#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
有消息称#小米#MIUI 安全组件即小米的杀毒引擎添加有国家反诈中心的扫描接口(nationalAntiFraudSingleAppScan)。
at com.miui.guardprovider.engine.mi.antidefraud.AntiDefraudAppManager.getSign(Unknown Source:0)
at com.miui.guardprovider.engine.mi.antidefraud.MiDetectAppsManager.virusInMiEngineRiskList(Unknown Source:30)
at com.miui.guardprovider.engine.mi.antidefraud.AntiDefraudAppManager.getDetectUnsafeAppStatus(Unknown Source:6)
at com.miui.guardprovider.manager.SecurityService$a.nationalAntiFraudSingleAppScan(Unknown Source:17)
at com.miui.guardprovider.aidl.IAntiVirusServer$Stub.onTransact(Unknown Source:38)
MIUI security components.apk源代码分析
viamogua.co