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.
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?
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?
- 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
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?
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.
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.
Sounds similar to https://docs.paperless-ngx.com/
Key difference I see is that you point it to a folder instead of uploading to a system.
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.
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?
Let me look into this
How about jpegs or other scanner images files? We have hundreds of scanned documents that were never pdf wrapped.
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).
But how’d you access teams when it’s work teams and don’t have api access ?
I creating DocuRepo as well. though not as fleshed out.
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
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. =]
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?
Not a fan of pushing every personal document to someone else's cloud. Nice to see a tool that keeps everything on disk instead.
The hardest part of these projects is usually not making documents searchable
Looks good, definitely going to try it. Extra thanks for creating something fully local, we need more projects like this one!
thankyou
Nice, what are you hoping to accomplish with this project?
- 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
Care to elaborate?
A resume
I'm a huge fan of recall, going to test this out. This looks very interesting.
Did you mean Recoll (https://www.recoll.org/)?
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?
Yes, not supporting it is an oversight I will correct.
How do you feel about supporting an S3 compatible target as a feature request?
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.
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?