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MaiBot
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feat: 新增知识库插件及相关功能 

新增了知识库插件，包括知识提取、存储、检索等功能。主要新增了以下模块：
- `knowledge_lib.py`: 知识库初始化及管理
- `qa_manager.py`: 问答系统管理
- `mem_active_manager.py`: 记忆激活管理
- `ie_process.py`: 信息提取处理
- `open_ie.py`: OpenIE数据处理
- `lpmmconfig.py`: 配置文件管理
- `prompt_template.py`: 提示模板管理
- `utils/`: 工具类模块，包括JSON修复、动态TopK选择、数据加载等

此外，还新增了相关工具`lpmm_get_knowledge.py`，用于从知识库中检索信息。同时更新了配置文件模板`lpmm_config_template.toml`和`bot_config_template.toml`，以支持新功能。

这些改动旨在增强麦麦的知识处理能力，使其能够更好地理解和回应复杂问题。
pull/822/head
墨梓柒 2025-04-21 22:54:10 +08:00
parent ea1a6401f8
commit 273b36a073
49 changed files with 3743 additions and 600 deletions
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1
.gitignore vendored
View File

@ -4,6 +4,7 @@ mongodb/
NapCat.Framework.Windows.Once/
log/
logs/
temp/
run_ad.bat
MaiBot-Napcat-Adapter-main
MaiBot-Napcat-Adapter

9
README.md
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@ -14,7 +14,7 @@
<p align="center">
<a href="https://github.com/MaiM-with-u/MaiBot/">
<img src="depends-data/maimai.png" alt="Logo" style="max-width: 200px">
<img src="depends-data/maimai.png" alt="Logo" width="200">
</a>
<br />
<a href="https://space.bilibili.com/1344099355">
@ -34,6 +34,7 @@
·
<a href="https://github.com/MaiM-with-u/MaiBot/issues">提出新特性</a>
</p>
</p>
## 新版0.6.x部署前先阅读：https://docs.mai-mai.org/manual/usage/mmc_q_a
@ -52,7 +53,7 @@
<div align="center">
<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
<img src="depends-data/video.png" style="max-width: 200px" alt="麦麦演示视频">
<img src="depends-data/video.png" width="200" alt="麦麦演示视频">
<br>
👆 点击观看麦麦演示视频 👆
</a>
@ -98,7 +99,7 @@
<div align="left">
<h2>📚 文档 </h2>
<h2>📚 文档</h2>
</div>
### (部分内容可能过时，请注意版本对应)
@ -185,7 +186,7 @@ MaiCore是一个开源项目，我们非常欢迎你的参与。你的贡献，
感谢各位大佬！
<a href="https://github.com/MaiM-with-u/MaiBot/graphs/contributors">
<img alt="contributors" src="https://contrib.rocks/image?repo=MaiM-with-u/MaiBot" />
<img src="https://contrib.rocks/image?repo=MaiM-with-u/MaiBot" />
</a>
**也感谢每一位给麦麦发展提出宝贵意见与建议的用户，感谢陪伴麦麦走到现在的你们**

171
import_openie.py 100644
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@ -0,0 +1,171 @@
# 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()

175
info_extraction.py 100644
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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()

84
raw_data_preprocessor.py 100644
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@ -0,0 +1,84 @@
import json
import os
from pathlib import Path
import sys # 新增系统模块导入
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)
print(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':
print("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"):
print("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"):
print("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:
print("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:
print(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)
print(f"处理完成，结果已保存到: {output_path}")
if __name__ == "__main__":
main()

70
src/config/config.py
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@ -186,18 +186,12 @@ class BotConfig:
ban_words = set()
ban_msgs_regex = set()
# [heartflow] # 启用启用heart_flowC(心流聊天)模式时生效, 需要填写token消耗量巨大的相关模型
# 启用后麦麦会自主选择进入heart_flowC模式(持续一段时间), 进行长时间高质量的聊天
enable_heart_flowC: bool = True # 是否启用heart_flowC(心流聊天, HFC)模式
reply_trigger_threshold: float = 3.0 # 心流聊天触发阈值，越低越容易触发
probability_decay_factor_per_second: float = 0.2 # 概率衰减因子，越大衰减越快
default_decay_rate_per_second: float = 0.98 # 默认衰减率，越大衰减越慢
initial_duration: int = 60 # 初始持续时间，越大心流聊天持续的时间越长
# sub_heart_flow_update_interval: int = 60 # 子心流更新频率，间隔 单位秒
# sub_heart_flow_freeze_time: int = 120 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
# heartflow
# enable_heartflow: bool = False # 是否启用心流
sub_heart_flow_update_interval: int = 60 # 子心流更新频率，间隔 单位秒
sub_heart_flow_freeze_time: int = 120 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
sub_heart_flow_stop_time: int = 600 # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
# heart_flow_update_interval: int = 300 # 心流更新频率，间隔 单位秒
heart_flow_update_interval: int = 300 # 心流更新频率，间隔 单位秒
observation_context_size: int = 20 # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
compressed_length: int = 5 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
compress_length_limit: int = 5 # 最多压缩份数，超过该数值的压缩上下文会被删除
@ -213,8 +207,8 @@ class BotConfig:
# response
response_mode: str = "heart_flow" # 回复策略
model_reasoning_probability: float = 0.7 # 麦麦回答时选择推理模型(主要)模型概率
model_normal_probability: float = 0.3 # 麦麦回答时选择一般模型(次要)模型概率
MODEL_R1_PROBABILITY: float = 0.8 # R1模型概率
MODEL_V3_PROBABILITY: float = 0.1 # V3模型概率
# MODEL_R1_DISTILL_PROBABILITY: float = 0.1 # R1蒸馏模型概率
# emoji
@ -407,34 +401,29 @@ class BotConfig:
def response(parent: dict):
response_config = parent["response"]
config.model_reasoning_probability = response_config.get(
"model_reasoning_probability", config.model_reasoning_probability
)
config.model_normal_probability = response_config.get(
"model_normal_probability", config.model_normal_probability
)
# 添加 enable_heart_flowC 的加载逻辑 (假设它在 [response] 部分)
if config.INNER_VERSION in SpecifierSet(">=1.4.0"):
config.enable_heart_flowC = response_config.get("enable_heart_flowC", config.enable_heart_flowC)
config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
# config.MODEL_R1_DISTILL_PROBABILITY = response_config.get(
# "model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY
# )
config.max_response_length = response_config.get("max_response_length", config.max_response_length)
if config.INNER_VERSION in SpecifierSet(">=1.0.4"):
config.response_mode = response_config.get("response_mode", config.response_mode)
def heartflow(parent: dict):
heartflow_config = parent["heartflow"]
# 加载新增的 heartflowC 参数
# 加载原有的 heartflow 参数
# config.sub_heart_flow_update_interval = heartflow_config.get(
# "sub_heart_flow_update_interval", config.sub_heart_flow_update_interval
# )
# config.sub_heart_flow_freeze_time = heartflow_config.get(
# "sub_heart_flow_freeze_time", config.sub_heart_flow_freeze_time
# )
config.sub_heart_flow_update_interval = heartflow_config.get(
"sub_heart_flow_update_interval", config.sub_heart_flow_update_interval
)
config.sub_heart_flow_freeze_time = heartflow_config.get(
"sub_heart_flow_freeze_time", config.sub_heart_flow_freeze_time
)
config.sub_heart_flow_stop_time = heartflow_config.get(
"sub_heart_flow_stop_time", config.sub_heart_flow_stop_time
)
# config.heart_flow_update_interval = heartflow_config.get(
# "heart_flow_update_interval", config.heart_flow_update_interval
# )
config.heart_flow_update_interval = heartflow_config.get(
"heart_flow_update_interval", config.heart_flow_update_interval
)
if config.INNER_VERSION in SpecifierSet(">=1.3.0"):
config.observation_context_size = heartflow_config.get(
"observation_context_size", config.observation_context_size
@ -443,17 +432,6 @@ class BotConfig:
config.compress_length_limit = heartflow_config.get(
"compress_length_limit", config.compress_length_limit
)
if config.INNER_VERSION in SpecifierSet(">=1.4.0"):
config.reply_trigger_threshold = heartflow_config.get(
"reply_trigger_threshold", config.reply_trigger_threshold
)
config.probability_decay_factor_per_second = heartflow_config.get(
"probability_decay_factor_per_second", config.probability_decay_factor_per_second
)
config.default_decay_rate_per_second = heartflow_config.get(
"default_decay_rate_per_second", config.default_decay_rate_per_second
)
config.initial_duration = heartflow_config.get("initial_duration", config.initial_duration)
def willing(parent: dict):
willing_config = parent["willing"]

138
src/do_tool/tool_can_use/lpmm_get_knowledge.py 100644
View File

@ -0,0 +1,138 @@
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

@ -47,6 +47,7 @@ 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)

101
src/heart_flow/heartflow.py
View File

@ -1,4 +1,5 @@
from .sub_heartflow import SubHeartflow, ChattingObservation
from .sub_heartflow import SubHeartflow
from .observation import ChattingObservation
from src.plugins.moods.moods import MoodManager
from src.plugins.models.utils_model import LLMRequest
from src.config.config import global_config
@ -9,8 +10,7 @@ from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONF
from src.individuality.individuality import Individuality
import time
import random
from typing import Dict, Any, Optional
import traceback
from typing import Dict, Any
heartflow_config = LogConfig(
# 使用海马体专用样式
@ -45,8 +45,6 @@ class CurrentState:
def __init__(self):
self.current_state_info = ""
self.chat_status = "IDLE"
self.mood_manager = MoodManager()
self.mood = self.mood_manager.get_prompt()
@ -72,27 +70,20 @@ class Heartflow:
"""定期清理不活跃的子心流"""
while True:
current_time = time.time()
inactive_subheartflows_ids = [] # 修改变量名以清晰表示存储的是ID
inactive_subheartflows = []
# 检查所有子心流
# 使用 list(self._subheartflows.items()) 避免在迭代时修改字典
for subheartflow_id, subheartflow in list(self._subheartflows.items()):
for subheartflow_id, subheartflow in self._subheartflows.items():
if (
current_time - subheartflow.last_active_time > global_config.sub_heart_flow_stop_time
): # 10分钟 = 600秒
logger.info(f"发现不活跃的子心流: {subheartflow_id}, 准备清理。")
# 1. 标记子心流让其后台任务停止
subheartflow.should_stop = True
# 2. 将ID添加到待清理列表
inactive_subheartflows_ids.append(subheartflow_id)
inactive_subheartflows.append(subheartflow_id)
logger.info(f"发现不活跃的子心流: {subheartflow_id}")
# 清理不活跃的子心流 (从字典中移除)
for subheartflow_id in inactive_subheartflows_ids:
if subheartflow_id in self._subheartflows:
del self._subheartflows[subheartflow_id]
logger.info(f"已从主心流移除子心流: {subheartflow_id}")
else:
logger.warning(f"尝试移除子心流 {subheartflow_id} 时发现其已被移除。")
# 清理不活跃的子心流
for subheartflow_id in inactive_subheartflows:
del self._subheartflows[subheartflow_id]
logger.info(f"已清理不活跃的子心流: {subheartflow_id}")
await asyncio.sleep(30) # 每分钟检查一次
@ -104,10 +95,8 @@ class Heartflow:
await asyncio.sleep(30) # 每分钟检查一次是否有新的子心流
continue
# await self.do_a_thinking()
# await asyncio.sleep(global_config.heart_flow_update_interval * 3) # 5分钟思考一次
await asyncio.sleep(300)
await self.do_a_thinking()
await asyncio.sleep(global_config.heart_flow_update_interval) # 5分钟思考一次
async def heartflow_start_working(self):
# 启动清理任务
@ -121,7 +110,7 @@ class Heartflow:
print("TODO")
async def do_a_thinking(self):
# logger.debug("麦麦大脑袋转起来了")
logger.debug("麦麦大脑袋转起来了")
self.current_state.update_current_state_info()
# 开始构建prompt
@ -227,55 +216,33 @@ class Heartflow:
return response
async def create_subheartflow(self, subheartflow_id: Any) -> Optional[SubHeartflow]:
async def create_subheartflow(self, subheartflow_id):
"""
获取或创建一个新的SubHeartflow实例。
如果实例已存在，则直接返回。
如果不存在，则创建实例、观察对象、启动后台任务，并返回新实例。
创建过程中发生任何错误将返回 None。
Args:
subheartflow_id: 用于标识子心流的ID (例如群聊ID)。
Returns:
对应的 SubHeartflow 实例，如果创建失败则返回 None。
创建一个新的SubHeartflow实例
添加一个SubHeartflow实例到self._subheartflows字典中
并根据subheartflow_id为子心流创建一个观察对象
"""
# 检查是否已存在
existing_subheartflow = self._subheartflows.get(subheartflow_id)
if existing_subheartflow:
logger.debug(f"返回已存在的 subheartflow: {subheartflow_id}")
return existing_subheartflow
# 如果不存在，则创建新的
logger.info(f"尝试创建新的 subheartflow: {subheartflow_id}")
try:
subheartflow = SubHeartflow(subheartflow_id)
# 创建并初始化观察对象
logger.debug(f"为 {subheartflow_id} 创建 observation")
observation = ChattingObservation(subheartflow_id)
await observation.initialize() # 等待初始化完成
subheartflow.add_observation(observation)
logger.debug(f"为 {subheartflow_id} 添加 observation 成功")
# 创建并存储后台任务
subheartflow.task = asyncio.create_task(subheartflow.subheartflow_start_working())
logger.debug(f"为 {subheartflow_id} 创建后台任务成功")
# 添加到管理字典
self._subheartflows[subheartflow_id] = subheartflow
logger.info(f"添加 subheartflow {subheartflow_id} 成功")
return subheartflow
if subheartflow_id not in self._subheartflows:
subheartflow = SubHeartflow(subheartflow_id)
# 创建一个观察对象，目前只可以用chat_id创建观察对象
logger.debug(f"创建 observation: {subheartflow_id}")
observation = ChattingObservation(subheartflow_id)
await observation.initialize()
subheartflow.add_observation(observation)
logger.debug("添加 observation 成功")
# 创建异步任务
asyncio.create_task(subheartflow.subheartflow_start_working())
logger.debug("创建异步任务 成功")
self._subheartflows[subheartflow_id] = subheartflow
logger.info("添加 subheartflow 成功")
return self._subheartflows[subheartflow_id]
except Exception as e:
# 记录详细错误信息
logger.error(f"创建 subheartflow {subheartflow_id} 失败: {e}")
logger.error(traceback.format_exc()) # 记录完整的 traceback
# 考虑是否需要更具体的错误处理或资源清理逻辑
logger.error(f"创建 subheartflow 失败: {e}")
return None
def get_subheartflow(self, observe_chat_id: Any) -> Optional[SubHeartflow]:
def get_subheartflow(self, observe_chat_id) -> SubHeartflow:
"""获取指定ID的SubHeartflow实例"""
return self._subheartflows.get(observe_chat_id)

2
src/heart_flow/observation.py
View File

@ -139,7 +139,7 @@ class ChattingObservation(Observation):
# traceback.print_exc() # 记录详细堆栈
# print(f"处理后self.talking_message：{self.talking_message}")
self.talking_message_str = await build_readable_messages(messages=self.talking_message, timestamp_mode="normal")
self.talking_message_str = await build_readable_messages(self.talking_message)
logger.trace(
f"Chat {self.chat_id} - 压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.talking_message_str}"

25
src/heart_flow/sub_heartflow.py
View File

@ -4,7 +4,8 @@ from src.plugins.moods.moods import MoodManager
from src.plugins.models.utils_model import LLMRequest
from src.config.config import global_config
import time
from typing import Optional, List
from typing import Optional
from datetime import datetime
import traceback
from src.plugins.chat.utils import parse_text_timestamps
@ -64,7 +65,7 @@ class SubHeartflow:
def __init__(self, subheartflow_id):
self.subheartflow_id = subheartflow_id
self.current_mind = "你什么也没想"
self.current_mind = ""
self.past_mind = []
self.current_state: CurrentState = CurrentState()
self.llm_model = LLMRequest(
@ -76,13 +77,15 @@ class SubHeartflow:
self.main_heartflow_info = ""
self.last_reply_time = time.time()
self.last_active_time = time.time() # 添加最后激活时间
self.should_stop = False # 添加停止标志
self.task: Optional[asyncio.Task] = None # 添加 task 属性
if not self.current_mind:
self.current_mind = "你什么也没想"
self.is_active = False
self.observations: List[ChattingObservation] = [] # 使用 List 类型提示
self.observations: list[ChattingObservation] = []
self.running_knowledges = []
@ -90,13 +93,19 @@ class SubHeartflow:
async def subheartflow_start_working(self):
while True:
current_time = time.time()
# --- 调整后台任务逻辑 --- #
# 这个后台循环现在主要负责检查是否需要自我销毁
# 不再主动进行思考或状态更新，这些由 HeartFC_Chat 驱动
# 检查是否被主心流标记为停止
if self.should_stop:
logger.info(f"子心流 {self.subheartflow_id} 被标记为停止，正在退出后台任务...")
# 检查是否超过指定时间没有激活 (例如，没有被调用进行思考)
if current_time - self.last_active_time > global_config.sub_heart_flow_stop_time: # 例如 5 分钟
logger.info(
f"子心流 {self.subheartflow_id} 超过 {global_config.sub_heart_flow_stop_time} 秒没有激活，正在销毁..."
f" (Last active: {datetime.fromtimestamp(self.last_active_time).strftime('%Y-%m-%d %H:%M:%S')})"
)
# 在这里添加实际的销毁逻辑，例如从主 Heartflow 管理器中移除自身
# heartflow.remove_subheartflow(self.subheartflow_id) # 假设有这样的方法
break # 退出循环以停止任务
await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 定期检查销毁条件

2
src/main.py
View File

@ -117,7 +117,7 @@ class MainSystem:
await interest_manager.start_background_tasks()
logger.success("兴趣管理器后台任务启动成功")
# 初始化并独立启动 HeartFCController
# 初始化并独立启动 HeartFC_Chat
HeartFCController()
heartfc_chat_instance = HeartFCController.get_instance()
if heartfc_chat_instance:

61
src/plugins/chat/bot.py
View File

@ -105,24 +105,53 @@ class ChatBot:
template_group_name = None
async def preprocess():
if groupinfo is None:
if global_config.enable_friend_chat:
if global_config.enable_pfc_chatting:
userinfo = message.message_info.user_info
messageinfo = message.message_info
# 创建聊天流
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform,
user_info=userinfo,
group_info=groupinfo,
)
message.update_chat_stream(chat)
await self.only_process_chat.process_message(message)
await self._create_pfc_chat(message)
if global_config.enable_pfc_chatting:
try:
if groupinfo is None:
if global_config.enable_friend_chat:
userinfo = message.message_info.user_info
messageinfo = message.message_info
# 创建聊天流
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform,
user_info=userinfo,
group_info=groupinfo,
)
message.update_chat_stream(chat)
await self.only_process_chat.process_message(message)
await self._create_pfc_chat(message)
else:
await self.heartFC_processor.process_message(message_data)
if groupinfo.group_id in global_config.talk_allowed_groups:
# logger.debug(f"开始群聊模式{str(message_data)[:50]}...")
if global_config.response_mode == "heart_flow":
# logger.info(f"启动最新最好的思维流FC模式{str(message_data)[:50]}...")
await self.heartFC_processor.process_message(message_data)
elif global_config.response_mode == "reasoning":
# logger.debug(f"开始推理模式{str(message_data)[:50]}...")
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
except Exception as e:
logger.error(f"处理PFC消息失败: {e}")
else:
await self.heartFC_processor.process_message(message_data)
if groupinfo is None:
if global_config.enable_friend_chat:
# 私聊处理流程
# await self._handle_private_chat(message)
if global_config.response_mode == "heart_flow":
await self.heartFC_processor.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
else: # 群聊处理
if groupinfo.group_id in global_config.talk_allowed_groups:
if global_config.response_mode == "heart_flow":
await self.heartFC_processor.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
if template_group_name:
async with global_prompt_manager.async_message_scope(template_group_name):

96
src/plugins/chat_module/heartFC_chat/heartFC_controler.py
View File

@ -1,7 +1,7 @@
import traceback
from typing import Optional, Dict
import asyncio
import threading # 导入 threading
from asyncio import Lock
from ...moods.moods import MoodManager
from ...chat.emoji_manager import emoji_manager
from .heartFC_generator import ResponseGenerator
@ -14,7 +14,6 @@ from .interest import InterestManager
from src.plugins.chat.chat_stream import chat_manager
from .pf_chatting import PFChatting
# 定义日志配置
chat_config = LogConfig(
console_format=CHAT_STYLE_CONFIG["console_format"],
@ -27,81 +26,44 @@ logger = get_module_logger("HeartFCController", config=chat_config)
INTEREST_MONITOR_INTERVAL_SECONDS = 1
# 合并后的版本：使用 __new__ + threading.Lock 实现线程安全单例，类名为 HeartFCController
class HeartFCController:
_instance = None
_lock = threading.Lock() # 使用 threading.Lock 保证 __new__ 线程安全
_initialized = False
def __new__(cls, *args, **kwargs):
if cls._instance is None:
with cls._lock:
# Double-checked locking
if cls._instance is None:
logger.debug("创建 HeartFCController 单例实例...")
cls._instance = super().__new__(cls)
return cls._instance
_instance = None # For potential singleton access if needed by MessageManager
def __init__(self):
# 使用 _initialized 标志确保 __init__ 只执行一次
if self._initialized:
# --- Updated Init ---
if HeartFCController._instance is not None:
# Prevent re-initialization if used as a singleton
return
# 虽然 __new__ 保证了只有一个实例，但为了防止意外重入或多线程下的初始化竞争，
# 再次使用类锁保护初始化过程是更严谨的做法。
# 如果确定 __init__ 逻辑本身是幂等的或非关键的，可以省略这里的锁。
# 但为了保持原始逻辑的意图（防止重复初始化），这里保留检查。
with self.__class__._lock: # 确保初始化逻辑线程安全
if self._initialized: # 再次检查，防止锁等待期间其他线程已完成初始化
return
logger.info("正在初始化 HeartFCController 单例...")
self.gpt = ResponseGenerator()
self.mood_manager = MoodManager.get_instance()
# 注意：mood_manager 的 start_mood_update 可能需要在应用主循环启动后调用，
# 或者确保其内部实现是安全的。这里保持原状。
self.mood_manager.start_mood_update()
self.tool_user = ToolUser()
# 注意：InterestManager() 可能是另一个单例或需要特定初始化。
# 假设 InterestManager() 返回的是正确配置的实例。
self.interest_manager = InterestManager()
self._interest_monitor_task: Optional[asyncio.Task] = None
self.pf_chatting_instances: Dict[str, PFChatting] = {}
# _pf_chatting_lock 用于保护 pf_chatting_instances 的异步操作
self._pf_chatting_lock = asyncio.Lock() # 这个是 asyncio.Lock，用于异步上下文
self.emoji_manager = emoji_manager # 假设是全局或已初始化的实例
self.relationship_manager = relationship_manager # 假设是全局或已初始化的实例
# MessageManager 可能是类本身或单例实例，根据其设计确定
self.MessageManager = MessageManager
self._initialized = True
logger.info("HeartFCController 单例初始化完成。")
self.gpt = ResponseGenerator()
self.mood_manager = MoodManager.get_instance()
self.mood_manager.start_mood_update()
self.tool_user = ToolUser()
self.interest_manager = InterestManager()
self._interest_monitor_task: Optional[asyncio.Task] = None
# --- New PFChatting Management ---
self.pf_chatting_instances: Dict[str, PFChatting] = {}
self._pf_chatting_lock = Lock()
# --- End New PFChatting Management ---
HeartFCController._instance = self # Register instance
# --- End Updated Init ---
# --- Make dependencies accessible for PFChatting ---
# These are accessed via the passed instance in PFChatting
self.emoji_manager = emoji_manager
self.relationship_manager = relationship_manager
self.MessageManager = MessageManager # Pass the class/singleton access
# --- End dependencies ---
# --- Added Class Method for Singleton Access ---
@classmethod
def get_instance(cls):
"""获取 HeartFCController 的单例实例。"""
# 如果实例尚未创建，调用构造函数（这将触发 __new__ 和 __init__）
if cls._instance is None:
# 在首次调用 get_instance 时创建实例。
# __new__ 中的锁会确保线程安全。
cls()
# 添加日志记录，说明实例是在 get_instance 调用时创建的
logger.info("HeartFCController 实例在首次 get_instance 时创建。")
elif not cls._initialized:
# 实例已创建但可能未初始化完成（理论上不太可能发生，除非 __init__ 异常）
logger.warning("HeartFCController 实例存在但尚未完成初始化。")
# This might indicate an issue if called before initialization
logger.warning("HeartFCController get_instance called before initialization.")
# Optionally, initialize here if a strict singleton pattern is desired
# cls._instance = cls()
return cls._instance
# --- 新增：检查 PFChatting 状态的方法 --- #
def is_pf_chatting_active(self, stream_id: str) -> bool:
"""检查指定 stream_id 的 PFChatting 循环是否处于活动状态。"""
# 注意：这里直接访问字典，不加锁，因为读取通常是安全的，
# 并且 PFChatting 实例的 _loop_active 状态由其自身的异步循环管理。
# 如果需要更强的保证，可以在访问 pf_instance 前获取 _pf_chatting_lock
pf_instance = self.pf_chatting_instances.get(stream_id)
if pf_instance and pf_instance._loop_active: # 直接检查 PFChatting 实例的 _loop_active 属性
return True
return False
# --- 结束新增 --- #
# --- End Added Class Method ---
async def start(self):
"""启动异步任务,如回复启动器"""

16
src/plugins/chat_module/heartFC_chat/heartFC_processor.py
View File

@ -13,7 +13,6 @@ from ...chat.message_buffer import message_buffer
from ...utils.timer_calculater import Timer
from .interest import InterestManager
from src.plugins.person_info.relationship_manager import relationship_manager
from .reasoning_chat import ReasoningChat
# 定义日志配置
processor_config = LogConfig(
@ -30,7 +29,7 @@ class HeartFCProcessor:
def __init__(self):
self.storage = MessageStorage()
self.interest_manager = InterestManager()
self.reasoning_chat = ReasoningChat.get_instance()
# self.chat_instance = chat_instance # 持有 HeartFC_Chat 实例
async def process_message(self, message_data: str) -> None:
"""处理接收到的原始消息数据，完成消息解析、缓冲、过滤、存储、兴趣度计算与更新等核心流程。
@ -73,11 +72,11 @@ class HeartFCProcessor:
user_info=userinfo,
group_info=groupinfo,
)
# --- 添加兴趣追踪启动 ---
# 在获取到 chat 对象后，启动对该聊天流的兴趣监控
await self.reasoning_chat.start_monitoring_interest(chat)
# --- 结束添加 ---
if not chat:
logger.error(
f"无法为消息创建或获取聊天流: user {userinfo.user_id}, group {groupinfo.group_id if groupinfo else 'None'}"
)
return
message.update_chat_stream(chat)
@ -91,6 +90,7 @@ class HeartFCProcessor:
message.raw_message, chat, userinfo
):
return
logger.trace(f"过滤词/正则表达式过滤成功: {message.processed_plain_text}")
# 查询缓冲器结果
buffer_result = await message_buffer.query_buffer_result(message)
@ -152,8 +152,6 @@ class HeartFCProcessor:
f"使用激活率 {interested_rate:.2f} 更新后 (通过缓冲后)，当前兴趣度: {current_interest:.2f}"
)
self.interest_manager.add_interest_dict(message, interested_rate, is_mentioned)
except Exception as e:
logger.error(f"更新兴趣度失败: {e}") # 调整日志消息
logger.error(traceback.format_exc())

12
src/plugins/chat_module/heartFC_chat/interest.py
View File

@ -6,7 +6,6 @@ import json # 引入 json
import os # 引入 os
from typing import Optional # <--- 添加导入
import random # <--- 添加导入 random
from src.plugins.chat.message import MessageRecv
from src.common.logger import get_module_logger, LogConfig, DEFAULT_CONFIG # 引入 DEFAULT_CONFIG
from src.plugins.chat.chat_stream import chat_manager # *** Import ChatManager ***
@ -67,13 +66,6 @@ class InterestChatting:
self.is_above_threshold: bool = False # 标记兴趣值是否高于阈值
# --- 结束：概率回复相关属性 ---
# 记录激发兴趣对(消息id,激活值)
self.interest_dict = {}
def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
# Store the MessageRecv object and the interest value as a tuple
self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
def _calculate_decay(self, current_time: float):
"""计算从上次更新到现在的衰减"""
time_delta = current_time - self.last_update_time
@ -453,10 +445,6 @@ class InterestManager:
stream_name = chat_manager.get_stream_name(stream_id) or stream_id # 获取流名称
logger.warning(f"尝试降低不存在的聊天流 {stream_name} 的兴趣度")
def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
interest_chatting = self._get_or_create_interest_chatting(message.chat_stream.stream_id)
interest_chatting.add_interest_dict(message, interest_value, is_mentioned)
def cleanup_inactive_chats(self, max_age_seconds=INACTIVE_THRESHOLD_SECONDS):
"""
清理长时间不活跃的聊天流记录

3
src/plugins/chat_module/heartFC_chat/messagesender.py
View File

@ -220,9 +220,10 @@ class MessageManager:
await asyncio.sleep(typing_time)
logger.debug(f"\n{message_earliest.processed_plain_text},{typing_time},等待输入时间结束\n")
await MessageSender().send_message(message_earliest)
await self.storage.store_message(message_earliest, message_earliest.chat_stream)
await MessageSender().send_message(message_earliest)
container.remove_message(message_earliest)
async def start_processor(self):

7
src/plugins/chat_module/heartFC_chat/pf_chatting.py
View File

@ -15,9 +15,6 @@ from src.config.config import global_config
from src.plugins.chat.utils_image import image_path_to_base64 # Local import needed after move
from src.plugins.utils.timer_calculater import Timer # <--- Import Timer
INITIAL_DURATION = 60.0
# 定义日志配置 (使用 loguru 格式)
interest_log_config = LogConfig(
console_format=PFC_STYLE_CONFIG["console_format"], # 使用默认控制台格式
@ -70,7 +67,7 @@ class PFChatting:
Args:
chat_id: The identifier for the chat stream (e.g., stream_id).
heartfc_controller_instance: 访问共享资源和方法的主HeartFCController实例。
heartfc_controller_instance: 访问共享资源和方法的主HeartFC_Controller实例。
"""
self.heartfc_controller = heartfc_controller_instance # Store the controller instance
self.stream_id: str = chat_id
@ -94,7 +91,7 @@ class PFChatting:
self._loop_active: bool = False # Is the loop currently running?
self._loop_task: Optional[asyncio.Task] = None # Stores the main loop task
self._trigger_count_this_activation: int = 0 # Counts triggers within an active period
self._initial_duration: float = INITIAL_DURATION # 首次触发增加的时间
self._initial_duration: float = 60.0 # 首次触发增加的时间
self._last_added_duration: float = self._initial_duration # <--- 新增：存储上次增加的时间
def _get_log_prefix(self) -> str:

43
src/plugins/chat_module/reasoning_chat/reasoning_chat.py
View File

@ -157,17 +157,17 @@ class ReasoningChat:
# 消息加入缓冲池
await message_buffer.start_caching_messages(message)
# logger.info("使用推理聊天模式")
# 创建聊天流
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform,
user_info=userinfo,
group_info=groupinfo,
)
message.update_chat_stream(chat)
await message.process()
logger.trace(f"消息处理成功: {message.processed_plain_text}")
# 过滤词/正则表达式过滤
if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
@ -175,13 +175,27 @@ class ReasoningChat:
):
return
await self.storage.store_message(message, chat)
# 记忆激活
with Timer("记忆激活", timing_results):
interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
message.processed_plain_text, fast_retrieval=True
)
# 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
buffer_result = await message_buffer.query_buffer_result(message)
# 处理提及
is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
# 意愿管理器：设置当前message信息
willing_manager.setup(message, chat, is_mentioned, interested_rate)
# 处理缓冲器结果
if not buffer_result:
# await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
# willing_manager.delete(message.message_info.message_id)
await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
willing_manager.delete(message.message_info.message_id)
f_type = "seglist"
if message.message_segment.type != "seglist":
f_type = message.message_segment.type
@ -200,27 +214,6 @@ class ReasoningChat:
logger.info("触发缓冲，已炸飞消息列")
return
try:
await self.storage.store_message(message, chat)
logger.trace(f"存储成功 (通过缓冲后): {message.processed_plain_text}")
except Exception as e:
logger.error(f"存储消息失败: {e}")
logger.error(traceback.format_exc())
# 存储失败可能仍需考虑是否继续，暂时返回
return
is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
# 记忆激活
with Timer("记忆激活", timing_results):
interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
message.processed_plain_text, fast_retrieval=True
)
# 处理提及
# 意愿管理器：设置当前message信息
willing_manager.setup(message, chat, is_mentioned, interested_rate)
# 获取回复概率
is_willing = False
if reply_probability != 1:

2
src/plugins/chat_module/reasoning_chat/reasoning_generator.py
View File

@ -44,7 +44,7 @@ class ResponseGenerator:
async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
"""根据当前模型类型选择对应的生成函数"""
# 从global_config中获取模型概率值并选择模型
if random.random() < global_config.model_reasoning_probability:
if random.random() < global_config.MODEL_R1_PROBABILITY:
self.current_model_type = "深深地"
current_model = self.model_reasoning
else:

230
src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py
View File

@ -1,9 +1,9 @@
import random
import time
from typing import Optional, Union
from typing import Optional
from ....common.database import db
from ...chat.utils import get_embedding, get_recent_group_detailed_plain_text, get_recent_group_speaker
# from ....common.database import db
from ...chat.utils import get_recent_group_detailed_plain_text, get_recent_group_speaker
from ...chat.chat_stream import chat_manager
from ...moods.moods import MoodManager
from ....individuality.individuality import Individuality
@ -13,6 +13,8 @@ from ....config.config import global_config
from ...person_info.relationship_manager import relationship_manager
from src.common.logger import get_module_logger
from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
from src.plugins.knowledge.knowledge_lib import qa_manager
from src.plugins.chat.chat_stream import ChatStream
logger = get_module_logger("prompt")
@ -53,7 +55,7 @@ class PromptBuilder:
self.activate_messages = ""
async def _build_prompt(
self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
self, chat_stream: ChatStream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
) -> tuple[str, str]:
# 开始构建prompt
prompt_personality = "你"
@ -101,14 +103,16 @@ class PromptBuilder:
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
)
related_memory_info = ""
if related_memory:
related_memory_info = ""
for memory in related_memory:
related_memory_info += memory[1]
# memory_prompt = f"你想起你之前见过的事情：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
memory_prompt = await global_prompt_manager.format_prompt(
"memory_prompt", related_memory_info=related_memory_info
)
else:
related_memory_info = ""
# print(f"相关记忆：{related_memory_info}")
@ -160,6 +164,7 @@ class PromptBuilder:
# 知识构建
start_time = time.time()
prompt_info = ""
prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
if prompt_info:
# prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
@ -221,225 +226,12 @@ class PromptBuilder:
return prompt
async def get_prompt_info(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)}")
related_info += qa_manager.get_knowledge(message)
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
# # 先尝试使用记忆系统的方法获取主题
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
# topics = []
# else:
# topics = [
# topic.strip()
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
if not topics:
logger.info("未能提取到任何主题，使用整个消息进行查询")
embedding = await get_embedding(message, request_type="prompt_build")
if not embedding:
logger.error("获取消息嵌入向量失败")
return ""
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
return related_info
# 2. 对每个主题进行知识库查询
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
# 优化：批量获取嵌入向量，减少API调用
embeddings = {}
topics_batch = [topic for topic in topics if len(topic) > 0]
if message: # 确保消息非空
topics_batch.append(message)
# 批量获取嵌入向量
embed_start_time = time.time()
for text in topics_batch:
if not text or len(text.strip()) == 0:
continue
try:
embedding = await get_embedding(text, request_type="prompt_build")
if embedding:
embeddings[text] = embedding
else:
logger.warning(f"获取'{text}'的嵌入向量失败")
except Exception as e:
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
if not embeddings:
logger.error("所有嵌入向量获取失败")
return ""
# 3. 对每个主题进行知识库查询
all_results = []
query_start_time = time.time()
# 首先添加原始消息的查询结果
if message in embeddings:
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
if original_results:
for result in original_results:
result["topic"] = "原始消息"
all_results.extend(original_results)
logger.info(f"原始消息查询到{len(original_results)}条结果")
# 然后添加每个主题的查询结果
for topic in topics:
if not topic or topic not in embeddings:
continue
try:
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
if topic_results:
# 添加主题标记
for result in topic_results:
result["topic"] = topic
all_results.extend(topic_results)
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
except Exception as e:
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
# 4. 去重和过滤
process_start_time = time.time()
unique_contents = set()
filtered_results = []
for result in all_results:
content = result["content"]
if content not in unique_contents:
unique_contents.add(content)
filtered_results.append(result)
# 5. 按相似度排序
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
# 6. 限制总数量（最多10条）
filtered_results = filtered_results[:10]
logger.info(
f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
)
# 7. 格式化输出
if filtered_results:
format_start_time = time.time()
grouped_results = {}
for result in filtered_results:
topic = result["topic"]
if topic not in grouped_results:
grouped_results[topic] = []
grouped_results[topic].append(result)
# 按主题组织输出
for topic, results in grouped_results.items():
related_info += f"【主题: {topic}】\n"
for _i, result in enumerate(results, 1):
_similarity = result["similarity"]
content = result["content"].strip()
# 调试：为内容添加序号和相似度信息
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
related_info += f"{content}\n"
related_info += "\n"
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info
@staticmethod
def get_info_from_db(
query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
) -> Union[str, list]:
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)
init_prompt()
prompt_builder = PromptBuilder()

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import random
import time
from typing import Optional
# from ....common.database import db
from ...chat.utils import get_recent_group_detailed_plain_text, get_recent_group_speaker
from ...chat.chat_stream import chat_manager
from ...moods.moods import MoodManager
from ....individuality.individuality import Individuality
from ...memory_system.Hippocampus import HippocampusManager
from ...schedule.schedule_generator import bot_schedule
from ...config.config import global_config
from ...person_info.relationship_manager import relationship_manager
from src.common.logger import get_module_logger
from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
from src.plugins.knowledge.knowledge_lib import qa_manager
logger = get_module_logger("prompt")
def init_prompt():
Prompt(
"""
{relation_prompt_all}
{memory_prompt}
{prompt_info}
{schedule_prompt}
{chat_target}
{chat_talking_prompt}
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
你的网名叫{bot_name}，有人也叫你{bot_other_names}，{prompt_personality}。
你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，然后给出日常且口语化的回复，平淡一些，
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话
请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
{moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""",
"reasoning_prompt_main",
)
Prompt(
"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。",
"relationship_prompt",
)
Prompt(
"你想起你之前见过的事情：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n",
"memory_prompt",
)
Prompt("你现在正在做的事情是：{schedule_info}", "schedule_prompt")
Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
class PromptBuilder:
def __init__(self):
self.prompt_built = ""
self.activate_messages = ""
async def _build_prompt(
self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
) -> tuple[str, str]:
# 开始构建prompt
prompt_personality = "你"
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# 关系
who_chat_in_group = [
(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
]
who_chat_in_group += get_recent_group_speaker(
stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id),
limit=global_config.MAX_CONTEXT_SIZE,
)
relation_prompt = ""
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
# relation_prompt_all = (
# f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
# f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
# )
# 心情
mood_manager = MoodManager.get_instance()
mood_prompt = mood_manager.get_prompt()
# logger.info(f"心情prompt: {mood_prompt}")
# 调取记忆
memory_prompt = ""
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
)
if related_memory:
related_memory_info = ""
for memory in related_memory:
related_memory_info += memory[1]
# memory_prompt = f"你想起你之前见过的事情：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
memory_prompt = await global_prompt_manager.format_prompt(
"memory_prompt", related_memory_info=related_memory_info
)
else:
related_memory_info = ""
# print(f"相关记忆：{related_memory_info}")
# 日程构建
# schedule_prompt = f"""你现在正在做的事情是：{bot_schedule.get_current_num_task(num=1, time_info=False)}"""
# 获取聊天上下文
chat_in_group = True
chat_talking_prompt = ""
if stream_id:
chat_talking_prompt = get_recent_group_detailed_plain_text(
stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
)
chat_stream = chat_manager.get_stream(stream_id)
if chat_stream.group_info:
chat_talking_prompt = chat_talking_prompt
else:
chat_in_group = False
chat_talking_prompt = chat_talking_prompt
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
# 关键词检测与反应
keywords_reaction_prompt = ""
for rule in global_config.keywords_reaction_rules:
if rule.get("enable", False):
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
logger.info(
f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
)
keywords_reaction_prompt += rule.get("reaction", "") + "，"
else:
for pattern in rule.get("regex", []):
result = pattern.search(message_txt)
if result:
reaction = rule.get("reaction", "")
for name, content in result.groupdict().items():
reaction = reaction.replace(f"[{name}]", content)
logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
keywords_reaction_prompt += reaction + "，"
break
# 中文高手(新加的好玩功能)
prompt_ger = ""
if random.random() < 0.04:
prompt_ger += "你喜欢用倒装句"
if random.random() < 0.02:
prompt_ger += "你喜欢用反问句"
if random.random() < 0.01:
prompt_ger += "你喜欢用文言文"
# 知识构建
start_time = time.time()
prompt_info = ""
prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
if prompt_info:
# prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
end_time = time.time()
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
# moderation_prompt = ""
# moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
# 涉及政治敏感以及违法违规的内容请规避。"""
logger.debug("开始构建prompt")
# prompt = f"""
# {relation_prompt_all}
# {memory_prompt}
# {prompt_info}
# {schedule_prompt}
# {chat_target}
# {chat_talking_prompt}
# 现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
# 你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
# 你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，然后给出日常且口语化的回复，平淡一些，
# 尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
# 请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话
# 请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
# {moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。"""
prompt = await global_prompt_manager.format_prompt(
"reasoning_prompt_main",
relation_prompt_all=await global_prompt_manager.get_prompt_async("relationship_prompt"),
relation_prompt=relation_prompt,
sender_name=sender_name,
memory_prompt=memory_prompt,
prompt_info=prompt_info,
schedule_prompt=await global_prompt_manager.format_prompt(
"schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
),
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
bot_other_names="/".join(
global_config.BOT_ALIAS_NAMES,
),
prompt_personality=prompt_personality,
mood_prompt=mood_prompt,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
return prompt
async def get_prompt_info(self, message: str, threshold: float):
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
related_info += qa_manager.get_knowledge(message)
return related_info
# 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:
# # 先尝试使用记忆系统的方法获取主题
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
# topics = []
# else:
# topics = [
# topic.strip()
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
# if not topics:
# logger.info("未能提取到任何主题，使用整个消息进行查询")
# embedding = await get_embedding(message, request_type="prompt_build")
# if not embedding:
# logger.error("获取消息嵌入向量失败")
# return ""
# related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
# logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
# return related_info
# # 2. 对每个主题进行知识库查询
# logger.info(f"开始处理{len(topics)}个主题的知识库查询")
# # 优化：批量获取嵌入向量，减少API调用
# embeddings = {}
# topics_batch = [topic for topic in topics if len(topic) > 0]
# if message: # 确保消息非空
# topics_batch.append(message)
# 批量获取嵌入向量
# embed_start_time = time.time()
# for text in topics_batch:
# if not text or len(text.strip()) == 0:
# continue
# try:
# embedding = await get_embedding(text, request_type="prompt_build")
# if embedding:
# embeddings[text] = embedding
# else:
# logger.warning(f"获取'{text}'的嵌入向量失败")
# except Exception as e:
# logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
# logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
# if not embeddings:
# logger.error("所有嵌入向量获取失败")
# return ""
# # 3. 对每个主题进行知识库查询
# all_results = []
# query_start_time = time.time()
# # 首先添加原始消息的查询结果
# if message in embeddings:
# original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
# if original_results:
# for result in original_results:
# result["topic"] = "原始消息"
# all_results.extend(original_results)
# logger.info(f"原始消息查询到{len(original_results)}条结果")
# # 然后添加每个主题的查询结果
# for topic in topics:
# if not topic or topic not in embeddings:
# continue
# try:
# # topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
# # if topic_results:
# # # 添加主题标记
# # for result in topic_results:
# # result["topic"] = topic
# # all_results.extend(topic_results)
# # logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
# except Exception as e:
# logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
# logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
# # 4. 去重和过滤
# process_start_time = time.time()
# unique_contents = set()
# filtered_results = []
# for result in all_results:
# content = result["content"]
# if content not in unique_contents:
# unique_contents.add(content)
# filtered_results.append(result)
# # 5. 按相似度排序
# filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
# # 6. 限制总数量（最多10条）
# filtered_results = filtered_results[:10]
# logger.info(
# f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
# )
# # 7. 格式化输出
# if filtered_results:
# format_start_time = time.time()
# grouped_results = {}
# for result in filtered_results:
# topic = result["topic"]
# if topic not in grouped_results:
# grouped_results[topic] = []
# grouped_results[topic].append(result)
# 按主题组织输出
# for topic, results in grouped_results.items():
# related_info += f"【主题: {topic}】\n"
# for _i, result in enumerate(results, 1):
# _similarity = result["similarity"]
# content = result["content"].strip()
# # 调试：为内容添加序号和相似度信息
# # related_info += f"{i}. [{similarity:.2f}] {content}\n"
# related_info += f"{content}\n"
# related_info += "\n"
# # logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
# logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
# return related_info
# def get_info_from_db(
# self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
# ) -> Union[str, list]:
# 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)
init_prompt()
prompt_builder = PromptBuilder()

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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
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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
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<https://www.gnu.org/licenses/why-not-lgpl.html>.

0
src/plugins/knowledge/__init__.py 100644
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65
src/plugins/knowledge/knowledge_lib.py 100644
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@ -0,0 +1,65 @@
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 100644
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251
src/plugins/knowledge/src/embedding_store.py 100644
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@ -0,0 +1,251 @@
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:
raise Exception("Faiss索引尚未构建")
if self.idx2hash is None:
raise Exception("idx2hash映射尚未构建")
# 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()

14
src/plugins/knowledge/src/global_logger.py 100644
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# Configure logger
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
console_logging_handler = logging.StreamHandler()
console_logging_handler.setFormatter(
logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
)
console_logging_handler.setLevel(logging.DEBUG)
logger.addHandler(console_logging_handler)

108
src/plugins/knowledge/src/ie_process.py 100644
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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

436
src/plugins/knowledge/src/kg_manager.py 100644
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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 100644
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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
)

143
src/plugins/knowledge/src/lpmmconfig.py 100644
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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)

36
src/plugins/knowledge/src/mem_active_manager.py 100644
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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

147
src/plugins/knowledge/src/open_ie.py 100644
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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

73
src/plugins/knowledge/src/prompt_template.py 100644
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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

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src/plugins/knowledge/src/qa_manager.py 100644
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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"],
)
# 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
def get_knowledge(self, question: str) -> str:
"""获取知识"""
# 处理查询
query_res, _ = self.process_query(question)
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

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src/plugins/knowledge/src/raw_processing.py 100644
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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 100644
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58
src/plugins/knowledge/src/utils/data_loader.py 100644
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@ -0,0 +1,58 @@
import jsonlines
from pathlib import Path
from typing import List, Dict, Any, Union, Optional
from src.config import global_config as config
class DataLoader:
"""数据加载工具类，用于从/data目录下加载各种格式的数据文件"""
def __init__(self, custom_data_dir: Optional[Union[str, Path]] = None):
"""
初始化数据加载器
Args:
custom_data_dir: 可选的自定义数据目录路径，如果不提供则使用配置文件中的默认路径
"""
self.data_dir = (
Path(custom_data_dir)
if custom_data_dir
else Path(config["persistence"]["data_root_path"])
)
if not self.data_dir.exists():
raise FileNotFoundError(f"数据目录 {self.data_dir} 不存在")
def _resolve_file_path(self, filename: str) -> Path:
"""
解析文件路径
Args:
filename: 文件名
Returns:
完整的文件路径
Raises:
FileNotFoundError: 当文件不存在时抛出
"""
file_path = self.data_dir / filename
if not file_path.exists():
raise FileNotFoundError(f"文件 {filename} 不存在")
return file_path
def load_jsonl(self, filename: str) -> List[Dict[str, Any]]:
"""
加载JSONL格式的文件
Args:
filename: 文件名
Returns:
包含所有数据的列表
"""
file_path = self._resolve_file_path(filename)
data = []
with jsonlines.open(file_path) as reader:
for obj in reader:
data.append(obj)
return data

51
src/plugins/knowledge/src/utils/dyn_topk.py 100644
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@ -0,0 +1,51 @@
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 100644
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@ -0,0 +1,8 @@
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()

79
src/plugins/knowledge/src/utils/json_fix.py 100644
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@ -0,0 +1,79 @@
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 100644
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@ -0,0 +1,17 @@
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()

9
src/plugins/memory_system/Hippocampus.py
View File

@ -1509,14 +1509,19 @@ class HippocampusManager:
return response
async def get_memory_from_topic(
self, valid_keywords: list[str], max_memory_num: int = 3, max_memory_length: int = 2, max_depth: int = 3
self,
valid_keywords: list[str],
max_memory_num: int = 3,
max_memory_length: int = 2,
max_depth: int = 3,
fast_retrieval: bool = False,
) -> list:
"""从文本中获取相关记忆的公共接口"""
if not self._initialized:
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
try:
response = await self._hippocampus.get_memory_from_topic(
valid_keywords, max_memory_num, max_memory_length, max_depth
valid_keywords, max_memory_num, max_memory_length, max_depth, fast_retrieval
)
except Exception as e:
logger.error(f"文本激活记忆失败: {e}")

79
template/bot_config_template.toml
View File

@ -1,7 +1,8 @@
[inner]
version = "1.4.0"
version = "1.3.1"
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
#以下是给开发人员阅读的，一般用户不需要阅读
#如果你想要修改配置文件，请在修改后将version的值进行变更
#如果新增项目，请在BotConfig类下新增相应的变量
#1.如果你修改的是[]层级项目，例如你新增了 [memory],那么请在config.py的 load_config函数中的include_configs字典中新增"内容":{
@ -18,12 +19,11 @@ version = "1.4.0"
# 次版本号：当你做了向下兼容的功能性新增，
# 修订号：当你做了向下兼容的问题修正。
# 先行版本号及版本编译信息可以加到“主版本号.次版本号.修订号”的后面，作为延伸。
#----以上是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
[bot]
qq = 1145141919810
qq = 114514
nickname = "麦麦"
alias_names = ["麦叠", "牢麦"] #该选项还在调试中，暂时未生效
alias_names = ["麦叠", "牢麦"]
[groups]
talk_allowed = [
@ -41,24 +41,23 @@ personality_sides = [
"用一句话或几句话描述人格的一些细节",
"用一句话或几句话描述人格的一些细节",
"用一句话或几句话描述人格的一些细节",
]# 条数任意，不能为0, 该选项还在调试中，可能未完全生效
]# 条数任意
[identity] #アイデンティティがない 生まれないらららら
# 兴趣爱好 未完善，有些条目未使用
identity_detail = [
"身份特点",
"身份特点",
]# 条数任意，不能为0, 该选项还在调试中，可能未完全生效
]# 条数任意
#外貌特征
height = 170 # 身高 单位厘米 该选项还在调试中，暂时未生效
weight = 50 # 体重 单位千克 该选项还在调试中，暂时未生效
age = 20 # 年龄 单位岁 该选项还在调试中，暂时未生效
gender = "男" # 性别 该选项还在调试中，暂时未生效
appearance = "用几句话描述外貌特征" # 外貌特征 该选项还在调试中，暂时未生效
height = 170 # 身高 单位厘米
weight = 50 # 体重 单位千克
age = 20 # 年龄 单位岁
gender = "男" # 性别
appearance = "用几句话描述外貌特征" # 外貌特征
[schedule]
enable_schedule_gen = true # 是否启用日程表
enable_schedule_interaction = true # 日程表是否影响回复模式
enable_schedule_gen = true # 是否启用日程表(尚未完成)
prompt_schedule_gen = "用几句话描述描述性格特点或行动规律，这个特征会用来生成日程表"
schedule_doing_update_interval = 900 # 日程表更新间隔 单位秒
schedule_temperature = 0.1 # 日程表温度，建议0.1-0.5
@ -68,25 +67,19 @@ time_zone = "Asia/Shanghai" # 给你的机器人设置时区，可以解决运
nonebot-qq="http://127.0.0.1:18002/api/message"
[response] #群聊的回复策略
enable_heart_flowC = true
# 该功能还在完善中
# 是否启用heart_flowC(心流聊天,HFC)模式
# 启用后麦麦会自主选择进入heart_flowC模式(持续一段时间），进行主动的观察和回复，并给出回复，比较消耗token
#reasoning：推理模式，麦麦会根据上下文进行推理，并给出回复
#heart_flow：结合了PFC模式和心流模式，麦麦会进行主动的观察和回复，并给出回复
response_mode = "heart_flow" # 回复策略，可选值：heart_flow（心流），reasoning（推理)
#一般回复参数
model_reasoning_probability = 0.7 # 麦麦回答时选择推理模型 模型的概率
model_normal_probability = 0.3 # 麦麦回答时选择一般模型 模型的概率
[heartflow] #启用启用heart_flowC(心流聊天)模式时生效，需要填写以下参数
reply_trigger_threshold = 3.0 # 心流聊天触发阈值，越低越容易进入心流聊天
probability_decay_factor_per_second = 0.2 # 概率衰减因子，越大衰减越快，越高越容易退出心流聊天
default_decay_rate_per_second = 0.98 # 默认衰减率，越大衰减越快，越高越难进入心流聊天
initial_duration = 60 # 初始持续时间，越大心流聊天持续的时间越长
#推理回复参数
model_r1_probability = 0.7 # 麦麦回答时选择主要回复模型1 模型的概率
model_v3_probability = 0.3 # 麦麦回答时选择次要回复模型2 模型的概率
[heartflow] # 注意：可能会消耗大量token，请谨慎开启，仅会使用v3模型
sub_heart_flow_update_interval = 60 # 子心流更新频率，间隔 单位秒
sub_heart_flow_freeze_time = 100 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
sub_heart_flow_stop_time = 500 # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
# sub_heart_flow_update_interval = 60
# sub_heart_flow_freeze_time = 100
# heart_flow_update_interval = 600
heart_flow_update_interval = 600 # 心流更新频率，间隔 单位秒
observation_context_size = 20 # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
compressed_length = 5 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
@ -94,13 +87,11 @@ compress_length_limit = 5 #最多压缩份数，超过该数值的压缩上下
[message]
max_context_size = 12 # 麦麦回复时获得的上文数量，建议12，太短太长都会导致脑袋尖尖
emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发
thinking_timeout = 100 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢）
max_response_length = 256 # 麦麦单次回答的最大token数
max_context_size = 12 # 麦麦获得的上文数量，建议12，太短太长都会导致脑袋尖尖
emoji_chance = 0.2 # 麦麦使用表情包的概率，设置为1让麦麦自己决定发不发
thinking_timeout = 60 # 麦麦最长思考时间，超过这个时间的思考会放弃
max_response_length = 256 # 麦麦回答的最大token数
message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟
# 以下是消息过滤，可以根据规则过滤特定消息，将不会读取这些消息
ban_words = [
# "403","张三"
]
@ -112,23 +103,22 @@ ban_msgs_regex = [
# "\\[CQ:at,qq=\\d+\\]" # 匹配@
]
[willing] # 一般回复模式的回复意愿设置
[willing]
willing_mode = "classical" # 回复意愿模式 —— 经典模式：classical，动态模式：dynamic，mxp模式：mxp，自定义模式：custom（需要你自己实现）
response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数
down_frequency_rate = 3 # 降低回复频率的群组回复意愿降低系数 除法
emoji_response_penalty = 0 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
emoji_response_penalty = 0.1 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
mentioned_bot_inevitable_reply = false # 提及 bot 必然回复
at_bot_inevitable_reply = false # @bot 必然回复
[emoji]
max_emoji_num = 90 # 表情包最大数量
max_emoji_num = 120 # 表情包最大数量
max_reach_deletion = true # 开启则在达到最大数量时删除表情包，关闭则达到最大数量时不删除，只是不会继续收集表情包
check_interval = 30 # 检查表情包（注册，破损，删除）的时间间隔(分钟)
auto_save = true # 是否保存表情包和图片
enable_check = false # 是否启用表情包过滤，只有符合该要求的表情包才会被保存
check_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存
enable_check = false # 是否启用表情包过滤
check_prompt = "符合公序良俗" # 表情包过滤要求
[memory]
build_memory_interval = 2000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多
@ -141,8 +131,7 @@ forget_memory_interval = 1000 # 记忆遗忘间隔 单位秒 间隔越低，
memory_forget_time = 24 #多长时间后的记忆会被遗忘 单位小时
memory_forget_percentage = 0.01 # 记忆遗忘比例 控制记忆遗忘程度 越大遗忘越多 建议保持默认
#不希望记忆的词，已经记忆的不会受到影响
memory_ban_words = [
memory_ban_words = [ #不希望记忆的词
# "403","张三"
]
@ -178,7 +167,7 @@ word_replace_rate=0.006 # 整词替换概率
[response_splitter]
enable_response_splitter = true # 是否启用回复分割器
response_max_length = 256 # 回复允许的最大长度
response_max_length = 100 # 回复允许的最大长度
response_max_sentence_num = 4 # 回复允许的最大句子数
[remote] #发送统计信息，主要是看全球有多少只麦麦

54
template/lpmm_config_template.toml 100644
View File

@ -0,0 +1,54 @@
# 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-R1-Distill-Qwen-32B" # 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-R1-Distill-Qwen-32B" # 模型名称
[embedding]
# 设置用于文本嵌入的Embedding模型
provider = "localhost" # 服务提供商
model = "text-embedding-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
[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

20
template/template.env
View File

@ -29,18 +29,8 @@ CHAT_ANY_WHERE_KEY=
SILICONFLOW_KEY=
# 定义日志相关配置
# 精简控制台输出格式
SIMPLE_OUTPUT=true
# 自定义日志的默认控制台输出日志级别
CONSOLE_LOG_LEVEL=INFO
# 自定义日志的默认文件输出日志级别
FILE_LOG_LEVEL=DEBUG
# 原生日志的控制台输出日志级别（nonebot就是这一类）
DEFAULT_CONSOLE_LOG_LEVEL=SUCCESS
# 原生日志的默认文件输出日志级别（nonebot就是这一类）
DEFAULT_FILE_LOG_LEVEL=DEBUG
SIMPLE_OUTPUT=true # 精简控制台输出格式
CONSOLE_LOG_LEVEL=INFO # 自定义日志的默认控制台输出日志级别
FILE_LOG_LEVEL=DEBUG # 自定义日志的默认文件输出日志级别
DEFAULT_CONSOLE_LOG_LEVEL=SUCCESS # 原生日志的控制台输出日志级别（nonebot就是这一类）
DEFAULT_FILE_LOG_LEVEL=DEBUG # 原生日志的默认文件输出日志级别（nonebot就是这一类）

46
麦麦开始学习.bat 100644
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@ -0,0 +1,46 @@
@echo off
CHCP 65001 > nul
setlocal enabledelayedexpansion
@REM REM 查找venv虚拟环境
@REM set "venv_path=%~dp0venv\Scripts\activate.bat"
@REM if not exist "%venv_path%" (
@REM echo 错误: 未找到虚拟环境，请确保venv目录存在
@REM pause
@REM exit /b 1
@REM )
@REM REM 激活虚拟环境
@REM call "%venv_path%"
@REM if %ERRORLEVEL% neq 0 (
@REM echo 错误: 虚拟环境激活失败
@REM pause
@REM exit /b 1
@REM )
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

56
（临时版）麦麦开始学习.bat
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@ -1,56 +0,0 @@
@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