mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-08-28 05:25:04 +00:00
118 lines
4.4 KiB
Python
118 lines
4.4 KiB
Python
"""Test module for ReMe memory system functionality."""
|
||
|
||
import asyncio
|
||
|
||
from reme import ReMe
|
||
from reme.core.schema import VectorNode, MemoryNode
|
||
|
||
|
||
async def test_reme():
|
||
"""Tests ReMe memory system with personal information storage and retrieval."""
|
||
# 构建一段包含个人信息的对话
|
||
reme = ReMe(vector_store={"collection_name": "reme"})
|
||
await reme.start()
|
||
# reme = await ReMe.create(vector_store={"collection_name": "reme"})
|
||
|
||
await reme.vector_store.delete_all()
|
||
|
||
messages = [
|
||
{
|
||
"role": "user",
|
||
"content": "你好，我是张伟，今年28岁，是一名软件工程师。",
|
||
"time_created": "2026-01-29 10:00:00",
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "你好张伟！很高兴认识你。作为一名软件工程师，你主要从事什么方向的开发工作呢？",
|
||
"time_created": "2026-01-29 10:00:05",
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "我主要做后端开发，擅长Python和Go语言。最近在研究AI Agent相关的技术。",
|
||
"time_created": "2026-01-29 10:00:30",
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "很棒！Python和Go都是非常实用的语言。AI Agent是当前很热门的方向，你在这方面有什么具体的研究重点吗？",
|
||
"time_created": "2026-01-29 10:00:35",
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "我特别关注记忆系统的设计，希望能让AI Agent具有长期记忆能力。我的工作地点在北京，平时喜欢看技术博客和参加技术分享会。",
|
||
"time_created": "2026-01-29 10:01:00",
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "记忆系统确实是AI Agent的核心能力之一。北京有很多优秀的技术社区和活动，相信你能找到很多志同道合的朋友。",
|
||
"time_created": "2026-01-29 10:01:05",
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "是的，我每周末都会去参加一些技术沙龙。对了，我的邮箱是zhangwei@example.com，如果有好的技术资料可以发给我。",
|
||
"time_created": "2026-01-29 10:01:30",
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "好的，我记下了。保持学习的热情很重要，祝你在AI Agent领域的研究越来越深入！",
|
||
"time_created": "2026-01-29 10:01:35",
|
||
},
|
||
]
|
||
|
||
print("=" * 60)
|
||
print("步骤1: 开始总结对话并生成记忆")
|
||
print("=" * 60)
|
||
|
||
# 对对话进行总结，生成记忆
|
||
await reme.summary_memory(
|
||
messages=messages,
|
||
user_name="zhangwei",
|
||
description="用户自我介绍和技术兴趣分享",
|
||
)
|
||
|
||
print("\n✓ 记忆总结完成")
|
||
|
||
print("\n" + "=" * 60)
|
||
print("步骤2: 查看已存储的记忆节点")
|
||
print("=" * 60)
|
||
|
||
# 列出所有存储的记忆节点
|
||
nodes: list[VectorNode] = await reme.vector_store.list()
|
||
for i, node in enumerate(nodes, 1):
|
||
memory_node = MemoryNode.from_vector_node(node)
|
||
print(f"{i} {memory_node.model_dump_json()}")
|
||
|
||
print("\n" + "=" * 60)
|
||
print("步骤3: 测试记忆检索 - 验证个人信息")
|
||
print("=" * 60)
|
||
|
||
# 测试问题1: 检索用户姓名
|
||
query1 = "用户叫什么名字？"
|
||
print(f"\n问题1: {query1}")
|
||
result1 = await reme.retrieve_memory(query=query1, user_name="zhangwei")
|
||
print(f"检索结果:\n{result1}")
|
||
|
||
# 测试问题2: 检索技术背景
|
||
query2 = "用户擅长什么编程语言和技术方向？"
|
||
print(f"\n问题2: {query2}")
|
||
result2 = await reme.retrieve_memory(query=query2, user_name="zhangwei")
|
||
print(f"检索结果:\n{result2}")
|
||
|
||
# 测试问题3: 检索个人信息
|
||
query3 = "用户的工作地点和联系方式是什么？"
|
||
print(f"\n问题3: {query3}")
|
||
result3 = await reme.retrieve_memory(query=query3, user_name="zhangwei")
|
||
print(f"检索结果:\n{result3}")
|
||
|
||
# 测试问题4: 检索兴趣爱好
|
||
query4 = "用户平时有什么爱好或活动？"
|
||
print(f"\n问题4: {query4}")
|
||
result4 = await reme.retrieve_memory(query=query4, user_name="zhangwei")
|
||
print(f"检索结果:\n{result4}")
|
||
|
||
print("\n" + "=" * 60)
|
||
print("测试完成！")
|
||
print("=" * 60)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(test_reme())
|