维护与文档更新

Whether you have tons of office files (pptx, docx, excels or pdfs) and you want to have your own AI consultant which master all your local knowledges?

I built DocMason. It's an open-source, entirely local workspace. The repo is the app. Codex is the runtime.

You just need to download the repo and open it by using Codex. Then you could start to use it. DocMason build Knowledge base in your laptop and leverage Codex strong AI Agent engine to conduct deep research.

I am currently using it in my daily working life, e.g. ask it to consider a complex ppt outline base on internal IT platform architecture & roadmaps. Or write a reports from all last years documents I have generated.

Repo is here: [https://github.com/JetXu-LLM/DocMason](https://github.com/JetXu-LLM/DocMason)

Would love to hear if the retrieval actually works for your complex files or if it completely falls over. Drop an issue if you find bugs!

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At this point, llama and mistral are outdated

Models like the qwen3.5 series are fine, if you don't take the smallest ones

Wow! Are you the author? I've been following you on GitHub. This isn't a simple weekend project! I'll have to thoroughly review the code, but it looks amazing. Thanks for sharing this repository.

The architecture and features pages are much more comprehensive than I expected, considering its low rating.

I really liked it. The memory flow is very well thought out; the extract/embezzle/retrieval flow with hybrid search (sqlite-vec + FTS5), along with time decay, is a great feature. And the mini-application feature within the chat is quite unusual; I wasn't expecting it.

My main concern for my use case is incremental synchronization. I haven't seen this addressed here. It would be great to know more about it, or at least have some notes on how to approach it, especially with external sources like repositories/wikis. I need the knowledge base to stay updated automatically without having to manually re-upload information. While the support for MCP servers can certainly help with this, perhaps that's the way to build it. Have you tried connecting it to an external power source of that type?

Wow! Great, thanks! Very useful information for when I can get started with all this.

I wasn't familiar with FastGPT, I'll check it out!

I understand the separation of aspects: wiki/documents + vectors/semantic search + agent orchestration + storage, all separate.

Regarding ETL, that's also clear: everything is standardized in a common format, so the agent doesn't need to know if it comes from Git, Notion, or a PDF. Clear domain and contracts, and encapsulation, right?

What I'm not clear on is how to orchestrate the different LLMs in practice. Is there an orchestrator that already decides which model to use based on the type of question, or do I have to implement it myself? I understand that for basic RAG or simple actions, Llama/Qwen are sufficient, but when you're in a continuous chat with the agent and the questions are more ambiguous... how do you manage that without the switch between models being obvious? I understand about caching the responses. I'll look into it when the time comes.

Thanks, Kong. I wasn't familiar with them, but the gateway idea makes sense, and I hadn't considered it. Thanks.

Regarding the indexing issues, I think they can be resolved with an incremental sync pipeline: Git/Outline detects changes, re-embeds only what has changed, and updates Qdrant. I'll review it when I have a clearer view of the stack. It's a good, interesting, and complex piece of work, but it's beautiful.

Thanks again!