLMClient,
):
self.embed_manager = embed_manager
self.embedding_client = llm_client_embedding
def get_activation(self, question: str) -> float:
"""获取记忆激活度"""
# 生成问题的Embedding
question_embedding = self.embedding_client.send_embedding_request("text-embedding", question)
# 查询关系库中的相似度
rel_search_res = self.embed_manager.relation_embedding_store.search_top_k(question_embedding, 10)
# 动态过滤阈值
rel_scores = dyn_select_top_k(rel_search_res, 0.5, 1.0)
if rel_scores[0][1] < global_config["qa"]["params"]["relation_threshold"]:
# 未找到相关关系
return 0.0
# 计算激活度
activation = sum([item[2] for item in rel_scores]) * 10
return activation

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src/plugins/knowledge/src/open_ie.py Normal file
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import json
from typing import Any, Dict, List
from .lpmmconfig import INVALID_ENTITY, global_config
def _filter_invalid_entities(entities: List[str]) -> List[str]:
"""过滤无效的实体"""
valid_entities = set()
for entity in entities:
if not isinstance(entity, str) or entity.strip() == "" or entity in INVALID_ENTITY or entity in valid_entities:
# 非字符串/空字符串/在无效实体列表中/重复
continue
valid_entities.add(entity)
return list(valid_entities)
def _filter_invalid_triples(triples: List[List[str]]) -> List[List[str]]:
"""过滤无效的三元组"""
unique_triples = set()
valid_triples = []
for triple in triples:
if len(triple) != 3 or (
(not isinstance(triple[0], str) or triple[0].strip() == "")
or (not isinstance(triple[1], str) or triple[1].strip() == "")
or (not isinstance(triple[2], str) or triple[2].strip() == "")
):
# 三元组长度不为3，或其中存在空值
continue
valid_triple = [str(item) for item in triple]
if tuple(valid_triple) not in unique_triples:
unique_triples.add(tuple(valid_triple))
valid_triples.append(valid_triple)
return valid_triples
class OpenIE:
"""
OpenIE规约的数据格式为如下
{
"docs": [
{
"idx": "文档的唯一标识符（通常是文本的SHA256哈希值）",
"passage": "文档的原始文本",
"extracted_entities": ["实体1", "实体2", ...],
"extracted_triples": [["主语", "谓语", "宾语"], ...]
},
...
],
"avg_ent_chars": "实体平均字符数",
"avg_ent_words": "实体平均词数"
}
"""
def __init__(
self,
docs: List[Dict[str, Any]],
avg_ent_chars,
avg_ent_words,
):
self.docs = docs
self.avg_ent_chars = avg_ent_chars
self.avg_ent_words = avg_ent_words
for doc in self.docs:
# 过滤实体列表
doc["extracted_entities"] = _filter_invalid_entities(doc["extracted_entities"])
# 过滤无效的三元组
doc["extracted_triples"] = _filter_invalid_triples(doc["extracted_triples"])
@staticmethod
def _from_dict(data):
"""从字典中获取OpenIE对象"""
return OpenIE(
docs=data["docs"],
avg_ent_chars=data["avg_ent_chars"],
avg_ent_words=data["avg_ent_words"],
)
def _to_dict(self):
"""转换为字典"""
return {
"docs": self.docs,
"avg_ent_chars": self.avg_ent_chars,
"avg_ent_words": self.avg_ent_words,
}
@staticmethod
def load() -> "OpenIE":
"""从文件中加载OpenIE数据"""
with open(global_config["persistence"]["openie_data_path"], "r", encoding="utf-8") as f:
data = json.loads(f.read())
openie_data = OpenIE._from_dict(data)
return openie_data
@staticmethod
def save(openie_data: "OpenIE"):
"""保存OpenIE数据到文件"""
with open(global_config["persistence"]["openie_data_path"], "w", encoding="utf-8") as f:
f.write(json.dumps(openie_data._to_dict(), ensure_ascii=False, indent=4))
def extract_entity_dict(self):
"""提取实体列表"""
ner_output_dict = dict(
{
doc_item["idx"]: doc_item["extracted_entities"]
for doc_item in self.docs
if len(doc_item["extracted_entities"]) > 0
}
)
return ner_output_dict
def extract_triple_dict(self):
"""提取三元组列表"""
triple_output_dict = dict(
{
doc_item["idx"]: doc_item["extracted_triples"]
for doc_item in self.docs
if len(doc_item["extracted_triples"]) > 0
}
)
return triple_output_dict
def extract_raw_paragraph_dict(self):
"""提取原始段落"""
raw_paragraph_dict = dict({doc_item["idx"]: doc_item["passage"] for doc_item in self.docs})
return raw_paragraph_dict

65
src/plugins/knowledge/src/prompt_template.py Normal file
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from typing import List
from .llm_client import LLMMessage
entity_extract_system_prompt = """你是一个性能优异的实体提取系统。请从段落中提取出所有实体，并以JSON列表的形式输出。
输出格式示例：
[ "实体A", "实体B", "实体C" ]
请注意以下要求：
- 将代词（如“你”、“我”、“他”、“她”、“它”等）转化为对应的实体命名，以避免指代不清。
- 尽可能多的提取出段落中的全部实体；
"""
def build_entity_extract_context(paragraph: str) -> List[LLMMessage]:
messages = [
LLMMessage("system", entity_extract_system_prompt).to_dict(),
LLMMessage("user", f"""段落：\n```\n{paragraph}```""").to_dict(),
]
return messages
rdf_triple_extract_system_prompt = """你是一个性能优异的RDF（资源描述框架，由节点和边组成，节点表示实体/资源、属性，边则表示了实体和实体之间的关系以及实体和属性的关系。）构造系统。你的任务是根据给定的段落和实体列表构建RDF图。
请使用JSON回复，使用三元组的JSON列表输出RDF图中的关系（每个三元组代表一个关系）。
输出格式示例：
[
["某实体","关系","某属性"],
["某实体","关系","某实体"],
["某资源","关系","某属性"]
]
请注意以下要求：
- 每个三元组应包含每个段落的实体命名列表中的至少一个命名实体，但最好是两个。
- 将代词（如“你”、“我”、“他”、“她”、“它”等）转化为对应的实体命名，以避免指代不清。
"""
def build_rdf_triple_extract_context(paragraph: str, entities: str) -> List[LLMMessage]:
messages = [
LLMMessage("system", rdf_triple_extract_system_prompt).to_dict(),
LLMMessage("user", f"""段落：\n```\n{paragraph}```\n\n实体列表：\n```\n{entities}```""").to_dict(),
]
return messages
qa_system_prompt = """
你是一个性能优异的QA系统。请根据给定的问题和一些可能对你有帮助的信息作出回答。
请注意以下要求：
- 你可以使用给定的信息来回答问题，但请不要直接引用它们。
- 你的回答应该简洁明了，避免冗长的解释。
- 如果你无法回答问题，请直接说“我不知道”。
"""
def build_qa_context(question: str, knowledge: list[(str, str, str)]) -> List[LLMMessage]:
knowledge = "\n".join([f"{i + 1}. 相关性：{k[0]}\n{k[1]}" for i, k in enumerate(knowledge)])
messages = [
LLMMessage("system", qa_system_prompt).to_dict(),
LLMMessage("user", f"问题：\n{question}\n\n可能有帮助的信息：\n{knowledge}").to_dict(),
]
return messages

120
src/plugins/knowledge/src/qa_manager.py Normal file
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import time
from typing import Tuple, List, Dict
from .global_logger import logger
# from . import prompt_template
from .embedding_store import EmbeddingManager
from .llm_client import LLMClient
from .kg_manager import KGManager
from .lpmmconfig import global_config
from .utils.dyn_topk import dyn_select_top_k
class QAManager:
def __init__(
self,
embed_manager: EmbeddingManager,
kg_manager: KGManager,
llm_client_embedding: LLMClient,
llm_client_filter: LLMClient,
llm_client_qa: LLMClient,
):
self.embed_manager = embed_manager
self.kg_manager = kg_manager
self.llm_client_list = {
"embedding": llm_client_embedding,
"filter": llm_client_filter,
"qa": llm_client_qa,
}
def process_query(self, question: str) -> Tuple[List[Tuple[str, float, float]], Dict[str, float] | None]:
"""处理查询"""
# 生成问题的Embedding
part_start_time = time.perf_counter()
question_embedding = self.llm_client_list["embedding"].send_embedding_request(
global_config["embedding"]["model"], question
)
part_end_time = time.perf_counter()
logger.debug(f"Embedding用时：{part_end_time - part_start_time:.5f}s")
# 根据问题Embedding查询Relation Embedding库
part_start_time = time.perf_counter()
relation_search_res = self.embed_manager.relation_embedding_store.search_top_k(
question_embedding,
global_config["qa"]["params"]["relation_search_top_k"],
)
if relation_search_res is not None:
# 过滤阈值
# 考虑动态阈值：当存在显著数值差异的结果时，保留显著结果；否则，保留所有结果
relation_search_res = dyn_select_top_k(relation_search_res, 0.5, 1.0)
if relation_search_res[0][1] < global_config["qa"]["params"]["relation_threshold"]:
# 未找到相关关系
relation_search_res = []
part_end_time = time.perf_counter()
logger.debug(f"关系检索用时：{part_end_time - part_start_time:.5f}s")
for res in relation_search_res:
rel_str = self.embed_manager.relation_embedding_store.store.get(res[0]).str
print(f"找到相关关系，相似度：{(res[1] * 100):.2f}% - {rel_str}")
# TODO: 使用LLM过滤三元组结果
# logger.info(f"LLM过滤三元组用时：{time.time() - part_start_time:.2f}s")
# part_start_time = time.time()
# 根据问题Embedding查询Paragraph Embedding库
part_start_time = time.perf_counter()
paragraph_search_res = self.embed_manager.paragraphs_embedding_store.search_top_k(
question_embedding,
global_config["qa"]["params"]["paragraph_search_top_k"],
)
part_end_time = time.perf_counter()
logger.debug(f"文段检索用时：{part_end_time - part_start_time:.5f}s")
if len(relation_search_res) != 0:
logger.info("找到相关关系，将使用RAG进行检索")
# 使用KG检索
part_start_time = time.perf_counter()
result, ppr_node_weights = self.kg_manager.kg_search(
relation_search_res, paragraph_search_res, self.embed_manager
)
part_end_time = time.perf_counter()
logger.info(f"RAG检索用时：{part_end_time - part_start_time:.5f}s")
else:
logger.info("未找到相关关系，将使用文段检索结果")
result = paragraph_search_res
ppr_node_weights = None
# 过滤阈值
result = dyn_select_top_k(result, 0.5, 1.0)
for res in result:
raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[res[0]].str
print(f"找到相关文段，相关系数：{res[1]:.8f}\n{raw_paragraph}\n\n")
return result, ppr_node_weights
else:
return None
def get_knowledge(self, question: str) -> str:
"""获取知识"""
# 处理查询
processed_result = self.process_query(question)
if processed_result is not None:
query_res = processed_result[0]
knowledge = [
(
self.embed_manager.paragraphs_embedding_store.store[res[0]].str,
res[1],
)
for res in query_res
]
found_knowledge = "\n".join(
[f"第{i + 1}条知识：{k[1]}\n 该条知识对于问题的相关性：{k[0]}" for i, k in enumerate(knowledge)]
)
return found_knowledge
else:
logger.info("LPMM知识库并未初始化，使用旧版数据库进行检索")
return None

44
src/plugins/knowledge/src/raw_processing.py Normal file
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import json
import os
from .global_logger import logger
from .lpmmconfig import global_config
from .utils.hash import get_sha256
def load_raw_data() -> tuple[list[str], list[str]]:
"""加载原始数据文件
读取原始数据文件，将原始数据加载到内存中
Returns:
- raw_data: 原始数据字典
- md5_set: 原始数据的SHA256集合
"""
# 读取import.json文件
if os.path.exists(global_config["persistence"]["raw_data_path"]) is True:
with open(global_config["persistence"]["raw_data_path"], "r", encoding="utf-8") as f:
import_json = json.loads(f.read())
else:
raise Exception("原始数据文件读取失败")
# import_json内容示例：
# import_json = [
# "The capital of China is Beijing. The capital of France is Paris.",
# ]
raw_data = []
sha256_list = []
sha256_set = set()
for item in import_json:
if not isinstance(item, str):
logger.warning("数据类型错误：{}".format(item))
continue
pg_hash = get_sha256(item)
if pg_hash in sha256_set:
logger.warning("重复数据：{}".format(item))
continue
sha256_set.add(pg_hash)
sha256_list.append(pg_hash)
raw_data.append(item)
logger.info("共读取到{}条数据".format(len(raw_data)))
return sha256_list, raw_data

0
src/plugins/knowledge/src/utils/__init__.py Normal file
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47
src/plugins/knowledge/src/utils/dyn_topk.py Normal file
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from typing import List, Any, Tuple
def dyn_select_top_k(
score: List[Tuple[Any, float]], jmp_factor: float, var_factor: float
) -> List[Tuple[Any, float, float]]:
"""动态TopK选择"""
# 按照分数排序（降序）
sorted_score = sorted(score, key=lambda x: x[1], reverse=True)
# 归一化
max_score = sorted_score[0][1]
min_score = sorted_score[-1][1]
normalized_score = []
for score_item in sorted_score:
normalized_score.append(
tuple(
[
score_item[0],
score_item[1],
(score_item[1] - min_score) / (max_score - min_score),
]
)
)
# 寻找跳变点：score变化最大的位置
jump_idx = 0
for i in range(1, len(normalized_score)):
if abs(normalized_score[i][2] - normalized_score[i - 1][2]) > abs(
normalized_score[jump_idx][2] - normalized_score[jump_idx - 1][2]
):
jump_idx = i
# 跳变阈值
jump_threshold = normalized_score[jump_idx][2]
# 计算均值
mean_score = sum([s[2] for s in normalized_score]) / len(normalized_score)
# 计算方差
var_score = sum([(s[2] - mean_score) ** 2 for s in normalized_score]) / len(normalized_score)
# 动态阈值
threshold = jmp_factor * jump_threshold + (1 - jmp_factor) * (mean_score + var_factor * var_score)
# 重新过滤
res = [s for s in normalized_score if s[2] > threshold]
return res

8
src/plugins/knowledge/src/utils/hash.py Normal file
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import hashlib
def get_sha256(string: str) -> str:
"""获取字符串的SHA256值"""
sha256 = hashlib.sha256()
sha256.update(string.encode("utf-8"))
return sha256.hexdigest()

76
src/plugins/knowledge/src/utils/json_fix.py Normal file
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import json
def _find_unclosed(json_str):
"""
Identifies the unclosed braces and brackets in the JSON string.
Args:
json_str (str): The JSON string to analyze.
Returns:
list: A list of unclosed elements in the order they were opened.
"""
unclosed = []
inside_string = False
escape_next = False
for char in json_str:
if inside_string:
if escape_next:
escape_next = False
elif char == "\\":
escape_next = True
elif char == '"':
inside_string = False
else:
if char == '"':
inside_string = True
elif char in "{[":
unclosed.append(char)
elif char in "}]":
if unclosed and ((char == "}" and unclosed[-1] == "{") or (char == "]" and unclosed[-1] == "[")):
unclosed.pop()
return unclosed
# The following code is used to fix a broken JSON string.
# From HippoRAG2 (GitHub: OSU-NLP-Group/HippoRAG)
def fix_broken_generated_json(json_str: str) -> str:
"""
Fixes a malformed JSON string by:
- Removing the last comma and any trailing content.
- Iterating over the JSON string once to determine and fix unclosed braces or brackets.
- Ensuring braces and brackets inside string literals are not considered.
If the original json_str string can be successfully loaded by json.loads(), will directly return it without any modification.
Args:
json_str (str): The malformed JSON string to be fixed.
Returns:
str: The corrected JSON string.
"""
try:
# Try to load the JSON to see if it is valid
json.loads(json_str)
return json_str # Return as-is if valid
except json.JSONDecodeError:
pass
# Step 1: Remove trailing content after the last comma.
last_comma_index = json_str.rfind(",")
if last_comma_index != -1:
json_str = json_str[:last_comma_index]
# Step 2: Identify unclosed braces and brackets.
unclosed_elements = _find_unclosed(json_str)
# Step 3: Append the necessary closing elements in reverse order of opening.
closing_map = {"{": "}", "[": "]"}
for open_char in reversed(unclosed_elements):
json_str += closing_map[open_char]
return json_str

17
src/plugins/knowledge/src/utils/visualize_graph.py Normal file
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import networkx as nx
from matplotlib import pyplot as plt
def draw_graph_and_show(graph):
"""绘制图并显示，画布大小1280*1280"""
fig = plt.figure(1, figsize=(12.8, 12.8), dpi=100)
nx.draw_networkx(
graph,
node_size=100,
width=0.5,
with_labels=True,
labels=nx.get_node_attributes(graph, "content"),
font_family="Sarasa Mono SC",
font_size=8,
)
fig.show()

57
template/lpmm_config_template.toml Normal file
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# LLM API 服务提供商，可配置多个
[[llm_providers]]
name = "localhost"
base_url = "http://127.0.0.1:8888/v1/"
api_key = "lm_studio"
[[llm_providers]]
name = "siliconflow"
base_url = "https://api.siliconflow.cn/v1/"
api_key = ""
[entity_extract.llm]
# 设置用于实体提取的LLM模型
provider = "siliconflow" # 服务提供商
model = "deepseek-ai/DeepSeek-V3" # 模型名称
[rdf_build.llm]
# 设置用于RDF构建的LLM模型
provider = "siliconflow" # 服务提供商
model = "deepseek-ai/DeepSeek-V3" # 模型名称
[embedding]
# 设置用于文本嵌入的Embedding模型
provider = "siliconflow" # 服务提供商
model = "Pro/BAAI/bge-m3" # 模型名称
dimension = 1024 # 嵌入维度
[rag.params]
# RAG参数配置
synonym_search_top_k = 10 # 同义词搜索TopK
synonym_threshold = 0.8 # 同义词阈值（相似度高于此阈值的词语会被认为是同义词）
[qa.llm]
# 设置用于QA的LLM模型
provider = "siliconflow" # 服务提供商
model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" # 模型名称
[info_extraction]
workers = 10
[qa.params]
# QA参数配置
relation_search_top_k = 10 # 关系搜索TopK
relation_threshold = 0.5 # 关系阈值（相似度高于此阈值的关系会被认为是相关的关系）
paragraph_search_top_k = 1000 # 段落搜索TopK（不能过小，可能影响搜索结果）
paragraph_node_weight = 0.05 # 段落节点权重（在图搜索&PPR计算中的权重，当搜索仅使用DPR时，此参数不起作用）
ent_filter_top_k = 10 # 实体过滤TopK
ppr_damping = 0.8 # PPR阻尼系数
res_top_k = 3 # 最终提供的文段TopK
[persistence]
# 持久化配置（存储中间数据，防止重复计算）
data_root_path = "data" # 数据根目录
raw_data_path = "data/import.json" # 原始数据路径
openie_data_path = "data/openie.json" # OpenIE数据路径
embedding_data_dir = "data/embedding" # 嵌入数据目录
rag_data_dir = "data/rag" # RAG数据目录

56
（临时版）麦麦开始学习.bat
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@echo off
chcp 65001 > nul
setlocal enabledelayedexpansion
cd /d %~dp0
title 麦麦学习系统
cls
echo ======================================
echo 警告提示
echo ======================================
echo 1.这是一个demo系统,不完善不稳定,仅用于体验/不要塞入过长过大的文本,这会导致信息提取迟缓
echo ======================================
echo.
echo ======================================
echo 请选择Python环境:
echo 1 - venv (推荐)
echo 2 - conda
echo ======================================
choice /c 12 /n /m "请输入数字选择(1或2): "
if errorlevel 2 (
echo ======================================
set "CONDA_ENV="
set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
:: 检查输入是否为空
if "!CONDA_ENV!"=="" (
echo 错误：环境名称不能为空
pause
exit /b 1
)
call conda activate !CONDA_ENV!
if errorlevel 1 (
echo 激活 conda 环境失败
pause
exit /b 1
)
echo Conda 环境 "!CONDA_ENV!" 激活成功
python src/plugins/zhishi/knowledge_library.py
) else (
if exist "venv\Scripts\python.exe" (
venv\Scripts\python src/plugins/zhishi/knowledge_library.py
) else (
echo ======================================
echo 错误: venv环境不存在，请先创建虚拟环境
pause
exit /b 1
)
)
endlocal
pause