问HN：我应该将全球知识、互联网搜索和用户RAG结合起来吗？
我正在斯里兰卡构建一个处理文档和其他敏感数据的SaaS平台。每个用户可以上传自己的文档和信息，平台使用RAG（检索增强生成）根据该用户的数据回答问题。这部分我觉得是合理的。
我主要担心的是，当用户没有上传足够的信息时会发生什么。我仍然希望大型语言模型（LLM）能够使用来自互联网（或经过筛选的知识库）的可靠信息提供准确的答案，并附上适当的引用。
我正在考虑以下两种架构：
选项1：
基础LLM（通过Azure AI Foundry或Amazon Bedrock的OpenAI/Anthropic）
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平台RAG（由我们管理的全球知识库）
↓
用户特定RAG
在这种方法中，我们维护一个全球知识库，由我们（平台管理员）进行策划和更新。每个用户都可以访问这个共享知识，而他们自己上传的文档则通过个人RAG进行搜索。
选项2：
开源LLM
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在斯里兰卡/特定领域数据上进行微调
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用户特定RAG
在这里，我们使用斯里兰卡或特定领域的数据对开源模型进行微调，每个用户仍然拥有自己的RAG来处理私有文档。
我关注的问题是：
微调在这里是否真的是正确的解决方案，还是不必要的？
全球/共享RAG是否比微调更好？
如果你想设计这个架构，你会如何做：
准确的领域知识回答
用户私有文档搜索
引用/来源
良好的可扩展性以支持数千个用户
我倾向于选项1，因为微调似乎成本高、耗时，并且我对此还没有经验。然而，我不确定我是否在正确思考这个问题。
我非常希望听到其他人如何解决这个问题。
查看原文
I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data.<p>Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data. That part makes sense to me.<p>My main concern is what happens when the user hasn't uploaded enough information. I still want the LLM to provide accurate answers using reliable information from the internet (or from a curated knowledge base), with proper citations.<p>These are the two architectures I'm considering:<p>Option 1:<p>Base LLM (OpenAI/Anthropic via Azure AI Foundry or Amazon Bedrock)
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Platform RAG (global knowledge base managed by us)
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User-specific RAG
In this approach, we maintain a global knowledge base that we (the platform admins) curate and update. Every user can access this shared knowledge, while their own uploaded documents are searched through their personal RAG.<p>Option 2:<p>Open-source LLM
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Fine-tuned on Sri Lankan/domain-specific data
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User-specific RAG
Here, we fine-tune an open-source model using Sri Lankan or domain-specific data, and each user still has their own RAG for their private documents.<p>My concerns are:<p>Is fine-tuning actually the right solution here, or is it unnecessary?<p>Is a global/shared RAG a better approach than fine-tuning?<p>How would you design this architecture if you wanted:<p>Accurate answers from domain knowledge<p>User-private document search<p>Citations/sources<p>Good scalability for thousands of users<p>I'm leaning toward Option 1 because fine-tuning seems expensive, time-consuming, and I have no experience with it yet. However, I'm not sure if I'm thinking about this correctly.<p>I'd really appreciate hearing how others would approach this problem