"""测试 ReMe 的 vector 搜索功能""" import asyncio from reme import ReMe async def main(): """测试 ReMe 的 vector 搜索功能""" # 初始化 ReMe reme = ReMe( working_dir=".reme", default_llm_config={ "backend": "openai", "model_name": "qwen3.5-plus", }, default_embedding_model_config={ "backend": "openai", "model_name": "text-embedding-v4", "dimensions": 1024, }, default_vector_store_config={ "backend": "local", # 支持 local/chroma/qdrant/elasticsearch }, ) await reme.start() messages = [ {"role": "user", "content": "帮我写一个 Python 脚本", "time_created": "2026-02-28 10:00:00"}, {"role": "assistant", "content": "好的,我来帮你写", "time_created": "2026-02-28 10:00:05"}, ] # 1. 从对话中总结记忆(自动提取用户偏好、任务经验等) result = await reme.summarize_memory( messages=messages, user_name="alice", # 个人记忆 # task_name="code_writing", # 任务记忆 ) print(f"总结结果: {result}") # 2. 检索相关记忆 memories = await reme.retrieve_memory( query="Python 编程", user_name="alice", # task_name="code_writing", ) print(f"检索结果: {memories}") # 3. 手动添加记忆 memory_node = await reme.add_memory( memory_content="用户喜欢简洁的代码风格", user_name="alice", ) print(f"添加的记忆: {memory_node}") memory_id = memory_node.memory_id # 4. 通过 ID 获取单条记忆 fetched_memory = await reme.get_memory(memory_id=memory_id) print(f"获取的记忆: {fetched_memory}") # 5. 更新记忆内容 updated_memory = await reme.update_memory( memory_id=memory_id, user_name="alice", memory_content="用户喜欢简洁且带注释的代码风格", ) print(f"更新后的记忆: {updated_memory}") # 6. 列出用户的所有记忆(支持过滤和排序) all_memories = await reme.list_memory( user_name="alice", limit=10, sort_key="time_created", reverse=True, ) print(f"用户记忆列表: {all_memories}") # 7. 删除指定记忆 await reme.delete_memory(memory_id=memory_id) print(f"已删除记忆: {memory_id}") # 8. 删除所有记忆(谨慎使用) # await reme.delete_all() await reme.close() if __name__ == "__main__": asyncio.run(main())