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.
gatnoodle 3 minutes ago [-]
This looks really cool. Can you tell me the minimum specs required to run this? It would nice if you could add it to the readme as well.
Key difference I see is that you point it to a folder instead of uploading to a system.
vsviridov 6 hours ago [-]
I think paperless devs are working on AI integration, and there are 3rd party solutions. I'm holding out for an official one, so far.
It's pretty cool, I've set up a share where the scanner scans, and it automatically picks it up from there and ingests it into the system.
bobim 10 hours ago [-]
Could it be extended so it also extracts pictures from pptx and xlsx and run vision to get a description to be added to the text content before indexing?
linuxrebe1 8 hours ago [-]
Let me look into this
clif_mcIrvin 6 hours ago [-]
How about jpegs or other scanner images files? We have hundreds of scanned documents that were never pdf wrapped.
password4321 7 hours ago [-]
Personal use? I need this at work, dragging useful info from tarpits like Teams and GitLab.
Also need to search git repos including all branches and history (TIL/xkcd#153'd GitLab's web search can basically only do one branch at a time).
linuxrebe1 4 hours ago [-]
I creating DocuRepo as well. though not as fleshed out.
rukshn 2 hours ago [-]
But how’d you access teams when it’s work teams and don’t have api access ?
nickweb 17 minutes ago [-]
Honestly. This with Paperless-NGX might be game changing if both pointed to the same folder.
karmakaze 4 hours ago [-]
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.
I have not set up Hister yet but it's on my list to try out. How would I do something like host it on my Unraid box but have it index/persist my local MacBook browsing history?
mune2gu-chan 2 hours ago [-]
Not a fan of pushing every personal document to someone else's cloud. Nice to see a tool that keeps everything on disk instead.
NKosmatos 10 hours ago [-]
Looks good, definitely going to try it. Extra thanks for creating something fully local, we need more projects like this one!
linuxrebe1 4 hours ago [-]
thankyou
Avery29 4 hours ago [-]
The hardest part of these projects is usually not making documents searchable
jphorism 8 hours ago [-]
Nice, what are you hoping to accomplish with this project?
linuxrebe1 4 hours ago [-]
- Filling a need I personally have.
- Learning how to leverage AI for real world use not just to fill up a data center.
- Personal knowledge
-developing skills
Pretty much in that order
passwordoops 6 hours ago [-]
Care to elaborate?
NamlchakKhandro 6 hours ago [-]
A resume
aucisson_masque 10 hours ago [-]
I'm a huge fan of recall, going to test this out. This looks very interesting.
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?
linuxrebe1 4 hours ago [-]
Yes, not supporting it is an oversight I will correct.
toomuchtodo 10 hours ago [-]
How do you feel about supporting an S3 compatible target as a feature request?
linuxrebe1 4 hours ago [-]
I'm actually thinking of this for a commercial product feature. However, if you use a tool like Rclone on Windows, Linux or Mac. Mount the s3 bucket and you can then run DocuBrowse as if the s3 bucket were local.
subhobroto 3 hours ago [-]
I love your project on many fronts. One, you're using Claude. Two, you used Python - but most importantly, you personally care about it.
I will be using this, and I will be making contributions to it as well.
> I'm actually thinking of this for a commercial product feature
Would you consider writing down which features you would like to make commercial product features and how you would like to price them?
1 hours ago [-]
Rendered at 08:07:31 GMT+0000 (Coordinated Universal Time) with Vercel.
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.
Key difference I see is that you point it to a folder instead of uploading to a system.
It's pretty cool, I've set up a share where the scanner scans, and it automatically picks it up from there and ingests it into the system.
Also need to search git repos including all branches and history (TIL/xkcd#153'd GitLab's web search can basically only do one branch at a time).
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
I'm working on a similar application called Hister (https://github.com/asciimoo/hister). I should borrow some of your ideas. =]
Pretty much in that order
I will be using this, and I will be making contributions to it as well.
> I'm actually thinking of this for a commercial product feature
Would you consider writing down which features you would like to make commercial product features and how you would like to price them?