Turning a pile of documents into a searchable useable knowledge base (github.com)

I had an issue. A documents folder with over 12k objects in it. A hodgepodge of folders and sub-folders. That over time had created a mess that no amount of file movement was ever going to make it usable. I wanted: 1) To keep my data local 2) be able to filter out PII and other data 3) Be able to find and delete duplicates 4) Get short synopsis of what a document is 5) Semantic and keyword search 6) All of this kept local to me requiring no internet access and no tokens spent to train someone elses AI.

The result I call DocuBrowser and in it's current form is FOSS (GPL-3) licensed for your personal use. The UI is in your browser. The AI models used are held local and are tiny, Available for Linux(RPM,Deb, and tgz) Windows and Mac. Let me know what you think and thanks for taking the time to try it out.

I'm a huge fan of recall, going to test this out. This looks very interesting.

We need projects like this. Automatically classifying the files is smart.

I'm working on a similar application called Hister (https://github.com/asciimoo/hister). I should borrow some of your ideas. =]

Looks good, definitely going to try it. Extra thanks for creating something fully local, we need more projects like this one!

How do you feel about supporting an S3 compatible target as a feature request?

I just installed this and, after a few hiccups, got it up and running on my Ubuntu system. Works great, looks great. Thank you for this. Half of my documents are OpenDocument format. Is there any chance you'll be supporting ODF in the future?

Nice, what are you hoping to accomplish with this project?

The hardest part of these projects is usually not making documents searchable

I learned a solution is to turn the documents into vectors in say PostgreSQL (with pgvector) and do a cosine similarity search with a search vector. Doing a search for embed models on HuggingFace shows nomic-ai/nomic-embed-text-v1.5 and Qwen/Qwen3-Embedding-0.6B. I might have used a larger one like Qwen/Qwen3-Embedding-4B.

There's some info for AnythingLLM[0] which supports RAG. AnythingLLM has LanceDB out of the box but also supports others including pgvector.

[0] https://docs.anythingllm.com/features/embedding-models

Not a fan of pushing every personal document to someone else's cloud. Nice to see a tool that keeps everything on disk instead.

Honestly. This with Paperless-NGX might be game changing if both pointed to the same folder.

Wanted to share Antfly which I think serves a similar niche:

https://antfly.io/

https://github.com/antflydb/antfly

They’ve put a lot of effort into optimizing the local llm pipelines and I have a lot of faith in the devs working on it.

Anyone getting a bunch of permission errors when running (e.g. Traceback (most recent call last): File "/Users/tron/Applications/DocuBrowse/venv/lib/python3.9/site-packages/psutil/_psosx.py", line 347, in wrapper return fun(self, args, *kwargs) File "/Users/tron/Applications/DocuBrowse/venv/lib/python3.9/site-packages/psutil/_psosx.py", line 508, in net_connections rawlist = cext.proc_net_connections(self.pid, families, types) PermissionError: [Errno 1] Operation not permitted (originated from proc_pidinfo(PROC_PIDLISTFDS) 1/2)

This is really cool. Can it play nice with gdrive or Dropbox? For better or worse, that's just where my data lives now but I'd love this layer.

Can you make a dockerfile and docker compose file?

Many such open source projects already (which is fantastic). I lose track of them.

Today it happened I needed a simple way to embed & query 1TB+ of documents, and I was looking at open source options. Can anyone tell me what their go-to solution is now? Could this be the one?

And what are the key differences vs. other open source RAG tools like kotaemon?