LLM Memory
一个专为大语言模型设计的记忆管理系统，支持基于重要性的记忆筛选和持久化存储。
安装
使用以下命令安装：
pip install llm-memory
使用示例：
from llm_memory import CombinedMemory
# 初始化记忆系统
memory = CombinedMemory(
max_size=100, # 最大记忆容量
system_prompt="你是一个助手", # 系统提示词
uid="user123", # 用户ID，用于数据库存储
recent_size=20 # 保留最近对话轮数
)
# 添加消息
memory.add_message("user", "你好", importance=1)
memory.add_message("assistant", "你好！有什么我可以帮你的吗？", importance=1)
# 获取上下文
context = memory.get_context()
# 保存到数据库
memory.save_memory_to_db()
参数说明：
- system_prompt: 系统提示词，可选
- uid: 用户唯一标识，用于数据库存储，可选
- max_size: 最大记忆容量，必需
- recent_size: 保留最近对话轮数，默认为20轮
特性：
- 基于重要性的记忆管理
- 自动保持最近的对话（可配置保留轮数）
- 支持数据库持久化
- 可配置的系统提示词
数据持久化：
当提供 uid 参数时，会自动将对话历史保存到本地数据库（使用 TinyDB）。数据库文件默认保存为 memory.json。
依赖：
Python >= 3.7 tinydb >= 4.7.0
许可证：
MIT License
1 maintainer
Metadata
1 maintainer
Release files for llm-memory 0.2.1
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Total release size: 8.5 kB
Release files / llm_memory-0.2.1.tar.gz
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