Background
检索增强生成(RAG)通常会引入搜索引擎等重型组件，但PocketFlow给出了轻量级实现方案。
Specific implementation steps
- 数据准备：将资料保存为txt/csv等简单格式
- 建立检索节点：
def retrieve(x): with open("data.txt") as f: return [line for line in f if x in line] flow.add_node("search", retrieve) - 连接生成节点：
flow.add_node("generate", lambda x: f"根据资料：{x[:100]}...") flow.connect("search", "generate")
Optimization Tips
- 对小文件使用内存缓存
- 对大文件建议先建立倒排索引
- combinable with
fuzzywuzzy等轻量库提升匹配精度
该方法可在100MB内存的设备上流畅运行。
This answer comes from the articlePocketFlow: A Minimalist Framework for AI Application Development in 100 Lines of CodeThe
May not be reproduced without permission:AI productivity tools " How to implement RAG functionality in a resource-limited environment?
